An algorithm for seizure onset detection using intracranial EEG

Abstract

This article addresses the problem of real-time seizure detection from intracranial EEG (IEEG). One difficulty in creating an approach that can be used for many patients is the heterogeneity of seizure IEEG patterns across different patients and even within a patient. In addition, simultaneously maximizing sensitivity and minimizing latency and false detection rates has been challenging as these are competing objectives. Automated machine learning systems provide a mechanism for dealing with these hurdles. Here we present and evaluate an algorithm for real-time seizure onset detection from IEEG using a machine-learning approach that permits a patient-specific solution. We extract temporal and spectral features across all intracranial EEG channels. A pattern recognition component is trained using these feature vectors and tested against unseen continuous data from the same patient. When tested on more than 875 hours of IEEG data from 10 patients, the algorithm detected 97% of 67 test seizures of several types with a median detection delay of 5 seconds and a median false alarm rate of 0.6 false alarms per 24-hour period. The sensitivity was 100% for 8 of 10 patients. These results indicate that a sensitive, specific, and relatively short-latency detection system based on machine learning can be employed for seizure detection from EEG using a full set of intracranial electrodes to individual patients.

Automatic seizure detection in SEEG using high frequency activities in wavelet domain

“…Features were extracted in time domain [13], frequency domain [14][15][16], both time and frequency domain [17] or time-frequency domain like wavelet transform. Since EEG is nonstationary, the methods using time-frequency domain usually provide higher success than the other two methods.…”

Automatic Seizure Onset Detection in Long Term Pediatric EEG Signals

“…In the last few decades' various algorithms have been proposed for detection of presence or absence of seizure on ongoing EEG record [4][5][6][7][8][9][10][11][12][13][14][15]. All seizure detection algorithms involve two steps.…”

“…In 2009, Shoeb [9] proposed a patient specific onset detection system which was tested on the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) database and shows detection accuracy and sensitivity of 96%, with a false-positive rate of 0.08 per hour and mean detection delay of 4.6 seconds. In a similar study Kharbouch et al [10] designed a method for seizure detection. For evaluation of algorithm, data of 10 patients was utilized to extract both temporal and spectral features.…”

Automatic Detection of Seizure ONSET in Pediatric EEG

“…But observing EEG continuously for a long time is a very tedious task, since EEG data recordings create lengthy data [4]. Hence automatic seizure detection is essential in clinical practice Automatic detection of seizures through the analysis of scalp EEG has been an important area of research for the last few decades [5][6][7][8][9][10][11][12][13][14]. In 1976, Gotman and Gloor [5] proposed a method of recognition and quantification of interictal epileptic activity (spikes and sharp waves) in human scalp EEG.…”

“…Shoeb and Guttag [9] reported 96% sensitivity and mean detection delay of 4.6 seconds when worked on CHB-MIT database [10]. In 2011, Kharbouch et al [11] proposed a method for seizure detection from iEEG. The data of 10 patients was utilized to extract both temporal and spectral features.…”