Objective To propose a lead attention network-based ST-T change recognition algorithm to detect ECG ST-T changes accurately.Methods Firstly,heartbeat signals were extracted through R-wave localization,and a 12-lead heartbeat matrix was generated by correlation-based screening and merging to realize data augmentation.Secondly,a lead attention module was constructed by combining depthwise convolution(DWConv)with the channel attention squeeze-and-excitation block(SE-block)structure to perceive the differences in ST-T status among electrocardiogram leads.Thirdly,the mapping output by two independent attention modules was fused and splicing with the original signal residual was carried out,so that attention information extraction and original information transfer were enhanced effectively.Finally,SE-ResNet was used as the backbone network to extract signal features to complete the classification and identification of ST-T changes.To validate the recognition performance of the proposed algorithm for ST-T changes in ECG,the 12-lead ECG data of 97 472 patients containing different ECG rhythms were collected for ablation and comparison experiments at the First Affiliated Hospital of Army Medical University.Results The proposed algorithm achieved an AUC of 0.965 with a sensitivity of 90.51%,specificity of 90.23%,positive predictive value of 89.24%and overall accuracy of 90.36%on an independent test set.Comparative analysis demonstrated superior performance to four benchmark architectures,including VGG16,ResNet18,MobileNetV3-Small and ShuffleNet,in terms of both classification accuracy and computational efficiency.Conclusion The algorithm designed can accurately detect ST-T changes and can be used for wearable ECG automatic analysis to assist in the early warning of cardiovascular diseases in both acute and chronic patients and highland residents.[Chinese Medical Equipment Journal,2025,46(7):1-11]

Objective To propose a lead attention network-based ST-T change recognition algorithm to detect ECG ST-T changes accurately.Methods Firstly,heartbeat signals were extracted through R-wave localization,and a 12-lead heartbeat matrix was generated by correlation-based screening and merging to realize data augmentation.Secondly,a lead attention module was constructed by combining depthwise convolution(DWConv)with the channel attention squeeze-and-excitation block(SE-block)structure to perceive the differences in ST-T status among electrocardiogram leads.Thirdly,the mapping output by two independent attention modules was fused and splicing with the original signal residual was carried out,so that attention information extraction and original information transfer were enhanced effectively.Finally,SE-ResNet was used as the backbone network to extract signal features to complete the classification and identification of ST-T changes.To validate the recognition performance of the proposed algorithm for ST-T changes in ECG,the 12-lead ECG data of 97 472 patients containing different ECG rhythms were collected for ablation and comparison experiments at the First Affiliated Hospital of Army Medical University.Results The proposed algorithm achieved an AUC of 0.965 with a sensitivity of 90.51%,specificity of 90.23%,positive predictive value of 89.24%and overall accuracy of 90.36%on an independent test set.Comparative analysis demonstrated superior performance to four benchmark architectures,including VGG16,ResNet18,MobileNetV3-Small and ShuffleNet,in terms of both classification accuracy and computational efficiency.Conclusion The algorithm designed can accurately detect ST-T changes and can be used for wearable ECG automatic analysis to assist in the early warning of cardiovascular diseases in both acute and chronic patients and highland residents.[Chinese Medical Equipment Journal,2025,46(7):1-11]

Child ; Humans ; Manometry ; Esophagus/physiology*

Objective: To evaluate the efficacy and safety of a domestic human plasma derived coagulation Factor Ⅸ concentrate (pd-FⅨ) in patients with hemophilia B. Methods: The study was a multicenter, open-label and single-arm study. The efficacy of pd-F Ⅸ was evaluated by objective performance criteria. The doses of pd-FⅨ were calculated according to the bleeding symptom and disease severity. The infusion efficiency of pd-FⅨ and improvement of bleeding symptoms were measured at 30 minutes and (24±4) h after the first infusion, respectively. Adverse events were recorded. Viral infection and FⅨ inhibitor were detected 90 d after the first infusion. Results: All 36 subjects with hemophilia B were enrolled in the study. The median age of these patients was 31 years old and the median injection doses were 4 (1-17) times. The hemostatic effect of 27/36 (75.00%) and 9/36 (25.00%) acute bleeding events were rated as "excellent" and "better" , respectively. The recovery rate was 111.92% (65.55%-194.28%) at 30 minutes after infusion of FⅨ. There was no adverse event related to FⅨ. No reactivation of HBV, HCV or HIV and FⅨ inhibitor was detected at 90-104 d after the first FⅨ infusion. Conclusion: This domestically made human plasma derived FⅨ concentrate is safe and effective in the treatment of acute bleeding in patients with hemophilia B. Clinical trial registration: China food and Durg Administration, 2016L08027.
Adult ; China ; Factor IX ; Hemophilia A ; Hemophilia B/therapy* ; Hemorrhage ; Humans ; Plasma