Version 1
: Received: 30 September 2024 / Approved: 30 September 2024 / Online: 1 October 2024 (08:40:05 CEST)
How to cite:
Martono, N. P.; Ohwada, H. Evaluating the Impact of Windowing Techniques on Fourier Transform Preprocessed Signals for Deep Learning-Based ECG Classification. Preprints2024, 2024092461. https://doi.org/10.20944/preprints202409.2461.v1
Martono, N. P.; Ohwada, H. Evaluating the Impact of Windowing Techniques on Fourier Transform Preprocessed Signals for Deep Learning-Based ECG Classification. Preprints 2024, 2024092461. https://doi.org/10.20944/preprints202409.2461.v1
Martono, N. P.; Ohwada, H. Evaluating the Impact of Windowing Techniques on Fourier Transform Preprocessed Signals for Deep Learning-Based ECG Classification. Preprints2024, 2024092461. https://doi.org/10.20944/preprints202409.2461.v1
APA Style
Martono, N. P., & Ohwada, H. (2024). Evaluating the Impact of Windowing Techniques on Fourier Transform Preprocessed Signals for Deep Learning-Based ECG Classification. Preprints. https://doi.org/10.20944/preprints202409.2461.v1
Chicago/Turabian Style
Martono, N. P. and Hayato Ohwada. 2024 "Evaluating the Impact of Windowing Techniques on Fourier Transform Preprocessed Signals for Deep Learning-Based ECG Classification" Preprints. https://doi.org/10.20944/preprints202409.2461.v1
Abstract
(1) Background: Arrhythmia, or irregular heart rhythms, are a prevalent cardiovascular condition and can be diagnosed using electrocardiogram (ECG) signals. Advances in deep learning have enabled automated analysis of these signals. However, the effectiveness of deep learning models depends heavily on the quality of signal preprocessing. This study evaluates the impact of different windowing techniques applied to Fourier Transform-preprocessed ECG signals on the classification accuracy of deep learning models. (2) Methods: We applied three windowing techniques—Hamming, Hann, and Blackman—to transform ECG signals into the frequency domain. A 1D Convolutional Neural Network (CNN) was employed to classify the ECG signals into five arrhythmia categories based on features extracted from each windowed signal.(3) Results: The Blackman window yielded the highest classification accuracy, with improved signal-to-noise ratio and reduced spectral leakage compared to the Hamming and Hann windows. (4) Conclusions: The choice of windowing technique significantly influences the effectiveness of deep learning models in ECG classification. Future studies should explore additional preprocessing methods and their clinical applications.
Keywords
arrhythmia; electrocardiogram (ECG); ECG classification; signal processing; deep learning; convolutional neural network (CNN)
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
