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ECG Classification With Event-Driven Sampling
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DOI：10.1109/ACCESS.2024.3364115.png)
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摘要
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Electrocardiogram (ECG) data's high dimensionality challenges real-time arrhythmia classification. Our approach employs functional approximation to condense ECG recordings into a compact feature set for simpler classification using Chebyshev polynomials. These polynomials, with 200 time points and 80 coefficients, accurately represent arrhythmias in an 81 x 1 feature vector. We prove Chebyshev polynomials act as implicit low-pass filters on input signals. Using MIT-BIH Arrhythmia and MIT-BIH Supraventricular Arrhythmia datasets, we introduce classifiers that achieve significant accuracy. A three-layered Artificial Neural Network yields high F1-scores (0.99, 0.90, 0.93, and 0.76 for classes N, S, V, and F) with minimal parameters (20,964), surpassing existing models. Furthermore, our proposed ECG classification system exhibits minimal computational demands, requiring only 0.1 MIPS per beat. We also propose efficient signal reconstruction methods, with the iterative approach showcasing accurate reconstruction with negligible error. This approach accommodates various data sampling types and determines optimal Chebyshev coefficients for capturing signal bandwidth.
Keyword:
Level-crossing ADC
electrocardiograms
functional approximation
Chebyshev polynomials
artificial neural networks
arrhythmia
support vector machines
bandwidth analysis
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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