Ali Shahidi Zandi

Automated Real-Time Epileptic Seizure Detection in Scalp EEG Recordings Using an Algorithm Based on Wavelet Packet Transform

A novel wavelet-based algorithm for real-time detection of epileptic seizures using scalp EEG is proposed. In a moving-window analysis, the EEG from each channel is decomposed by wavelet packet transform. Using wavelet coefficients from seizure and nonseizure references, a patient-specific measure is developed to quantify the separation between seizure and nonseizure states for the frequency range of 1-30 Hz. Utilizing this measure, a frequency band representing the maximum separation between the two states is determined and employed to develop a normalized index, called combined seizure index (CSI). CSI is derived for each epoch of every EEG channel based on both rhythmicity and relative energy of that epoch as well as consistency among different channels. Increasing significantly during ictal states, CSI is inspected using one-sided cumulative sum test to generate proper channel alarms. Analyzing alarms from all channels, a seizure alarm is finally generated. The algorithm was tested on scalp EEG recordings from 14 patients, totaling 75.8 h with 63 seizures. Results revealed a high sensitivity of 90.5% , a false detection rate of 0.51 h-1 and a median detection delay of 7 s. The algorithm could also lateralize the focus side for patients with temporal lobe epilepsy.

Predicting Epileptic Seizures in Scalp EEG Based on a Variational Bayesian Gaussian Mixture Model of Zero-Crossing Intervals

A novel patient-specific seizure prediction method based on the analysis of positive zero-crossing intervals in scalp electroencephalogram (EEG) is proposed. In a moving-window analysis, the histogram of these intervals for the current EEG epoch is computed, and the values corresponding to specific bins are selected as an observation. Then, the set of observations from the last 5 min is compared with two reference sets of data points (preictal and interictal) through novel measures of similarity and dissimilarity based on a variational Bayesian Gaussian mixture model of the data. A combined index is then computed and compared with a patient-specific threshold, resulting in a cumulative measure which is utilized to form an alarm sequence for each channel. Finally, this channel-based information is used to generate a seizure prediction alarm. The proposed method was evaluated using ~ 561 h of scalp EEG including a total of 86 seizures in 20 patients. A high sensitivity of 88.34% was achieved with a false prediction rate of 0.155 h-1 and an average prediction time of 22.5 min for the test dataset. The proposed method was also tested against a Poisson-based random predictor.

An entropy-based approach to predict seizures in temporal lobe epilepsy using scalp EEG

We describe a novel algorithm for the prediction of epileptic seizures using scalp EEG. The method is based on the analysis of the positive zero-crossing interval series of the EEG signal and its first and second derivatives as a measure of brain dynamics. In a moving-window analysis, we estimated the probability density of these intervals and computed the differential entropy. The resultant entropy time series were then inspected using the cumulative sum (CUSUM) procedure to detect decreases as precursors of upcoming seizures. In the next step, the alarm sequences resulting from analysis of the EEG waveform and its derivatives were combined. Finally, a seizure prediction index was generated based on the spatio-temporal processing of the combined CUSUM alarms. We evaluated our algorithm using a dataset of ∼21.5 hours of multichannel scalp EEG recordings from four patients with temporal lobe epilepsy, resulting in 87.5% sensitivity, a false prediction rate of 0.28/hr, and an average prediction time of 25 min.

A novel wavelet-based index to detect epileptic seizures using scalp EEG signals

In this paper, we propose a novel wavelet-based algorithm for the detection of epileptic seizures. The algorithm is based on the recognition of rhythmic activities associated with ictal states in surface EEG recordings. Using a moving-window analysis, we first decomposed each EEG segment into a wavelet packet tree. Then, we extracted the coefficients corresponding to the frequency band of interest defined for rhythmic activities. Finally, a normalized index sensitive to both the rhythmicity and energy of the EEG signal was derived, based on the resulting coefficients. In our study, we evaluated this combined index for real-time detection of epileptic seizures using a dataset of ∼11.5 hours of multichannel scalp EEG recordings from three patients and compared it to our previously proposed wavelet-based index. In this dataset, the novel combined index detected all epileptic seizures with a false detection rate of 0.52/hr.

Scalp EEG Acquisition in a Low-Noise Environment: A Quantitative Assessment

This pilot study investigates effects of an ultrashielded capsule at the low-noise underground laboratory (LSBB), Rustrel, France, when used to acquire scalp electroencephalogram (EEG). Analysis of EEG recordings from three volunteers confirms that clean EEG signals can be acquired in the LSBB capsule without the need for notch filtering. In addition, using different setups for acquiring EEG in the capsule, statistical analysis of power spectral densities based on a geodesic distance measure reveals that a laptop and a patient module do not introduce any noise on recorded signals. Moreover, the current study shows that the backward counting task as a mental activity can be better detected using the EEG acquired in the capsule due to the higher level of β-band activities. The counting-relaxed β -band energy ratio is calculated using the S transform and compared between the hospital and capsule, revealing significantly higher values in the capsule (p <; 0.05). Exploring the relative β-band energy (ratio of β-band energy to that of 0-12 Hz in the counting state) reveals that the average of this measure is higher in the capsule for all subjects. Those results demonstrate the potential of the LSBB capsule for novel EEG studies, including the establishment of novel low-noise EEG benchmarks.

Predicting temporal lobe epileptic seizures based on zero-crossing interval analysis in scalp EEG

A novel real-time patient-specific algorithm to predict epileptic seizures is proposed. The method is based on the analysis of the positive zero-crossing intervals in the scalp electroencephalogram (EEG), describing the brain dynamics. In a moving-window analysis, the histogram of these intervals in each EEG epoch is computed, and the distribution of the histogram value in specific bins, selected using interictal and preictal references, is estimated based on the values obtained from the current epoch and the epochs of the last 5 min. The resulting distribution for each selected bin is then compared to two reference distributions (interictal and preictal), and a seizure prediction index is developed. Comparing this index with a patient-specific threshold for all EEG channels, a seizure prediction alarm is finally generated. The algorithm was tested on ∼15.5 hours of multichannel scalp EEG recordings from three patients with temporal lobe epilepsy, including 14 seizures. 86% of seizures were predicted with an average prediction time of 20.8 min and a false prediction rate of 0.12/hr.

Detection of epileptic seizures in scalp electroencephalogram: an automated real-time wavelet-based approach.

Summary This study evaluates a new automated patient-specific method for epileptic seizure detection using scalp electroencephalogram (EEG). The method relies on a normalized wavelet-based index, named the combined seizure index (CSI), and requires a seizure example and a nonseizure EEG interval as reference. The CSI is derived for every epoch in each EEG channel and is sensitive to both the rhythmicity and relative energy of that epoch and the consistency of EEG patterns among different channels. Increasing significantly as seizures occur, the CSI is monitored using a one-sided cumulative sum test to generate appropriate alarms in each channel. A seizure alarm is finally generated according to channel-based information. The proposed method was evaluated using the scalp EEG test data of approximately 236 hours from 26 patients with a total of 79 focal seizures, achieving a high sensitivity of approximately 91% with a false detection rate of 0.33 per hour and a median detection latency of 7 seconds. In addition, statistical analysis revealed that the average CSI around the onset on the side of the focus in patients with temporal lobe epilepsy (TLE) is significantly greater than that of the opposite side (P < 0.001), indicating the capability of this index in lateralizing the seizure focus in this type of epilepsy.

Electroconvulsive Therapy: A Model for Seizure Detection by a Wavelet Packet Algorithm

Electroconvulsive therapy (ECT) is an effective treatment for severe depression. In this paper, we have used an algorithm based on wavelet packet (WP) analysis of EEG signals to detect seizures induced by ECT. After determining dominant frequency bands in the ictal period during ECT, the energy ratio of these bands was computed using the corresponding WP coefficients. This ratio was then used as an index to recognize seizure periods. Four different approaches to detect ECT seizures were employed in 41 EEG recordings from nine patients. Sensitivity in ECT seizure detection ranged from 76 to 95% while the false detection rate ranged from 6 to 13.

Epileptic seizure prediction using variational mixture of Gaussians

We propose a novel patient-specific method for predicting epileptic seizures by analysis of positive zero-crossing intervals in scalp electroencephalogram (EEG). In real-time analysis, the histogram of these intervals for the current EEG epoch is computed, and the values which correspond to the bins discriminating between interictal and preictal references are selected as an observation. Then, the set of observations from the last 5 min is compared with two reference sets of data points (interictal and preictal) using a variational Gaussian mixture model (GMM) of the data, and a combined index is computed. Comparing this index with a patient-specific threshold, an alarm sequence is produced for each channel. Finally, a seizure prediction alarm is generated according to channel-based information. The proposed method was evaluated using ∼40.3 h of scalp EEG recordings from 6 patients with total of 28 partial seizures. A high sensitivity of 95% was achieved with a false prediction rate of 0.134/h and an average prediction time of 22.8 min for the test dataset.

Scalp EEG quantitative analysis : automated real-time detection and prediction of epileptic seizures

As a chronic neurological disorder, epilepsy is associated with recurrent, unprovoked epileptic seizures resulting from a sudden disturbance of brain function. Long-term monitoring of epileptic patients’ Electroencephalogram (EEG) is often needed for diagnosis of seizures, which is tedious, expensive, and time-consuming. Also, clinical staff may not identify the seizure early enough to determine the semiology at the onset. This motivates EEG-based automated real-time detection of seizures. Apart from their possible severe side effects, common treatments for epilepsy (medication and surgery) fail to satisfactorily control seizures in ∼25% of patients. EEG-based seizure prediction systems would significantly enhance the chance of controlling/aborting seizures and improve safety and quality of life for patients. This thesis proposes novel EEG-based patient-specific techniques for real-time detection and prediction of epileptic seizures and also presents a pilot study of scalp EEGs acquired in a unique low-noise underground environment. The proposed detection method is based on the wavelet packet analysis of EEG. A novel index, termed the combined seizure index, is introduced which is sensitive to both the rhythmicity and relative energy of the EEG in a given channel and considers the consistency among different channels at the same time. This index is monitored by a cumulative sum procedure in each channel. This channel-based information is then used to generate the final seizure alarm. In this thesis, a prediction method based on a variational Bayesian Gaussian mixture model of the EEG positive zero-crossing intervals is proposed. Novel indices of similarity and dissimilarity are introduced to compare current observations with the preictal and interictal references and monitor the changes for each channel. Information from individual channels is finally combined to trigger an alarm