Automatic Identification of Systolic Time Intervals in Seismocardiogram
Automatic Identification of Systolic Time Intervals in Seismocardiogram
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DOI：
10.1038/srep37524
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发表时间：
2016-11-22
影响因子：
4.6
通讯作者：
Veluvolu, Kalyana C.
中科院分区：
文献类型：
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作者：
Shafiq, Ghufran;Tatinati, Sivanagaraja;Veluvolu, Kalyana C.
通过不显眼的可穿戴传感器对血流动力学参数进行连续、非侵入性监测可能有助于早期发现心脏异常，并为慢性心血管疾病患者的长期随访提供可行的解决方案，而无需中断日常生活活动。心电图 (ECG) 和心电图 (SCG) 信号可以分别从轻型电极和加速度计轻松获取，这些信号可用于导出收缩时间间隔 (STI)。为此，需要对这些信号中的相关峰进行自动和准确的注释，由于 SCG 信号的受试者间形态变异性和易产生噪声的性质，这具有挑战性。本文提出了一种方法，利用滑动模板中检测到的峰值信息来自动注释SCG信号中与STI相关的所需峰值，以缩小对实际SCG信号中所需峰值的搜索范围。在传统/受控仰卧和现实/具有挑战性的坐姿条件下对该方法进行的实验验证（包含超过 5600 个心跳周期）显示了该方法在噪声条件下的良好性能和鲁棒性。可穿戴配置中的 STI 自动测量可以为患者、高危老年人和健康爱好者的长期监测提供量化的心脏健康指数。
Continuous and non-invasive monitoring of hemodynamic parameters through unobtrusive wearable sensors can potentially aid in early detection of cardiac abnormalities, and provides a viable solution for long-term follow-up of patients with chronic cardiovascular diseases without disrupting the daily life activities. Electrocardiogram (ECG) and siesmocardiogram (SCG) signals can be readily acquired from light-weight electrodes and accelerometers respectively, which can be employed to derive systolic time intervals (STI). For this purpose, automated and accurate annotation of the relevant peaks in these signals is required, which is challenging due to the inter-subject morphological variability and noise prone nature of SCG signal. In this paper, an approach is proposed to automatically annotate the desired peaks in SCG signal that are related to STI by utilizing the information of peak detected in the sliding template to narrow-down the search for the desired peak in actual SCG signal. Experimental validation of this approach performed in conventional/controlled supine and realistic/challenging seated conditions, containing over 5600 heart beat cycles shows good performance and robustness of the proposed approach in noisy conditions. Automated measurement of STI in wearable configuration can provide a quantified cardiac health index for long-term monitoring of patients, elderly people at risk and health-enthusiasts.