Diagnosis of atrial fibrillation based on lightweight detail-semantic network

Atrial fibrillation has become an important disease that threatens people's health. Wearable long-range dynamic ECG signals are effective means to screen for AF, but the complexity of existing algorithms limits the integration of signal acquisition and analysis in wearable ECG monitors. Developing an algorithm that satisfies the computational power of low-end chips is the key to the problem. In this paper, we propose a lightweight detail-semantic network (LDS Net) consisting of detail path, semantic path, and an improved cross-guidance mechanism. The detail path uses multiple layers of depthwise separable convolution to extract deep information about AF, the semantic path uses hourglass residual modules to compensate for the disadvantage of obstructed information flow of depthwise separable convolution, and an improved cross-guidance mechanism uses the idea of mutual guidance to achieve information fusion between the detail path and the semantic path. The network has only 0.2 M parametric count and 327.36 M computational volume. It achieves 99.57% accuracy on the MIT-BIH Atrial Fibrillation Database and 90.89% accuracy on the clinical dataset. The results show that LDS Net is significantly better than the classical convolutional neural network in terms of computational cost and accuracy, and has good clinical application prospects and generalizability.