November 1, 2019

A Novel Application based on Spectrogram and Convolutional Neural Network for ECG Classification

ADAykut Di̇kerBandırma Onyedi Eylül University ZCZafer CömertSamsun University EAEngin AvcıFırat University

Structured PICO

Population

Publicly available ECG signal data set from Physionet

Intervention

Spectrogram generation followed by convolutional neural network (CNN) classification (AlexNet, VGG-16, and ResNet-18) using transfer learning and fine-tuning

Outcome

ECG signal classification accuracy

A novel approach using spectrograms and convolutional neural networks (specifically AlexNet) achieved 83.82% accuracy in classifying ECG signals, demonstrating potential for automated ECG analysis.

Abstract

Electrocardiogram (ECG) is a biomedical signal which represents the electrical activity of the human heart. Various cardiac diseases have been detected using the outputs of ECG devices. Recently, advances in signal processing techniques bring out a new horizon for processing the ECG signals. In this scope, a novel application based on the spectrogram, which is a graphical representation of time-frequency information of the signal, and the convolutional neural network (CNN) is proposed so as to distinguish ECG signals. To this aim, a publicly available data set in Physionet was utilized. Firstly, the spectrograms of each signal were obtained. Then, these colorful spectrogram images were applied as the input to CNNs that are AlexNet, VGG-16, and ResNet-18. The transfer learning and fine-tuning approach were used for training and validation of the models. As a result, the most efficient results were provided by AlexNet with an accuracy of 83.82%. The experimental results of this study show that the proposed model ensures promising results for the ECG signal classification.

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