IEEE Trans Biomed Eng. 2018 Oct;65(10):2248-2258. doi: 10.1109/TBME.2017.2788701. Epub 2018 Jan 26.
Novel minimum-contact vital signs monitoring techniques like textile or capacitive electrocardiogram (ECG) provide new opportunities for health monitoring. These techniques are sensitive to artifacts and require handling of unstable signal quality. Spatio-temporal blind source separation (BSS) is capable of processing suchlike multichannel signals. However, BSS's permutation indeterminacy requires the selection of the cardiac signal (i.e., the component resembling the electric cardiac activity) after its separation from artifacts. This study evaluates different concepts for solving permutation indeterminacy.
Novel automated component selection routines based on heartbeat detections are compared with standard concepts, as using higher order moments or frequency-domain features, for solving permutation indeterminacy in spatio-temporal BSS. BSS was applied to a textile and a capacitive ECG dataset of healthy subjects performing a motion protocol, and to the MIT-BIH Arrhythmia Database. The performance of the subsequent component selection was evaluated by means of the heartbeat detection accuracy (ACC) using an automatically selected single component.
The proposed heartbeat-detection-based selection routines significantly outperformed the standard selectors based on Skewness, Kurtosis, and frequency-domain features, especially for datasets containing motion artifacts. For arrhythmia data, beat analysis by sparse coding outperformed simple periodicity tests of the detected heartbeats.
Component selection routines based on heartbeat detections are capable of reliably selecting cardiac signals after spatio-temporal BSS in case of severe motion artifacts and arrhythmia.
The availability of robust cardiac component selectors for solving permutation indeterminacy facilitates the usage of spatio-temporal BSS to extract cardiac signals in artifact-sensitive minimum-contact vital signs monitoring techniques.
新型微创生命体征监测技术，如纺织或电容心电图（ECG），为健康监测提供了新的机会。这些技术对伪影很敏感，需要处理不稳定的信号质量。时空盲源分离（BSS）能够处理此类多通道信号。然而，BSS 的排列不定需要在从伪影中分离出心脏信号（即类似于电心脏活动的分量）后选择。本研究评估了解决排列不定的不同概念。
基于心跳检测的新型自动化分量选择程序与标准概念进行了比较，标准概念使用高阶矩或频域特征来解决时空 BSS 中的排列不定。BSS 应用于进行运动协议的健康受试者的纺织和电容 ECG 数据集以及麻省理工学院-贝思以色列医院心律失常数据库。通过使用自动选择的单个分量进行心跳检测准确性（ACC）评估，评估了后续分量选择的性能。
所提出的基于心跳检测的选择程序显著优于基于偏度、峰度和频域特征的标准选择程序，特别是对于包含运动伪影的数据集。对于心律失常数据，稀疏编码的心跳分析优于检测到的心跳的简单周期性测试。
在存在严重运动伪影和心律失常的情况下，基于心跳检测的分量选择程序能够可靠地选择时空 BSS 后的心电信号。
用于解决排列不定的稳健心脏分量选择器的可用性，促进了时空 BSS 在对伪影敏感的微创生命体征监测技术中提取心脏信号的使用。
