86e860f7e84a32eed4820fd1a83ba0cb pmc_11860660.pdf 13f564f3957b57ab6e72f57b3f7a1515fbc66734 pmc_11860660.pdf 13007a509beb9458d22ca70c3de5c4fb5e7597ee534cc3479178adbee380af12 pmc_11860660.pdf Title: Identification of Athleticism and Sports Profiles Throughout Machine Learning Applied to Heart Rate Variability Subject: Heart rate variability (HRV) is a non-invasive health and fitness indicator, and machine learning (ML) has emerged as a powerful tool for analysing large HRV datasets. This study aims to identify athletic characteristics using the HRV test and ML algorithms. Two models were developed: Model 1 (M1) classified athletes and non-athletes using 856 observations from high-performance athletes and 494 from non-athletes. Model 2 (M2) identified an individual soccer player within a team based on 105 observations from the player and 514 from other team members. Three ML algorithms were applied —Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)— and SHAP values were used to interpret the results. In M1, the SVM algorithm achieved the highest performance (accuracy = 0.84, ROC AUC = 0.91), while in M2 Random Forest performed best (accuracy = 0.92, ROC AUC = 0.94). Based on these results, we propose an athleticism index and a soccer identification index derived from HRV data. The findings suggest that ML algorithms, such as SVM and RF, can effectively generate indices based on HRV for identifying individuals with athletic characteristics or distinguishing athletes with specific sports profiles. These insights underscore the importance of integrating HRV assessments systematically into training regimens for enhanced athletic evaluation. Keywords: heart rate variability; machine learning; athletes; sport profiles; team sports; random forest; support vector machine; SHAP values; training load Author: Tony Estrella and Lluis Capdevila Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Wed Jan 22 14:20:12 2025 CET ModDate: Wed Jan 22 14:22:48 2025 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 16 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 1533546 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- MLTPMG+VnURWPalladioL Type 1 Custom yes yes yes 10 0 LBBUOR+URWPalladioL-Roma Type 1 Custom yes yes yes 16 0 FFOXFE+URWPalladioL-Bold Type 1 Custom yes yes yes 22 0 JUMVBF+URWPalladioL-Ital Type 1 Custom yes yes yes 27 0 DVCMRV+CMSY10 Type 1 Builtin yes yes yes 59 0 HLHDBK+PalatinoLinotype-Bold CID TrueType Identity-H yes yes yes 67 0 HLHDCK+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 73 0 HLHDCL+PalatinoLinotype-Italic CID TrueType Identity-H yes yes yes 79 0 HLHDCM+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 85 0 HLHDCN+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 88 0 HLHDCO+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 91 0 HLHDGP+PalatinoLinotype-Roman TrueType MacRoman yes yes no 94 0 HLHDCK+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 113 0 HLHDCL+PalatinoLinotype-Italic CID TrueType Identity-H yes yes yes 119 0 HLHDCM+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 125 0 HLHDCN+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 128 0 HLHDCO+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 131 0 HLHDGP+PalatinoLinotype-Roman TrueType MacRoman yes yes no 134 0 HLHDBK+PalatinoLinotype-Bold CID TrueType Identity-H yes yes yes 147 0 HLHDCK+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 153 0 HLHDCL+PalatinoLinotype-Italic CID TrueType Identity-H yes yes yes 159 0 HLHDCM+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 165 0 HLHDCN+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 168 0 HLHDCO+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 171 0 HLHDGP+PalatinoLinotype-Roman TrueType MacRoman yes yes no 174 0 HLHDBK+PalatinoLinotype-Bold CID TrueType Identity-H yes yes yes 183 0 HLHDCK+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 189 0 HLHDCL+PalatinoLinotype-Italic CID TrueType Identity-H yes yes yes 195 0 HLHDCM+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 201 0 HLHDCN+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 204 0 HLHDCO+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 207 0 HLHDGP+PalatinoLinotype-Roman TrueType MacRoman yes yes no 210 0 HLHIMP+PalatinoLinotype-Italic TrueType MacRoman yes yes no 213 0 HLHDBK+PalatinoLinotype-Bold CID TrueType Identity-H yes yes yes 223 0 HLHDCK+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 229 0 HLHDCL+PalatinoLinotype-Italic CID TrueType Identity-H yes yes yes 235 0 HLHDCM+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 241 0 HLHDCN+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 244 0 HLHDCO+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 247 0 HLHDGP+PalatinoLinotype-Roman TrueType MacRoman yes yes no 250 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-03-28 02:08:25 CET RepresentationInformation: pmc_11860660.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-03-27 02:09:43 CET Size: 1533546 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 478 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Study Sample Destination: subsection.2.1 Item: Title: Procedure and Measures Destination: subsection.2.2 Item: Title: Data Preparation Destination: subsection.2.3 Item: Title: Machine Learning Analysis Destination: subsection.2.4 Item: Title: Results Destination: section.3 Children: Item: Title: Descriptive Parameters Destination: subsection.3.1 Item: Title: Machine Learning Evaluation Destination: subsection.3.2 Item: Title: Interpretability with SHAP Values Destination: subsection.3.3 Item: Title: Athleticism Index Destination: subsection.3.4 Item: Title: Soccer Identification Index Destination: subsection.3.5 Item: Title: Discussion Destination: section.4 Item: Title: Conclusions Destination: section.5 Item: Title: References Destination: appendix.A. Info: Title: Identification of Athleticism and Sports Profiles Throughout Machine Learning Applied to Heart Rate Variability Author: Tony Estrella and Lluis Capdevila Subject: Heart rate variability (HRV) is a non-invasive health and fitness indicator, and machine learning (ML) has emerged as a powerful tool for analysing large HRV datasets. This study aims to identify athletic characteristics using the HRV test and ML algorithms. Two models were developed: Model 1 (M1) classified athletes and non-athletes using 856 observations from high-performance athletes and 494 from non-athletes. Model 2 (M2) identified an individual soccer player within a team based on 105 observations from the player and 514 from other team members. Three ML algorithms were applied  Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)  and SHAP values were used to interpret the results. In M1, the SVM algorithm achieved the highest performance (accuracy = 0.84, ROC AUC = 0.91), while in M2 Random Forest performed best (accuracy = 0.92, ROC AUC = 0.94). Based on these results, we propose an athleticism index and a soccer identification index derived from HRV data. The findings suggest that ML algorithms, such as SVM and RF, can effectively generate indices based on HRV for identifying individuals with athletic characteristics or distinguishing athletes with specific sports profiles. These insights underscore the importance of integrating HRV assessments systematically into training regimens for enhanced athletic evaluation. Keywords: heart rate variability; machine learning; athletes; sport profiles; team sports; random forest; support vector machine; SHAP values; training load Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Wed Jan 22 14:20:12 CET 2025 ModDate: Wed Jan 22 14:22:48 CET 2025 ID: 0x1d4a5825bcbc96567b5c07094143ea75, 0x1d4a5825bcbc96567b5c07094143ea75 Filters: FilterPipeline: FlateDecode Fonts: Type1: Font: BaseFont: LBBUOR+URWPalladioL-Roma FontSubset: true FirstChar: 2 LastChar: 243 FontDescriptor: FontName: LBBUOR+URWPalladioL-Roma Flags: Symbolic FontBBox: -166, -283, 1021, 943 FontFile: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: FFOXFE+URWPalladioL-Bold FontSubset: true FirstChar: 2 LastChar: 121 FontDescriptor: FontName: FFOXFE+URWPalladioL-Bold Flags: Symbolic FontBBox: -152, -301, 1000, 935 FontFile: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: MLTPMG+VnURWPalladioL FontSubset: true FirstChar: 146 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