Md Kafiul Islam

Translational System and Signal Processing Group

Dept. of Electrical & Computer Engineering

National University of Singapore

Purpose:

◦ Diagnosis/Detection of Epilepsy Seizure by Long-term EEG Monitoring (up

to 72 hours)

◦ Early warning of seizures (prediction) onset in order to stop seizure

Special Features

◦ Continuous monitoring

◦ Wireless EEG (up to 10 m or so)

◦ Usually Dry electrode

◦ Few no. of channels

typically 4-8 channels

◦ Patient can lead normal daily life

Except few tasks (e.g. taking bath)

Head Movement ( < 10 Hz )

◦ Shake

◦ Nod

◦ Roll

Hand Movement

Eye Movement

◦ Eyebrow Movement

◦ Eye blinking

◦ Horizontal and Vertical Eye Movement

◦ Extra-ocular muscle activity

Jaw Clenching/Eating/Drinking

Walking

Running

Sleeping

◦ Drowsiness, Light Sleep, Deep Sleep, REM

Talking

Working on PC

Watching TV

And Many More!

Consequence:

Too Many Motion Artifacts!

Challenge: Differentiate

between artifacts, epileptic

seizures and normal EEG activities!

How …!???

Horizontal Eye

Movement (T1-T2)

and

Blink Artifacts (Frontal channels)

EOG

*Chang, B. S., Schachter, S. C., & Schomer, D. L. (2005). Atlas of ambulatory EEG.

Amsterdam ; London: Amsterdam ; London : Elsevier Academic Press, c2005.

Rhythmic eye flutter can occur at quite high frequencies, in this case 5–8 Hz, and trigger automated seizure detection algorithms.

This artifact can be distinguished from ictal events in different ways, including the lack of evolution in frequency or amplitude over time.

Eye Flutter Artifact

recorded by Seizure

Detection Algorithm

Electrode Tapping

Artifact

With ambulatory EEG recording, there may be periods in which the patientis scratching or pressing on the scalp electrodes.

Jaw-Clenching

Artifact

Periods of jaw clenching and biting during ambulatory EEG recording can be associated with artifact.

This activity is often most prominent over the temporal regions, presumably due to temporalis or masseter muscle contraction.

Chewing Artifact

Chewing artifact can often have rhythmic features and at times may resemble ictal activity.

This artifacts are characterized by the rhythmic appearance of muscle artifact centered mostly over the temporal regions bilaterally.

Chewing Artifact

Recorded by

Spike Detection

Algorithm

Chewing artifact can have rhythmic and sharp features, and can thus be picked up by automated detection algorithms.

Here, the spike detections at 23:53:23 and at 23:53:34 capture high frequency sharp waveforms that are artifactual in nature, in association with a nighttime snack.

Dry Electrode Artifact

One disadvantage of ambulatory EEG monitoring is the inability of technologists to check on recording quality frequently in real time and to respond with appropriate technical adjustments as needed throughout the day, as would typically occur in an inpatient monitoring unit.

Here, a dry electrode at F8 causes an artifact seen throughout this excerpt.

Forehead Rubbing

Artifact

As with many other patient movement artifacts, rhythmic rubbing artifact can be a potentially confusing finding on ambulatory EEG monitoring, particularly in the absence of concomitant video recording. Here, rhythmic rubbing of the forehead produces an artifact in the anterior channels, extending from the beginning of this excerpt through 20:50:16.

Differentiation of such artifact from an ictal event, based on the lack of evolution of the artifact in frequency or amplitude, is critical.

Pulse Artifact

Pulse artifact is caused by the rhythmic movement of scalp electrodes, usually over the temporal regions, by the pulsations of blood through vessels close to the skin, such as the superficial temporal artery. It is to be distinguished from the more common electrocardiographic (EKG) artifact in that pulse artifact is typically not as sharply contoured, resembles a slow wave, and follows the QRS complex rather than being simultaneous to it. Here, pulse artifact is seen in channels 9 and 10 over the left temporal region throughout this entire excerpt.

Delta: (< 4 Hz)

Theta: (4 - 8 Hz)

Alpha: (8 - 13 Hz)

Beta: (14 - 30 Hz)

Gamma: (> 30 Hz)

Mu: (7.5 - 12.5 Hz)

Gamma

Use of an extra sensor

◦ Adaptive filtering

Accelerometer

Gyroscope

Contact Impedance Measurement

A-priori user input required

◦ Wiener/Kalman/Particle Filtering

Blind Source Separation

◦ ICA/CCA/MCA

Time-series Analysis

◦ Wavelet transform / STFT

Empirical Technique

◦ HHT/EMD/EEMD

Artifact Generation Protocol (From Literature)

Domain◦ Time◦ Frequency◦ Wavelet

Statistical Features◦ Entropy

ApEn, MSE, CMSE, MMSE, etc.

◦ Kurtosis◦ Skew◦ RMS/Variance/S.D.◦ Max/Min/Mean◦ Maxima/Minima

High Frequency Oscillations (HFO)80 Hz – 200 Hz

Ripple200 Hz – 600 Hz

Typical Scalp EEG!0.05Hz – 128 Hz

Seizure Appears in Scalp EEG0.5 Hz - 29 Hz [1,2]

1. J. Gotman, “Automatic recognition of epileptic seizures in the EEG,”Electoencephalogr Clin. Neurophysiol., vol. 54, pp. 530–540, 1982

2. M. E. Saab and J. Gotman, “A system to detect the onset of epilepticseizures in scalp EEG,” Clin. Neurophysiol., vol. 116, pp. 427–442,2005.

Seizure Appears between 0.5 Hz to 29 Hz

Raw EEG

D1

(64 – 128 Hz)

A1

(0 – 64 Hz)

D2

(32 – 64 Hz)

A2

(0 – 32 Hz)

D3

(16 – 32 Hz)

A3

(0 – 16 Hz)

D4

(8 – 16 Hz)

A4

(0 – 8 Hz)

D5

(4 –8 Hz)

A5

(0 – 4 Hz)

D6

(2 – 4 Hz)

A6

(0 – 2 Hz)

D7

(1 – 2 Hz)

A7

(0 – 1 Hz)

D8

(0.5 – 1 Hz)

A8

(0 – 0.5 Hz)

Multilevel Wavelet DecompositionFs = 256 Hz, L = 8, Haar Wavelet

Artifact Removal Applied on Simulated Data

o Simulated Clean EEG (downloaded from [1])

o Simulated Seizure (downloaded from [2])

o Artifact Simulation (to mimic extreme ambulatory condition)

[1]. http://www.cs.bris.ac.uk/~rafal/phasereset/[2]. Newborn EEG Seizure Simulation

Performance Evaluation of Proposed Artifact Removal

-0.8 -0.7 -0.6 -0.520

25

30

35

40

45

50

55

Artifact SNR in dB

%ar

tifac

t red

ucti

on, l

amda

o The target is to remove artifacts as much as possible

o Artifact removal should enhance the performance of later signal processing stage (e.g. increase seizure detection rate)

o Average amount of artifact reduction is around 35% when artifact SNR is around 0 dB

o Not all artifacts affect seizure detection performance

EEG Features before and after Artifact Removal

Features Extracted: (i) Entropy (ii) Kurtosis (iii) Line Length (iv) Peak(v) NEO (vi) Variance (vii) FFT (viii) FFT Peak

Note: The features between seizure and non-seizure data are more separable after artifact removal which suggests that it increases the detection rate and minimizes false alarms (false alarms are due to artifacts).

Characterize movement artifacts by doing EEG experiments

Use real seizure data marked by the epilepsy specialists

Improve the algorithm by avoiding ICA

Use classification techniques to differentiate seizure from non-seizure data and evaluate the performance of proposed algorithm