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I
Neurophysiological Characterization of a Non-Human Primate Model
of Spinal Cord Injury Utilizing Intramuscular Electromyography
by
Farah Masood
A Thesis
presented to
The University of Guelph
In partial fulfilment of the requirements for the degree of
NEUROPHYSIOLOGICAL CHARACTERIZATION OF A NON-HUMAN PRIMATE MODEL OF SPINAL CORD INJURY UTILIZING INTRAMUSCULAR
ELECTROMYOGRAPHY
The lack of a valid non-human primate (NHP) model of spinal cord injury (SCI) and
a robust assessment tool are the key reasons behind the low rate of successful clinical
trials for the development of SCI medication. Accordingly, developing such an NHP model
would fill the gap between preclinical and clinical studies. A histopathological analysis
would help to validate a developed NHP model, while neurophysiological
characterizations could offer a reliable assessment tool for the effect of the injury. The
goal of this work is to submit a neurophysiological analysis using intramuscular
electromyography (EMG) signals collected from the agonist-antagonist pair of tail
muscles of Macaca fasicularis during pre- and post-lesion, and for a treatment and control
groups. The main goal was achieved by setting three sub-goals to be addressed in this
work. First, the general volitional muscle activity was analyzed utilizing some basic
amplitude features. Second, an analysis of the motor unit discharge properties was
submitted using wavelet transforms (WT) and the relative power (RP) of the multi-
resolution EMG signals. Third, the development of an SCI classification system was
studied and analyzed by employing various classification approaches, including regular
machine learning (ML) and deep learning (DL) classification techniques
Farah Masood
University of Guelph, 2019
Dr. Hussein A. Abdullah
III
The findings were consistent with that of the literature. Evidence suggested that
the newly proposed general volitional muscle metric (Q-metric) was related directly to the
gross recruitment of motor units required for volitional muscle control pre- and post-lesion.
As well, the results of the multi-resolution analysis demonstrated the capability of utilizing
the WT and the RP as a decomposition method to characterize the discharge properties
of the dynamic muscle activity. The findings of the SCI classification analysis implied that
the proposed Q-metric and WT-RPs features could be used to build an SCI classification
system using K-nearest neighbors (KNN) technique. Finally, the deep learning
classification, which consisted exclusively of convolutional neural network (CNN) using
raw EMG segments as inputs, showed high potential and promising results for use as an
SCI classification system with an F-measure of 84.0% and 86.9%. for the left and the right
side.
III
DEDICATION
To the soul of my great father with love …
Mohammed Ridha Masood
To my wonderful mother and my first teacher …
Sabah Yousif
To my best friends and my sisters …
Noor and Shahad
To my wonderful husband who supports me and has always believed in me …
Dr. Wisam Al-Wajidi
To my lovely daughter and son who support me with their continuous love and patience…
Mina & Yousif
Thank you all for being my strong, inspiring, and devoted family…
You have made me what I am today
IV
ACKNOWLEDGEMENTS
First and foremost, I would like to thank and praise Allah, the Almighty, the most merciful
and compassionate, for His support, help, and generosity, and I pray to Him for guidance
in the future.
I would like to express my deepest appreciation to all those who have contributed directly
and indirectly to the completion of this dissertation.
I am very grateful to my academic supervisor, Dr. Hussein A. Abdullah, for his valuable
guidance, and patient encouragement throughout the work for this dissertation. Great
appreciation goes to Dr. Shanker Nesathurai for his mentoring and kind support through
the long journey of this research and for giving me the great opportunity to be part of his
distinguished team. I would like to extend my thanks to the members of my doctoral
advisory committee, Dr. Bob Dony, Dr. Karen Gordon, Dr. Gerarda Darlington, and Dr.
David Chiu for their constructive suggestions and recommendations. My appreciation also
to Dr. Graham Tylor, and my colleagues Dr. Nitin Seth, Dr. Noor Al-Qazzaz, and Dr. Abeer
Al-Hyari, for their time, accessibility, and advice throughout the research period. Personal
thanks are given to the Iraqi Cultural Counselor in Ottawa, Dr. Asaad Toma Omran, for
his help and support. Thank you to my colleagues in the Robotics Institutes Lab,
especially Cole Tarry and Patrick Wspanialy for their support.
I gratefully acknowledge the Iraqi Ministry of Higher Education and Scientific Research
(IMHESR) for providing me a scholarship to finish my Ph.D. degree in Canada.
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Table of Contents
Contents ABSTRACT............................................................................................................................................... III
DEDICATION ............................................................................................................................................ III
ACKNOWLEDGEMENTS ...................................................................................................................... IV
Table of Contents .................................................................................................................................... V
List of Figures ...................................................................................................................................... VIII
List of Tables ......................................................................................................................................... XII
List of Abbreviations .......................................................................................................................... XIII
List of Publications .............................................................................................................................. 142
VIII
List of Figures
Figure 2.1 The main causes of SCI [4] ____________________________________________________ 8
Figure 2.2 The different types of paralysis resulted from SCI [19] _______________________________ 9
Figure 2.3 The complete course of motor neurons starting from the motor cortex (brain) to the spinal
IDPC-CNN is significantly better than other applied methods
2 May / 2019
[153] CNN Spectrogram images
Pattern recognition
CNN classification accuracy of 88.04 %
3 April / 2019
[154] Regression CNN Raw EMG signal
Simultaneous EMG control
CNN-based system outperformed SVM-based system
4 Apr / 2019
[154] Compact CNN Raw EMG signal
Gesture classification
CNN outperformed SVM
5 Jan / 2019
[155] CNN + transfer learning
Raw EMG, spectrograms and continuous wavelet transform (CWT)
Gesture classification
Transfer learning enhanced the performance for the CWT-based CNN
6 Oct / 2017
[156] CNN sEMG Gesture recognition
CNN achieved an average accuracy of 97.81%
7 Jul / 2017
[157] CNN sEMG Pattern recognition
CNN showed a better performance compared to SVM
8 Feb / 2017
[158] CNN sEMG images Hand gesture recognition
CNN is better than LDA, SVM, KNN, and RF (state-of-the-art methods)
9 Nov/ 2016
[159] CNN sEMG images Gesture recognition
CNN is better than LDA, SVM, KNN, MLP and RF
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10 Oct / 2016
[160] CNN sEMG spectrograms
Robotic arm guidance
CNN achieved state-of- the-art results
11 Sep / 2016
[148] CNN Raw sEMG Pattern recognition
CNN produced accurate results with a simple architecture compared to the classical methods
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Chapter 3
Materials and Methodology
3.1. Introduction
This chapter describes the work methodology, including all the details regarding the
EMG acquisition process. This includes the equipment, the experimental setting, and the
protocol. All of these details are described in the Materials and Experimental Procedures
section of this chapter. A brief description is made of the subjects which were enrolled. A
detailed description is then made of the surgical procedures that were required to implant
the intramuscular EMG electrodes and to create the designed SCI. The monitoring of the
subjects, euthanasia, and postmortem examinations are also explained at the end of this
section. The EMG Analysis Methods section discusses all the applied EMG analyzing
steps and methods that were implemented.
3.2. Materials and Experimental Procedures
3.2.1. Subjects enrolled
The subjects were healthy, research-naïve, adult male cynomolgus macaques
(Macaca fascicularis; n = 6; weight, 6 to 13 kg). The first four subjects received a
combination of pharmacologic treatment; the remaining two subjects did not receive
treatment.
3.2.2. The initial surgical procedure to facilitate the collection of EMG data
On the lower back of each macaque, a midline incision was placed superior to the
proximal tail, and a small pocket was dissected between the underlying muscle and the
subcutaneous fat. Additional small incisions were made on the left and the right side of
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the tail to expose the flexor cauda longus and brevis muscles; these muscles are an
agonist-antagonist pair. A small telemetry device (PhysioTel D70, Data Sciences
International, Minneapolis, MN) was inserted into this pocket in the lower back. The
telemetry device was attached through a set of intramuscular electrodes, which were
implanted into the left and the right flexor cauda longus and brevis. The EMG signals
were measured using intramuscular electrodes with a 10 mm inter-electrode distance, a
10 mm exposed wire length, a 1000 Hz sampling frequency, and a common ground.
Figure (3.1) illustrates the experimental setup. The tail of Macaca fascicularis can
be considered a fifth limb, which is involved in the performance of functional tasks and
balance [37], [161], [162]. The tail has well-developed sensory and motor areas. EMG
data were collected by a radio frequency link to a computer for 30 days to determine
baseline tail movements. Data collection occurred Monday through Friday (excluding
holidays) for one hour daily. The attached intramuscular electrodes were implanted into
the left and the right flexor cauda longus and brevis muscles (tail muscles) and used to
measure EMG signals. In a NHP, the tail is integral to functional tasks; this includes
reciprocal movements that aid in balance during ambulation [37]. As such, EMG data from
the tail is analogous to EMG data obtained from the limb of a human being with SCI.
Baseline EMG data were collected during voluntary movement of the NHPs within their
home enclosures for 30 days. Intramuscular electrodes allow for long-term implantation
in subjects and are generally well tolerated. When compared to surface electrodes, there
is less cross-talk. As a conceptual argument, data from intramuscular electrodes
aggregates the EMG signal from muscle fibers in multiple motor units.
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EMG data were collected from all six subjects. However, for one subject that
received treatment, the information from one side was not recorded. As such, its raw data
was not included in the work analysis. The resulting analyses represent the data of only
three of the subjects that received the combination therapy (Subjects 1, 2, and 3), and
two subjects who did not receive treatment (Subjects 4 and 5). The EMG data were
collected during the free movement of normal daily activities of the subjects (non-
stimulated manner). Figure (3.2) illustrates a sample of the recorded raw EMG data.
Figure 3.1The main components of the utilized experimental setting [5]
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Figure 3.2 Graphical representation of the collected EMG signal [163]
3.2.3. The subsequent surgical procedure to create an experimental spinal cord
injury
At 30 days after the implantation of the transmitter, the subjects underwent a
second surgery. The anesthesia and the postsurgical pain management plan was the
same as described earlier. A small laminotomy was performed at the level of the fifth
lumbar vertebra. An epidural balloon catheter was inserted and advanced approximately
10 cm cranial, to the level of the lower thoracic spinal cord. The balloon was inflated
rapidly and remained inflated for 1 min. Conceptually, this procedure corresponds to
human SCI, in that there is a rapid transfer of energy (that is, initial balloon inflation),
followed by residual displacement of tissues such as disk material, boney fragments, or
hematoma (that is, continued balloon inflation for 60 s). The balloon was then deflated.
The catheter was removed, and the surgical incision was closed. The focus of this model
was to create a lesion that mimics human SCI. The SCI was designed to cause only
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pathological and neurophysiological impairments without any clinical impairments that
could affect the function of the subject’s internal organs.
The radiographic image in Figure (3.3) highlights the location of the experimental
lesion in the lower thoracic vertebra marked by the red arrow. The CT image in Figure
(3.4) demonstrates how the lesion is created and the displacement of the thoracic spinal
cord by the epidural catheter. After the lesion was created, the subject remained
anesthetized for 1 h. This time period is a typical duration between injury and the
availability of emergency medical treatment in humans.
After one hour, four macaques (treatment group) received an intravenous bolus of
thyrotropin releasing hormone (TRH) (dose, 0.2 mg/kg) followed by a continuous
intravenous infusion for one hour of selenium (60 mg, 0.2 mg/kg/h). In addition, vitamin E
(dose, 80 IU) was administered orally once daily starting one day after surgery and
continuing for 90 d. In addition, two macaques (untreated control group) received an
infusion of normal saline for 1 h. These control subjects did not receive any selenium or
vitamin E. All the subjects experienced a spinal cord lesion.
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Figure 3.3 The CT image of the inflated balloon in the epidural space of the thoracic spine. The balloon is inflated with air, which appears black in the spinal column (red arrow). Note that 60% of the column is occupied by the balloon, and the spinal cord is displaced. This image is from a cadaveric subject [6]
Figure 3.4 Standard radiograph of the catheter inserted into the epidural space via laminotomy in a cadaveric subject. The balloon is not inflated in this image [6]
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3.2.4. Monitoring of subjects
The investigators and the veterinary staff closely monitored all subjects. The
frequency of assessment was at least twice daily and was more frequent during the
immediate post-surgical periods. The macaques were assessed and monitored for clinical
indicators of illness including limb weakness, vomiting, diarrhea, jaundice, bleeding, and
anorexia. The animals were weighed at the time of the surgical procedure, and prior to
euthanasia. The subjects were monitored for wasting and emaciation, as clinical
indicators of weight loss.
3.2.5. Euthanasia and post-mortem examination
On day 90, the subjects were euthanized by sedation with ketamine (10 to 20 mg/kg
IM) followed by sodium pentobarbital (>50 mg/kg IV). The postmortem examination was
performed immediately after euthanasia. The gross and microscopic examination
included assessment of the brain, spinal cord, heart, liver, spleen, kidneys, and bladder.
Venous blood was obtained immediately prior to euthanasia; cerebrospinal fluid was also
obtained. The experimental SCI resulted in both a physiological and a histopathological
perturbation [6] as shown in Figure (3.5).
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Figure 3.5 (A) Photomicrograph of the spinal cord at the epicenter of the lesion. The gray and white parenchyma are disrupted by dilated myelin sheaths (spongiosis; arrows), which are widespread. These findings are noted caudal and cephalad to the lesion, in both treated and untreated subjects. Hematoxylin and eosin stain; magnification, 40×. (B) Photomicrograph of the spinal cord (white matter funiculi) at epicenter of experimental injury. There are many dilated myelin sheaths (spongiosis; black arrows), some of which contain degenerating or swollen axons (red arrows). These findings are noted caudal and cephalad to the lesion, in both treated and untreated subjects. Haematoxylin and eosin stain; magnification, 400× [6].
The research involving the first two subjects was completed at the New England
Primate Research Center, where the animals were cared for in accordance with the
National Research Council’s Guide for the Care and Use of Laboratory Animals (8th
47
edition) and the standards of the Harvard Medical School Standing Committee on
Animals. Research on the remaining four subjects was completed at the Wisconsin
National Primate Research Center and was approved by the IACUC at the University of
Wisconsin at Madison. The macaques were under close supervision by the veterinary
staff and were monitored for any adverse effects.
3.3. EMG Analysis Methods
This work represents the only analysis that has been applied using this dataset. The
recorded EMG signal was analyzed using a methodology that consisted of four stages:
EMG preprocessing, general muscle activity metric analysis, motor unit discharge
analysis, and development of an SCI classification system. The first stage included the
filtering, the removal of outliers, and the segmentation steps of EMG signals. The second
stage was performed using the most common EMG features (area, root mean square,
turns, and zero-crossing) as well as a newly proposed metric named Q-metric. The third
stage was performed using a conventional decomposition method, and a proposed
wavelet and relative power method. The fourth stage included developing SCI
classification systems using the different EMG features and classical ML techniques
(KNN and SVM). It also included a new deep learning application for an SCI classification
system using CNN and raw EMG signals. Figure (3.6) illustrates the first three stages with
the main steps of each analysis, while Figure (3.7) represents the fourth stage of the
methodology.
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Figure 3.6 A general block diagram for the EMG analysis - Part 1
49
Figure 3.7 A general block diagram for the EMG analysis- Part 2
50
3.3.1. EMG signal preprocessing
The EMG signals were processed using three main steps which includes:
a) Removing outliers
EMG data were collected from all six subjects. However, in one subject, the
information from one side was not recorded due to a technical error. As such, the raw
data in this work represents the experience of three subjects that received the
combination therapy, and two subjects who did not receive treatment. As well, due to
some technical issues during the EMG recording process, EMG data from a few days
during the experiment were discarded.
b) Segmentation
The recording session from each day collected between 2 to 4 million EMG data
points, of which 1.5 million points were selected representing approximately 25 minutes
of data. These subsets were chosen to avoid the outliers that often occurred at the
beginning or the end of some of the recording sessions while still maintaining an equal
sample selected from each session. According to the stochastic nature of the EMG signal,
the instantaneous EMG data sample held relatively little information regarding the whole
muscle activity; therefore, another segmentation step was applied using a window of size
(1000 ms) on the EMG data of each experimental day.
c) Filtering
The physiological changes in the muscle fibers produced a myoelectric signal which
is known as the EMG signal [85]. When the EMG signal is obtained from dynamic
activities, it is usually contaminated with unwanted artifacts that might affect the
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developed signal analysis and its interpretation. It must thus be carefully filtered before
proceeding to any forward processing step. The filtering signal process usually tries to
keep as much of the original signal information as possible, and then reduce or attenuate
the accompanying noise signals [164]. The quality of the EMG signal depends mainly on
the applied processing steps. Filtering represents an important step that eliminates the
combined noises from the raw EMG signal. The raw EMG data, obtained from daily
recordings, was filtered using:
1. Zero offset filter: eliminates the baseline offset noise and is implemented by using one
of the offset correction methods (“detrend”) in MATLAB.
2. Power-line noise filter: a notch filter with 60 Hz was also applied to eliminate the power
line noise.
3. Zero-phase bandpass filter: extracts the most effective band frequency region from
the raw signal by applying a bandpass filter (4th order Butterworth filter with a lower
and an upper cut off frequency of 10 and 450 Hz respectively). A phase shift problem
was solved by processing the input signal in both the forward and the backward
directions.
The EMG conditioning steps have been implemented using MATLAB software
(MathWorks, Natick, MA, USA).
3.3.2. General volitional muscle activity metric
3.3.2.1. Conventional EMG metrics
The goal of this section is to evaluate the ability to use some of the conventional
EMG amplitude metrics to evaluate volitional muscle activity pre- and post-SCI, as well
as while performing dynamic and multilevel muscle contractions. During the muscle
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contraction, motor units recruit sequentially according to their size starting from the
smallest to the largest ones. First, the earliest MUs that are recruited, which are small in
size, generate a weak tension. Then to increase the level of contraction, the larger MUs
fire progressively until they reach the desired level [165]. A strong muscle contraction
generates a complex EMG signal, which is known as the interference pattern EMG signal.
This type of signal is built up from the activity of multiple motor units, and it results when
the MUs recruit at the same time, their potentials get overlapped, and they interfere with
each other [165].
A conventional EMG signal analysis, using signals detected during constant force
and isometric contractions, is useful in many applications. However, experiments that
represent real-life conditions are important to extend the reliability of these methods
especially for clinical applications [166]. It remains unclear whether existing EMG
features, which are largely used based on signals collected during constant-force
isometric contractions, are still effective for use with this type of EMG signal (dynamic
activity and multilevel muscle contractions).
An EMG signal has a stochastic and complex nature [167]. Therefore, to describe
the overall muscle activity, the averaged/windowed signal is utilized because it is more
informative than instantaneous EMG samples. EMG features are commonly calculated
by using a sliding window over the EMG signal [86], [138], [168]. The window length differs
based on the characterization of the signal (sampling frequency, total length), and the
application. The segmentation step could be applied using either a joint (overlapped) or
a disjoint windowing process [169], [170]. It has been observed that the overlapped
windowing process has a better classification performance compared to the disjoint
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windowing [169]. The overlapped method does, however, require high computational
costs and time for some of the classification methods.
In this analysis, the filtered EMG signal for each individual day was used, then
followed with a segmentation step using a window of size 1000 ms. Initially, a group of
standard and popular EMG amplitude features were measured including area, RMS, turns
number and zero-crossing number. These features were selected according to their easy
implementation, fast computation, and class discrimination factors [71], [85].
Subsequently, the calculated features were tested in a linear mixed model to check their
ability to differentiate between the two experimental phases (pre- and post-lesion), and to
test the effect of the utilized treatment. The EMG features were calculated as follows:
1) Area is the integration of the absolute value of each EMG sample over the selected
duration and it reflects the quantity of the electrical impulse. It can be expressed as follows
[69], [71]:
𝐴𝑟𝑒𝑎 = ∑|𝑥𝑖| 𝑖 = 1, 2, … , 𝑁
𝑁
𝑖=1
(3.1)
𝑁: 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑒𝑠 , 𝑥𝑖 = 𝑡ℎ𝑒 𝑖 − 𝑠𝑎𝑚𝑝𝑙𝑒
2) Root mean square (RMS) is a statistical measure that represents the square root
of the mean of the squares of all the EMG signal samples. The RMS reflects the average
power of the EMG signal, and it is given by the following formula [69], [71]:
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𝑅𝑀𝑆 = √1
𝑁∑ 𝑥𝑖
2
𝑁
𝑖=1
(3.2)
𝑁: 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑒𝑠 , 𝑥𝑖 = 𝑡ℎ𝑒 𝑖 − 𝑠𝑎𝑚𝑝𝑙𝑒
3) Zero-crossing is a simple frequency measure in the time domain which
represents the number of times that the EMG waveform crosses the amplitude zero line.
The zero-crossing counter will increase if two consecutive EMG samples satisfy the
following condition [69], [71]:
{𝑋𝑛 > 0 𝑎𝑛𝑑 𝑋𝑛+1 < 0} 𝑜𝑟 {𝑋𝑛 < 0 𝑎𝑛𝑑 𝑋𝑛+1 > 0}
4) Turns number is another feature that may measure the frequency content of the
signal by counting the number of times that the slope changes its sign from negative to
positive and vice-versa. The turns counter is incremented if three consecutive EMG data
Table 3.2 Parameters used in the equation of the mixed model
Parameter Definition
𝑹𝑷𝒊𝒋𝒌 response (relative power)
𝑫𝒂𝒚𝒊𝒋𝒌 experiment day
𝑭𝒓𝒆𝒒𝒋𝒌 frequency sub-band (D1, D2, D3, D4, D4, D6, and A6)
EFFECT the main effect in the models, so for:
Model 1: the side effect [categorical variable with two levels (left and right
side)]
Model 2: the lesion effect [categorical variable with two levels (pre and post-
lesion)]
Model 3: the treatment effect [categorical variable with 2 levels (control and
treatment group)]
𝜷𝟎 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐨 𝜷𝟕 the fixed effect associated with the following: intercept, Day, EFFECT,
EFFECT*Day, Freq, Freq*Day, Freq* EFFECT, and Freq* EFFECT*Day
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𝜶𝟎𝒌, 𝜶𝟏𝒌 the subject random effect associated with the intercept and Day slope,
respectively
𝜶𝟎𝒋𝒌, 𝜶𝟏𝒋𝒌 the random effect of a frequency sub-band nested within a subject associated
with the intercept and Day slope respectively
𝜺𝒊𝒋𝒌 random error
Figure 3.12 Collected Data Structure: The data in this work was collected from the left (L) and the right (R) side of the tail for five subjects throughout the days of the experiment (d1, d2, …, dn) for the pre- and post-lesion period. The data for each day was decomposed into seven frequency sub-bands (D1, D2, D3, …, A6). Three of the subjects received a treatment combining TRH, selenium, and vitamin E post-lesion (treatment group); the remaining two subjects did not receive any treatment post-lesion (control group) [163]
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3..4 SCI Classification System
3.4.1 SCI classification system using classical ML classification technique
In this analysis, an SCI classification system has been investigated using various EMG
features and ML classification techniques. This analysis has been performed using the
following steps:
1. EMG features extraction and pre-processing
All the features that were measured in the previous sections, including the four EMG
amplitude features (area, RMS, turn number, and ZC number), the Q-metric, and the RP
of the EMG frequency sub-bands (RPA6, RPD6, RPD5, RPD4, RPD3, RPD2, and RPD1),
have been used as input data for the generated SCI classification systems. Two systems
were built; the first system utilized the EMG amplitude features, while the second system
was based on the two newly proposed features.
According to the experimental setting, more post-lesion instances were available than
the pre-lesion class, which generates a class imbalanced dataset. The imbalanced
dataset, in turn, leads to a misclassification problem that reduces the accuracy of a
classifier. The degree of imbalance is usually small, but it appears extensively in certain
applications, such as medical diagnoses, and fraud detection applications. As an
example, the medical dataset often included a higher percentage of ‘normal’ class
samples as compared to the ‘abnormal’ or ‘interesting’ class samples. In this case, the
misclassification rate of considering an ‘abnormal’ instance as a ‘normal’ instance is
higher than vice versa, which results in a poor diagnostic system. To resolve this issue,
there are some pre-processing steps that could be applied to rebalance the classes within
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a dataset. In the literature, the problem of an imbalanced dataset was solved by re-
sampling the data in two ways, either under-sampling the majority class or oversampling
the minority class, such as in the synthetic minority oversampling technique (SMOTE)
[194].
In this work, SMOTE was applied to address the imbalanced class issue. SMOTE is
one of the pre-processing techniques that over-sample the pre-lesion (minority class) and
generate a balanced dataset. The algorithm takes samples of the feature space for the
pre-lesion class and its nearest neighbors and merges them to generate new instances
[194], [195].
2. Classification techniques
In this study, two different machine learning classification algorithms were used.
These classifiers were chosen according to the requirements and the feasibility of the
EMG signal dataset. The classification results were analyzed and compared to find the
most reliable type. The SVM and KNN algorithms were the two classification techniques
that were employed. The SMOTE approach and the classification algorithms were
implemented using a special software (Weka) by the University of Waikato, New Zealand.
The two different machine learning classification algorithms are as follow:
a) Support vector machine
The SVM technique is one of the machine learning algorithms that has been
applied to different biomedical tasks, including classification and regression [196]. The
SVM is a binary classifier that aims to differentiate between two classes by creating an
optimal hyperplane that separates the classes and satisfies the following equation:
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𝑤. x + b = 0 (3.5)
where 𝑤 is a weight vector, 𝑥 is an input vector, and 𝑏 is a bias term [197]. For the linearly
separable classification problem, the SVM algorithm tries to find the separating
hyperplane with a maximum (margin). Given that the two classes are -1 and +1, the
separability assumption refers to the existence of some values of w and b in which all the
input vectors satisfy the following constraints in Equations (3.6) and (3.7) [198]:
𝑤. x + b ≥ +1 for ∀ y = +1 (3.6)
𝑤. x + b ≥ +1 for ∀ y = −1 (3.7)
These two can be combined into Equation (3.8):
𝑦(𝑤. x + b) − 1 ≥ 0 for ∀ y (3.8)
In this study, the kernel function of the SVM was tested empirically and the optimal
result was obtained using the Pearson VII function-based universal kernel (PUK) [199],
[200]. Figure (3.13) illustrates a general schematic of the SVM classification.
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Figure 3.13 General schematic illustrating the SVM classification technique
b) K-nearest neighbors
KNN is a simple classification technique; however, it is an effective method that has
been applied to various applications such as healthcare, handwriting detection, and
image recognition [201]. The KNN is a non-parametric and a lazy approach, which refers
to the fact that no assumptions are made, and the structure of the model is based entirely
on the data given to it. This algorithm is popular for its speed and simplicity, and it has
been utilized widely in EMG classification studies [146], [147], [202]. Like any other
machine learning algorithm, KNN has some drawbacks or limitations that include
choosing the appropriate value of K [146], [203]; however, by varying the value of K, the
increase in generalization and accuracy of the KNN classifier can be achieved. Figure
(3.14) illustrates the three main steps of applying the KNN technique. These steps are
listed below [204], so as to predict a label for point (?) in Figure (3.14):
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1. Firstly, calculating the distance between point (?) and all the remaining points by using
distance measuring methods, such as Euclidean distance, Manhattan distance,
Minkowski distance, or Hamming distance.
2. Secondly, finding the closest K neighbors to point (?).
3. Thirdly, classifying point (?) by the majority voting of the K neighbors, so if K=3, it
would be labelled as an orange star, while if K=7, it would be labelled as a green dot.
Figure 3.14 A graphical illustration of the K-nearest neighbor classification technique
3.4.2 SCI classification using DL technique compared to ML technique
A comparison study has been performed between two SCI classification systems
using DL classification (CNN) and ML (KNN), the main steps of the analysis as follows:
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1. EMG data
In this section, the filtered EMG signal for each individual day of the experiment was
segmented into a series of disjoint windows of size 1000 ms. A different number of EMG
segments were next tested as inputs to the classification system. After multiple trials,
53,350 pre-lesion EMG segments and 135,300 post-lesion EMG segments combined for
all the subjects were chosen to be used as classifier inputs. This number was selected as
a compromise between the improvement in the classification accuracy and the time
consumed. According to the nature of the experiment, the collected data was imbalanced
since it was collected for more days during the post-lesion period compared to the pre-
lesion period. An imbalanced dataset leads to inaccurate classification results [195]. To
address this problem, the dataset was corrected by applying a random over-sampling
technique for the pre-lesion class. The number of samples in the pre-lesion class was
increased to be equal to the number of samples in the post-lesion class, so a total of
270,600 instances were used as inputs to the classification system.
2. Classification techniques
Two classification techniques have been used in this comparison. These techniques are
as follows:
i) KNN classification
Using the prepared EMG segments, four of the standard EMG amplitude features
were extracted, which included the area, the RMS, the turns number and the zero-
crossing number. All the features were then standardized and normalized. The created
feature vector was then utilized as an input to the KNN classifier. Different K values were
tested, and the best accuracy was obtained using k=9.
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ii) CNN classification
In this work, the CNN architecture was chosen empirically by running multiple
experiments with different architectures by considering various numbers of layers, and
different numbers of filters with different sizes and numbers of strides. The final selected
network consisted of five blocks, illustrated in Figure (3.15) and Table (3.3). The CNN
was structured as follows:
Input: An EMG segment (1000 x 1)
Block 1 was composed of:
- A convolutional layer with 32 filters having a size 5 x 1 and a stride of 2
- An activation layer using rectified linear units (ReLUs) as activation functions
- A subsampling layer (max-pooling); also, a dropout of 0.2 was applied to reduce
the overfitting.
Block 2 was composed of:
- A convolutional layer with 32 filters having a size of 5 x 1 and a stride of 2
- An activation layer of ReLUs used as activation functions
- A subsampling layer (max-pooling), and a dropout of 0.2 was applied
Block 3 was composed of:
- A convolutional layer with 64 filters having a size of 3 x 1 and a stride of 1
- An activation layer of ReLUs used as activation functions
- A subsampling layer (max-pooling), and a dropout of 0.2 was applied
Block 4 was composed of:
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- A convolutional layer with 128 filters having a size of 3 x 1 and a stride of 1
- An activation layer of ReLUs used as activation functions
- A subsampling layer (max-pooling), and a dropout of 0.2 was applied
These four blocks were followed by:
A global average pooling layer (GAP) which computed the average value of each
individual input feature map and yielded a single feature map obtained by concatenating
the computed average values.
A fully connected layer (FC) of size 100 x 1 with ReLUs used as activation functions
and a dropout of 0.3 was also applied.
An output layer: A sigmoid activation function was chosen for the output layer to satisfy
the binary classification requirement. This was done to classify the input data using the
output from the FC layer into either a pre-lesion class (< 0.5) or a post-lesion class (>
0.5).
Table 3.3 Optimized CNN architecture
No Architecture Parameters Output Shape No. of Parameters
1 CONV1: 32 x (5,1), stride: (2) 500 x 32 192
2 Maxpool: (2,1), stride: (2) 250 x 32 0
3 Dropout1: (0.2) 250 x 32 0
4 CONV2: 32 x (5,1), stride: (2) 125 x 32 5152
5 Maxpoo2: (2,1), stride: (2) 63 x 32 0
6 Dropout2: (0.2) 63 x 32 0
7 CONV3: 64 x (3,1), stride: (1) 63 x 64 6208
8 Maxpoo3: (2,1), stride: (2) 32 x 64 0
9 Dropout3: (0.2) 32 x 64 0
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10 CONV4: 128 x (3,1), stride: (1) 32 x 128 24704
11 Maxpoo4: (2,1), stride: (2) 16 x 128 0
12 Dropout4: (0.2) 16 x 128 0
13 Global Average Pooling 128 0
14 FC: 100 (ReLU) 100 12900
15 Dropout1: (0.3) 100 0
16 Output: 1 (Sigmoid) 1 101
Total No of Parameters 49257
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Figure 3.15 Convolutional neural network structures utilized to build the EMG-based SCI classification system
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Chapter 4
Results and Discussions
4.1. Introduction
This chapter describes the detailed results obtained after the implementation of the
proposed methodology in Chapter 3. The current chapter is organized as follows:
• The general volitional muscle activity analysis is illustrated in two sections.
o The first section reports the results of the fitted statistical models for the four
conventional EMG amplitude features.
o The second section reports and illustrates the results of the proposed peak
number method (Q-metric).
• The analysis of motor unit discharge properties is described in another two sections.
o The third section presents and illustrates the results of using a conventional
EMG decomposition method.
o The fourth section reports and illustrates the results of the proposed EMG
discharge analysis approach using a wavelet decomposition technique and
relative power.
• The SCI classification system is described in four separate sections.
o The first section explains and illustrates the definition and the mathematical
formula for the classifier performance evaluation methods which were used.
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o The second section includes the results of the SCI classification system using
the four EMG amplitude features.
o The third section describes the results of the SCI classification system using
the proposed Q-metric and WT-RPs features.
o The fourth section presents and compares the results of the new deep learning
(CNN) application as an SCI classification system to those of the classical KNN
classification technique.
4.2. General Volitional Muscle Activity Metric
4.2.1. Conventional EMG metrics
In this section, the popular features in the analysis of the EMG signal and the most
utilized features that describe the EMG signal amplitude have been calculated. These
features are area, RMS, turns, and ZC, which have been chosen based on easy
implementation, fast computation, and class discrimination factors [71], [85]. Each
individual feature was used as a response variable in a statistical mixed model to test the
effect of the created lesion and the combination of treatments that was utilized.
Tables (4.1) and (4.2) present the results of the statistical models of the left side EMG
signals that related to the effect of the created lesion and the treatment that was used
respectively. In Table (4.1), the results showed a significant difference (p-value <0.05) in
the turns and the ZC value between the pre- and the post-lesion period, with increments
in the post-lesion period of Turn/pre = 0.420, Turn/post = 0.49, ZC/pre = 0.507, and
ZC/post = 0.544. In comparison, the statistical models of the lesion effect using area and
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RMS had nonsignificant differences (p-value>0.05), as seen in Table (4.1). The treatment
effect models showed nonsignificant results using all four of the calculated features (p-
value>0.05), as seen in Table (4.2).
Tables (4.3) and (4.4) present the results of the statistical models of the right side
EMG signals that are related to the effect of the lesion that was created and the treatment
that was used respectively. ZC showed a significant difference between the pre- and post-
lesion values (p-value<0.05), with a reduction in the post-lesion phase of ZC/pre = 0.801
and ZC/post = 0.636, while the area, the RMS, and the turns had a nonsignificant
difference between the two phases. The results related to the treatment effect models
showed nonsignificant results using all four of the calculated features (p-value>0.05), as
seen in Table (4.4).
As can be seen from Tables (4.1) to (4.4), there is no consistency in the results.
On the left side, the Turn and the ZC showed a significant difference with the increments
in their values in the post-lesion period. In comparison, on the right side only the ZC
showed a significant difference with a reduction during the post-lesion period. However,
the significant results in the ZC on both sides may imply a potential effect of the created
lesion on the frequency content of the EMG signals because ZC represents a simple
frequency measure in the time domain. This disturbance or change in the parameters of
the frequency fits the inferences of previous works that referred to an alteration in the
recruitment pattern and firing rate of motor units after the occurrence of a UMN lesion
[54], [55], [205].
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Table 4.1 The results of the statistical model (lesion effect) of the conventional EMG features for the left side
Feature Mean (Pre) Mean (Post) p-value
Area 0.459 0.244 0.964
RMS 0.504 0.315 0.075
Turns 0.420 0.492 < 0.001
ZC 0.507 0.544 < 0.001
Table 4.2 The results of the statistical model (treatment effect) of the conventional EMG features for the left side
Feature Mean (Ctrl) Mean (Tr) p-value
Area 0.365 0.338 0.171
RMS 0.434 0.384 0.230
Turns 0.478 0.435 0.208
ZC 0.450 0.601 0.797
Table 4.3 The results of the statistical model (lesion effect) of the conventional EMG features for the right side
Feature Mean (Pre) Mean (Post) p-value
Area 0.260 0.321 0.576
RMS 0.079 0.328 0.106
Turns 0.765 0.565 0.894
ZC 0.801 0.636 < 0.01
Table 4.4 The results of the statistical model (treatment effect) of the conventional EMG features for the right side
Feature Mean (Ctrl) Mean (Tr) p-value
Area 0.282 0.299 0.589
RMS 0.206 0.202 0.802
Turns 0.718 0.613 0.083
ZC 0.689 0.749 0.438
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4.2.2. The proposed peak number method (Q-metric)
According to the results obtained from the conventional EMG metrics and the unique
dataset (longitudinal data recordings, dynamic activity, and different levels of contraction),
a new muscle activity metric was needed. In the methodology section, a new metric,
called Q-metric, was proposed by applying multiple signal processing steps, which
included filtering, rectifying, smoothing, thresholding, and counting the number of peaks
that satisfied the calculated threshold. Next, a mixed model was fitted to test the effect of
the created lesion and the treatment that was used on the EMG signal represented in the
Q-metric.
Table (4.5) summarizes the Q values at key time points, and Figure (4.1) represents
a fitted linear regression model for the aggregate data of Q before and after the creation
of the lesion. Consistent with a maturation of the wire–muscle interface, the value of Q
for both the left and the right side of the tail increased during the pre-lesion period. Im-
mediately after the lesion, the value of Q decreased on both the left and the right side in
both the treatment and control groups. The Q scores before and after the creation of the
spinal cord lesion were significantly different (left side, P = 0.021; right side, P = 0.01)
with a reduction in the mean value of the Q-metric during the post-lesion period. This
finding suggests that the lesion led to decreased EMG activity, resulting in decreased tail
movement, and supports the construct that Q can be used as a measure of impairment
after experimental SCI. On the left side, the treatment group was associated with a trend
toward higher Q-values when compared with the untreated control group (P = 0.075); this
effect was not noted on the right side (P = 0.519). Post-lesion data was compared to the
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pre-lesion baseline to identify consistent changes across the subjects. Fitted Q values
(Table 4.1) increased with time in both groups, although the values on both sides were
higher after treatment. Overall, the EMG data were insufficient to attribute the effect to
the treatment.
Figure 4.1 The aggregate fitted Q-values, representing tail movement, are plotted for the left (top) and the right (bottom) side. Values are normalized to a range between 0 and 1, with higher values indicating greater muscle activity. Of note, on both the left and the right side, the Q values decreased immediately after the creation of the lesion. On the left side, the effect of the lesion in the treatment group (orange) was attenuated, compared with that of the nontreatment group (grey). On both the left and the right side, the Q-metric improved over time [6]
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Table 4.5 Standardized Q-metric
Subject Day of
implantation
Pre-lesion,
midpoint
Postlesion,
day 1
Post-lesion,
midpoint
Study end
Right side
1 0.548 0.693 0.016 0.322 0.645
2 0.553 0.696 0.016 0.320 0.639
3 0.552 0.696 0.016 0.320 0.641
4 0.604 0.731 0.014 0.283 0.566
5 0.585 0.719 0.015 0.296 0.591
Left side
1 0.855 0.902 0.005 0.103 0.207
2 0.695 0.793 0.011 0.217 0.435
3 0.798 0.863 0.007 0.144 0.288
4 0.836 0.888 0.006 0.117 0.235
5 0.948 0.965 0.002 0.037 0.073
The Q-metric for each subject increased after the time of insertion and reached a
plateau during the pre-lesion period. This pattern reflects the maturation of the wire–
muscle interface. The Q-metric decreased immediately after the creation of the lesion and
gradually increased (improved) over time. The values of Q relative to time are nonlinear.
Data from the pre- and post-lesion period suggest that a change is taking place and can
be measured in a quantifiable method. Given the EMG data from these experiments, Q
can be used to evaluate impairment after TSCI. Specifically, Q decreased immediately
after the experimental SCI and increased over time; however, the evidence was
insufficient to ascribe a treatment-associated effect. Regardless, the results from these
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experiments have provided insights into the statistical variances of Q and will serve as a
guide or future experiments.
4.3. An Analysis of the EMG Discharge Properties
The discharge properties of the collected EMG signals were studied using two
different approaches. First, one of the conventional EMG decomposition techniques was
used. It was similar to that of Stashuk [106], which had been widely used in the literature.
This method was applied to test its efficacy with the unique nature of the EMG data
(dynamic, longitudinal, and multi-levels of contraction) from the current study. Second, a
combination of wavelet transforms, and relative power was used as a decomposition
method. The frequency content of the EMG signals was studied by decomposing the
signal into its constituent frequency sub-bands. The relative power of each sub-band was
then calculated and analyzed statistically using a mixed linear model.
4.3.1. Conventional EMG decomposition
In this section, the results after the implementation of the conventional EMG
decomposition method (isolating MUPs) are presented. This method includes three main
steps: MUP detection and segmentation, MUP clustering, and MUP classification into
MUPTs. The segmentation and MUP detection steps were applied using a thresholding
technique. This step requires choosing a certain threshold level and a specific length for
the segmentation window; in this study, it was 1.5 RMS [43] and 180 ms respectively.
According to this method, the EMG peaks that exceeded the selected threshold level were
then used as the center point for the segmentation window. The EMG data points that fell
within the selected window were finally segmented and saved as candidate MUPs.
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Next, various types of morphological, time, and frequency domain features were
measured for each MUP segment, including area, RMS, ZC, turn number, peak to peak,
length, mean frequency, and median frequency. The feature vector that was calculated
was later used as input for the clustering step. The goal of the clustering step was to find
the initial MUP templates to be used as labels to classify the candidate MUPs, using a
supervised classification technique in the third and final step.
Figure (4.2) illustrates some results from a previous study in the literature [43]; the first
column represents the candidate MUPs, the second shows valid MUPT templates, and
the last column shows the firing pattern for the MUs that was achieved by tracking the
MUP templates within the signal.
Figure 4.2 Example of valid and invalid MUPs, their MUP templates, and firing patterns [43]
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Generally, valid MUP templates should resemble the results shown in the second
column of Figure (4.2). In this work, after applying the first two steps of the method (MUP
detection and segmentation, followed by the clustering step using the K-means method
with different values of K), invalid MUP templates were obtained, as shown in Figure (4.3).
The templates that were obtained confirmed that the applied decomposition method was
not applicable and could require EMG signals with certain characteristics that were not
available in the collected EMG dataset including high sampling frequency and isometric
muscle activity with a low level of contraction. Based on the obtained results (invalid MUP
templates), it was not possible to continue with this method, and another strategy needed
to be investigated.
(1)
(2)
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(3)
(4)
(5)
92
(6)
Figure 4.3 From (1) to (6), different samples of the detected and clustered MUPs using the K-means clustering method
and fifteen various EMG features
4.3.2. The proposed EMG discharge properties analysis utilizing wavelet
transform decomposition and relative power
In this section, the results were presented in the form of answers to the three research
questions that were stated in Chapter 3. These questions are as follows:
1) During the pre-lesion period, do the subjects demonstrate symmetrical volitional tail
activity as measured by EMG activity?
To test the symmetry of the tail muscle activity on both sides, the RP of the EMG
signals during the pre-lesion period were analyzed using a linear mixed model. The
interaction of the frequency sub-band and the side variable in the statistical model
demonstrated a significant effect (p-value < 0.001). Given that the side*frequency
interaction is significant, Tukey’s mean comparisons were generated to identify what
means differed significantly. The results in Figure (4.4) suggest that the side variable had
a significant effect on the RP value of the D1, D2, D4, and D5 sub-bands. The estimated
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mean of the RP for the D1, D2 and D4 sub-bands of the left side was significantly higher
than that of the right side. In contrast, the estimated mean of the RP for the D5 sub-band
on the left side was significantly lower than that of the right side. The estimated mean for
the RP across the different frequency sub-bands for both sides is illustrated in Figure
(4.4). Taken together, these results showed that the left and right tail muscles had
asymmetrical activation.
Figure 4.4 The estimated mean of the RP of seven reconstructed EMG sub-bands prior to the creation of SCI for the left and the right side of the tail. Of note for each band, there is a difference in the RP value when compared to the left and the right side of the tail. The D2 sub-band of the left and the right side has the maximum RP, and the significant difference between the two sides is at the D1, D2, D4, and D5 sub-bands. The star indicates a significant difference
[163]
In human beings, the construct of limb dominance or preference is well accepted;
i.e., most people use their right arm for functional tasks, and a minority use the left arm.
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A select few people can use both arms equally well (ambidextrous). The question of
whether monkeys have limb dominance remains a subject of scientific inquiry. In this
analysis, there is asymmetry related to the RP of EMG data derived from the left and the
right tail muscles. These results suggest that the long-tailed macaque (Macaca
fascicularis) exhibits limb preference and/or dominance. This is also consistent with the
observations of the veterinary staff involved with this project. Generally, limb dominance
refers to the preferential use of one limb to perform functional tasks [206]. This asymmetry
in the pre-lesion (normal control) period has implications for any analysis subsequent to
the experimental SCI. Conceptually, volitional motor control involves a two-circuit
pathway; i.e., the upper motor neurons and the lower motor neurons of the corticospinal
tracts. The upper motor neurons originate in the cortex, travel the internal capsule and
pyramids, and terminate into the grey matter of the spinal cord. The lower motor neurons
originate in the grey matter of the spinal cord, exit via the nerve roots and reach the
muscles via the plexus and peripheral nerves. The data suggest that the neural network
that controls the left and right neurophysiological circuit is not symmetrical; therefore, it
stands to reason that experimental SCI will have different effects on the separate circuits.
This is consistent with human disease, to the extent that a lesion in one part of the central
nervous system may have different manifestations related to the limbs. As such, the
effects of the lesion, from a neurological perspective, were analyzed in Model Two
separately for both the left and the right side.
2) In the post-lesion period, is there a change in the EMG activity attributed to the
experimental spinal cord injury and how it could be characterized in terms of frequency
content?
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To answer this question, the effect of the created lesion was analyzed using a linear
mixed model. The effect of the lesion was studied by testing the difference between the
EMG characteristics during the pre- and post-lesion period. The interaction of the
frequency sub-band and the lesion variable in the statistical models of both sides
demonstrated a significant effect (p-value < 0.001). Given that the lesion*frequency
interaction was significant, Tukey’s mean comparisons were generated to identify which
means differed significantly. Figures (4.5) and (4.6) summarized the estimated mean of
the RP values for different frequency sub-bands of the pre- and post-lesion group for the
two sides. On the left side, the estimated mean of the RP for the D4, D6 and A6 sub-
bands was significantly higher in the post-lesion period compared to the pre-lesion period.
On the right side, the estimated mean of the RP for the D4, D6, and A6 sub-band was
also significantly higher in the post-lesion period. In comparison, the estimated mean of
the RP for the D1 and D2 sub-bands was significantly lower in the post-lesion period
compared to the pre-lesion period. The results suggest that the created lesion had a clear
effect on the discharge properties of the MUs, and with this technique, changes in
discharge properties could be detected even when there is no clinical evidence.
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Figure 4.5 The estimated mean of the RP of seven reconstructed EMG sub-bands prior and post to the creation of the SCI for the left side of the tail. Of note, the RP values for the frequency sub-bands (D4, D6, and A6) are significantly higher in the post-lesion period. The star indicates a significant difference [163]
In this analysis, there was a difference in the RP values when comparing the pre-
and the post-lesion data; this was statistically significant. Specifically, the low frequency
(LF) sub-bands (D4, D6, and A6) increased significantly during the post-lesion period on
both sides. As well, the high frequency (HF) sub-bands (D1 and D2) decreased
significantly during the post-lesion period on the right side. These results are consistent
with the literature [54]. As a general construct, neurological disease is associated with
abnormalities in the firing of the motor units and leads to a lower discharge frequency
[54], [55], [205]. This, in turn, can affect the distribution of the high and the low-frequency
components of the EMG signals. This shift in the RP value may be related to the
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disorganized, spontaneous firing of single or multiple motor units, which affect the
frequency distribution. Speculatively, a component of these abnormalities was related to
spasticity; in SCI, the recruitment of Type I or Type II motor units could be preferentially
affected, which could in turn distort the frequency content. The elucidation of these
relationships requires further research. With this method, a change in the recruitment
behavior post-SCI might be detectable even before there is clinical evidence.
Figure 4.6 The estimated mean of the RP of seven reconstructed EMG sub-bands prior to and post to the creation of the SCI for the right side of the tail. Of note, the RP values for the lower frequency sub-bands (D4, D6, and A6) are significantly higher in the post-lesion period, while the higher frequency sub-bands (D1 and D2) are significantly lower in the post-lesion period. The star indicates a significant difference [163]
3) What is the difference in the EMG activity between the control and the treatment group
in the post-lesion period (treatment effect)?
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The post-lesion data were utilized to generate two separate mixed models, one for
each side. The potential effect of the treatment was analyzed using the RP of the left and
the right sides, incorporating an analysis that compared the treatment and the control
groups. The interaction of the frequency sub-band and the treatment variables in the
models of both sides demonstrated a significant effect (p-value < 0.001). Given that the
treatment*frequency interaction was significant, Tukey’s mean comparisons were
generated to identify what means differed significantly. Figures (4.7) and (4.8) summarize
the estimated mean of the RP values for different sub-bands of the control and treatment
group for the two sides. On the left side, the D1, D2, D3, and D6 sub-bands have a
significant difference, while on the right side the effect was significant in all the sub-bands
except the D2. The results suggest that there is a significant difference in the discharge
properties of the MUs in the treatment and the control groups during the post-lesion
period.
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Figure 4.7 The estimated mean of the RP of seven reconstructed EMG sub-bands post-lesion for the left side of the treatment (Tr) and the control (Ctrl) groups; of note, the RP values for the frequency sub-bands (D1, D2, D3, and D6) are significantly different. Subjectively, the distribution of the RP in the treatment group is similar to the RP distribution
in the pre-lesion period for all the subjects. The star indicates a significant difference [163]
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Figure 4.8 The estimated mean of the RP of seven reconstructed EMG sub-bands post-lesion for the right side of the treatment (Tr) and the control (Ctrl) group; of note, the RP values for frequency sub-bands (D1, D3, D4, D5, D6, and A6) are significantly different. Subjectively, the distribution of the RP in the treatment group is similar to the RP
distribution in the pre-lesion period for all the subjects. The star indicates a significant difference [163]
The combination of TRH, selenium and vitamin E is associated with a treatment effect.
One of the goals of the current experiments was to gain a preliminary understanding of
the safety and efficacy of the combination of these three agents, which had not been
previously evaluated in animal models or in human studies. In a clinical trial involving
human patients who experienced SCI, TRH was administered as a 0.2-mg/kg bolus,
followed by an infusion of 0.2 mg/kg/h for 6 h [207]. For the current study, a similar weight-
based dosing paradigm was used: macaques in the treatment group received a bolus of
0.2 mg/kg with continuous infusion of 0.2 mg/kg/h for 1 h while anesthetized. Specifically,
three subjects received a combination treatment and two subjects did not receive
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treatment. In the post-lesion period, there was a significant difference in the RP values
for most of the frequency sub-bands of the treatment and the control groups. These
results suggested there is a clear difference in the discharge properties of the MUs of the
treatment group during the post-lesion period. This finding was present for both the left
and the right sides. However, in the context of a limited number of subjects, this should
be considered as a preliminary inference. The rationale for this combination of treatment
for SCI and the implications thereof are published elsewhere [168].
4.4. SCI Classification System
EMG signals are one of the powerful biomarkers that have been used to detect and
track various neuromuscular abnormalities, such as myopathy and neuropathy disorders.
EMG signal interpretation is typically based on the visual inspection of expert clinicians.
Consequently, the diagnostic process might vary because it depends on the experience
of the clinician; therefore, developing a reliable EMG classification system may offer an
efficient assessment tool. Nonetheless, an analysis of EMG classifications may present
a robust and accurate detection method of the presence of SCI. As well, such an analysis
may expand the understanding of an SCI because a computerized analysis offers a
quantitative measure instead of a subjective visual inspection. In this study, EMG features
have been extracted using different methods, including the conventional amplitude
metrics, the Q-metric, and the RP of the WT sub-bands. All these features have been
utilized to build different classification systems using two artificial intelligence approaches:
classical machine learning techniques, which include KNN and SVM, as well as a new
application of deep learning (CNN), which has been submitted in this study.
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4.4.1 Classification performance evaluation
The Performance of the employed classification techniques has been analyzed using
the following approaches:
1. Confusion matrix
The performance of a classification system can be described in a matrix table known
as a confusion matrix. The typical properties and the ratios that are included in the
confusion matrix for a binary classifier is illustrated in Figure (4.9). Each row of the matrix
represents the instances number in an actual class while each column represents the
instances number in a predicted class. In this study, the positive and negative classes
refer to the post- and pre-lesion data respectively. The confusion matrix consists of four
basic measures, which are as follow:
a. True Positives (TP): the number of correctly classified post-lesion instances.
b. True Negatives (TN): the number of correctly classified pre-lesion instances.
c. False Positives (FP): the number of pre-lesion instances, which have been
incorrectly classified as a post-lesion class.
d. False Negatives (FN): the number of post-lesion instances, which have been
incorrectly classified as a pre-lesion class.
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Predicted Class
Positive Negative
Actu
al
Cla
ss
Po
sit
ive
True Positive
TP
False Negative
FN
Neg
ati
ve
False Positive
FP
True Negative
TN
Figure 4.9 The basic components of a confusion matrix [208], [209]
Figure (4.10) represents the confusion matrix for one of the classifiers that was
employed in this study. This classifier has been used as an example to explain the main
elements of the confusion matrix. The EMG data were labelled into two classes: pre- and
post-lesion. During the training step, the classifier learned how to distinguish between the
two classes (for a binary type) and build a model; later, it used this model to predict the
labels of the unseen instances during the testing step. In Figure (4.10), 15503 out of
24,600 total post-lesion samples were correctly classified as belonging to the post-lesion
class. In comparison, 15854 out of 24,250 total pre-lesion instances were accurately
classified as belonging to the pre-lesion class. As well, the FP = 8396 and the FN =
9097samples, which represent the pre- and post-lesion instances that were misclassified
respectively. The values of FP and FN are important error metrics that need to be reduced
depending on the application itself and cannot be generalized for all applications; for
example, minimizing FN is important when there is a serious life-threatening disease that
can be treated effectively during the early stages. But then again, in the case of high costs
or a high risk of follow-up therapy, minimizing FP is important when the disease is not life-
104
threatening for the patient. Therefore, to assess the performance of the classifiers,
especially for medical applications, the metrics that consider the effect of these two errors
(FP and FN) need to be considered.
Predicted Classes
Post-lesion Pre-lesion
Ac
tual
Cla
sse
s
Post-
lesion TP= 15503 FN= 9097 24600
Pre-
lesion FP= 8396 TN= 15854 24250
Figure 4.10 An example of the confusion matrix results
2. Statistical performance metrics
To evaluate the performance of the applied KNN and CNN classification approaches,
five statistical metrics, including sensitivity (recall), specificity, precision, accuracy, and F-
measure, were used [142]. These metrics were calculated using the four basic measures
that are included in the confusion matrix of a classifier, including TP, TN, FP, and FN. The
five metrics were defined and calculated as follows:
Sensitivity (Recall): This measures the ratio of the actual positives that are correctly
identified.
𝑇𝑃
𝑇𝑃 + 𝐹𝑁𝑥 100%
Specificity: This measures the ratio of the actual negatives that are correctly identified.
105
𝑇𝑁
𝑇𝑁 + 𝐹𝑃𝑥 100%
Precision: This measures the ratio of the predicted instances that were correctly identified
as positive to the total predicted instances as positive.
𝑇𝑃
𝑇𝑃 + 𝐹𝑃𝑥 100%
Accuracy: This measure how well a classifier has classified the classes correctly.
𝑇𝑃 + 𝑇𝑁
𝑇𝑃 + 𝐹𝑁 + 𝑇𝑁 + 𝐹𝑃𝑥 100%
F-measure: This is the harmonic mean of sensitivity and precision.
2𝑇𝑃
2𝑇𝑃 + 𝐹𝑁 + 𝐹𝑃𝑥 100%
4.4.2. SCI classification system using classical ML classification technique
In this work, two different groups of EMG features have been utilized with the classical
ML classification techniques. The results of these different features have been illustrated
as follows:
1. SCI classification using EMG amplitude features
The four amplitude EMG features (area, RMS, turn number, and ZC number)
mentioned in Section (3.3.2.1) were combined to form one feature set. This set was used
as an input for the first SCI classification system that was developed. The most common
machine learning classification techniques in the EMG area were employed, including
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KNN and SVM. Figures (4.11), (4.12), (4.14), and (4.15) demonstrate the confusion
matrices of the classification techniques that were employed to the EMG data regarding
the left and the right EMG sides. The x-axis and the y-axis indicate the class of data (pre-
or post-lesion), and the diagonal cells of these figures show the rate of correctly classified
samples for both classes.
Using the SVM classifier, the post-lesion samples of the left side data were classified
correctly at a higher percentage (81%) compared to the pre-lesion sample (29%), as seen
in Figure (4.11). In comparison, the KNN confusion matrix demonstrated a balanced
prediction performance in terms of the number of correctly classified instances for both
classes (63% and 65%), as seen in Figure (4.12).
Figure 4.11 Confusion matrix of the SVM classifier using EMG amplitude features / left side
107
Figure 4.12 Confusion matrix of the KNN classifier using EMG amplitude features / left side
The performance of the classifiers was also evaluated using the five statistical
metrics. sensitivity, specificity, accuracy, precision, and F-measure values of the left side
are outlined in Figure (4.13). This figure shows that KNN scored the highest values of
specificity, accuracy, and precision metrics across the two classification methods. The
KNN classified the data with an accuracy of 64.1%, and the F-measure, 63.9%; in
contrast, the SVM classified the data with an accuracy of 54.9% and the F-measure,
64.2%. The SVM method with the worst performance was found to be biased when it
classified the pre-lesion samples (negative class) with a low specificity of 28.9%, while
the post-lesion samples (positive class) were classified with a reasonable degree of
sensitivity at 80.5%. According to the findings, the KNN is a more appropriate
classification method for the left side data using the EMG amplitude features.
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Figure 4.13 Evaluation metrics of the KNN and the SVM classifier using EMG amplitude features/ left side
Using the SVM classifier with the right side data, the pre-lesion samples were
correctly classified at a higher percentage (87%) compared to the post-lesion sample
(56%), as seen in Figure (4.14). The KNN technique also demonstrated an imbalanced
prediction performance with a rate of 68% for the post-lesion class and 87% for the pre-
lesion class, as seen in Figure (4.15).
Figure (4.16) represents the sensitivity, specificity, accuracy, precision, and F-
measure values of the SVM and KNN classification using the right side data. The KNN
scored the highest values of all the metrics (except for specificity) across the two
classification methods. The KNN classified the data with an accuracy of 77.4%, and the
F-measure, 75.1%; in contrast, the SVM classified the data with an accuracy of 71.9%
and the F-measure, 66.7%. The SVM classifier also showed some biased behavior with
the right side data when it classified the pre-lesion samples (negative class) with a
specificity of 87.3%, while the post-lesion samples (positive class) were classified with a
The main CNN power is its automated feature learning ability, which may play an
important role in the EMG signal field according to its complexity and nonstationary
nature; therefore, such an automated feature learning system might help in encoding the
hidden message of the motor control system to the skeletal muscles. As well, it may help
in characterizing the neuromuscular abnormalities (perturbations in the electrical activity
of the muscle) that occur as a result of neuromuscular diseases such as SCI.
Electromyography is a reliable and cost-efficient muscle activity assessment tool.
However, due to the complex process of generating this signal, analyzing and
understanding such a complicated signal is a challenging task. In this project particularly,
the EMG signal has more complexity because it was collected while the subjects were
performing their daily activities without any restrictions (freestyle movement). Even
though the CNN classification did not outperform the classical KNN method, it can still be
considered a reasonable method of preliminary classification. In future work, the SCI
classification system could be applied using a CNN system with a more advanced
structure.
4.5. Summary
Chapter 4 has presented a comprehensive intramuscular EMG analysis of the
NHP model of SCI. In order to examine the reliability of using dynamic EMG signals to
describe the SCI effects, this analysis considered the main characterization aspects of
EMG signals in terms of signal amplitude features, frequency content (pattern of
recruitment), and SCI classification ability using various techniques. Of note was the
challenge of using conventional EMG features to describe the SCI effects. As well, this
122
study submitted a preliminary volitional muscle activity metric (Q-metric) utilizing the peak
number of the EMG signal over a period of time. This metric could be advanced in the
future to be used as an SCI assessment tool. Moreover, this work proposed an EMG
decomposition method that included wavelet analysis and relative power calculations.
This method was able to detect the expected perturbations in the frequency components
of the EMG signals post-SCI. As well, it provided a preliminary inference that treatment
with a combination of TRH, selenium and vitamin E may improve SCI recovery. Together,
these results confirmed the reliability of using EMG signals as a measure of volitional
muscle activity for an NHP model.
An SCI diagnosis is currently based on a subjective visual assessment of different
biomarkers by specialists. The development of an SCI classification system would be an
essential means of advancing the diagnostic process because such a system would offer
an objective assessment tool using EMG signals. In chapter 3, different SCI classification
systems were developed using four selected EMG amplitude features, RP-WT features,
and Q-metric values, along with two different classification techniques (KNN and SVM).
A comparison study was performed to investigate the reliability of using a deep
learning (CNN) classifier with a raw EMG signal as an SCI classification system. The
results of the comparison indicate that a CNN with a simple architecture can provide a
level of performance close to the classical ML classification technique (KNN) for the
purpose of neuromuscular disease (SCI) classification. CNN has an advantage as it is
easier to implement in terms of the input data since it uses raw data. As well, throughout
123
the training process, it learns some representations (extracts some features
automatically) from the input data; therefore, it reduces the effort required for preparing
the engineered features.
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Chapter 5
Conclusions and Future Work
5.1. Conclusions
• This chapter summarizes the main outcomes of the current work. The overriding goal
of this research is to build a valid NHP model for SCI with a reliable assessment tool
to characterize the injury neurophysiologically using intramuscular EMG signals. This
contribution could facilitate the development of a model to test new treatments for SCI,
and thereby assist in improving the functionality and quality of life for human beings.
• The work presents the results of experiments involving Macaca fascicularis, in which
a model of spinal cord injury (SCI) was created by using a balloon catheter inserted
into the epidural space. Prior to the creation of the lesion, an EMG telemetry device
was inserted to facilitate the measurement of tail movement and muscle activity before
and after SCI. The telemetry device was attached to two pairs of wire electrodes,
which were tunneled into the tail. This model is unique in that the impairment was
limited to the tail: the subjects did not experience limb weakness, bladder impairment,
or bowel dysfunction. The focus of the SCI model was to create a lesion that mimics
human SCI without clinical impairments.
• The tail of Macaca fascicularis can be considered a fifth limb, which is involved in the
performance of functional tasks and balance. The tail has well-developed sensory and
motor areas.
125
• The intramuscular recordings from this model are unique. Specifically, the EMG data
were obtained prior to and subsequent to SCI. Additionally, the data were obtained
while the subjects were moving freely (dynamic movement), over an extended period
of time (longitudinal).
• Some potential limitations included the limited numbers of subjects; this is, in part
related to the cost. As well, the longitudinal nature of the collection precluded a very
high sampling rate.
• Different analysis techniques were used to develop reliable EMG markers for the
detection and the classification of SCI.
• In this study, three specific objectives were set, and they were achieved using the
methodology suggested in Chapter 3. The results are summarized in the following:
1. Regarding the development of a metric for general volitional muscle activity, two
methods were examined, which included the following:
• First, four main EMG amplitude features (area, RMS, turn, and ZC) were selected
to be used as a metric of muscle activity. Based on the statistical analysis of the
results, the selected features showed inconsistent behavior regarding the effect of
the lesion that had been created and the treatment that was utilized. This could
indicate that conventional EMG metrics are overwhelmed if they are used as
characterization metrics for EMG signals that are collected in a dynamic fashion
and with different levels of contractions. A more specialized metric was therefore
needed to overcome the additional complexity of this type of EMG signal.
126
• The alternative EMG analysis paradigm that was proposed focused on the number
of peaks in the continuous signal of the EMG and it was called the Q-metric. Given
the EMG data from these experiments, the Q-metric could be used to evaluate
impairment after SCI. Specifically, the Q-metric decreased immediately after the
experimental SCI was implemented, and then increased over time. Evidence was,
however, insufficient to ascribe the changes to a treatment-associated effect.
2. In terms of the characterization of the discharge properties of the muscle motor units
during pre- and post-SCI for the treatment and the control group, and for agonist-
antagonist pairs of muscles, the following methods were examined:
• First, the most common EMG decomposition technique found in the literature was
applied to test its efficacy with the unique nature of the EMG signal in this study.
According to the acquired invalid results, this method was not applicable for such
a unique type of EMG data; therefore, another technique needed to be
investigated.
• Wavelet analysis and the relative power of EMG frequency sub-bands were
proposed as alternative decomposition methods. The RP of these sub-bands was
used as a response variable in various mixed models to analyze the effect of the
muscle side, the induced lesion, and the treatment that was used, on the collected
EMG data. In the absence of the SCI, the EMG data demonstrated an
asymmetrical activation of the two sides; this is consistent with the human
phenomenon of limb preference and/or dominance. Perturbation with an
experimental lesion resulted in a clear EMG consequence. Of note, the effect of
127
the lesion was different on the left and the right side. As well, there was a
preliminary inference that treatment with a combination of TRH, selenium and
vitamin E could improve recovery. The results indicated that the RP of the
decomposed EMG data added a new dimension to the evaluation of impairment
and recovery in this NHP model of experimental SCI. This analysis could lead to a
new assessment index for motor unit activity and progression of recovery from
SCI.
3. In terms of designing the SCI classification system:
• All the extracted EMG features in this work, including EMG amplitude features, Q-
metric, and the RP of the WT sub-bands, were utilized to develop the SCI
classification system using machine learning techniques that included SVM and
KNN.
• Using the EMG amplitude features, KNN and SVM techniques generally classified
the data with a low performance. The KNN outperformed the SVM with an F-
measure of 63.9.0% and 75.1% for the left and the right side respectively.
• In contrast, these two classification techniques showed a higher performance
using the Q-metric combined with the RP of the WT sub-bands features. Using
these features, the KNN technique outperformed the SVM method with an F-
measure of 81.8% and 91.4% for the left and the right side respectively.
• This study also presented a new application of deep learning (CNN) as an SCI
classification system using raw EMG segments as inputs and compared the results
to the classical machine learning classification (KNN).
128
• The performance of the two classifiers was measured and compared using five
performance metrics. The KNN technique outperformed the CNN methods with an
F-measure of 89.7% and 92.7% for the left and the right side respectively. The
new application of combining CNN and a raw EMG signal for neuromuscular
disease classification achieved a competitive degree of classification accuracy
when compared to the classical machine learning techniques. CNN classified the
data with an F-measure of 84.0% and 86.9%. for the left and the right side, while
the KNN, using hand-crafted EMG features, achieved an F-measure of 89.7% and
92.7% for the left and the right side respectively. As the CNN technique is still a
new application, more investigations need to be performed in order to build a clear
understanding of the ability and the reliability of using this deep learning technique
for SCI detection and characterization using the automated learned features.
• In conclusion, the EMG and the histopathologic data from this study have
advanced the understanding of SCI. The additional research required may
translate into novel treatments for people and animals with spinal cord injuries.
5.2. Future Work
• Several potential alternative approaches to analyzing EMG data were found. The
EMG analysis paradigms that were used in the current study focused on hand-crafted
EMG features. An alternative approach is to employ automated learned feature
methods, such as CNN, AE, and RNN, and check their efficacy in translating the
hidden changes in the EMG signal as a result of an SCI. In this study, a preliminary
application of CNN showed reasonable results. The automated learned features
129
approach could also help to make other conservative EMG analysis methods more
applicable for dynamic EMG data; e.g. it could help to overcome the problem of
applying the conventional decomposition method (MUPT method).
130
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List of Publications Some of the work described in this thesis previously appeared in:
1- Masood, F., Abdullah, H.A., Seth, N., Simmons, H., Brunner, K., Sejdic, E., & Schalk, D.R., et al. "Neurophysiological Characterization of a Non-Human Primate Model of Traumatic Spinal Cord Injury Utilizing Fine-Wire EMG Electrodes." Sensors 19, no. 15 (2019): 3303.
2- Seth, N., Simmons, H.A., Masood, F., Graham, W.A., Rosene, D.L., Westmoreland, S.V., & Cummings, S.M. et al. "Model of Traumatic Spinal Cord Injury for Evaluating Pharmacologic Treatments in Cynomolgus Macaques (Macaca fasicularis)." Comparative Medicine 68, no. 1 (2018): 63-73.
3- Masood, F., Farzana, M., Nesathurai, S., & Abdullah, H., "Comparison Study of Classification Methods of Intramuscular EMG Data for Non-Human Primate Model of Traumatic Spinal Cord Injury." Journal of Engineering in Medicine (under review)
4- Masood, F., Abdullah, H. A., Sharma, M., Mand, D., Simmons, H., Brunner, K., Schalk, D., Sledge, J., & Nesathurai, S., “A Novel Application of Deep Learning (Convolutional Neural Network) for Spinal Cord Injury Classification Using Raw EMG Signal.” (under review)
5- Seth, N., Masood, F., Sledge, J.B., Graham, W.A., Rosene, D.L., Westmoreland, S., & Abdullah, H.A. “Humane Non-human Primate Model of Traumatic Spinal Cord Injury: Quantitative Analysis of Electromyographic Data.” Open Journal of Veterinary Medicine, 5(07), (2015): p.161.
6- Gwardjan, B., Nitin, S., Masood, F., Sledge, J., Rosene, D., Westmoreland, S., & Abdullah, H. “Treating Acute Traumatic Spinal Cord Injury with Combination Therapy of Thyrotropin Releasing Hormone, Selenium and Vitamin E.” Journal of Neuropathology and Experimental Neurology Vol. 75, No. (6), (2016, June): pp. 605-606.
7- Fayez, O., Johnson, K., Simmons, H., Brunner, K., Schalk, D., Mensinkai, A., & Sedjic, E. “Pathological and Radiological Features of Traumatic Spinal Cord Injury”. In Journal of Neuropathology and Experimental Neurology 78(6), (2019, June): pp. 579-579.