An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction.

This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that aims to address the problems in arrhythmia diagnosis. The model performs pre-processing of the heartbeat signal by automatically and efficiently extracting time-domain, time-frequency-domain and multi-scale features at different scales. These features are imported into an adaptive online convolutional network-based classification inference module for arrhythmia diagnosis. Experimental results show that the AOCT-based deep learning neural network diagnostic module has excellent parallel computing and classification inference capabilities, and the overall performance of the model improves with increasing scales. In particular, when multi-scale features are used as inputs, the model is able to learn both time-frequency domain information and other rich information, thus significantly improving the performance of the end-to-end diagnostic model. The final results show that the AOCT-based deep learning neural network model has an average accuracy of 99.72%, a recall of 99.62%, and an F1 score of 99.3% in diagnosing four common heart diseases.

Application of tracing methodology in nursing management of cardiology department / 现代临床护理

Objective To explore the effect of tracing methodology on nursing management of cardiology department. Methods From January 2014 to December 2016, the two departments were divided into two groups: the observation group and the control group, 81 cases in each group. The patients in the control group were given routine nursing management, and the observation group was given tracing methodological nursing management. The two groups were compared in terms of basic care pass rate, incidence of adverse care events, patient's satisfaction level. Result The prevalence of basic nursing care of the observation group was significantly higher than that of the control, the incidence of nursing adverse events was significantly lower and the patient's satisfaction level was significantly higher (P<0.05). Conclusion In the department of cardiology, the tracing methodology can effectively improve the quality of clinical nursing and patient 's nursing satisfaction.