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Epileptic seizure classification using ConvLSTM deep classifier and rotation short-time Fourier Transform

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Abstract

Epilepsy is one of the world’s most common neurological disorders. Timely diagnosis of this disease improves the quality of life of patients. In this research, we used deep learning to diagnose and predict epileptic seizures. While Long Short Term Memory (LSTM) learn the concept of time and Convolutional Neural Network (CNN) learn images well, Convolutional Long Short Term Memories (ConvLSTMs) as a new type of LSTMs use both capabilities. How to prepare the input is important and effective in using deep neural networks. The use of raw signals also forces us to make full use of time-domain features. We employed the short-time Fourier transform (STFT) to use both time and frequency domain information. But the output images have a fixed resolution due to the fixed size of the window. We solved this problem by calculating STFT with different window sizes and adding a third dimension. Then, with rotation, we put the dimensions in positions appropriate to their meaning. So we provided a set of images that convey the concept of time to ConvLSTMs to learn the signal pattern and generalize it. We tested our deep learning model on dataset from the University of Bonn in Germany. We compared the findings with those obtained from the other state-of-the-art models. The obtained accuracies demonstrate that the proposed model is both effective and reliable.

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  1. Babol Noshirvani University of Technology, Babol, Mazandaran, Iran, Islamic Republic of

    Mona Chalaki & Hesam Omranpour

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  1. Mona Chalaki
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  2. Hesam Omranpour
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Correspondence to Hesam Omranpour.

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Chalaki, M., Omranpour, H. Epileptic seizure classification using ConvLSTM deep classifier and rotation short-time Fourier Transform. J Ambient Intell Human Comput 14, 3809–3825 (2023). https://doi.org/10.1007/s12652-022-04204-1

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