Machine Learning-Based Classification of Alertness Levels in Elite Shooting Athletes Using Heart Rate Variability.
This study aims to develop a predictive model for alertness levels in elite shooting athletes by analyzing heart rate variability (HRV) dynamics under simulated competitive stress. 83 national-level shooting athletes completed a 60-minute Psychomotor Vigilance Task (PVT) protocol designed to mimic t...
| Publicado en: | Journal of Sports Science & Medicine Vol. 25; no. 2; pp. 476 - 487 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Hakan Gur, Journal of Sports Science & Medicine
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195156793&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195156793 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13032968 FYN jtl: Journal of Sports Science & Medicine issn: 13032968 maglogo: N pubinfo: dt: Jun2026 vid: 25 iid: 2 pid: 26030 pub: Hakan Gur, Journal of Sports Science & Medicine artinfo: ui: 195156793 195156793 195156793 10.52082/jssm.2026.476 195156793 ppf: 476 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Machine Learning-Based Classification of Alertness Levels in Elite Shooting Athletes Using Heart Rate Variability. aug: au: Lu, Jiaojiao Qiu, Jun An, Yan affil: School of Exercise and Health, Shanghai University of Sport, Shanghai, China sug: subj: Boosting Machine Learning Algorithms Prediction Models Heart Rate Variability Athletes, Elite Psychosocial Factors Target Sports Attention Classification Algorithms Human Funding Source Male Female Adolescence Young Adult Validation Studies Competitive Behavior Prediction Algorithms Psychomotor Performance Random Forest Sports Physiology Support Vector Machine Reliability Electrocardiography Signal Processing, Computer Assisted Autonomic Nervous System Physiopathology Descriptive Statistics Comparative Studies ROC Curve Data Analysis Software Pearson's Correlation Coefficient Sensitivity and Specificity One-Way Analysis of Variance Conceptual Framework Adolescent: 13-18 years Male Female ab: This study aims to develop a predictive model for alertness levels in elite shooting athletes by analyzing heart rate variability (HRV) dynamics under simulated competitive stress. 83 national-level shooting athletes completed a 60-minute Psychomotor Vigilance Task (PVT) protocol designed to mimic the sustained attentional demands of a competition, while HRV data were continuously recorded. Pearson correlation analysis identified HRV features associated with behavioral performance. Key predictors were selected via recursive feature elimination with Random Forest. Four machine learning algorithms--Support Vector Machine (SVM), Random Forest (RF), XGBoost, and AdaBoost--were employed to construct classification models for alertness. Model performance was evaluated using accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC). SHAP analysis was applied to interpret feature contributions. The binary classification framework (optimal vs. sub-optimal alertness) demonstrated superior reliability over multi-class approaches. The AdaBoost model achieved the best performance, with an accuracy of 0.75, an F1-score of 0.73, and an AUC of 0.77. SHAP analysis revealed that the very low frequency percentage (VLF%) was the most critical predictor, followed by the SD2/SD1 ratio. Notably, elevated VLF% values were associated with lower alertness levels. The binary classification model, integrating key HRV indices (notably VLF%) with the AdaBoost algorithm, can effectively distinguish alertness levels in shooting athletes during simulated competitive stress. This approach provides a validated, non-invasive tool for objective psychophysiological monitoring in training, offering actionable insights for pre-competition readiness assessment. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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