IEEE Trans Biomed Eng. 2020 Dec;67(12):3464-3473. doi: 10.1109/TBME.2020.2987759. Epub 2020 Nov 19.
A wearable armband electrocardiogram (ECG) monitor has been used for daily life monitoring. The armband records three ECG channels, one electromyogram (EMG) channel, and tri-axial accelerometer signals. Contrary to conventional Holter monitors, the armband-based ECG device is convenient for long-term daily life monitoring because it uses no obstructive leads and has dry electrodes (no hydrogels), which do not cause skin irritation even after a few days. Principal component analysis (PCA) and normalized least mean squares (NLMS) adaptive filtering were used to reduce the EMG noise from the ECG channels. An artifact detector and an optimal channel selector were developed based on a support vector machine (SVM) classifier with a radial basis function (RBF) kernel using features that are related to the ECG signal quality. Mean HR was estimated from the 24-hour armband recordings from 16 volunteers in segments of 10 seconds each. In addition, four classical HR variability (HRV) parameters (SDNN, RMSSD, and powers at low and high frequency bands) were computed. For comparison purposes, the same parameters were estimated also for data from a commercial Holter monitor. The armband provided usable data (difference less than 10% from Holter-estimated mean HR) during 75.25%/11.02% (inter-subject median/interquartile range) of segments when the user was not in bed, and during 98.49%/0.79% of the bed segments. The automatic artifact detector found 53.85%/17.09% of the data to be usable during the non-bed time, and 95.00%/2.35% to be usable during the time in bed. The HRV analysis obtained a relative error with respect to the Holter data not higher than 1.37% (inter-subject median/interquartile range). Although further studies have to be conducted for specific applications, results suggest that the armband device has a good potential for daily life HR monitoring, especially for applications such as arrhythmia or seizure detection, stress assessment, or sleep studies.
一种可穿戴的手臂式心电图（ECG）监测器已被用于日常生活监测。该臂带记录三个 ECG 通道、一个肌电图（EMG）通道和三轴加速度计信号。与传统的 Holter 监测器不同，基于臂带的 ECG 设备由于使用无阻碍导联和干式电极（无水凝胶），即使在几天后也不会引起皮肤刺激，因此非常适合长期日常监测。主成分分析（PCA）和归一化最小均方（NLMS）自适应滤波用于减少 ECG 通道中的 EMG 噪声。基于支持向量机（SVM）分类器和具有径向基函数（RBF）核的最优通道选择器，使用与 ECG 信号质量相关的特征，开发了一种伪迹检测器。从 16 名志愿者的 24 小时臂带记录中，使用 10 秒的片段估计平均 HR。此外，计算了四个经典心率变异性（HRV）参数（SDNN、RMSSD 和低频和高频带的功率）。为了比较目的，还使用商业 Holter 监测器的数据估计了相同的参数。当用户不在床上时，臂带提供了可使用的数据（与 Holter 估计的平均 HR 的差异小于 10%），在非床时间内，75.25%/11.02%（中位数/四分位距）的时间段内和在床时间内的 98.49%/0.79%。自动伪迹检测器发现，在非卧床时间内，53.85%/17.09%的数据可用，在卧床时间内，95.00%/2.35%的数据可用。与 Holter 数据相比，HRV 分析的相对误差不高于 1.37%（中位数/四分位距）。尽管还需要针对特定应用进行进一步研究，但结果表明，臂带设备具有很好的日常生活 HR 监测潜力，特别是在心律失常或癫痫检测、压力评估或睡眠研究等应用中。
