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.

译文

:可穿戴的袖标心电图(ECG)监视器已用于日常生活监视。袖标记录了三个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监护仪相反,基于袖章的ECG设备使用方便,因为它不使用阻塞性导线,并且电极干燥(无水凝胶),即使在几天后也不会引起皮肤刺激,因此可用于长期的日常生活监测。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通道的EMG噪声。基于具有径向基函数(RBF)内核的支持向量机(SVM)分类器,使用与ECG信号质量相关的功能,开发了伪像检测器和最佳通道选择器。平均HR是根据16位志愿者的24小时臂章录音估算出来的,每节10秒。此外,还计算了四个经典的HR可变性(HRV)参数(SDNN,RMSSD以及低频段和高频段的功率)。为了进行比较,对商用Holter监测仪的数据也估算了相同的参数。当用户不在床上时,臂章提供了有用的数据(与Holter估计的平均HR的差异小于10%),当用户未躺在床上时,这些段的数据为98.49%/ 0.79%(受试者之间的中位数/四分位间距)床段。自动伪像检测器发现,在非就寝时间可以使用53.85%/ 17.09%的数据,在就寝时间可以使用95.00%/ 2.35%的数据。 HRV分析获得的Holter数据的相对误差不高于1.37%(受试者间中位数/四分位间距)。尽管必须针对特定的应用进行进一步的研究,但结果表明,该袖标设备具有用于日常HR监测的良好潜力,尤其是对于心律不齐或癫痫发作检测,压力评估或睡眠研究等应用。

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