A hybrid 1D CNN-BiLSTM model for epileptic seizure detection using multichannel EEG feature fusion

Apr 5, 2024Biomedical physics & engineering express

Using a combined 1D CNN and BiLSTM model to detect epileptic seizures from multiple EEG signals

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Circadian Biology on OpenScience ↗PubMed ↗DOI ↗

Abstract

The proposed model achieved an average accuracy of 95.90% in detecting seizures using EEG data.

The hybrid model combines spatial features from a convolutional neural network (CNN) and temporal features from a bi-directional long short-term memory network (Bi-LSTM).
An average sensitivity of 97.18% was observed, indicating high detection capability for actual seizure events.
The model maintains a low false positivity rate of 0.05 per hour, suggesting reliability in distinguishing between seizure and non-seizure states.
No additional preprocessing steps are required, which simplifies the diagnostic process for clinicians.
The method leverages a parallel path network to enhance both memory retention and feature extraction efficiency.

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Epilepsy, a chronic non-communicable disease is characterized by repeated unprovoked seizures, which are transient episodes of abnormal electrical activity in the brain. While Electroencephalography (EEG) is considered as the gold standard for diagnosis in current clinical practice, manual inspection of EEG is time consuming and biased. This paper presents a novel hybrid 1D CNN-Bi LSTM feature fusion model for automatically detecting seizures. The proposed model leverages spatial features extracted by one dimensional convolutional neural network and temporal features extracted by bi directional long short-term memory network. Ictal and inter ictal data is first acquired from the long multichannel EEG record. The acquired data is segmented and labelled using small fixed windows. Signal features are then extracted from the segments concurrently by the parallel combination of CNN and Bi-LSTM. The spatial and temporal features thus captured are then fused to enhance classification accuracy of model. The approach is validated using benchmark CHB-MIT dataset and 5-fold cross validation which resulted in an average accuracy of 95.90%, with precision 94.78%, F1 score 95.95%. Notably model achieved average sensitivity of 97.18% with false positivity rate at 0.05/hr. The significantly lower false positivity and false negativity rates indicate that the proposed model is a promising tool for detecting seizures in epilepsy patients. The employed parallel path network benefits from memory function of Bi-LSTM and strong feature extraction capabilities of CNN. Moreover, eliminating the need for any domain transformation or additional preprocessing steps, model effectively reduces complexity and enhances efficiency, making it suitable for use by clinicians during the epilepsy diagnostic process.

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A hybrid 1D CNN-BiLSTM model for epileptic seizure detection using multichannel EEG feature fusion

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