Athlete Exercise Intensity Recognition Method based on ECG and Convolutional Neural Network

Zhu, Yu; Wang, Baiyang; Zhang, Fuchun; Zhu, Haiyan

doi:10.1088/1742-6596/2289/1/012029

Cited by 1 publication

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“…The heart is a very important component of our human organism [1]. According to related studies, the incidence of major diseases is much higher in first-tier cities than in smaller cities [2]. There are many reasons for this phenomenon, one of which is the urban pollution and the fact that in the process of urbanization, only economic development is emphasized without paying attention to the human factor, and a large number of tall buildings are erected but the construction of urban greenery is neglected; all these factors lead to the aggravation of urban pollution, and more importantly, people live in a depressing environment without humanistic care for a long time, which greatly affects the human body.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based ECG Abnormality Identification Prediction and Analysis

Liang

2022

Journal of Sensors

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In recent years, China’s economy has been developing rapidly, and people’s life and quality of life have been improving; more importantly, people’s habits and living habits have also developed from the previous “unhygienic” and “not very careful” to the current “Healthier, more hygienic, greener and more sophisticated” direction. In the process of this development, due to the rapid development of the economy and the industrialization of cities, the incidence of heart disease is also increasing year by year. According to relevant studies, China’s urbanization process has been unprecedented, the number of urbanized people in China has exploded in recent years, and the ratio of urban to rural population has increased from about 20 percent to about 75 percent today. The effects of the population and the urbanization of urban architecture are affecting people’s physical and mental health, both consciously and unconsciously, causing both positive and negative physical and mental effects on the psychological and physical levels. In this paper, the concept of deep learning is fully utilized to train the CNN neural network model and apply it to the ECG abnormality recognition and prediction examination. In order to fully validate the application and significance of deep learning in ECG abnormality identification and prediction, the whole project was completed through subjective and objective experiments. The experimental results show that, from the subjective aspect, the ECG examination has been accepted by most people for different age groups, and the analysis results of the ECG examination with the deep learning model in this paper are more satisfactory; from the objective aspect, the CNN-ECG abnormality recognition prediction network model trained in this paper has high. The accuracy of the model for ECG abnormality recognition prediction can reach 86% when the learning rate is set to 0.0001 and the batch size is set to 120, and the model can reduce the burden of medical personnel to a certain extent.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based ECG Abnormality Identification Prediction and Analysis

Liang

2022

Journal of Sensors

View full text Add to dashboard Cite

show abstract

Athlete Exercise Intensity Recognition Method based on ECG and Convolutional Neural Network

Cited by 1 publication

References 7 publications

Deep Learning-Based ECG Abnormality Identification Prediction and Analysis

Deep Learning-Based ECG Abnormality Identification Prediction and Analysis

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