Physiological data acquisition and data analytics for …...Physiological data acquisition and data analytics for neurologists Chou-Ching Lin, MD PhD Department of Neurology & Department

Physiological data acquisition and data analytics for neurologists

Chou-Ching Lin, MD PhD

Department of Neurology & Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan

2

Introduction

2

Aims of this talk

This lecture is important to provide the dual

understanding on

• what neurologists should know about data acquisition and

basic methods/types of data analysis.

• also to describe to engineers what types of data clinicians

would be looking for in the clinical interpretation and how

current technology can assist in this translation.

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4

Physiological signals

4

Types of physiological signals

• Single shot Example

– 1D: a number NCV

– 2D: a figure MRS

– 3D: a volume of figures PET, SPECT

• Time series

– 1D: a number EEG (today’s focus)

– 2D: a figure EEG mapping

– 3D: a volume of figures functional MRI

5

Physical nature of physiological signals

• Electrophysiology

• Sonography

• CT

• MRI

• Nuclear radiation

• Movement parameters

• …

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7

Electrophysiological signals

7

Basic terms in electrophysiology

e- e- e- e- e-

Voltage

Potential

Charge

CurrentResistance

Impedance

V = I RVoltage Current Resistance

8

Membrane potential

• Goldman constant-field equation

V = ln ( )RT Pk·[Ko+] + PNa·[Nao

+] + PCl·[Cli-]

F Pk·[Ki+] + PNa·[Nai

+] + PCl·[Clo-]

[Na+] 15 mM

[K+] 150 mM[Cl-] 10 mM

[Na+] 150 mM[K+] 5 mM

[Cl-] 120 mM

e-e-

e-e-

e-

e+

e+e+

e+e+

Separation of ions leads

to potential field

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How potential field is generated

• Mainly by separation of

ions by ion pump

– Needs consumption of ATP

• Potential x charge equals to

potential energy

– Can be used to produce ATP

10

Ion channels on cell membrane

Potential

change

11

Voltage Gated Ion Channel

12

Scope of electrophysiological signals

There is at least one electrophysiological study for

an anatomical site interested by Neurologists

• Cerebral cortex EEG

• Major sensory tracts VEP, BAEP, SSEP

• Major motor tract MEP

• Peripheral nerve NCV

• Muscle EMG

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14

Scheme of signal handling

14

Detection

Collection

Digitization Processing

Analysis

Physiological data Conditioning

Signal flow

15

16

Signal detection

• The nature of the signal

• What kind of device to use

• Impedance match

16

Volume conductor

e- e- e-

e-e-

e-V A / rCharge DistancePotential

17

Properties of volume conductor

though

• Signal amplitude proportional

inversely to distance

to conductivity

• Additive from different sources

e-

e-

All signals in the body will be detected

18

Detection of electrical signal

• Amplitude of EEG ~ 10-6 V

• AAA battery: 1.5 VDifferential amplifier

Scalp

Skull

Cerebral cortex

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Homogenous

populationWhole brain

Action

potential

Regional

circuit

Level to detect: what technique?

Single cell

Field

potential

EEGMembrane

potential

20

Patch clamp

Intracellular

recording

Extracellular

recording

Probe array

Extracellular

recording

Single probe

Extra-cortical

recording

Multiple probes

Natural filtering by tissues

(E. Niedermeyer, 1999)

Action potentials in nerve fiber

Low pass filtering by tissue

High pass filtering by EEG machine

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Signals recorded by EEG machine

Heart

Muscle

Noise

(Phone,vibration)

EEG Machine

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23

Signal conditioning

• To facilitate identification

• To facilitate collection

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Amplification and sensitivity

1 x

1/2 x

1/4 x

50 (mV)

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• Amplification is determined by the hardware capability

• Sensitivity is adjustable by the software

Example _Sensitivity

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LPF: 70 Hz

HPF: 0.3 Hz

Sens: 200 uV p-p

LPF: 70 Hz

HPF: 0.3 Hz

Sens: 100 uV p-p

Signal/Noise ratio (S/N ratio)

• Just make the signal larger won’t make it clearer

• What is more important is S/N ratio

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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-4

-2

0

2

4

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-4

-2

0

2

4

X 1

X 2

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Signal digitization

• Need to determine the sampling rate

• Need to consider the resolution

Sampling rate

• How precise in horizontal axis

• Usually defined as points in a second (Hz)

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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-1.5

-1

-0.5

0

0.5

1

1.5

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-1.5

-1

-0.5

0

0.5

1

1.5

100 Hz

5 Hz

Shanon’s theory:

Nyquist frequency

Resolution

• How precise in vertical axis

• Usually defined in bit. – Ex. 4 bits means 16 possible values.

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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-1.5

-1

-0.5

0

0.5

1

1.5

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-1.5

-1

-0.5

0

0.5

1

1.5

< 0.01

0.25

Matching amplification and resolution

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0

2

4

6

0

14

0

2

4

6

0

7

0

2

4

6

0

3.5

Range of signal amplitude after amplification

Adequate Too smallToo large

Amplification

Range of signal amplitude after amplification

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Signal processing

• Filtering

• Phase lock averaging

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Basic concept of a filter

High pass filter

Pass the fast changing component

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Low pass filter

Pass the slow changing component

Purpose of using filter

1. To retain certain portion of frequencies

2. Also to remove noise

• Frequency– Unit: Hz (How many swings per second)

– Ex. Heart rate 60/min is equal to ? Hz

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The concept of domain

• Time domain

• Frequency domain

Time (second)

0 1 0 1

Frequency (Hz)

2 3 4 5 6 7 8 9

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Fourier Transform

• To switch a signal from time domain to frequency

domain

• Inverse Fourier transform does the contrary

• In fact, there are a lot of transform in Engineering

– A hot example: HHT (Hilbert-Huang Transform)

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The concept of filter

0 1

Frequency (Hz)

2 3 4 5 6 7 8 9

1

0

0 1 2 3 4 5 6 7 8 9

FT

IFT

0 1

Time (second)0 1

36Time (second) Frequency (Hz)

Types of filter

• Low pass

• High pass

• Band pass

• Band stop

37Frequency

Cut-off frequency

• In real world, the filter cannot be perfect. There is

a gradual transition from pass zone to stop zone.

• Cut-off frequency is defined as the frequency that

the amplitude drop 3 dB.

Frequency

1

-3 dB (~ = 0.7)

Cut-off frequency

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Low pass filter

0 50 (Hz)

0 10 (Hz)

0 2 (Hz)

0 1 (Hz)

39

High pass filter

0 50 (Hz)

0.4 50 (Hz)

0.8 50 (Hz)

10 50 (Hz)

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Example _HPF

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LPF: 70 Hz

HPF: 1.0 Hz

Sens: 200 uV p-p

LPF: 70 Hz

HPF: 0.3 Hz

Sens: 200 uV p-p

Example _LPF

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LPF: 15 Hz

HPF: 0.3 Hz

Sens: 200 uV p-p

LPF: 70 Hz

HPF: 0.3 Hz

Sens: 200 uV p-p

Example _over filter

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LPF: 70 Hz

HPF: 2 Hz

Sens: 200 uV p-p

LPF: 70 Hz

HPF: 0.3 Hz

Sens: 200 uV p-p

Phase Locked Averaging

• A technique frequently used in EP

• For a measured signal

T = S + N

• Phase locked averaging

mean (T) = mean (S) + mean (N)

but mean (N) tends to 0,

if N is white noise

stimulus

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Signal / Noise Ratio _Example

0

2

4

0

2

4

0 5 100

2

4

0

2

4

0

2

4

0 5 100

2

4

N = 0.62 N2 = N/5

S = 0.1 S = 0.1

S + N S + N2

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Signal analysis

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Linear Methods

• Spectral analyses

– Fourier transformation & power spectrum

• Least estimator

• Principal component analysis

• Common spatial processing or filtering

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Non-linear Methods

• Independent component analysis

• Wavelet decomposition

• Chaotic system

– Dimension estimation

• Hilbert-Huang Transformation (HHT)

• Neural network

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Neural Networks (NN)

• Shallow NN

– Ensemble learning with stacking

– Multilayer perceptron (MLP)

• Deep NN

– Convolutional NN

– Recurrent NN

– LSTM (Long Short-Term Memory)

– GAN (Generative Adversarial Network)

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WNN _Example of shallow NN

1st layer

16

neurons

Input

16

neurons

Output

layer

4 wavelet

decomposition

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6 s

ample

s/ s

ec

Ex

per

imen

t d

ata Identification

Results

2nd layer

16

neurons

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Training template

EMG

(mV)

EEG

(mV

)

Beeper

(V) Output

target

3

1

-1

10

-10

200

-200

0

2 4 6 8 10

Time(sec)

0

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Example Results

0~15 sec

15~30 sec

30~45 sec

45~60 sec

60~75 sec

75~90 sec

90~105 sec

105~120 sec

120~135 sec

135~150 sec

EMG

WNN Results

Movement identified

No movement identifiedAccuracy：60%

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Deep NN _Example

• Augmented reality (AR) through

an intelligent glasses

MS student Ho

Image processing with DNN

• Textboxes

• CRNN

• MobileNetV2

Real world scenario

Training at a PC with GPU cards

Label recognition with DNN

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