A Personalized Monitoring Model for Electrocardiogram (ECG) Signals: Diagnostic Accuracy Study (Preprint)

BACKGROUND Lately, the demand for remote ECG monitoring has increased drastically because of the COVID-19 pandemic. To prevent the spread of the virus and keep individuals with less severe cases out of hospitals, more patients are having heart disease diagnosis and monitoring remotely at home. The efficiency and accuracy of the ECG signal classifier are becoming more important because false alarms can overwhelm the system. Therefore, how to classify the ECG signals accurately and send alerts to healthcare professionals in timely fashion is an urgent problem to be addressed. OBJECTIVE The primary aim of this research is to create a robust and easy-to-configure solution for monitoring ECG signal in real-world settings. We developed a technique for building personalized prediction models to address the issues of generalized models because of the uniqueness of heartbeats [19]. In most cases, doctors and nurses do not have data science background and the existing Machine Learning models might be hard to configure. Hence a new technique is required if Remote Patient Monitoring will take off on a grand scale as is needed due to COVID-19. The main goal is to develop a technique that allows doctors, nurses, and other medical practitioners to easily configure a personalized model for remote patient monitoring. The proposed model can be easily understood and configured by medical practitioners since it requires less training data and fewer parameters to configure. METHODS In this paper, we propose a Personalized Monitoring Model (PMM) for ECG signal based on time series motif discovery to address this challenge. The main strategy here is to individually extract personalized motifs for each individual patient and then use motifs to predict the rest of readings of that patient by an artificial logical network. RESULTS In 32 study patients, each patient contains 30 mins of ECG signals/readings. Using our proposed Personalized Monitoring Model (PMM), the best diagnostic accuracy reached 100%. Overall, the average accuracy of PMM was always maintained above 90% with different parameter settings. For Generalized Monitoring Models (GMM1 and GMM2), the average accuracies were only around 80% with much more running time than PMM. Regardless of parameter settings, it normally took 3-4 mins for PMM to generate the training model. However, for GMM1 and GMM2, it took around 1 hour and even more with the increase of training data. The proposed model substantially speeds up the ECG diagnostics and effectively improve the accuracy of ECG diagnostics. CONCLUSIONS Our proposed PMM almost eliminates much training and small sample issues and is completely understandable and configurable by a doctor or a nurse.