Novel deep learning framework for detection of epileptic seizures using EEG signals.

Front Comput Neurosci

Cognitive Neuroscience Laboratory, Department of Biological Sciences, BITS Pilani, KK Birla Goa Campus, Pilani, Goa, India.

Published: March 2024

AI Article Synopsis

  • Epilepsy, affecting 50 million people globally, is characterized by recurrent seizures and has traditionally relied on time-consuming expert analysis of EEG signals for diagnosis.
  • Researchers are now using machine learning techniques to automate the detection process, addressing inefficiencies and human error in manual inspection.
  • A novel method utilizing 1-D Convolutional layers, LSTMs, and GRUs has demonstrated high accuracy (up to 100%) in classifying EEG waveforms, making significant strides in automated seizure detection.

Article Abstract

Introduction: Epilepsy is a chronic neurological disorder characterized by abnormal electrical activity in the brain, often leading to recurrent seizures. With 50 million people worldwide affected by epilepsy, there is a pressing need for efficient and accurate methods to detect and diagnose seizures. Electroencephalogram (EEG) signals have emerged as a valuable tool in detecting epilepsy and other neurological disorders. Traditionally, the process of analyzing EEG signals for seizure detection has relied on manual inspection by experts, which is time-consuming, labor-intensive, and susceptible to human error. To address these limitations, researchers have turned to machine learning and deep learning techniques to automate the seizure detection process.

Methods: In this work, we propose a novel method for epileptic seizure detection, leveraging the power of 1-D Convolutional layers in combination with Bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and Average pooling Layer as a single unit. This unit is repeatedly used in the proposed model to extract the features. The features are then passed to the Dense layers to predict the class of the EEG waveform. The performance of the proposed model is verified on the Bonn dataset. To assess the robustness and generalizability of our proposed architecture, we employ five-fold cross-validation. By dividing the dataset into five subsets and iteratively training and testing the model on different combinations of these subsets, we obtain robust performance measures, including accuracy, sensitivity, and specificity.

Results: Our proposed model achieves an accuracy of 99-100% for binary classifications into seizure and normal waveforms, 97.2%-99.2% accuracy for classifications into normal-interictal-seizure waveforms, 96.2%-98.4% accuracy for four class classification and accuracy of 95.81%-98% for five class classification.

Discussion: Our proposed models have achieved significant improvements in the performance metrics for the binary classifications and multiclass classifications. We demonstrate the effectiveness of the proposed architecture in accurately detecting epileptic seizures from EEG signals by using EEG signals of varying lengths. The results indicate its potential as a reliable and efficient tool for automated seizure detection, paving the way for improved diagnosis and management of epilepsy.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11000706PMC
http://dx.doi.org/10.3389/fncom.2024.1340251DOI Listing

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