Post-exercise evaluation based on the fusion of electrocardiography (ECG) and near-infrared spectroscopy (NIRS)
This study investigated elite athletes’ aerobic exercise recovery period based on ECG and near-infrared spectroscopy. This study explores the integration of electrocardiogram (ECG) signals and cerebral oxygenation levels to classify aerobic exercise recovery. Skiers perform 20 min of aerobic exercis...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-05-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0233363 |
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| _version_ | 1850209069618429952 |
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| author | Jiaqi Sun Shiqing Sun Wenjie Cui Yubing Sun Guangda Liu Jing Cai |
| author_facet | Jiaqi Sun Shiqing Sun Wenjie Cui Yubing Sun Guangda Liu Jing Cai |
| author_sort | Jiaqi Sun |
| collection | DOAJ |
| description | This study investigated elite athletes’ aerobic exercise recovery period based on ECG and near-infrared spectroscopy. This study explores the integration of electrocardiogram (ECG) signals and cerebral oxygenation levels to classify aerobic exercise recovery. Skiers perform 20 min of aerobic exercise on roller skis. The ensemble empirical mode decomposition is employed to decompose the ECG signal into intrinsic mode functions. ECG non-linear indices and cerebral oxygenation based on near-infrared spectroscopy were calculated during the recovery period after aerobic exercise. t-distributed stochastic neighbor embedding (t-SNE) is applied to visualize the combined feature space of ECG and cerebral oxygenation. The results show that there is a decrease in sample entropy, approximate entropy, and Shannon entropy, closely related to an increase in the concentration of oxygenated hemoglobin and a decrease in the deoxygenated hemoglobin during the recovery. Classification accuracies of 96.2%, 85.6%, and 91.3% are achieved using the combined features in 2D t-SNE and 99.5%, 97.9%, and 96.1% in 3D t-SNE using K-Nearest Neighbors, Support Vector Machine, and Tree classifiers, respectively. These findings underscore the potential of integrating ECG and cerebral oxygenation for aerobic exercise recovery classification. |
| format | Article |
| id | doaj-art-fa1432d41bef4131acaf824541df4463 |
| institution | OA Journals |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-fa1432d41bef4131acaf824541df44632025-08-20T02:10:06ZengAIP Publishing LLCAIP Advances2158-32262025-05-01155055222055222-910.1063/5.0233363Post-exercise evaluation based on the fusion of electrocardiography (ECG) and near-infrared spectroscopy (NIRS)Jiaqi Sun0Shiqing Sun1Wenjie Cui2Yubing Sun3Guangda Liu4Jing Cai5College of Instrumentation and Electrical Engineering, Jilin University, Changchun, ChinaCollege of Instrumentation and Electrical Engineering, Jilin University, Changchun, ChinaCollege of Instrumentation and Electrical Engineering, Jilin University, Changchun, ChinaCollege of Instrumentation and Electrical Engineering, Jilin University, Changchun, ChinaCollege of Instrumentation and Electrical Engineering, Jilin University, Changchun, ChinaCollege of Instrumentation and Electrical Engineering, Jilin University, Changchun, ChinaThis study investigated elite athletes’ aerobic exercise recovery period based on ECG and near-infrared spectroscopy. This study explores the integration of electrocardiogram (ECG) signals and cerebral oxygenation levels to classify aerobic exercise recovery. Skiers perform 20 min of aerobic exercise on roller skis. The ensemble empirical mode decomposition is employed to decompose the ECG signal into intrinsic mode functions. ECG non-linear indices and cerebral oxygenation based on near-infrared spectroscopy were calculated during the recovery period after aerobic exercise. t-distributed stochastic neighbor embedding (t-SNE) is applied to visualize the combined feature space of ECG and cerebral oxygenation. The results show that there is a decrease in sample entropy, approximate entropy, and Shannon entropy, closely related to an increase in the concentration of oxygenated hemoglobin and a decrease in the deoxygenated hemoglobin during the recovery. Classification accuracies of 96.2%, 85.6%, and 91.3% are achieved using the combined features in 2D t-SNE and 99.5%, 97.9%, and 96.1% in 3D t-SNE using K-Nearest Neighbors, Support Vector Machine, and Tree classifiers, respectively. These findings underscore the potential of integrating ECG and cerebral oxygenation for aerobic exercise recovery classification.http://dx.doi.org/10.1063/5.0233363 |
| spellingShingle | Jiaqi Sun Shiqing Sun Wenjie Cui Yubing Sun Guangda Liu Jing Cai Post-exercise evaluation based on the fusion of electrocardiography (ECG) and near-infrared spectroscopy (NIRS) AIP Advances |
| title | Post-exercise evaluation based on the fusion of electrocardiography (ECG) and near-infrared spectroscopy (NIRS) |
| title_full | Post-exercise evaluation based on the fusion of electrocardiography (ECG) and near-infrared spectroscopy (NIRS) |
| title_fullStr | Post-exercise evaluation based on the fusion of electrocardiography (ECG) and near-infrared spectroscopy (NIRS) |
| title_full_unstemmed | Post-exercise evaluation based on the fusion of electrocardiography (ECG) and near-infrared spectroscopy (NIRS) |
| title_short | Post-exercise evaluation based on the fusion of electrocardiography (ECG) and near-infrared spectroscopy (NIRS) |
| title_sort | post exercise evaluation based on the fusion of electrocardiography ecg and near infrared spectroscopy nirs |
| url | http://dx.doi.org/10.1063/5.0233363 |
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