In current clinical practices, electroencephalograms (EEG) are reviewed and analyzed by trained neurologists to provide supports for therapeutic decisions. Manual reviews can be laborious and error prone. Automatic and accurate seizure non-seizure classification methods are desirable. A critical challenge is that seizure morphologies exhibit considerable variabilities. In order to capture essential seizure features, this paper leverages an emerging deep learning model, the independently recurrent neural network (IndRNN), to construct a new approach for the seizure non-seizure classification. This new approach gradually expands the time scales, thereby extracting temporal and spatial features from the local time duration to the entire record. Evaluations are conducted with cross-validation experiments across subjects over the noisy data of CHB-MIT. Experimental results demonstrate that the proposed approach outperforms the current state-of-the-art methods. In addition, we explore how the segment length affects the classification performance. Thirteen different segment lengths are assessed, showing that the classification performance varies over the segment lengths, and the maximal fluctuating margin is more than 4 . Thus, the segment length is an important factor influencing the classification performance.
Shoeb and Guttag propose a method to construct a patient-specific detector for seizure detection by using the support vector machine (SVM) @cite_1 . The method leverages filters to extract spectral features over each channel, and then stacks feature vectors to catch time-evolution information. A sensitivity of 96 propose a wavelet-based algorithm for real-time detection of epileptic seizures using scalp EEG @cite_3 . In this algorithm, the EEG from each channel is decomposed by wavelet packet transform, and a patient-specific measure is deployed by using wavelet coefficients to separate the seizure and non-seizure states. Utilizing the measure, a combined seizure index is derived for each epoch of every EEG channel. Through inspecting the combined seizure index, proper channel alarms are generated.
@cite_1 @cite_3
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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. EEG is one the most effective tools used in the diagnosis of epilepsy. However, proper diagnosis of epilepsy requires the detection and analysis of epileptic seizures for a long period of time. Manual monitoring of long term EEG is tedious and costly. Therefore, a reliable automated seizure detection system is desirable. Most current state-of-the-art methods use hand crafted feature extraction and simple classification techniques, which often leads to sub-par performance due to lack of generalizability across patient dataset. Consequently, this work introduces novel deep recurrent neural network (DRNN) architecture to perform automated patient specific seizure detection using scalp EEG. We further propose a unique mapping of seizure EEG signal for efficient processing with the DRNN. This mapping allows the proposed deep architecture to simultaneously learn both temporal and spatial features of raw seizure EEG respectively. The proposed DRNN architecture is tested with long term patient specific scalp EEG data of 5 subjects with approximately 34 hours of EEG extracted from a publically available dataset. Overall, the proposed network successfully detects 100 of total seizure events with an average detection delay of 7.0 sec. The results demonstrate superior performance to that of the current state-of-art seizure detection methods. The proposed DRNN architecture also obtains a runtime of approximately 30ms for 1 sec segment of 18 channel EEG. The low processing time with sparse use of computing resources and superior performance make the proposed architecture appropriate for real-time use.