{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T16:45:13Z","timestamp":1772729113975,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T00:00:00Z","timestamp":1723680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102076"],"award-info":[{"award-number":["62102076"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Heart Rate Variability (HRV) refers to the capability of the heart rhythm to vary at different times, typically reflecting the regulation of the heart by the autonomic nervous system. In recent years, with advancements in Electrocardiogram (ECG) signal processing technology, HRV features reflect various aspects of cardiac activity, such as variability in heart rate, cardiac health status, and responses. We extracted key features of HRV and used them to develop and evaluate an automatic recognition model for cardiac diseases. Consequently, we proposed the HRV Heart Disease Recognition (HHDR) method, employing the Spectral Magnitude Quantification (SMQ) technique for feature extraction. Firstly, the HRV signals are extracted through electrocardiogram signal processing. Then, by analyzing parts of the HRV signal within various frequency ranges, the SMQ method extracts rich features of partial information. Finally, the Random Forest (RF) classification computational method is employed to classify the extracted information, achieving efficient and accurate cardiac disease recognition. Experimental results indicate that this method surpasses current technologies in recognizing cardiac diseases, with an average accuracy rate of 95.1% for normal\/diseased classification, and an average accuracy of 84.8% in classifying five different disease categories. Thus, the proposed HHDR method effectively utilizes the local information of HRV signals for efficient and accurate cardiac disease recognition, providing strong support for cardiac disease research in the medical field.<\/jats:p>","DOI":"10.3390\/s24165296","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T04:29:57Z","timestamp":1723782597000},"page":"5296","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Heart Diseases Recognition Model Based on HRV Feature Extraction over 12-Lead ECG Signals"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8956-0511","authenticated-orcid":false,"given":"Ling","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin 132013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8325-3184","authenticated-orcid":false,"given":"Tianshuo","family":"Bi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin 132013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayu","family":"Hao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin 132013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3258-0244","authenticated-orcid":false,"given":"Tie Hua","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin 132013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4286","DOI":"10.1007\/s11227-023-05583-8","article-title":"An intelligent hybrid classification model for heart disease detection using imbalanced electrocardiogram signals","volume":"80","author":"Ketu","year":"2024","journal-title":"J. Supercomput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chandrasekhar, N., and Peddakrishna, S. (2023). Enhancing heart disease prediction accuracy through machine-learning techniques and optimization. Processes, 11.","DOI":"10.3390\/pr11041210"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dhara, S.K., Bhanja, N., and Khampariya, P. (2024). An adaptive heart disease diagnosis via ECG signal analysis with deep feature extraction and enhanced radial basis function. Comput. Methods Biomech. Biomed. Eng. Imaging Vis., 11.","DOI":"10.1080\/21681163.2023.2245927"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8755","DOI":"10.1007\/s00521-022-06889-z","article-title":"Automated ECG multi-class classification system based on combining deep-learning features with HRV and ECG measures","volume":"34","author":"Eltrass","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s11831-022-09823-7","article-title":"A systematic review on artificial intelligence-based techniques for diagnosis of cardiovascular arrhythmia diseases: Challenges and opportunities","volume":"30","author":"Singhal","year":"2023","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e12903","DOI":"10.1111\/exsy.12903","article-title":"Expert system for detection of congestive heart failure using optimal wavelet and heart rate variability signals for wireless cloud-based environment","volume":"40","author":"Sharma","year":"2023","journal-title":"Expert Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"012051","DOI":"10.1088\/1742-6596\/2325\/1\/012051","article-title":"Early heart disease prediction using ensemble learning techniques","volume":"2325","author":"Chowdary","year":"2022","journal-title":"Proc. J. Phys. Conf. Ser."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, J., Dong, X., Zhao, H., and Tian, Y. (2022). Predictive classifier for cardiovascular disease based on stacking model fusion. Processes, 10.","DOI":"10.3390\/pr10040749"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Faust, O., Hong, W., Loh, H.W., Xu, S., Tan, R.S., Chakraborty, S., Barua, P.D., Molinari, F., and Acharya, U.R. (2022). Heart rate variability for medical decision support systems: A review. Comput. Biol. Med., 145.","DOI":"10.1016\/j.compbiomed.2022.105407"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Esgalhado, F., Batista, A., Vassilenko, V., Russo, S., and Ortigueira, M. (2022). Peak detection and HRV feature evaluation on ECG and PPG signals. Symmetry, 14.","DOI":"10.3390\/sym14061139"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s42979-020-00365-y","article-title":"Heart disease prediction using machine-learning techniques","volume":"1","author":"Shah","year":"2020","journal-title":"SN Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s12553-020-00499-2","article-title":"Comparing different feature selection algorithms for cardiovascular disease prediction","volume":"11","author":"Hasan","year":"2021","journal-title":"Health Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.compbiomed.2018.12.012","article-title":"Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types","volume":"105","author":"Oh","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1109\/TIM.2019.2910342","article-title":"LSTM-based auto-encoder model for ECG arrhythmias classification","volume":"69","author":"Hou","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"\u015amigiel, S., Pa\u0142czy\u0144ski, K., and Ledzi\u0144ski, D. (2021). ECG signal classification using deep-learning techniques based on the PTB-XL dataset. Entropy, 23.","DOI":"10.3390\/e23091121"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pa\u0142czy\u0144ski, K., \u015amigiel, S., Ledzi\u0144ski, D., and Bujnowski, S. (2022). Study of the few-shot learning for ECG classification based on the PTB-XL dataset. Sensors, 22.","DOI":"10.3390\/s22030904"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sieci\u0144ski, S., Tkacz, E.J., and Kostka, P.S. (2023). Heart rate variability analysis on electrocardiograms, seismocardiograms and gyrocardiograms of healthy volunteers and patients with valvular heart diseases. Sensors, 23.","DOI":"10.3390\/s23042152"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1007\/s42979-023-02062-y","article-title":"Assessment of Sympathetic and Parasympathetic Activities of Nervous System from Heart Rate Variability Using Machine Learning Techniques","volume":"4","author":"Banu","year":"2023","journal-title":"SN Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, K., Cardoso, C., Moctezuma-Ramirez, A., Elgalad, A., and Perin, E. (2023). Heart Rate Variability Measurement through a Smart Wearable Device: Another Breakthrough for Personal Health Monitoring?. Int. J. Environ. Res. Public Health, 20.","DOI":"10.20944\/preprints202308.0732.v1"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bahameish, M., Stockman, T., and Requena Carri\u00f3n, J. (2024). Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features. Sensors, 24.","DOI":"10.3390\/s24103210"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.28991\/CEJ-2023-09-09-013","article-title":"Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver\u2019s Physiological Condition","volume":"9","author":"Razak","year":"2023","journal-title":"Civ. Eng. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"92710","DOI":"10.1109\/ACCESS.2022.3201911","article-title":"On the generalization of sleep apnea detection methods based on heart rate variability and machine learning","volume":"10","author":"Padovano","year":"2022","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mu, S., Liao, S., Tao, K., and Shen, Y. (2024). Intelligent fatigue detection based on hierarchical multi-scale ECG representations and HRV measures. Biomed. Signal Process. Control, 92.","DOI":"10.1016\/j.bspc.2024.106127"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, L., Song, F., Zhou, T.H., Hao, J., and Ryu, K.H. (2023). EEG and ECG-based multi-sensor fusion computing for real-time fatigue driving recognition based on feedback mechanism. Sensors, 23.","DOI":"10.3390\/s23208386"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, L., Liu, H., Zhou, T., Liang, W., and Shan, M. (2021). Multidimensional emotion recognition based on semantic analysis of biomedical eeg signal for knowledge discovery in psychological healthcare. Appl. Sci., 11.","DOI":"10.3390\/app11031338"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, L., Hao, J., and Zhou, T.H. (2023). ECG Multi-Emotion Recognition Based on Heart Rate Variability Signal Features Mining. Sensors, 23.","DOI":"10.3390\/s23208636"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1038\/s41597-020-0495-6","article-title":"PTB-XL, a large publicly available electrocardiography dataset","volume":"7","author":"Wagner","year":"2020","journal-title":"Sci. Data"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Botros, J., Mourad-Chehade, F., and Laplanche, D. (2022). Cnn and svm-based models for the detection of heart failure using electrocardiogram signals. Sensors, 22.","DOI":"10.3390\/s22239190"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shin, S., Kang, M., Zhang, G., Jung, J., and Kim, Y.T. (2022). Lightweight Ensemble Network for detecting heart disease using ECG signals. Appl. Sci., 12.","DOI":"10.3390\/app12073291"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ahmed, A.A., Ali, W., Abdullah, T.A., and Malebary, S.J. (2023). Classifying cardiac arrhythmia from ECG signal using 1D CNN deep learning model. Mathematics, 11.","DOI":"10.3390\/math11030562"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5296\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:37:20Z","timestamp":1760110640000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5296"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,15]]},"references-count":30,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24165296"],"URL":"https:\/\/doi.org\/10.3390\/s24165296","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,15]]}}}