MS-QI: A Modulation Spectrum-Based ECG Quality Index for Telehealth Applications

Abstract

As telehealth applications emerge, the need for accurate and reliable biosignal quality indices has increased. One typical modality used in remote patient monitoring is the electrocardiogram (ECG), which is inherently susceptible to several different noise sources, including environmental (e.g., powerline interference), experimental (e.g., movement artifacts), and physiological (e.g., muscle and breathing artifacts). Accurate measurement of ECG quality can allow for automated decision support systems to make intelligent decisions about patient conditions. This is particularly true for in-home monitoring applications, where the patient is mobile and the ECG signal can be severely corrupted by movement artifacts. In this paper, we propose an innovative ECG quality index based on the so-called modulation spectral signal representation. The representation quantifies the rate of change of ECG spectral components, which are shown to be different from the rate of change of typical ECG noise sources. The proposed modulation spectral-based quality index, MS-QI, was tested on 1) synthetic ECG signals corrupted by varying levels of noise, 2) single-lead recorded data using the Hexoskin garment during three activity levels (sitting, walking, running), 3) 12-lead recorded data using conventional ECG machines (Computing in Cardiology 2011 dataset), and 4) two-lead ambulatory ECG recorded from arrhythmia patients (MIT-BIH Arrhythmia Database). Experimental results showed the proposed index outperforming two conventional benchmark quality measures, particularly in the scenarios involving recorded data in real-world environments.

Spectro-Temporal Electrocardiogram Analysis for Noise-Robust Heart Rate and Heart Rate Variability Measurement

“…Here, modulation spectrograms are estimated every 5 seconds with 75% overlap. While alternate parameter configurations (e.g., base window, modulation window, overlap, and number of FFT points) can be used, the values reported herein were empirically found to be optimal for the task and sampling rate at hand [44] . For this particular configuration, a 5-second duration ECG signal will generate an RR series with five points.…”

“…The represented modulation spectrogram shows the rate-of-change of the different ECG spectral components. Synthesized clean ECG data were used to optimize the base and modulation window sizes, as well as the overlap rates reported above for 256 Hz sampled ECG data, as detailed in [44] . This parameter configuration requires a minimum of 4 seconds of ECG data in order to calculate one modulation spectrogram.…”

“…More specifically, we measure the rate-of-change of ECG short-term spectral magnitude components and show that cardiac components and noise components become separable in this new domain. Modulation spectrum processing has been shown in the past to accurately separate signal and noise components for speech enhancement [41] , [42] , auscultatory sound analysis [43] , and, more recently, for automated ECG quality assessment [44] . Within this new domain, robust HR measurement in extremely noisy cases is possible without the need for a priori ECG enhancement nor time-domain peak detection.…”

EPOWT: A denoising technique of the electrocardiography signal transmission via 5G wireless communications

“…These screens are either transferring the crude information towards a primary station or save the information in memory on behalf of later investigation. They are not able to give clinical crucial investigation and support in decision creation and therefore these gadgets are not broadly utilized for continuous checking . Hence, the main goal of this study is to build a communal ECG monitoring structure aimed at the wireless detection of ECG signals and real time examination of ECG statistics, in addition to giving community‐dwelling cardiac outpatients through customized medicinal services and a functioning type of communication between therapeutic experts and patients .…”

“…They are not able to give clinical crucial investigation and support in decision creation and therefore these gadgets are not broadly utilized for continuous checking. 17,18 Hence, the main goal of this study is to build a communal ECG monitoring structure aimed at the wireless detection of ECG signals and real time examination of ECG statistics, in addition to giving community-dwelling cardiac outpatients through customized medicinal services and a functioning type of communication between therapeutic experts and patients. 19 The framework is equipped for overseeing individual medical complaints of cardiovascular patients for the experts and gives dynamic communications between heart patients and PCs, relatives, and restorative experts and underlines a simpler method to suit numerous clients in a network.…”