Shintomi Ayaka, Izumi Shintaro, Yoshimoto Masahiko, Kawaguchi Hiroshi
Graduate School of System Informatics Kobe University 1-1 Rokkodai-cho Nada-ku Kobe Hyogo Japan.
Healthc Technol Lett. 2022 Mar 8;9(1-2):9-15. doi: 10.1049/htl2.12023. eCollection 2022 Feb-Apr.
The purpose of this study is to evaluate the effectiveness of heartbeat error and compensation methods on heart rate variability (HRV) with mobile and wearable sensor devices. The HRV analysis extracts multiple indices related to the heart and autonomic nervous system from beat-to-beat intervals. These HRV analysis indices are affected by the heartbeat interval mismatch, which is caused by sampling error from measurement hardware and inherent errors from the state of human body. Although the sampling rate reduction is a common method to reduce power consumption on wearable devices, it degrades the accuracy of the heartbeat interval. Furthermore, wearable devices often use photoplethysmography (PPG) instead of electrocardiogram (ECG) to measure heart rate. However, there are inherent errors between PPG and ECG, because the PPG is affected by blood pressure fluctuations, vascular stiffness, and body movements. This paper evaluates the impact of these errors on HRV analysis using dataset including both ECG and PPG from 28 subjects. The evaluation results showed that the error compensation method improved the accuracy of HRV analysis in time domain, frequency domain and non-linear analysis. Furthermore, the error compensation by the algorithm was found to be effective for both PPG and ECG.
本研究的目的是评估使用移动和可穿戴传感器设备时心跳误差及补偿方法对心率变异性（HRV）的有效性。HRV分析从逐搏间期提取与心脏和自主神经系统相关的多个指标。这些HRV分析指标会受到心跳间期不匹配的影响，心跳间期不匹配是由测量硬件的采样误差以及人体状态的固有误差引起的。尽管降低采样率是可穿戴设备上降低功耗的常用方法，但这会降低心跳间期的准确性。此外，可穿戴设备通常使用光电容积脉搏波描记法（PPG）而非心电图（ECG）来测量心率。然而，PPG和ECG之间存在固有误差，因为PPG会受到血压波动、血管硬度和身体运动的影响。本文使用包含28名受试者的ECG和PPG的数据集评估了这些误差对HRV分析的影响。评估结果表明，误差补偿方法提高了HRV在时域、频域和非线性分析中的准确性。此外，发现算法进行的误差补偿对PPG和ECG均有效。
