A simple model to detect atrial fibrillation via visual imaging

Non-Contact Atrial Fibrillation Detection From Face Videos by Learning Systolic Peaks

OBJECTIVE

We propose a non-contact approach for atrial fibrillation (AF) detection from face videos.

METHODS

Face videos, electrocardiography (ECG), and contact photoplethysmography (PPG) from 100 healthy subjects and 100 AF patients are recorded. Data recordings from healthy subjects are all labeled as healthy. Two cardiologists evaluated ECG recordings of patients and labeled each recording as AF, sinus rhythm (SR), or atrial flutter (AFL). We use the 3D convolutional neural network for remote PPG monitoring and propose a novel loss function (Wasserstein distance) to use the timing of systolic peaks from contact PPG as the label for our model training. Then a set of heart rate variability (HRV) features are calculated from the inter-beat intervals, and a support vector machine (SVM) classifier is trained with HRV features.

RESULTS

Our proposed method can accurately extract systolic peaks from face videos for AF detection. The proposed method is trained with subject-independent 10-fold cross-validation with 30s video clips and tested on two tasks. 1) Classification of healthy versus AF: the accuracy, sensitivity, and specificity are 96.00%, 95.36%, and 96.12%. 2) Classification of SR versus AF: the accuracy, sensitivity, and specificity are 95.23%, 98.53%, and 91.12%. In addition, we also demonstrate the feasibility of non-contact AFL detection.

CONCLUSION

We achieve good performance of non-contact AF detection by learning systolic peaks.

SIGNIFICANCE

non-contact AF detection can be used for self-screening of AF symptoms for suspectable populations at home or self-monitoring of AF recurrence after treatment for chronic patients.

VidAF: A Motion-Robust Model for Screening Atrial Fibrillation from Facial Videos

Atrial fibrillation (AF) is the most common arrhythmia, but an estimated 30% of patients with AF are unaware of their conditions. The purpose of this work is to design a model for AF screening from facial videos, with a focus on addressing typical motion disturbances in our real life, such as head movements and expression changes. This model detects a pulse signal from the skin color changes in a facial video by a convolution neural network, incorporating a phase-driven attention mechanism to suppress motion signals in the space domain. It then encodes the pulse signal into discriminative features for AF classification by a coding neural network, using a de-noise coding strategy to improve the robustness of the features to motion signals in the time domain. The proposed model was tested on a dataset containing 1200 samples of 100 AF patients and 100 non-AF subjects. Experimental results demonstrated that VidAF had significant robustness to facial motions, predicting clean pulse signals with the mean absolute error of inter-pulse intervals less than 100 milliseconds. Besides, the model achieved promising performance in AF identification, showing an accuracy of more than 90% in multiple challenging scenarios. VidAF provides a more convenient and cost-effective approach for opportunistic AF screening in the community.