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- W4283744888 abstract "• Deep learning coupled with heart sounds to diagnose exercised-induced cardiac fatigue (EICF). • Establishment of heart sound database targeted to EICF. • Convolutional block attention module combined with residual mapping. Exercised-induced cardiac fatigue (EICF) refers to an impermanent decline in systolic and diastolic function caused by high-intensity and multi-frequency exercise. Long-term EICF may lead to heart fatigue, myocardial damage, and even sudden cardiac death. However, common cardiac fatigue diagnoses rely on expensive devices or time. Further, cardiac inotropy as an essential reference has not been applied to measure cardiac fatigue. In this paper, heart sound was proposed to evaluate EICF based on its ability to reflect changes of cardiac contractility, with a specialized deep learning network designed to recognize subjects with EICF. First, heart sounds were collected by a physiological signal collection system, and a heart sound database for 20 subjects was established. Then, discrete wavelet transform and logistic regression hidden semi-Markov model were applied to denoise and segment signals respectively, and 1770 samples in total were obtained. Finally, a network unifying residual mapping and attention modules was designed to recognize heart sounds, and the accuracy, precision, and recall of the model were 98.85%, 98.94%, and 98.9% respectively. This study suggests that heart sounds combined with the deep learning model can achieve accurate and objective diagnosis, which is potential for a reliable alternative to diagnose EICF." @default.
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- W4283744888 date "2022-08-01" @default.
- W4283744888 modified "2023-09-24" @default.
- W4283744888 title "Diagnosis of exercise-induced cardiac fatigue based on deep learning and heart sounds" @default.
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- W4283744888 doi "https://doi.org/10.1016/j.apacoust.2022.108900" @default.
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