Arrhythmia diagnosis of young martial arts athletes based on deep learning for smart medical care

Zhuang, Jing; Sun, Jing; Yuan, Guoliang

doi:10.1007/s00521-021-06159-4

Cited by 11 publications

(7 citation statements)

References 33 publications

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“…Therefore, it is important to collect ECG signals in physiological signal extraction. In normal sinus rhythm, the pacing point of the heart is the starting point of the cardiac conduction system, sinoatrial node [ 7 ]. After the generation of SAAN, the impulse is transmitted to the bundle between nodes and common atrial muscle and finally to the atrial honey node, and the left atrium conduction velocity of impulse in the atrioventricular node becomes very slow, and it will accelerate only when it reaches his bundle.…”

Section: Construction Of the Mental Health State Detection Model Base...mentioning

confidence: 99%

Model Construction of Using Physiological Signals to Detect Mental Health Status

Liu

2021

Journal of Healthcare Engineering

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Background. Mental health is a direct indicator of human mental activity, and it also affects all aspects of the human body. It plays a very important role in monitoring human mental health. Objectives. To design a mental health state detection model based on physiological signals to detect human mental health. Methods. For the detection of mental health, the sliding window method is used to divide the physiological signal dataset and the corresponding time into several segments and then calculate the physiological signal data in the sliding window for each physiological signal to form a sequence of characteristic values; according to the heart rate variability of the physiological signal, the heart rate variability (HRV) is extracted from the interval spectrum waveform: through the discrete trend analysis in statistics, the change characteristics of the ECG signal are analyzed, and the sequence statistical indicators of the physiological signal are calculated. With the help of a support vector machine used for the significant accuracy with less computation power, the physiological signals of the mental state are classified, and the discriminant function of the mental health state signals is normalized. A mental health state detection model is constructed according to the index system, the optimal solution of the model is obtained through the optimization function, and the mental health state detection is completed. Result. The detection error of the proposed model is less which improves the detection accuracy and is less time consuming. Conclusion. The detection model using physiological signals is proposed to evaluate the mental health status. As compared to the other detection models, its detection time is short and method error is always less than 2% which shows its accuracy and effectiveness.

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Section: Construction Of the Mental Health State Detection Model Base...mentioning

confidence: 99%

Model Construction of Using Physiological Signals to Detect Mental Health Status

Liu

2021

Journal of Healthcare Engineering

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“…The doctor then diagnoses the abnormal heart rate from the ECG post-exercise. They can provide appropriate treatment according to different heart rate types [4]. An electrocardiogram (ECG) is a waveform that records potential changes during human heart activity [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale and Multi-Channel Information Fusion for Exercise Electrocardiogram Feature Extraction and Classification

Wang,

Zhang,

et al. 2024

IEEE Access

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Increased physical activity can help reduce the occurrence of cardiovascular disease. However, cardiovascular disease during strenuous exercise also brings certain risks, so a convenient and effective method is needed to accurately identify heart rate. Due to the low amplitude characteristics of ECG signals, automatic classification of the imperceptibility and irregularities of ECG signals remains a great challenge. To address this issue, we propose an automatic heart rate detection method using a two-dimensional convolutional neural network. First, the ECG data is preprocessed to convert the multi-lead single-channel ECG data into dual-channel ECG data. Then, the ECG image is input into the convolutional neural network. To enhance the diagnostic accuracy of an abnormal heart rate diagnosis model, this study integrates a multiscale pyramid module into the network to fully extract image contextual information. Experimental analysis utilizes MIT-BIH and sudden cardiac death holter public datasets for training and testing. The results show that the final classification accuracy reaches 99.12% and 98.40%, respectively. The method requires no manual pre-processing of converted ECG images, has high accuracy, can effectively monitor abnormal heart rate, and minimize sudden death caused by abnormal heart rate.

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“…Recently, data-driven deep learning algorithms have shown great advantages over traditional methods in several fields due to their low computational cost and high accuracy. [14][15][16][17][18][19][20][21][22][23][24] Although a large number of research studies have explored the application of deep learning at the nanoscale, these deep learning frameworks usually focus on solids by extracting lowdimensional feature vectors to characterize the microscopic structure of ordered crystals, 25 but the effective extraction of microscopic features of liquids faces challenges. In our previous work, point features of liquids were extracted in large quantities to succeed in predicting the two-dimensional (2D) distribution of density and temperature for liquids using our developed deep learning frameworks.…”

Section: Introductionmentioning

confidence: 99%

Prediction of water transport properties on an anisotropic wetting surface via deep learning

Guo

Sun

et al. 2023

Nanoscale

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Arrhythmia diagnosis of young martial arts athletes based on deep learning for smart medical care

Cited by 11 publications

References 33 publications

Model Construction of Using Physiological Signals to Detect Mental Health Status

Model Construction of Using Physiological Signals to Detect Mental Health Status

Multi-Scale and Multi-Channel Information Fusion for Exercise Electrocardiogram Feature Extraction and Classification

Prediction of water transport properties on an anisotropic wetting surface via deep learning

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