Frequency and Uncertainty driven Deep Learning Approach to Segment Electrocardiogram Signals for Effective Heart Parameters Estimation

Sayna Rotbei, Gennaro Esposito Mocerino, Muhammad Salman Haleem, Leandro Pecchia, Alessio Botta

Keywords: Artificial intelligence, Deep Learning, Electrocardiogram Signals, Heart Rhythm

TL;DR: This paper is about deploying short time fourier transform and entropy to inform effective ECG signal segmentation

Abstract: Accurate classification of electrocardiogram signals is reliant on accurate heart rhythm parameters detection which require effective electrocardiogram segmentation at the beat level. In this study, we propose and evaluate integrating temporal, frequency and uncertainty informed deep learning approach for classifying electrocardiogram signals into three main categories: PQ, QRST, and TP. Utilizing PhysioNet’s QT public database, we preprocess the data, including noise filtering, gap removal, and normalization, to prepare it for deep learning model input. We employ multilayer deep learning architectures, integrate them with additional features such as short-time fourier transform and approximate entropy to determine uncertainty enhanced classification performance. Through extensive experimentation and five-fold cross-validation, we analyze the impact of layer duplication and additional features on classification accuracy. Our results demonstrate that our proposed methodologies achieve up to 96% accuracy in classifying ECG signals, providing valuable insights for improving heart rhythm parameter detection and suggesting avenues for further research in ECG signal processing.

Track: 11. General Track

Registration Id: JZNBZFLS7M8

Submission Number: 137