Key result
An automatic detector combining CNN-LSTM and Laplacian Support Vector Machine achieved 99.63% accuracy, 99.70% sensitivity, and 99.57% specificity for detecting atrial fibrillation.
Why the study?
Early detection of AF is critical, but identifying potential AF in massive wearable long-term ECG monitoring data is challenging given the difficulty of obtaining large amounts of labeled clinical data.
Does an automatic detector combining CNN-LSTM and LapSVM accurately detect atrial fibrillation in long-term ECG data?
Does an automatic detector combining CNN-LSTM and LapSVM accurately detect atrial fibrillation in long-term ECG data?
A novel deep learning and semi-supervised machine learning model demonstrated high accuracy (>99%) for detecting atrial fibrillation from ECG data, even with limited labeled training data.
Atrial fibrillation (AF) is a common arrhythmia associated with cardiac death and stroke. Therefore, the early detection of AF is of critical importance and the wearable long-term ECG monitoring system is one promising way. In order to assist the cardiologists in identifying potential AF in a tremendous amount of long-term ECG data, this study proposed an automatic detector combining deep learning and semi-supervised learning in view of the difficulty of obtaining a large number of labeled data in clinical practice. Three R-R interval features and two nonlinear features extracted from ECG samples, combined with 16-dimension deep learning features extracted by CNN-LSTM, are put into the semi-supervised machine learning model Laplacian Support Vector Machine (LapSVM) for AF detection. The proposed method showed very promising performance, with an accuracy of 99.63%, a sensitivity of 99.70%, a specificity of 99.57% and an F_score of 99.59% on the AFDB dataset. It still achieved an accuracy of 98% when the proportion of the training set was reduced, and achieved an accuracy of 96% on the SPHD collected clinically. The results show that the proposed method can classify AF and non-AF with a higher accuracy, and has excellent generalization performance in different categories of subjects, which is in line with clinical scenarios. The proposed method is also conducive to solving the clinical cases with little labeled data.
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Wang et al. (2023) studied Atrial fibrillation. Automatic detector combining CNN-LSTM and Laplacian Support Vector Machine (LapSVM) was evaluated on Accuracy of AF detection. An automatic detector combining CNN-LSTM and Laplacian Support Vector Machine achieved 99.63% accuracy, 99.70% sensitivity, and 99.57% specificity for detecting atrial fibrillation.
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