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Accuracy Enhancement of Epileptic Seizure Detection: A Deep Learning Approach with Hardware Realization of STFT

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Abstract

Electroencephalogram (EEG) signals, generated during the neuron firing, are an effective way of predicting such seizure and it is used widely in recent days for classifying and predicting seizure activity. But EEG signals generated during an epileptic seizure are highly nonstationary and dynamic in nature and contain very crucial information about the state of the brain. Due to this randomness, the accuracy of analysis of EEG data by conventional and visual methods is reduced drastically. This paper aims at enhancing epilepsy seizure detection using deep learning models with an FPGA implementation of the short-time Fourier transform block. Detection of seizure has been achieved in the following stages: (1) time–frequency analysis of EEG segments using STFT; (2) extraction of frequency bands and features of interest; and (3) seizure detection using convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM). For this work, the Bonn EEG dataset has been used. The maximum error of ~ 0.13% was encountered while the comparison of STFT output generated via proposed hardware architecture vs the output generated via simulation was done. The average classification accuracy of 93.9% and 97.2% was achieved by CNN and Bi-LSTM models, respectively, considering all frequency bands for epileptic and non-epileptic patients.

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Data Availability

The data for this paper are acquired from the University of Bonn, Germany [42]. This dataset consists of data of subjects who are healthy, interictal, and ictal, which are divided into five exclusive segments. These are denoted as A, B, C, D, and E. There are 100 text files in every segment. Each text file consists the data of 4096 pulse per sample. With the help of a 128-channel amplifier, the EEG signals were recorded. The sampling frequency used while recording was set to 173.61 Hz. The duration of each channel of each segment was restricted to 23.6 s. Segments A and B are of a healthy, an ictal-free, person. These two segments are different even though taken from a healthy person because the data of Segment A represents a healthy person with eyes open and that of B represents a healthy person with closed eyes, while the remaining three segments consist of seizure. The segment C and D are taken from an interictal person. Segment C represents an interictal patient, and data recorded from hippocampal formation. Similarly, segment D represents an interictal patient, but the data recorded from the epileptogenic zone. Lastly, segment E represents an ictal person, who contains prominent seizure activity.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Sai Manohar Beeraka, Abhash Kumar, Mustafa Sameer & Bharat Gupta

  2. Department of Information Technology, Institute of Engineering and Management, Kolkata, West Bengal, India

    Sanchita Ghosh

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  1. Sai Manohar Beeraka
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Beeraka, S.M., Kumar, A., Sameer, M. et al. Accuracy Enhancement of Epileptic Seizure Detection: A Deep Learning Approach with Hardware Realization of STFT. Circuits Syst Signal Process 41, 461–484 (2022). https://doi.org/10.1007/s00034-021-01789-4

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