Smital Lukas, Haider Clifton R, Vitek Martin, Leinveber Pavel, Jurak Pavel, Nemcova Andrea, Smisek Radovan, Marsanova Lucie, Provaznik Ivo, Felton Christopher L, Gilbert Barry K, Holmes Iii David R
IEEE Trans Biomed Eng. 2020 Oct;67(10):2721-2734. doi: 10.1109/TBME.2020.2969719. Epub 2020 Jan 27.
Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed.
The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing.
The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device.
The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions.
The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals.
如今，心电图质量评估方法大多旨在对整个信号的质量进行二元区分，即区分质量好坏。这种分类不适用于可穿戴设备收集的长期数据。本文提出了一种估计长期心电图信号质量的新方法。
通过计算连续信噪比（SNR）曲线在局部时间窗口内进行实时质量估计。通过分析SNR波形确定数据质量段的布局。将心电图信号质量分为三个级别：适合全波心电图分析的信号、仅适合QRS检测的信号以及不适合进一步处理的信号。
可靠的QRS检测和全心电图波形分析的SNR限值分别为5 dB和18 dB。该方法使用合成数据进行开发和测试，并在可穿戴设备的真实数据上进行了验证。
所提出的解决方案是一种用于注释心电图信号质量的强大、准确且计算高效的算法，将有助于对在自由生活条件下记录的心电图信号进行后续的定制分析。
可穿戴设备进行长期心电图信号自我监测的领域正在迅速发展。分析大量收集的数据非常耗时。预先表征数据质量并因此将后续分析限制在可用信号上是有利的。
