Development of severality diagnosis model of atrial fibrillation using XGBoost from electrocardiogram

Abstract

Atrial fibrillation (AF) is a type of arrhythmia in that atria fail to adequately function. Frequent AF may lead to the formation of blood clots in the atria, which may lead to cerebral or myocardial infarction. AF is usually diagnosed by a cardiologist with a visual check of electrocardiogram (ECG) data measured with a Holter electrocardiograph over a 24-hour period, which is burdensome and time-consuming. In this study, we develop a model that automatically diagnoses the severity of AF from ECG data by using machine learning technologies. The ECG data of 75 patients with suspected AF were collected from the Mitsubishi Kyoto Hospital. The 30-beats RRI data were clipped from the collected ECG data, and heart rate variability (HRV) data were extracted from the clipped RRI data. We trained an XGBoost model that can diagnose normal, mild, and severe AF using the extracted HRV data. The overall rate of correct answers of the trained model was 86.2%, 1.53% of severe AFs misdiagnosed healthy or mild as severe, and 11.4% misdiagnosed severe as healthy. The performance was high enough for clinical practice. The severity of AF will be easily and rapidly diagnosed with the developed model in the future.