Machine Learning Model for Early Detection of COVID-19 by Heart Rhythm Abnormalities

Abstract

Introduction. Electronic devices capable of collecting individual telemetry data have opened up prospects for preclinical detection of COVID-19 signs. Known solutions involve the analysis of information that is difficult to obtain at the moment. We are talking, specifically, about the blood condition or a PCR test. This significantly limits the possibility of integrating algorithms with wrist gadgets. At the same time, the cardiovascular system as an object of observation is quite informative, the data collection is well developed. The article describes the problem of detecting covid anomalies in rhythm strips. The work aims at creating a mathematical model based on machine learning algorithms to automate the process of detecting covid abnormalities in the heart rhythm. The possibility of integrating the results obtained with fitness bracelets and smart watches is shown.Materials and Methods. The work involved an open technology stack: Python, Scikit-learn, Lightgbm. When assessing the quality of models for binary classification, metric F1 was used. 229 cardiac rhythm strips (сardiointervalographies) of patients with COVID-19 were studied. The presence or absence of signs of an anomaly was determined taking into account the time of the rhythm strip and the intervals between heartbeats. Deviations that could indicate infection were shown graphically. Based on the exploratory analysis results, a list of signs indicating an anomaly was made.Results. As a result of the work done, a mathematical model was obtained that detected heart rate abnormalities specific to COVID-19 with an accuracy of 83 %. The basic features determining the predictive ability of the model were identified and ranked. They included the current value of the interval between heartbeats, the derivatives at the subsequent and previous points of measuring the duration of the heartbeat, the first derivative at the current point, and the deviation of the current value of the duration of the RR-interval from the median. The first indicator in this list was recognized as the most significant, the last — the least. For machine learning purposes, the potential of five algorithms was evaluated: IsolationForest, LGBMClassifier, RandomForestClassifier, ExtraTreesClassifier, SGDOneClassSVM. The normal and abnormal results of observations in isolation trees were visualized. A parameter was set that corresponded to the probability of regular observation outside the norm, and its value was selected — 0.11. Taking into account this indicator, a graph was constructed for the SGDOneClassSVM model. Based on the data set, using the cross-validation technique, the quality metric was calculated. The case in hand was a rhythm strip with a time series of observations taken in one continuous time interval from one person. A step-by-step process of obtaining averaged metric values for each model was described. In comparison, the highest indicator was recorded for the LGBMClassifier model, the lowest — for SGDOneClassSVM and IsolationForest.Discussion and Conclusions. The resulting mathematical model takes up little space in the memory of a mobile device, i.e., it does not impose significant requirements on computing resources. The solution has an acceptable detection quality for preclinical screening of COVID-19-related cardiovascular disorders. The algorithm detects anomalies in 83 % of cases. Four minutes is enough to record a rhythm strip. The proposed scenario for using an integrated solution is concise and easy to implement. Widespread use of the development can contribute to the detection of COVID-19 at an early stage.