A Real-Time Tunable ECG Noise-Aware System for IoT-Enabled Devices

Abstract

The Internet of Things (IoT)-enabled electrocardiogram (ECG) monitoring system is an evolution in healthcare, and it is used to monitor heart conditions during daily living conditions. However, the ECG signals from the wearable device are often contaminated with noise, such as power line interference, baseline wander, and muscle artefact. The presence of these noises increases the communication cost of IoT-enabled devices by transmitting unusable noisy signals as well as affecting the clinical decision-making at the application level. A real-time ECG noise detection system at the IoT-enabled gateway can reduce this communication cost and improve the overall quality of the transmitted signal. In this article, we propose a tunable ECG noise localization system to detect noisy ECG segments with a selected percentage of noise-free ECG levels. The proposed system will hold the following: 1) deployable ECG noise detector in the low-computational devices, such as smart phone; 2) reduce the data dropping rate at the network layer; 3) reduce the transmission cost; and 4) improve the reliability of the automated analysis of ECG at the IoT-enabled gateway. We evaluated the performance of the proposed ECG noise detector using publicly available and real-time ECG datasets from wearable ECG sensors. We also measured the data drop rate and compared it with the state-of-the-art algorithms. The impact of reduced data dropping rate also justifies using two commonly used R-peak detection algorithms. The proposed system reduces the data dropping rate by 21.09% on average over public and real-time datasets. Similarly, it increases the number of R-Peak detection by 15.33% on average compared with the existing binary noise classification system by saving partial noisy ECG segments. Thus, the proposed noise detection system improves the reliability of the automated ECG analysis. In summary, the study results support the necessity of a noise detection system for wearable ECG monitoring in daily living conditions to improve the data acquisition efficiency. In addition, a noise localizer can be used for dropping a noisy portion of the signal or filtering the only noisy segment of the ECG signal without blindly imposing a filter on both noise-free and noisy ECG signals.