Study on Cardiovascular Monitoring and Diagnosis of Athletes

Xiao, Zhangbo; Sun, Chang; Bai, Jie; Li, Xingjiang

doi:10.1590/1517-8692202127082021_0351

Cited by 1 publication

(1 citation statement)

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“…These signals contain less information and cannot accurately and comprehensively reflect the state of exercise. Therefore, many researchers began to explore the use of other physical indicators that contain more information to evaluate physical status [ 3 – 6 ]. Kulawiec et al [ 7 ] observed the blood glucose levels of athletes after exercise to judge recovery of athletes.…”

Section: Introductionmentioning

confidence: 99%

The Recognition Method of Athlete Exercise Intensity Based on ECG and PCG

Wang

Zhu

2022

Computational and Mathematical Methods in Medicine

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Athletes usually arrange their training plans and determine their training intensity according to the coach’s experience and simple physical indicators such as heart rate during exercise. However, the accuracy of this method is poor, and the training plan and exercise intensity arranged according to this method can easily cause physical damage, or the training cannot meet the actual needs. Therefore, in order to realize the reasonable arrangement and monitoring of athletes’ training, a method of human exercise intensity recognition based on ECG (electrocardiogram) and PCG (Phonocardiogram) is proposed. First, the ECG and PCG signals are fused into a two-dimensional image, and the dataset is marked and divided according to the different motion intensities. Then, the training set is trained with a CNN (convolutional neural network) to obtain the prediction model of the neural network. Finally, the neural network model is used to identify the ECG and PCG signals to judge the exercise intensity of the athlete, so as to adjust the training plan according to the exercise intensity. The recognition accuracy of the model on the dataset can reach 95.68%. Compared with the use of heart rate to detect the physical state during exercise, ECG records the total potential changes in the process of depolarization and repolarization of the heart, and PCG records the waveform of the beating sound of the heart, which contains richer feature information. Combined with the CNN method, the athlete’s exercise intensity prediction model constructed by extracting the features of the athlete’s ECG and PCG signals realizes the real-time monitoring of the athlete’s exercise intensity and has high accuracy and generalization ability.

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Section: Introductionmentioning

confidence: 99%