Detection of Atrial Fibrillation in Short-Lead Electrocardiogram Recordings Obtained using a Smart Scale

Abstract

Atrial fibrillation (AF) is the type of arrhythmia that raises possibility of severe health problems such as heart failure and stroke and it is known that a major risk factor of AF includes overweight and obesity. Based on this association between such health-related indicators, we propose a smart scale that is capable of measuring weight and electrocardiography (ECG) simultaneously. The scale was developed using Arduino Uno, a Wheatstone bridge load cell, and ECG sensors. The ECG signals were processed to compute heart rate (in other words, RR interval). The smart scale was evaluated with four healthy volunteers in terms of reliability showing high agreement with a commercial device for ECG monitoring. In addition, it implements Atrial Fibrillation (AF) detection using machine-learning classifiers including a k-Nearest Neighbor (kNN) method, a Decision Tree (DT), and a Neural Network (NN) on relatively short recordings of ECG obtained while using the scale. The root mean square of the successive differences between heart beats (RMSSD) and the Shannon entropy of the RR interval (ECG features) were extracted from ECG signals for AF detection. Performance of AF detection was tested with patients who were treated at a Cardiology Center after balancing data by applying over- and under-sampling techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and the Tomek Link (T-Link) algorithm. After addressing the data imbalance, the AF detection performance of each classifier (kNN, DT, and NNs) was 98.9%, 97.8%, and 98.9% respectively. This work has successfully demonstrated weight and cardio activity monitoring features while using a scale that may help keep the records of sensitive health related indexes on a daily basis.