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Communication Dans Un Congrès Année : 2021

Event-driven ECG classification using an open-source, LC-ADC based non-uniformly sampled dataset

Résumé

In this article, non-uniformly sampled electrocardiogram (ECG) signals obtained from level-crossing analog-to-digital converters (LC-ADCs) are analyzed for event-driven classification and compression performance. The signal compression results show that it is important to assess the distortion in event-driven signals when simulating LC-ADC models, especially at lower resolutions and larger quantization steps. The effects of varying the LC-ADC parameters for the application of cardiac arrhythmia classifiers are also assessed using an artificial neural network (ANN) and the MIT-BIH Arrhythmia Database. In comparison with uniformly-sampled data, it is possible to achieve comparable classification accuracy at a much lower complexity with event-driven ECG signals. The results show the best event-driven model achieves over 97% accuracy with 79% reduction in ANN complexity with signal-to-distortion ratio (S/D)≥21dB. For S/D<21dB, the best event-driven model achieves 93% accuracy with a 96% reduction in ANN complexity. An open-source event-driven arrhythmia database is also presented.
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Dates et versions

hal-03362265 , version 1 (17-12-2021)

Identifiants

Citer

M. Saeed, Q. Wang, O. Märtens, Benoit Larras, Antoine Frappé, et al.. Event-driven ECG classification using an open-source, LC-ADC based non-uniformly sampled dataset. 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021, May 2021, Daegu, South Korea. pp.9401333, ⟨10.1109/ISCAS51556.2021.9401333⟩. ⟨hal-03362265⟩
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