Key result
ANN with Chebyshev features accurately classifies arrhythmias requiring only 0.1 MIPS per beat.
Why the study?
High dimensionality of ECG data challenges real-time arrhythmia classification.
Does functional approximation using Chebyshev polynomials improve arrhythmia classification accuracy in event-driven ECG sampling?
Does functional approximation using Chebyshev polynomials improve arrhythmia classification accuracy in event-driven ECG sampling?
Functional approximation using Chebyshev polynomials enables accurate and computationally efficient real-time arrhythmia classification from event-driven ECG sampling.
May support efficient real-time arrhythmia detection on low-power devices; hypothesis-generating and requires prospective validation.
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 × 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.
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Saeed et al. (2024) studied Arrhythmia (n=123,615). Functional approximation with Chebyshev polynomials and Artificial Neural Network vs. k-NN and SVM classifiers was evaluated on F1-score for arrhythmia classification. A three-layered Artificial Neural Network using Chebyshev polynomial features achieved high F1-scores (0.99, 0.90, 0.93, and 0.76 for N, S, V, and F classes) with minimal computational demands of 0.1 MIPS per beat.
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