Signal Quality Analysis for Long-Term ECG Monitoring Using a Health Patch in Cardiac Patients

Abstract

Cardiovascular diseases (CVD) represent a serious health problem worldwide, of which atrial fibrillation (AF) is one of the most common conditions. Early and timely diagnosis of CVD is essential for successful treatment. When implemented in the healthcare system this can ease the existing socio-economic burden on health institutions and government. Therefore, developing technologies and tools to diagnose CVD in a timely way and detect AF is an important research topic. ECG monitoring patches allowing ambulatory patient monitoring over several days represent a novel technology, while we witness a significant proliferation of ECG monitoring patches on the market and in the research labs, their performance over a long period of time is not fully characterized. This paper analyzes the signal quality of ECG signals obtained using a single-lead ECG patch featuring self-adhesive dry electrode technology collected from six cardiac patients for 5 days. In particular, we provide insights into signal quality degradation over time, while changes in the average ECG quality per day were present, these changes were not statistically significant. It was observed that the quality was higher during the nights, confirming the link with motion artifacts. These results can improve CVD diagnosis and AF detection in real-world scenarios.

The Utility of a Novel Electrocardiogram Patch Using Dry Electrodes Technology for Arrhythmia Detection During Exercise and Prolonged Monitoring: Proof-of-Concept Study

“…However, the percentage of time with high signal quality in all collected data remained stable over time and was around 60%. Moreover, further analysis of signal quality showed no significant difference in the quality changes between the days [27]. Other research has shown that monitoring for up to 14 days is already feasible with the self-adhesive Zio Patch [18], but the influence of high-intensity activities was not addressed.…”

“…Quality analysis was performed using Python (Python Software Foundation). The quality of the ECG recording from the patch during exercise was analyzed using a Signal Quality Indicator (SQI) which resulted in being the best performing one when comparing different SQI metrics with annotated quality levels [27]. This SQI is based on the comparison of successive QRS complexes.…”

“…This was then averaged across all participants. Segments of ECG with a quality above 80% are considered high-quality signals [27].…”

Gender and age characteristics and atrial fibrillation during long-term telemonitoring of the ECG

“…За час досліджень цієї технології, що наразі тривають, з'явились роботи, що демонструють її певні діагностичні переваги. За даними I. Campero-Jurado і співавторів порівняно зі стандартним підходом, патч-монітори на 56,7 % більше ідентифікували прояви аритмії протягом тривалого періоду використання, що має принципове значення для прогнозування та профілактики серцевосудинних подій [7].…”

EgoActive: Integrated Wireless Wearable Sensors for Capturing Infant Egocentric Auditory–Visual Statistics and Autonomic Nervous System Function ‘in the Wild’

“…With adult ECG, a high-pass filter of 0.5 Hz is used in combination with a notch filter at the mains-electricity frequency (e.g., 50 Hz or 60 Hz). The 0.5 Hz threshold along with a notch filter is the default setting in the Neurokit2 open source Python (Python Software Foundation, Wilmington, DE, USA) library for ECG peak detection [ 116 ] and in other studies (e.g., [ 117 ]), while also being the same lower bound used for monitor-quality ECGs [ 118 ]. The Neurokit2 peak detection method (default settings) was used to detect R peaks on the preprocessed ECG signal [ 116 ], where the QRS complexes are identified based on the steepness of the absolute gradient of the ECG, and the local maxima are identified as the R waves.…”