Wahlstrom Johan, Skog Isaac, Handel Peter, Khosrow-Khavar Farzad, Tavakolian Kouhyar, Stein Phyllis K, Nehorai Arye
IEEE Trans Biomed Eng. 2017 Oct;64(10):2361-2372. doi: 10.1109/TBME.2017.2648741. Epub 2017 Jan 9.
We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogram-based heart rate and heart rate variability estimation. Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and [Formula: see text], respectively. The proposed algorithm can be applied to any set of inertial sensors; does not require access to any additional sensor modalities; does not make any assumptions on the seismocardiogram morphology; and explicitly models sensor noise and beat-to-beat variations (both in amplitude and temporal scaling) in the seismocardiogram morphology. As such, it is well suited for low-cost implementations using off-the-shelf inertial sensors and targeting, e.g., at-home medical services.
我们提出一种用于处理心震图的隐马尔可夫模型方法。使用期望最大化算法学习心震图形态，并通过维特比算法估计给定时刻的心脏状态。从获得的维特比序列中，然后可以直接估计瞬时心率、心率变异性测量值和心脏时间间隔（后者需要少量手动标注）。如所进行的实验研究所表明的，所提出的算法在基于心震图的心率和心率变异性估计方面优于现有技术。此外，等容收缩时间和左心室射血时间的估计平均绝对误差分别约为5 [毫秒]和[公式：见原文]。所提出的算法可应用于任何一组惯性传感器；不需要访问任何其他传感器模态；不对心震图形态做任何假设；并明确对心震图形态中的传感器噪声和逐搏变化（包括幅度和时间尺度）进行建模。因此，它非常适合使用现成惯性传感器的低成本实现，并适用于例如家庭医疗服务等目标应用。
