CDAD: A Novel High-Efficiency Cross-age Domain Adaptation ECG Diagnosis Algorithm for Adolescents

Cardiovascular diseases are rapidly becoming one of the major health problems in adolescents. With the advancement of deep learning techniques, smart ECG diagnostic tools based on these techniques show great potential for application in real-world healthcare settings. However, the scarcity of ECG data in adolescents compared to older adults is a key challenge for deep learning techniques, the accuracy of which relies on extensive labeled training data. In this paper, we propose a Cross-age Domain Adaptation Diagnosis CDAD approach and introduce a domain adaptation network, Squeeze and Excitation Widekernel Neural Network SEWNN , aiming to alleviate the constraints imposed by unlabeled data and cross-domain diagnosis. Firstly, Largescale labeled ECG data from elderly individuals are employed for feature extraction and model training. Subsequently, an adversarial learning approach is employed to enhance the model’s cross-domain transfer capabilities. In addition, Ensemble learning techniques that consider information from multiple cues to improve prediction accuracy are applied. In this study, we validate the effectiveness of the proposed method by applying it to three public ECG diagnostic datasets and evaluating its applicability from the elderly to adolescents. By comparing the experimental results with other methods, we demonstrate the validity of the method in adolescents diagnosing ECG, as well as its robustness in cross-dataset diagnosis.