Analysis of Temporal Lobe Paroxysmal Events Using Independent Component Analysis Jonathan J. Halford MD

Department of Neuroscience, Medical University of South Carolina, Charleston SC

ObjectiveAbstract

Background

Materials & Methods

Discussion

ReferencesThe mICA algorithm is implemented in MATLAB using an Infomax ICA algorithm provided as part of the EEGLAB software analysis toolbox.

A Delorme & S Makeig. "EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics," Journal of Neuroscience Methods 134:9-21 (2004)

Rationale: Determining whether a paroxysmal EEG event in the temporal lobe is normal or abnormal is difficult. Normal temporal lobe paroxysmal events such as exaggerations of alpha activity, wicket spikes, and small sharp spikes can have an epileptiform appearance. This study uses a new implementation of independent component analysis (ICA) called multi-taper ICA (mICA) to analyze temporal lobe paroxysmal EEG discharges. The mICA technique performs ICA multiple times with multiple window lengths on an EEG dataset. It is able to detect paroxysmal EEG events and represent each event with its own independent component and scalp distribution.

Methods: A database of 101 de-identified 30-second segments of routine EEGs was prepared. This database included 61 segments containing temporal paroxysmal discharges from 50 patients without of history of epilepsy which were judged to be normal but difficult to interpret. Also placed in this database were 40 segments containing abnormal epileptiform discharges from 29 patients with known epilepsy. These abnormal epileptiform discharges were chosen for being subtle due to their relatively low amplitude. The mICA technique was used to generate independent components (ICs) containing the 101 paroxysmal discharges. Various characteristics of the ICs and scalp distributions of these discharges were studied to find characteristics which differentiated normal from abnormal temporal discharges. Characteristics studied include the scalp distribution, the number of ICs detected by mICA for each discharge, and the peak spectral frequency of the ICs.

Results: Characteristics associated with abnormal epileptiform paroxysmal discharges include: (1) mICA detection of three or more ICs for the discharge (2) discharges which produced ICs longer than one second in duration (3) ICs with scalp distributions which were not bitemporal (4) ICs with scalp distributions in which the temporal lobes had opposite polarities (5) ICs with scalp distributions which did not involve the occipital region (6) ICs with scalp distributions which had a low weights polarity (7) ICs with scalp distributions which were very focal due to involving only two or three electrodes in a standard 10-20 montage (8) and ICs which had a peak spectral frequency in the delta range due to an associated slow wave. Using these eight characteristics, the mICA technique for identifying abnormal epileptiform discharges labeled the discharges as abnormal with a sensitivity of 71% and a specificity of 82%.

Conclusions: The study demonstrates subtle differences between normal and abnormal paroxysmal temporal EEG discharges. This technique could be implemented in routine EEG interpretation by allowing the EEG reader to highlight a short portion of one channel of EEG for running mICA analysis on. This study could be improved by including characteristics of the waveform shape (such as half-wave analysis and/or wavelet analysis) and more detailed frequency spectrum analysis.

STEP ONE: ICA is calculated multiple times using multiple overlapping window sizes and locations throughout the EEG record. The independent components (ICs) produced by these many ICA calculations are stored in a database

This study demonstrates that the mICA algorithm can capture paroxysmal EEG discharges and help characterize them. Characteristics of abnormal paroxysmal discharges

(1) The suggests that the discharge is very paroxysmal because it is easy to capture with mICA

(2) Alpha activity spreading into the temporal region often has a bitemporal scalp distribution.

(3) This is evidence of a dipole projecting across the head.(4) This suggests the discharge is a brief accentuation of alpha activity.This suggests that there is an associated slow wave which is part of the

discharge.

The purpose of this study is to use a new computer algorithm called multi-taper independent component analysis (mICA) to study temporal EEG paroxysmal discharges. The mICA algorithm has been developed by the author and uses ICA in a new way to provide an automated computer method for detecting, collecting, and studying EEG paroxysmal discharge.

Results

This is an example of a discharge with a bitemporal scalp distribution, suggesting that it is not abnormal and likely an accentuation of normal alpha activity extending into the temporal region:

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The interpretation of paroxysmal EEG events in the temporal regions can be difficult. Patients with partial epilepsy commonly have abnormal spike and sharp-wave discharges in this region. But other normal temporal paroxysmal discharges such as wicket spikes, small sharp spikes and asymmetrical temporal alpha activity as well as artifacts such as electrode pop artifact, movement artifact, and EMG artifact can have a similar appearance. The incorrect labeling of normal discharges and artifacts as abnormal epileptiform discharges is one of the most common causes of misdiagnosis in EEG interpretation.

Independent component analysis (ICA) is a computerized mathematical algorithm for separating source signals from a mixture of signals. It can be useful for studying paroxysmal events since it can separate out EEG events from other background activity and define the scalp distribution. The limitations for using conventional ICA to isolate paroxysmal EEG events are twofold. First, ICA does not provide a method for measuring the length of EEG events since the length of the event is determined by the window length a priori. Secondly, conventional ICA is limited to isolating a number of event types equal to the number of scalp electrodes, which could lead to events being missed. A new method of using ICA was developed as part of this study to circumvent these problems.

The mICA algorithm involves three steps:

STEP TWO: ICs in the database which are paroxysmal are retained and others are discarded. Paroxysmal ICs are defined as having higher integrated amplitude in Zone A in comparison to Zone B. A paroxysmal event index (PEI) is calculated for each IC as the ratio of the integrated amplitude in Zone A divided by the integrated amplitude in Zone B.

STEP THREE: Often many independent components are present in the database for each paroxysmal discharge in the EEG record. Because of this, the independent components in the database are compared to each other to find out which represent the same pattern. Only one independent component representing each pattern in the EEG record is retained.

Below is an example of a 10 second epoch of EEG showing the paroxysmal ICs captured by the mICA algorithm:

The mICA analysis detected all of the 101 paroxysmal EEG discharges except for three. All three were non-epileptiform normal discharges.

Characteristics of ICs which were associated with epileptiform abnormality include:

(1) More than two ICs are captured by mICA for the paroxysmal discharge. (2) The ICA of longest duration captured for that paroxysmal discharge

was longer than one second.(3) The IC does not have a bitemporal scalp distribution. (4) The scalp distribution of the IC shows the ipsilateral temporal lobe to be

negative and the contralateral temporal lobe to be positive. (5) The scalp distribution of the discharge does not extend into the occipital

region. (6) The scalp distribution does not contain both a very negative region and

a very positive region.(7) The discharge is very focal and involves only three electrodes or less.(8) There is a low peak spectral frequency of less than six Hz.

Using these 8 characteristics there was a sensitivity of 71% and a specificity of 82% for labeling which discharges were abnormal epileptiform discharges.

This is an example of a paroxysmal discharge which is very focal (involving mainly three electrodes), suggesting that it is an abnormal epileptiform discharge.

This is an example of a discharge which has a low peak spectral frequency (below 2 Hz) due to the large associated slow wave. This suggests that it is an abnormal epileptiform paroxysmal temporal discharge.

One year of routine EEGs performed at the MUSC Neurophysiology Lab were reviewed by the author (over 1000 EEGs). 351 EEGs read at MUSC as “normal” were reviewed in detail by the author. A database of 101 de-identified 30-second segments of routine EEGs was prepared. This database included 61 segments containing temporal paroxysmal discharges from 50 patients without of history of epilepsy which were judged to be normal but difficult to interpret. Also placed in this database were 40 segments containing abnormal epileptiform discharges from 29 patients with known epilepsy. These abnormal epileptiform discharges were chosen for being subtle due to their relatively low amplitude. Another parallel database of ‘EEG tags’ was created using short segments of EEG from just one EEG channel which best represented each paroxysmal discharge.

The mICA analysis was performed in these steps:(1) The mICA analysis was run on four-second windows of EEG data

containing the 101 paroxysmal discharges. This mICA analysis produced 991 ICs

(2) These 991 ICs were compared with each other and it was determined that they represent 379 unique paroxysmal EEG events. (Many more paroxysmal events are captured than just the 101 paroxysmal events being studied.)

(3) To determine which of these 379 ICs represent one of the 101 paroxysmal events being studied, the 101 EEG tags are compared to the 379 ICs the ICs which match one of the 101 paroxysmal events were labeled.

(4) The ICs which matched with tags were studied to determine which IC characteristics were associated with normal versus abnormal paroxysmal events. IC characteristics studied included the peak spectral frequency of the IC, the length of the IC, the ease with which mICA captured the discharge, and the scalp distribution of the IC.