In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor’s diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F₁ score of 0.92 on the test set (n = 7270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection.

Objective : Detecting atrial fibrillation (AF) from electrocardiogram (ECG) recordings remains a challenging task. In this paper, a new AF detection method was proposed to classify the ECG recordings into one of four classes: Normal rhythm, AF, Other rhythm, and Noisy recordings. Approach : The proposed method comprised preprocessing, feature extraction, and classification. In preprocessing, R-peaks were detected, and RR intervals and delta RR intervals were extracted. In feature extraction, 30 multi-level features were extracted, including AF features ( n = 4), morphology features ( n = 20), RR interval features ( n = 2), and features of similarity index between beats ( n = 4). In classification, these features were used to train an AdaBoosted decision tree ensemble for classifying ECG recordings into the four classes. The decision tree ensemble was trained with 100-fold cross-validation on the training dataset ( n = 8528) provided by the 2017 PhysioNet/Computing in Cardiology (CinC) Challenge. Main results : The trained classifier was submitted to the Challenge for testing on the unavailable test dataset ( n = 3658); the official F ₁ scores for ‘Normal’, ‘AF’, ‘Other’ were 0.91, 0.82, and 0.73, respectively, and the overall F ₁ score was 0.82 (ranking equal 5th with eight other algorithms in the 2017 PhysioNet/CinC Challenge). Significance : The proposed algorithm may be used as a new method for AF detection.