Involutional neural networks for ECG spectrogram classification and person identification
by Hatem Zehir; Toufik Hafs; Sara Daas
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 13, No. 1, 2024

Abstract: Reliable personal identification is crucial. This study explores an ECG-based system using spectrograms and involutional neural networks (INNs) for accurate person identification. The system first preprocesses ECG signals with a Butterworth filter and segments them using the Pan-Tompkins++ algorithm. Each segment, representing a heartbeat, is then converted into a spectrogram using the short-time Fourier transform (STFT). Finally, an INN classifies the spectrograms for identification. Tested on the MIT-BIH Arrhythmia and Physikalisch-Technische Bundesanstalt (PTB) databases, the system achieved accuracies of 97.93% and 97.63%, respectively, surpassing conventional methods like convolutional neural networks (CNNs). This improvement is attributed to INNs' ability to better capture both local and temporal patterns in the spectrogram data.

Online publication date: Mon, 15-Jul-2024

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