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Article Dans Une Revue Physiological Measurement Année : 2022

Arrhythmia classification of 12-lead and reduced-lead electrocardiograms via recurrent networks, scattering, and phase harmonic correlation

Résumé

We describe an automatic classifier of arrhythmias based on 12-lead and reduced-lead electrocardiograms. Our classifier comprises four modules: scattering transform (ST), phase harmonic correlation (PHC), depthwise separable convolutions (DSC), and a long short-term memory (LSTM) network. It is trained on PhysioNet/Computing in Cardiology Challenge 2021 data. The ST captures short-term temporal ECG modulations while the PHC characterizes the phase dependence of coherent ECG components. Both reduce the sampling rate to a few samples per typical heart beat. We pass the output of the ST and PHC to a depthwise-separable convolution layer (DSC) which combines lead responses separately for each ST or PHC coefficient and then combines resulting values across all coefficients. At a deeper level, two LSTM layers integrate local variations of the input over long time scales. We train in an end-to-end fashion as a multilabel classification problem with a normal and 25 arrhythmia classes. Lastly, we use canonical correlation analysis (CCA) for transfer learning from 12-lead ST and PHC representations to reduced-lead ones. After local cross-validation on the public data from the challenge, our team ``BitScattered'' achieved the following results: 0.682±0.0095 for 12-lead; 0.666±0.0257 for 6-lead; 0.674±0.0185 for 4-lead; 0.661±0.0098 for 3-lead; and 0.662±0.0151 for 2-lead.
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Dates et versions

hal-03768767 , version 1 (31-10-2023)

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Philip Warrick, Vincent Lostanlen, Michael Eickenberg, Masun Nabhan Homsi, Adrián Campoy Rodriguez, et al.. Arrhythmia classification of 12-lead and reduced-lead electrocardiograms via recurrent networks, scattering, and phase harmonic correlation. Physiological Measurement, 2022, 43 (9), pp.094002. ⟨10.1088/1361-6579/ac77d1⟩. ⟨hal-03768767⟩
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