Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions

Objective: 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. Methods: 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. Results: 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. Conclusion: 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. Significance: 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.

I. Introduction

Aaccording to the World Health Organization (report from May 2017), cardiovascular diseases (CVD) are responsible for approximately 30% of all deaths worldwide [1]. In addition, nearly 80% of all cardiovascular-related deaths occur in low- and middle-income countries [2]–[4]. The most common diagnostic method used to detect heart disease is measuring the heart's electrical activity by electrocardiography (ECG). Nowadays, the telemedicine area and the field of long-term self-monitoring not only of patients but also of athletes are swiftly developing. For this purpose, wearable devices are often used. Their advantage is small size, sufficient battery life and relative affordability. On the other hand, the record quality generally fluctuates because sensing is done in free-living conditions in comparison with ambulatory resting ECG. Long-term ECG monitoring data often contain a variety of artifacts (e.g., powerline interference, drift, impulse noise, and muscle noise) that complicate subsequent analysis [5]. The change of noise intensity over time and overall non-stationarity of the signal also complicate the processing of the long-term signals.