Why the study?
Wearable biosensors for heart disease analysis generate large amounts of heart sound signal data, presenting an urgent management problem.
A novel parallel compressive sensing model for multi-channel heart sound signals achieves 9-10 times faster reconstruction speeds than existing algorithms, facilitating long-term wearable cardiac monitoring.
May enable faster wearable heart sound monitoring; leaves open prospective clinical validation before adoption.
The rapid development of wearable biosensors based on the Internet of Things provides a new solution for heart disease analysis and prediction of sudden death from heart failure. However, how to deal with a large amount of data generated by heart sound signals is an urgent problem to be solved. Based on the characteristics of heart sound signals, we propose a parallel compressive sensing model for the multi-channel synchronous acquisition of heart sound signals. Furthermore, we provide a series of experiments to assess the performance of the model. The experimental results demonstrate that the reconstruction speed of the proposed model is 9-10 times faster than that of the block sparse Bayesian learning algorithm and the orthogonal matching pursuit algorithm, and the reconstruction effect is better. Meanwhile, the proposed model can effectively reconstruct the normal heart sound signal and abnormal heart sound signal of four-channel synchronous acquisition. Therefore, the proposed model is feasibility.
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Cheng et al. (2019) studied this question.
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