Approach to Electrocardiographic Signal Assessment Based on Multifactor Regression Analysis of Time Variability Function
Keywords:
time variability function, multifactor regression model, electrocardiographic signal, cardiac diagnosis, stepwise regression, extrasystole, left bundle branch block, CDPCTVF prediction coefficient, statistical predictors, automatic classification of cardiac pathologies, cyclic discrete random processes, cardiovascular systemAbstract
The study presents the development of a multifactor regression model designed for automated identification of cardiac conditions through the analysis of statistical parameters of electrocardiographic data temporal variability. The developed model is based on five key statistical indicators: arithmetic mean, median, mode, root mean square deviation, and kurtosis. These parameters were identified through a sequential regression selection procedure from a total set of thirteen primary characteristics at a threshold level of statistical significance p < 0.05. The weight parameters of each indicator were calculated using quadratic deviation optimization. The arithmetic mean demonstrates the dominant influence on the diagnostic outcome (coefficient β = 153.952), while the remaining indicators provide a refining effect with negative weight coefficients.
The developed model demonstrates high efficiency in distinguishing three categories of cardiac activity: physiological norm, arrhythmic disorders such as premature cardiac contractions, and structural myocardial conduction disturbances (partial left bundle branch block). The modeling quality is confirmed by the coefficient of determination R² = 0.97636 (adjusted R² = 0.97577). The physiological state is characterized by minimal variance of time intervals (0.00003-0.00004 s²), while premature contractions demonstrate a thousand-fold increase in this indicator (0.011…0.012 s²). Validation on an experimental dataset of 204 cardiac activity records confirmed the high reliability of the system (Fisher's F-statistic = 1635.7 at p < 0.001) and compliance with fundamental requirements of regression modeling. The analysis of prediction errors revealed their correspondence to Gaussian distribution and variance homogeneity, confirming the correctness of the developed system.
The proposed approach combines the advantages of classical statistical methods with the proposed use of the time variability function for comprehensive analysis of morphological and rhythmic characteristics of cardiac signals. The practical significance lies in creating mathematical tools for automated cardiovascular disease diagnostic systems and clinical decision support systems.
References
H. Franchevska, M. Khvostivskyi, V. Dozorskyi, E. Yavorska, and O. Zastavnyy, “The Method and Algorithm for Detecting the Fetal ECG Signal in the Presence of Interference,” in Proceedings of the 1st International Workshop on Computer Information Technologies in Industry 4.0 (CITI 2023), CEUR Workshop Proceedings, Ternopil, Ukraine, June 14-16, 2023, pp. 263-272. ISSN 1613-0073.
L. Mosiy, and A. Sverstiuk, “Methods of modeling and classification of electrocardiograms,” Computer-Integrated Technologies: Education, Science, Production, no. 58, pp. 104-115, 2025. https://doi.org/10.36910/6775-2524-0560-2025-58-12 .
L. Mosiy, and A. Sverstiuk, “Information technology for electrocardiographic signal analysis based on mathematical models of temporal and amplitude variability,” Computer Systems and Information Technologies, no. 2, pp. 36-44, 2025. https://doi.org/10.31891/csit-2025-2-4 .
Y. Kim, et al., “Predicting Future Incidences of Cardiac Arrhythmias Using Discrete Heartbeats from Normal Sinus Rhythm ECG Signals via Deep Learning Methods,” Diagnostics, vol. 13, no. 17, p. 2849, 2023. https://doi.org/10.3390/diagnostics13172849 .
P. K. P. Shanmuganathan, and V. Sivaratri, “Integrating Deep Learning for Arrhythmia Detection with Automated Drug Delivery: A Comprehensive Approach to Cardiac Health Monitoring and Treatment,” arXiv, 2024. https://doi.org/10.48550/ARXIV.2410.19827 .
R. Patil, B. Narkhede, S. Varma, S. Suraliya, and N. Mehendale, “Auto Lead Extraction and Digitization of ECG Paper Records using cGAN,” arXiv, 2022. https://doi.org/10.48550/ARXIV.2211.06720 .
S. Lupenko, and R. Butsiy, “Isomorphic Multidimensional Structures of the Cyclic Random Process in Problems of Modeling Cyclic Signals with Regular and Irregular Rhythms,” Fractal and Fractional, vol. 8, no. 4, p. 203, 2024. https://doi.org/10.3390/fractalfract8040203 .
S. Lupenko, “The Mathematical Model of Cyclic Signals in Dynamic Systems as a Cyclically Correlated Random Process,” Mathematics, vol. 10, no. 18, pр. 3406, 2022. https://doi.org/10.3390/math10183406 .
S. Lupenko, “Rhythm-adaptive statistical estimation methods of probabilistic characteristics of cyclic random processes,” Digital Signal Processing, vol. 151, pр. 104563, 2024. https://doi.org/10.1016/j.dsp.2024.104563 .
V. Martsenyuk, A. Sverstiuk, A. Klos-Witkowska, A. Horkunenko, and S. Rajba, “Vector of Diagnostic Features in the Form of Decomposition Coefficients of Statistical Estimates Using a Cyclic Random Process Model of Cardiosignal,” in 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Metz, France, 2019, pp. 298-303. https://doi.org/10.1109/idaacs.2019.8924398 .
S. Lupenko, I. Lytvynenko, A. Sverstiuk, B. Shelestovskyi, and A. Horkunenko, “Software for Statistical Processing and Modeling of a Set of Synchronously Registered Cardio Signals of Different Physical Nature,” CEUR Workshop Proceedings, vol. 2864, pp. 194-205, 2021. https://doi.org/10.32782/cmis/2864-17 .
V. Kalidas and L. S. Tamil, “Detection of atrial fibrillation using discrete-state Markov models and Random Forests,” Computers in Biology and Medicine, vol. 113, pр. 103386, 2019. https://doi.org/10.1016/j.compbiomed.2019.103386 .
T. K. Lin, D. Wendelken, B. O’Rourke, S. R. Jones, N. M. Punjabi, and D. DeMazumder, “Abstract 453: Phenotyping by ECG Dynamics During Sleep Predicts Cardiovascular Disease Risk Factors in a Multicenter Study of Asymptomatic Middle-aged Community Adults,” Circulation Research, vol. 125, Suppl. 1, 2019. https://doi.org/10.1161/res.125.suppl_1.453 .
A. Mombereau, Y. Serinagaoglu Dogrusoz, R. Dubois, and L. R. Bear, “Improved Performance of Data-Adaptive Regression Framework Based on Multivariate Adaptive Regression Splines for Electrocardiographic Imaging,” in Computing in Cardiology Conference (CinC), vol. 51, 2024. https://doi.org/10.22489/cinc.2024.294 .
M. Malik, “Relation between QT and RR intervals is highly individual among healthy subjects: implications for heart rate correction of the QT interval,” Heart, vol. 87, no. 3, pp. 2200-228, 2002. https://doi.org/10.1136/heart.87.3.220 .
A. Paul, et al., “Development of Automated Cardiac Arrhythmia Detection Methods Using Single Channel ECG Signal,” 2023.
M. Lavanga, et al., “Maturation of the Autonomic Nervous System in Premature Infants: Estimating Development Based on Heart-Rate Variability Analysis,” Frontiers in Physiology, vol. 11, 2021. https://doi.org/10.3389/fphys.2020.581250 .
B. A. Shah, et al., “Heart rate variability and amplitude-integrated electroencephalography measured shortly after birth and time to reach clinical milestones: a pilot study in late preterm infants,” Frontiers in Pediatrics, vol. 13, 2025. https://doi.org/10.3389/fped.2025.1579197 .
R. Sassi, et al., “Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society,” Europace, vol. 17, no. 9, pp. 1341-1353, 2015. https://doi.org/10.1093/europace/euv015 .
M. Pagani, D. Lucini, and A. Porta, “Sympathovagal balance from heart rate variability: time for a second round?” Experimental Physiology, vol. 97, no. 10, pp. 1141-1142, 2012. https://doi.org/10.1113/expphysiol.2012.066977 .
B. Dhananjay, and J. Sivaraman, “Prediction of cardiac rhythm based on heart rate variability features in sinus tachycardia conditions,” in 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, India, 2021, pp. 1-6. https://doi.org/10.1109/icbsii51839.2021.9445167 .
A. Smiley and J. Finkelstein, “Regression Analysis for Prediction of Exercise Exertion Levels Using Physiological Data,” in 2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA, 2023, pp. 0416-0419. https://doi.org/10.1109/uemcon59035.2023.10315969 .
C. D. Zuluaga-Ríos, M. A. Álvarez-López, and Á. A. Orozco-Gutiérrez, “Comparación de métodos de filtro de Kalman robustos para corrección de artefactos en el análisis de la variabilidad de la frecuencia cardiaca,” TecnoLógicas, vol. 18, no. 34, p. 25, 2015. https://doi.org/10.22430/22565337.213 .
P. Sardar, R. Gupta, and S. Mukhopadhyay, “Multiclass Signal Quality Assessment of Electrocardiogram using Entropy-based Features and Machine Learning Technique,” in 2022 IEEE Silchar Subsection Conference (SILCON), Silchar, India, 2022, pp. 1-7. https://doi.org/10.1109/silcon55242.2022.10028787 .
A. Lyon, A. Mincholé, J. P. Martínez, P. Laguna, and B. Rodriguez, “Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances,” Journal of The Royal Society Interface, vol. 15, no. 138, pp. 20170821, 2018. https://doi.org/10.1098/rsif.2017.0821 .
J. Li, M. Wang, F. Zhang, G. Liu, and W. Wen, “Chronic Stress Recognition Based on Time-Slot Analysis of Ambulatory Electrocardiogram and Tri-Axial Acceleration,” IEEE Transactions on Affective Computing, vol. 15, no. 3, pp. 1178-1189, 2024. https://doi.org/10.1109/taffc.2023.3326747 .
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
