A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms
A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms
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DOI：
10.1088/0967-3334/37/11/1885
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发表时间：
2016-11-01
影响因子：
3.2
通讯作者：
Koivisto, Tero
中科院分区：
文献类型：
--
作者：
Tadi, Mojtaba Jafari;Lehtonen, Eero;Koivisto, Tero
心率监测有助于评估心血管系统的功能和状况。我们提出了一种新的实时适用的方法，用于估计心跳到心跳的时间间隔和心率在地震心电图从三轴微机电加速度计采集。地震心动图（SCG）是一种非侵入性的心脏监测方法，可测量心脏的机械活动。从SCG测量真正的心跳到心跳的时间间隔可以用于监测心律、用于心率变异性分析以及用于许多其他临床应用。在本文中，我们提出了希尔伯特自适应搏动识别技术的检测心跳定时和心跳间的时间间隔从健康志愿者在三个不同的位置，即仰卧，左，右横卧。我们的方法是心电图（ECG）独立的，因为它不需要任何ECG基准点来估计心跳到心跳的间隔。该算法的性能进行了测试，对标准的心电图测量。不同体位的平均真阳性率、阳性预测值和检测错误率分别为仰卧位95.8%、96.0%和相似或等于0.6%，左侧99.3%、98.8%和相似或等于0.001%，右侧99.53%、99.3%和相似或等于0.01%。对于所有位置，在SCG和ECG心跳间间隔（r > 0.99）之间观察到高度相关性和一致性，这突出了该算法从不同位置进行SCG心脏监测的能力。此外，我们证明了所提出的方法在智能手机基于SCG的适用性。总之，所提出的算法可用于实时连续非侵入性心脏监测、智能手机心电图以及针对健康和福祉应用的可穿戴设备。
Heart rate monitoring helps in assessing the functionality and condition of the cardiovascular system. We present a new real-time applicable approach for estimating beat-to-beat time intervals and heart rate in seismocardiograms acquired from a tri-axial microelectromechanical accelerometer. Seismo-cardiography (SCG) is a non-invasive method for heart monitoring which measures the mechanical activity of the heart. Measuring true beat-to-beat time intervals from SCG could be used for monitoring of the heart rhythm, for heart rate variability analysis and for many other clinical applications. In this paper we present the Hilbert adaptive beat identification technique for the detection of heartbeat timings and inter-beat time intervals in SCG from healthy volunteers in three different positions, i.e. supine, left and right recumbent. Our method is electrocardiogram (ECG) independent, as it does not require any ECG fiducial points to estimate the beat-to-beat intervals. The performance of the algorithm was tested against standard ECG measurements. The average true positive rate, positive prediction value and detection error rate for the different positions were, respectively, supine (95.8%, 96.0% and similar or equal to 0.6%), left (99.3%, 98.8% and similar or equal to 0.001%) and right (99.53%, 99.3% and similar or equal to 0.01%). High correlation and agreement was observed between SCG and ECG inter-beat intervals (r > 0.99) for all positions, which highlights the capability of the algorithm for SCG heart monitoring from different positions. Additionally, we demonstrate the applicability of the proposed method in smartphone based SCG. In conclusion, the proposed algorithm can be used for real-time continuous unobtrusive cardiac monitoring, smartphone cardiography, and in wearable devices aimed at health and well-being applications.