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Evaluation of Level-Crossing ADCs for Event-Driven ECG Classification

Abstract : In this paper, a new methodology for choosing design parameters of level-crossing analog-to-digital converters (LC-ADCs) is presented that improves sampling accuracy and reduces the data stream rate. Using the MIT-BIH Arrhythmia dataset, several LC-ADC models are designed, simulated and then evaluated in terms of compression and signal-to-distortion ratio. A new one-dimensional convolutional neural network (1D-CNN) based classifier is presented. The 1D-CNN is used to evaluate the event-driven data from several LC-ADC models. With uniformly sampled data, the 1D-CNN has 99.49%, 92.4% and 94.78% overall accuracy, sensitivity and specificity, respectively. In comparison, a 7-bit LC-ADC with 2385Hz clock frequency and 6-bit clock resolution offers 99.2%, 89.98% and 91.64% overall accuracy, sensitivity and specificity, respectively. It also offers 3x data compression while maintaining a signal-to-distortion ratio of 21.19dB. Furthermore, it only requires 49% floating-point operations per second (FLOPS) for cardiac arrhythmia classification in comparison with the uniformly sampled ADC. Finally, an open-source event-driven arrhythmia database is presented.
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https://hal.archives-ouvertes.fr/hal-03497825
Contributor : Antoine Frappé Connect in order to contact the contributor
Submitted on : Monday, December 20, 2021 - 5:06:44 PM
Last modification on : Tuesday, April 26, 2022 - 4:06:04 PM

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Maryam Saeed, Qingyuan Wang, Olev Martens, Benoit Larras, Antoine Frappé, et al.. Evaluation of Level-Crossing ADCs for Event-Driven ECG Classification. IEEE Transactions on Biomedical Circuits and Systems, Institute of Electrical and Electronics Engineers, In press, pp.1-1. ⟨10.1109/TBCAS.2021.3136206⟩. ⟨hal-03497825⟩

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