ML algorithms to estimate data reliability metric of ECG from inter-patient data for trustable AI-based cardiac monitors

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Improvement in remote healthcare systems has been conducted utilizing cyber-physical systems (CPS), although data reliability is still the biggest challenge in unsupervised settings. Cardiac monitoring remotely using wearable sensors and implementing trustable artificial intelligence (AI) algorithms are one of the most challenging problems in Smart Health (sHealth). Remote cardiac monitoring using wearable electrocardiogram (ECG) devices is prone to noise and artifacts, which can make the data unreliable as false detection, can happen due to the presence of noise. Checking the reliability of data manually is time-consuming and not possible for long-term recorded data from unsupervised settings. To mitigate the data reliability issue in remote cardiac monitoring, we propose a novel Data Reliability Metric (DReM), where we predict the reliability of inter-patient ECG data using machine learning (ML) algorithms from data statistics itself. We predicted DReM on a scale of 0–1, where 0 means an extremely noisy signal and 1 means a noise-free signal. In this work, we have used Lasso regression (LR), Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR) algorithms. To evaluate the model performance R 2 , Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) was used. Decision tree regression was able to predict the DReM with a high correlation (R 2 = 1) and low prediction error. We tested our model using different patients’ data and observed that DTR performed best in all cases. Our proposed data reliability approach can be beneficial in cardiac disease detection by identifying false predictions from unreliable data. • A novel Data Reliability Metric (DReM) is proposed to estimate the reliability of inter-patient ECG data on a scale of 0–1. • Both time-series features (statistical, spectral, and temporal) and morphological features were extracted from the noise-contaminated ECG data. • For evaluating model performance R 2 , RMSE, and MAE are used as analysis indices. • Decision tree regression (DTR) is the best-performed model that achieved R 2 value 1. • Our proposed automated method can be useful in remote cardiac monitoring to identify false alarms due to the less reliable data in ECG.