Key result
The Personalized Monitoring Model achieved an average diagnostic accuracy of over 90% for ECG signal classification, compared to 80% for generalized models, with training completed in 3-4 minutes.
Why the study?
Surging demand for remote ECG monitoring required an easily configurable, accurate, and understandable solution to classify ECG signals and send timely alerts.
Does a Personalized Monitoring Model (PMM) improve diagnostic accuracy compared to Generalized Monitoring Models (GMM) for remote ECG monitoring?
Does a Personalized Monitoring Model (PMM) improve diagnostic accuracy compared to Generalized Monitoring Models (GMM) for remote ECG monitoring?
Absolute Event Rate: 90% vs 80%
A personalized motif-based ECG monitoring model achieved higher diagnostic accuracy and faster training times than generalized models, offering a potential solution for remote patient monitoring.
May enhance remote ECG accuracy; leaves open validation in larger prospective trials.
BACKGROUND: Due to the COVID-19 pandemic, the demand for remote electrocardiogram (ECG) monitoring has increased drastically in an attempt to prevent the spread of the virus and keep vulnerable individuals with less severe cases out of hospitals. Enabling clinicians to set up remote patient ECG monitoring easily and determining how to classify the ECG signals accurately so relevant alerts are sent in a timely fashion is an urgent problem to be addressed for remote patient monitoring (RPM) to be adopted widely. Hence, a new technique is required to enable routine and widespread use of RPM, as is needed due to COVID-19. OBJECTIVE: The primary aim of this research is to create a robust and easy-to-use solution for personalized ECG monitoring in real-world settings that is precise, easily configurable, and understandable by clinicians. METHODS: In this paper, we propose a Personalized Monitoring Model (PMM) for ECG data based on motif discovery. Motif discovery finds meaningful or frequently recurring patterns in patient ECG readings. The main strategy is to use motif discovery to extract a small sample of personalized motifs for each individual patient and then use these motifs to predict abnormalities in real-time readings of that patient using an artificial logical network configured by a physician. RESULTS: Our approach was tested on 30 minutes of ECG readings from 32 patients. The average diagnostic accuracy of the PMM was always above 90% and reached 100% for some parameters, compared to 80% accuracy for the Generalized Monitoring Models (GMM). Regardless of parameter settings, PMM training models were generated within 3-4 minutes, compared to 1 hour (or longer, with increasing amounts of training data) for the GMM. CONCLUSIONS: Our proposed PMM almost eliminates many of the training and small sample issues associated with GMMs. It also addresses accuracy and computational cost issues of the GMM, caused by the uniqueness of heartbeats and training issues. In addition, it addresses the fact that doctors and nurses typically do not have data science training and the skills needed to configure, understand, and even trust existing black box machine learning models.
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Kotorov et al. (2020) studied Electrocardiogram (ECG) monitoring (n=32). Personalized Monitoring Model (PMM) vs. Generalized Monitoring Models (GMM) was evaluated on Average diagnostic accuracy of ECG signal classification. The Personalized Monitoring Model achieved an average diagnostic accuracy of over 90% for ECG signal classification, compared to 80% for generalized models, with training completed in 3-4 minutes.
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