Wearable System for Cardiac Diagnosis and Monitoring: Clustering Analysis and Usability Assessment Using Fractal Geometry
Heart rate abnormalities, including tachycardia and bradycardia, are among the leading global causes of morbidity, necessitating continuous and accurate monitoring for early detection and intervention. This study introduces an innovative smartwatch-based cardiac monitoring system that integrates fra...
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2025-01-01
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| author | Jose Sulla Torres Cusirramos Montesinos Roderick Nestor Sandra Catalina Correa Herrera Jairo Jattin Balcazar Herwin Huillcen Baca Agueda Munoz del Carpio Toia |
| author_facet | Jose Sulla Torres Cusirramos Montesinos Roderick Nestor Sandra Catalina Correa Herrera Jairo Jattin Balcazar Herwin Huillcen Baca Agueda Munoz del Carpio Toia |
| author_sort | Jose Sulla Torres |
| collection | DOAJ |
| description | Heart rate abnormalities, including tachycardia and bradycardia, are among the leading global causes of morbidity, necessitating continuous and accurate monitoring for early detection and intervention. This study introduces an innovative smartwatch-based cardiac monitoring system that integrates fractal geometry analysis, dynamical systems modeling, and clustering techniques to enhance diagnostic precision. Unlike conventional smartwatch-based monitoring systems, this approach employs advanced mathematical modeling to identify nonlinear patterns in heart rate dynamics, enabling more precise differentiation between normal and pathological conditions. The system was developed using the CRISP-DM methodology, ensuring a structured and data-driven implementation. A mobile application, “My Cardio,” was designed for Android-based smartwatches, enabling the collection of real-time heart rate data and cloud storage for subsequent fractal-based processing. Additionally, a clustering analysis was performed on data from patients with and without cardiac history, identifying three distinct patient groups based on heart rate characteristics. Cluster 0 included individuals with lower, stable heart rates; Cluster 1 represented intermediate variations; and Cluster 2 comprised patients with significantly elevated heart rates associated with higher clinical risk. The findings were statistically validated and visualized, demonstrating that integrating clustering techniques with fractal geometry enhances the detection of clinically relevant cardiac patterns. Furthermore, a usability assessment using the System Usability Scale (SUS) yielded a score of 80.3 or higher, confirming high user acceptance and feasibility for widespread adoption. This study differentiates itself from existing approaches by combining wearable technology with advanced computational techniques to enhance cardiac diagnosis and monitoring. The results underscore the potential of smartwatch-based systems as a noninvasive, intelligent alternative for continuous cardiovascular assessment, paving the way for future applications in digital cardiology and telemedicine. |
| format | Article |
| id | doaj-art-a07569775456460ca84ddcc4fa6e188d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a07569775456460ca84ddcc4fa6e188d2025-08-20T03:29:27ZengIEEEIEEE Access2169-35362025-01-011310474210475510.1109/ACCESS.2025.357925611031443Wearable System for Cardiac Diagnosis and Monitoring: Clustering Analysis and Usability Assessment Using Fractal GeometryJose Sulla Torres0https://orcid.org/0000-0001-5129-430XCusirramos Montesinos Roderick Nestor1https://orcid.org/0000-0003-2897-3878Sandra Catalina Correa Herrera2Jairo Jattin Balcazar3Herwin Huillcen Baca4https://orcid.org/0000-0001-9385-7940Agueda Munoz del Carpio Toia5https://orcid.org/0000-0003-0501-7314Vicerrectorado de Investigación, Universidad Católica de Santa María, Arequipa, PeruAgile Corporation, Arequipa, PeruCentro de Investigación y Atención Psicosocial Hanami, Bogotá, ColombiaCentro de Investigación y Atención Psicosocial Hanami, Bogotá, ColombiaDepartamento Académico de Ingeniería y Tecnología Informática, Universidad Nacional José María Arguedas, Apurímac, Andahuaylas, PerúVicerrectorado de Investigación, Universidad Católica de Santa María, Arequipa, PeruHeart rate abnormalities, including tachycardia and bradycardia, are among the leading global causes of morbidity, necessitating continuous and accurate monitoring for early detection and intervention. This study introduces an innovative smartwatch-based cardiac monitoring system that integrates fractal geometry analysis, dynamical systems modeling, and clustering techniques to enhance diagnostic precision. Unlike conventional smartwatch-based monitoring systems, this approach employs advanced mathematical modeling to identify nonlinear patterns in heart rate dynamics, enabling more precise differentiation between normal and pathological conditions. The system was developed using the CRISP-DM methodology, ensuring a structured and data-driven implementation. A mobile application, “My Cardio,” was designed for Android-based smartwatches, enabling the collection of real-time heart rate data and cloud storage for subsequent fractal-based processing. Additionally, a clustering analysis was performed on data from patients with and without cardiac history, identifying three distinct patient groups based on heart rate characteristics. Cluster 0 included individuals with lower, stable heart rates; Cluster 1 represented intermediate variations; and Cluster 2 comprised patients with significantly elevated heart rates associated with higher clinical risk. The findings were statistically validated and visualized, demonstrating that integrating clustering techniques with fractal geometry enhances the detection of clinically relevant cardiac patterns. Furthermore, a usability assessment using the System Usability Scale (SUS) yielded a score of 80.3 or higher, confirming high user acceptance and feasibility for widespread adoption. This study differentiates itself from existing approaches by combining wearable technology with advanced computational techniques to enhance cardiac diagnosis and monitoring. The results underscore the potential of smartwatch-based systems as a noninvasive, intelligent alternative for continuous cardiovascular assessment, paving the way for future applications in digital cardiology and telemedicine.https://ieeexplore.ieee.org/document/11031443/Clusteringfractal geometrycardiac monitoringsmartwatchusability |
| spellingShingle | Jose Sulla Torres Cusirramos Montesinos Roderick Nestor Sandra Catalina Correa Herrera Jairo Jattin Balcazar Herwin Huillcen Baca Agueda Munoz del Carpio Toia Wearable System for Cardiac Diagnosis and Monitoring: Clustering Analysis and Usability Assessment Using Fractal Geometry IEEE Access Clustering fractal geometry cardiac monitoring smartwatch usability |
| title | Wearable System for Cardiac Diagnosis and Monitoring: Clustering Analysis and Usability Assessment Using Fractal Geometry |
| title_full | Wearable System for Cardiac Diagnosis and Monitoring: Clustering Analysis and Usability Assessment Using Fractal Geometry |
| title_fullStr | Wearable System for Cardiac Diagnosis and Monitoring: Clustering Analysis and Usability Assessment Using Fractal Geometry |
| title_full_unstemmed | Wearable System for Cardiac Diagnosis and Monitoring: Clustering Analysis and Usability Assessment Using Fractal Geometry |
| title_short | Wearable System for Cardiac Diagnosis and Monitoring: Clustering Analysis and Usability Assessment Using Fractal Geometry |
| title_sort | wearable system for cardiac diagnosis and monitoring clustering analysis and usability assessment using fractal geometry |
| topic | Clustering fractal geometry cardiac monitoring smartwatch usability |
| url | https://ieeexplore.ieee.org/document/11031443/ |
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