Academic Journal
CIRCULAR PHASE-SPACE INDICES: NEW BIOMARKERS FOR HEART RATE MODELING AND CLASSIFICATION.
| Τίτλος: | CIRCULAR PHASE-SPACE INDICES: NEW BIOMARKERS FOR HEART RATE MODELING AND CLASSIFICATION. |
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| Συγγραφείς: | Goshvarpour, Ateke |
| Πηγή: | Biomedical Engineering: Applications, Basis & Communications; Jun2026, Vol. 38 Issue 3, p1-11, 11p |
| Θεματικοί όροι: | Biomarkers, Phase space, Heart beat, Computer simulation, Geometric shapes, Classification, Biomedical signal processing, Nonlinear analysis |
| Περίληψη: | Background/Introduction: In recent decades, nonlinear biomedical signal processing has captured the attention of many researchers. While existing methods (e.g. entropy, Lyapunov exponents) focus on abstract measures of signal irregularity, this study introduces a geometrically interpretable framework based on a novel circular phase-space reconstruction. This study intends to propose innovative biomarkers by analyzing the spatial density of the signal's trajectory in this unique phase-space. Methods: Unlike traditional Poincaré plots, a circular phase-space is constructed by mapping the time series into (sin(X i ), cos(X i )), confining all points to a unit circle. To quantify the trajectory's geometry, hypothetical circles (radii 0.1-1.0) are superimposed, and the normalized intersection counts between the trajectory and each circle are computed. These indices are extracted from the meditators' and nonmeditators heart rate (HR) signals. The efficiency of the measures is assessed in both classification and modeling problems. For a typical classification problem, the performance of the k -nearest neighbor (k NN), support vector machine (SVM), and Naïve Bayes (NB) is appraised. For a modeling problem, a simple polynomial equation of degree two is proposed. Results: The results indicated that the proposed biomarkers, derived from the geometric structure of the circular phase-space, achieve 90% classification accuracy and enable dynamical modeling via a quadratic polynomial. Conclusions: In conclusion, this work pioneers the use of circular phase-space intersections to characterize HR dynamics, offering simplicity and interpretability over conventional nonlinear methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Biomedical Engineering: Applications, Basis & Communications is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Biomedical Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edm DbLabel: Biomedical Index An: 191334239 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.4189453125 |
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| Items | – Name: Title Label: Title Group: Ti Data: CIRCULAR PHASE-SPACE INDICES: NEW BIOMARKERS FOR HEART RATE MODELING AND CLASSIFICATION. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Goshvarpour%2C+Ateke%22">Goshvarpour, Ateke</searchLink> – Name: TitleSource Label: Source Group: Src Data: Biomedical Engineering: Applications, Basis & Communications; Jun2026, Vol. 38 Issue 3, p1-11, 11p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Biomarkers%22">Biomarkers</searchLink><br /><searchLink fieldCode="DE" term="%22Phase+space%22">Phase space</searchLink><br /><searchLink fieldCode="DE" term="%22Heart+beat%22">Heart beat</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Geometric+shapes%22">Geometric shapes</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Biomedical+signal+processing%22">Biomedical signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+analysis%22">Nonlinear analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background/Introduction: In recent decades, nonlinear biomedical signal processing has captured the attention of many researchers. While existing methods (e.g. entropy, Lyapunov exponents) focus on abstract measures of signal irregularity, this study introduces a geometrically interpretable framework based on a novel circular phase-space reconstruction. This study intends to propose innovative biomarkers by analyzing the spatial density of the signal's trajectory in this unique phase-space. Methods: Unlike traditional Poincaré plots, a circular phase-space is constructed by mapping the time series into (sin(X i ), cos(X i )), confining all points to a unit circle. To quantify the trajectory's geometry, hypothetical circles (radii 0.1-1.0) are superimposed, and the normalized intersection counts between the trajectory and each circle are computed. These indices are extracted from the meditators' and nonmeditators heart rate (HR) signals. The efficiency of the measures is assessed in both classification and modeling problems. For a typical classification problem, the performance of the k -nearest neighbor (k NN), support vector machine (SVM), and Naïve Bayes (NB) is appraised. For a modeling problem, a simple polynomial equation of degree two is proposed. Results: The results indicated that the proposed biomarkers, derived from the geometric structure of the circular phase-space, achieve 90% classification accuracy and enable dynamical modeling via a quadratic polynomial. Conclusions: In conclusion, this work pioneers the use of circular phase-space intersections to characterize HR dynamics, offering simplicity and interpretability over conventional nonlinear methods. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Biomedical Engineering: Applications, Basis & Communications is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.4015/S1016237225500358 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: Biomarkers Type: general – SubjectFull: Phase space Type: general – SubjectFull: Heart beat Type: general – SubjectFull: Computer simulation Type: general – SubjectFull: Geometric shapes Type: general – SubjectFull: Classification Type: general – SubjectFull: Biomedical signal processing Type: general – SubjectFull: Nonlinear analysis Type: general Titles: – TitleFull: CIRCULAR PHASE-SPACE INDICES: NEW BIOMARKERS FOR HEART RATE MODELING AND CLASSIFICATION. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Goshvarpour, Ateke IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10162372 Numbering: – Type: volume Value: 38 – Type: issue Value: 3 Titles: – TitleFull: Biomedical Engineering: Applications, Basis & Communications Type: main |
| ResultId | 1 |