Design and validation of Withings ECG Software 2, a tiny neural network based algorithm for detection of atrial fibrillation

Abstract

Background:

Atrial Fibrillation (AF) is the most common form of arrhythmia in the world with a prevalence of 1%–2%. AF is also associated with an increased risk of cardiovascular diseases (CVD), such as stroke, heart failure, and coronary artery diseases, making it a leading cause of death. Asymptomatic patients are a common case (30%–40%). This highlights the importance of early diagnosis or screening. Wearable and home devices offer new opportunities in this regard.

Methods:

We present WECG-SW2, a lightweight algorithm that classifies 30-second lead I ECG strips as ‘NSR’, ‘AF’, ‘Other’ or ‘Noise’. By detecting the location of QRS complexes in the signal, the information can be organized into a low dimensionality input which is fed to a tiny Convolutional Neural Network (CNN) with only 3,633 parameters. This approach allows for the algorithm to run directly on the ECG acquisition devices, and improves accuracy by making the most out of the training set.

Results:

WECG-SW2 was evaluated on a database which combines three clinical studies sponsored by Withings with three hardware devices, as well as the MIT-BIH Arrhythmia Database. On the proprietary clinical database, the sensitivity and specificity of AF detection were 99.63% (95% CI: 99.15–99.84) and 99.85% (95% CI: 99.61–99.94), respectively, based on 4646 strips taken from 1441 participants. On the MIT-BIH Arrhythmia Database, the sensitivity and specificity were 99.87% (95% CI: 99.53, 99.98) and 100% (95% CI: 98.31, 100.0), respectively, across 2624 analyzed segments.

Conclusion:

WECG-SW2 demonstrates high sensitivity and specificity in the detection of AF using a wide variety of ECG recording hardware. The binary of WECG-SW2 is available upon request to the corresponding author for research purposes.