Spectrogram-Based Arrhythmia Classification Using Three-Channel Deep Learning Model with Feature Fusion

This paper introduces a novel deep learning model for ECG signal classification using feature fusion. The proposed methodology transforms the ECG time series into a spectrogram image using a short-time Fourier transform (STFT). This spectrogram is further processed to generate a histogram of oriente...

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Bibliographic Details
Published in:Applied sciences 2024-11, Vol.14 (21), p.9936
Main Authors: Eleyan, Alaa, Bayram, Fatih, Eleyan, Gülden
Format: Article
Language:English
Subjects:
Algorithms
Arrhythmia
CNN
Computational linguistics
Datasets
deep learning
ECG
Electrocardiogram
Electrocardiography
GRU
heartbeat
Language processing
Natural language interfaces
Neural networks
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Summary:This paper introduces a novel deep learning model for ECG signal classification using feature fusion. The proposed methodology transforms the ECG time series into a spectrogram image using a short-time Fourier transform (STFT). This spectrogram is further processed to generate a histogram of oriented gradients (HOG) and local binary pattern (LBP) features. Three separate 2D convolutional neural networks (CNNs) then analyze these three image representations in parallel. To enhance performance, the extracted features are concatenated before feeding them into a gated recurrent unit (GRU) model. The proposed approach is extensively evaluated on two ECG datasets (MIT-BIH + BIDMC and MIT-BIH) with three and five classes, respectively. The experimental results demonstrate that the proposed approach achieves superior classification accuracy compared to existing algorithms in the literature. This suggests that the model has the potential to be a valuable tool for accurate ECG signal classification, aiding in the diagnosis and treatment of various cardiovascular disorders.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14219936