Department of Computer and Information Engineering, Daegu University, Kyeongsan 38453, Republic of Korea.
Department of Biomedical Engineering, School of Medicine, Keimyung University, Daegu 42601, Republic of Korea.
Sensors (Basel). 2023 Feb 22;23(5):2423. doi: 10.3390/s23052423.
A sudden cardiac event in patients with heart disease can lead to a heart attack in extreme cases. Therefore, prompt interventions for the particular heart situation and periodic monitoring are critical. This study focuses on a heart sound analysis method that can be monitored daily using multimodal signals acquired with wearable devices. The dual deterministic model-based heart sound analysis is designed in a parallel structure that uses two bio-signals (PCG and PPG signals) related to the heartbeat, enabling more accurate heart sound identification. The experimental results show promising performance of the proposed Model III (DDM-HSA with window and envelope filter), which had the highest performance, and S1 and S2 showed average accuracy (unit: %) of 95.39 (±2.14) and 92.55 (±3.74), respectively. The findings of this study are anticipated to provide improved technology to detect heart sounds and analyze cardiac activities using only bio-signals that can be measured using wearable devices in a mobile environment.
心脏病患者的突发性心脏事件在极端情况下可能导致心脏病发作。因此，对特定心脏情况的及时干预和定期监测至关重要。本研究关注一种可以使用可穿戴设备获取的多模态信号进行日常监测的心音分析方法。双确定性模型为基础的心音分析采用并行结构设计，使用两个与心跳相关的生物信号（心音图和光电容积脉搏波信号），从而实现更准确的心音识别。实验结果表明，所提出的模型 III（带窗口和包络滤波器的双确定性模型心音分析）具有有前景的性能，其性能最高，S1 和 S2 的平均准确率（单位：%）分别为 95.39（±2.14）和 92.55（±3.74）。本研究的结果有望提供改进的技术，使用可穿戴设备在移动环境中测量的生物信号来检测心音并分析心脏活动。
