Nontraditional Electrocardiogram and Algorithms for Inconspicuous In-Home Monitoring: Comparative Study.

Nicholas J Conn,Karl Q Schwarz,David A Borkholder

doi:10.2196/mhealth.9604

Nicholas J Conn, Karl Q Schwarz + Show 1 more

Open Access

https://doi.org/10.2196/mhealth.9604

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Abstract

BackgroundWearable and connected in-home medical devices are typically utilized in uncontrolled environments and often measure physiologic signals at suboptimal locations. Motion artifacts and reduced signal-to-noise ratio, compared with clinical grade equipment, results in a highly variable signal quality that can change significantly from moment to moment. The use of signal quality classification algorithms and robust feature delineation algorithms designed to achieve high accuracy on poor quality physiologic signals can prove beneficial in addressing concerns associated with measurement accuracy, confidence, and clinical validity.ObjectiveThe objective of this study was to demonstrate the successful extraction of clinical grade measures using a custom signal quality classification algorithm for the rejection of poor-quality regions and a robust QRS delineation algorithm from a nonstandard electrocardiogram (ECG) integrated into a toilet seat; a device plagued by many of the same challenges as wearable technologies and other Internet of Things–based medical devices.MethodsThe present algorithms were validated using a study of 25 normative subjects and 29 heart failure (HF) subjects. Measurements captured from a toilet seat-based buttocks electrocardiogram were compared with a simultaneously captured 12-lead clinical grade ECG. The ECG lead with the highest morphological correlation to buttocks electrocardiogram was used to determine the accuracy of the heart rate (HR), heart rate variability (HRV), which used the standard deviation of the normal-to-normal (SDNN) intervals between sinus beats, QRS duration, and the corrected QT interval (QTc). These algorithms were benchmarked using the MIT-BIH Arrhythmia Database (MITDB) and European ST-T Database (EDB), which are standardized databases commonly used to test QRS detection algorithms.ResultsClinical grade accuracy was achieved for all buttocks electrocardiogram measures compared with standard Lead II. For the normative cohort, the mean was −0.0 (SD 0.3) bpm (N=141 recordings) for HR accuracy and −1.0 (SD 3.4) ms for HRV (N=135). The QRS duration and the QTc interval had an accuracy of −0.5 (SD 6.6) ms (N=85) and 14.5 (SD 11.1) ms (N=85), respectively. In the HF cohort, the accuracy for HR, HRV, QRS duration, and QTc interval was 0.0 (SD 0.3) bpm (N=109), −6.6 (SD 13.2) ms (N=99), 2.9 (SD 11.5) ms (N=59), and 11.2 (SD 19.1) ms (N=58), respectively. When tested on MITDB and EDB, the algorithms presented herein had an overall sensitivity and positive predictive value of over 99.82% (N=900,059 total beats), which is comparable to best in-class algorithms tuned specifically for use with these databases.ConclusionsThe present algorithmic approach to data analysis of noisy physiologic data was successfully demonstrated using a toilet seat-based ECG remote monitoring system. This approach to the analysis of physiologic data captured from wearable and connected devices has future potential to enable new types of monitoring devices, providing new insights through daily, inconspicuous in-home monitoring.

Highlights

BackgroundWearable and internet-connected medical devices have the potential to fill a gap in patient monitoring, providing insights into disease progression and cardiovascular health between office visits, as well as enabling prevention-focused personalized care
The ECG lead with the highest morphological correlation to buttocks electrocardiogram was used to determine the accuracy of the heart rate (HR), heart rate variability (HRV), which used the standard deviation of the normal-to-normal (SDNN) intervals between sinus beats, QRS duration, and the corrected QT interval (QTc)
Clinical grade accuracy was achieved for all buttocks electrocardiogram measures compared with standard Lead II

Summary

Introduction

BackgroundWearable and internet-connected medical devices have the potential to fill a gap in patient monitoring, providing insights into disease progression and cardiovascular health between office visits, as well as enabling prevention-focused personalized care. The sluggish adoption of such technology within health care can be attributed to a lack of clinical value due to the large volume of difficult-to-interpret data and poor confidence in measurement accuracy [1,2]. This is due to use in uncontrolled environments and limited signal quality, resulting in noisy and highly variable signals that change from moment to moment. Many wearable and connected devices do not meet the requirements for medical use and instead target self-management of fitness and well-being The literature approaches this problem by directly addressing noise in physiologic signals, through signal enhancement and noise removal techniques. The use of signal quality classification algorithms and robust feature delineation algorithms designed to achieve high accuracy on poor quality physiologic signals can prove beneficial in addressing concerns associated with measurement accuracy, confidence, and clinical validity

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