OBJECTIVES: To design a deep learning model that balances model complexity and performance to enable its integration into wearable ECG monitoring devices for automated diagnosis of atrial fibrillation. METHODS: This study was performed based on data from 84 patients with atrial fibrillation, 25 patients with atrial fibrillation, and 18 subjects without obvious arrhythmia collected from the publicly available datasets LTAFDB, AFDB, and NSRDB, respectively. A lightweight attention network based on depthwise separable convolution and fusion of channel-spatial information, namely DSC-AttNet, was proposed. Depthwise separable convolution was introduced to replace standard convolution and reduce model parameters and computational complexity to realize high efficiency and light weight of the model. The multilayer hybrid attention mechanism was embedded to compute the attentional weights of the channels and spatial information at different scales to improve the feature expression ability of the model. Ten-fold cross-validation was performed on LTAFDB, and external independent testing was conducted on AFDB and NSRDB datasets. RESULTS: DSC-AttNet achieved a ten-fold average accuracy of 97.33% and a precision of 97.30% on the test set, both of which outperformed the other 4 comparison models as well as the 3 classical models. The accuracy of the model on the external test set reached 92.78%, better than those of the 3 classical models. The number of parameters of DSC-AttNet was 1.01M, and the computational volume was 27.19G, both smaller than the 3 classical models. CONCLUSIONS This proposed method has a smaller complexity, achieves better classification performance, and has a better generalization ability for atrial fibrillation classification.
Atrial Fibrillation/diagnosis* ; Humans ; Electrocardiography ; Deep Learning ; Wearable Electronic Devices ; Neural Networks, Computer

OBJECTIVES: We propose a myocardial infarction (MI) detection and localization model for improving the diagnostic accuracy for MI to provide assistance to clinical decision-making. METHODS: The proposed model was constructed based on multi-scale field residual blocks fusion modified channel attention (MSF-RB-MCA). The model utilizes lead II electrocardiogram (ECG) signals to detect and localize MI, and extracts different levels of feature information through the multi-scale field residual block. A modified channel attention for automatic adjustment of the feature weights was introduced to enhance the model's ability to focus on the MI region, thereby improving the accuracy of MI detection and localization. RESULTS: A 5-fold cross-validation test of the model was performed using the publicly available Physikalisch-Technische Bundesanstalt (PTB) dataset. For MI detection, the model achieved an accuracy of 99.96% on the test set with a specificity of 99.84% and a sensitivity of 99.99%. For MI localization, the accuracy, specificity and sensitivity were 99.81%, 99.98% and 99.65%, respectively. The performances of the model for MI detection and localization were superior to those of other comparison models. CONCLUSIONS The proposed MSF-RB-MCA model shows excellent performance in AI detection and localization based on lead II ECG signals, demonstrating its great potential for application in wearable devices.
Myocardial Infarction/diagnosis* ; Humans ; Electrocardiography/methods* ; Signal Processing, Computer-Assisted ; Algorithms ; Sensitivity and Specificity

Left atrial appendage closure(LAAC)is an important intervention method for preventing thromboembolic events in patients with non-valvular atrial fibrillation(NVAF).However,incomplete endothelialization following the procedure can impact its long-term efficacy and safety.This article proposes the view of qi flourishment promoting tissue regeneration based on the traditional Chinese medicine(TCM)theory of qi and blood,and explores diagnostic and therapeutic strategies for post-LAAC incomplete endothelialization by examining the theoretical connotation of qi flourishment promoting tissue regeneration and the relationship between qi and vascular endothelial cells.It is proposed that the primary pathogenesis in patients after LAAC is due to qi deficiency.Guided by the view of qi flourishment promoting tissue regeneration,therapeutic approaches such as tonifying qi to promote granulation,supplementing qi to activate blood circulation,and harmonizing the viscera can be employed to address incomplete endothelialization in NVAF patients following LAAC.Clinically,the qi-supplementing and blood-activating classic formula Neituo Shengji San,mainly composed of Astragali Radix,Glycyrrhizae Radix et Rhizoma,Olibanum,Myrrha,Paeoniae Radix Alb,Trichosanthis Radix,Salviae Miltiorrhizae Radix et Rhizoma,etc.,is usually utilized for modified use.Depending on the specific symptom patterns or pathogenesis characteristics of patients with incomplete endothelialization,this basic formula may be used by combining with Shengmai San or augmented with qi-supplementing and blood-activating herbs such as Chuanxiong Rhizoma,Fici Simplicissimae Radix,and Notoginseng Radix et Rhizoma to promote endothelialization.Diagnosing and treating post-LAAC incomplete endothelialization in NVAF patients following the view of qi flourishment promoting tissue regeneration,it is expected to offer a novel TCM perspective and therapeutic strategy to enhance post-LAAC outcomes and address the challenge of incomplete endothelialization.This approach can further serve as a reference for TCM clinicians to manage endothelialization issues following implantation procedures.