{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T10:56:19Z","timestamp":1772967379085,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T00:00:00Z","timestamp":1621382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanghai Municipal Science and Economic and Informatization Commission Project","award":["GYQJ-2018-2-05"],"award-info":[{"award-number":["GYQJ-2018-2-05"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["1171009, 61801123"],"award-info":[{"award-number":["1171009, 61801123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2019M651367"],"award-info":[{"award-number":["2019M651367"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2017SHZDZX01, 16441907900"],"award-info":[{"award-number":["2017SHZDZX01, 16441907900"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.<\/jats:p>","DOI":"10.3390\/s21103524","type":"journal-article","created":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T21:49:21Z","timestamp":1621460961000},"page":"3524","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8836-0662","authenticated-orcid":false,"given":"Rongru","family":"Wan","sequence":"first","affiliation":[{"name":"Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8517-0142","authenticated-orcid":false,"given":"Yanqi","family":"Huang","sequence":"additional","affiliation":[{"name":"Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}]},{"given":"Xiaomei","family":"Wu","sequence":"additional","affiliation":[{"name":"Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China"},{"name":"Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.hlc.2018.08.026","article-title":"Epidemiology of sudden cardiac death: Global and regional perspectives","volume":"28","author":"Wong","year":"2019","journal-title":"Hear. Lung Circ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Panjaitan, F., Nurmaini, S., Akbar, M., Mirza, A.H., Syaputra, H., Kurniawan, T.B., and P, R.U. (2019, January 2\u20133). Identification of classification method for sudden cardiac death: A review. Proceedings of the 2019 International Conference on Electrical Engineering and Computer Science (ICECOS), Batam, Indonesia.","DOI":"10.1109\/ICECOS47637.2019.8984465"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e254","DOI":"10.1161\/CIR.0000000000000950","article-title":"Heart disease and stroke statistics-2021 update: A report from the American heart association","volume":"143","author":"Virani","year":"2021","journal-title":"Circulation"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/0002-8703(89)90670-4","article-title":"Ambulatory sudden cardiac death: Mechanisms of production of fatal arrhythmia on the basis of data from 157 cases","volume":"117","author":"Luna","year":"1989","journal-title":"Am. Heart J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/RBME.2019.2912313","article-title":"Prediction of sudden cardiac death in implantable cardioverter defibrillators: A review and comparative study of heart rate variability features","volume":"13","author":"Parsi","year":"2020","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1146\/annurev.physiol.62.1.25","article-title":"Ventricular fibrillation: Mechanisms of initiation and maintenance","volume":"62","author":"Jalife","year":"2000","journal-title":"Annu. Rev. Physiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.cmpb.2017.02.010","article-title":"Ventricular fibrillation and tachycardia detection from surface ECG using time-frequency representation images as input dataset for machine learning","volume":"141","author":"Mjahad","year":"2017","journal-title":"Comput. Meth. Prog. Bio."},{"key":"ref_8","first-page":"242","article-title":"Early defibrillation through first responders\u2019 triples survivals rates from out of hospital cardiac arrest in an Italian community","volume":"22","author":"Villani","year":"2001","journal-title":"Eur. Heart J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3308","DOI":"10.1161\/01.CIR.96.10.3308","article-title":"Estimating effectiveness of cardiac arrest interventions: A logistic regression survival model","volume":"96","author":"Valenzuela","year":"1997","journal-title":"Circulation"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1016\/j.jacc.2017.07.778","article-title":"Defibrillation for ventricular fibrillation: A shocking update","volume":"70","author":"Nichol","year":"2017","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1056\/NEJMra0803409","article-title":"Implantable cardioverter\u2013defibrillators after myocardial infarction","volume":"359","author":"Myerburg","year":"2008","journal-title":"N. Engl. J. Med."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Calderon, A., P\u00e9rez, A., and Valente, J. (2019, January 5). ECG feature extraction and ventricular fibrillation (VF) prediction using data mining techniques. Proceedings of the 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), Cordoba, Spain.","DOI":"10.1109\/CBMS.2019.00014"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1109\/10.58594","article-title":"Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm","volume":"37","author":"Thakor","year":"1990","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/BF02447420","article-title":"Ventricular fibrillation detection by a regression test on the autocorrelation function","volume":"25","author":"Chen","year":"1987","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/1475-925X-4-60","article-title":"Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators","volume":"4","author":"Amann","year":"2005","journal-title":"BioMed. Eng. Online"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3862","DOI":"10.1016\/j.eswa.2011.09.097","article-title":"Prediction of spontaneous ventricular tachyarrhythmia by an artificial neural network using parameters gleaned from short-term heart rate variability","volume":"39","author":"Joo","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_17","unstructured":"Kuo, S., and Dillman, R. (1978, January 6\u201310). Computer Detection of Ventricular Fibrillation. Proceedings of the 1987 Computing in Cardiology, San Francisco, CA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/0141-5425(89)90067-8","article-title":"Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: A diagnostic system","volume":"11","author":"Barro","year":"1989","journal-title":"J. Biomed. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1109\/51.870237","article-title":"Evaluating arrhythmias in ECG signals using wavelet transforms","volume":"19","author":"Watson","year":"2000","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_20","unstructured":"Alonso-Atienza, F., Morgado, E., Fernandez-Mart\u0131nez, L., Garc\u0131a-Alberola, A., and Rojo-Alvarez, J.L. (2012, January 9\u201312). Combination of ECG parameters with support vector machines for the detection of life-threatening arrhythmias. Proceedings of the 2012 Computing in Cardiology, Krakow, Poland."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/BF02518922","article-title":"Recognition of ventricular fibrillation using neural networks","volume":"32","author":"Clayton","year":"1994","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s13534-011-0012-0","article-title":"Monitoring physiological signals using nonintrusive sensors installed in daily life equipment","volume":"1","author":"Lim","year":"2011","journal-title":"Biomed. Eng. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/RBME.2015.2414661","article-title":"Ambient and unobtrusive cardiorespiratory monitoring techniques","volume":"8","author":"Antink","year":"2015","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_24","unstructured":"Tavakolian, K., Ngai, B., Akhbardeh, A., Kaminska, B., and Blaber, A. (2009, January 13\u201316). Comparative analysis of infrasonic cardiac signals. Proceedings of the 2009 36th Annual Computers in Cardiology Conference (CinC), Park City, UT, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1152\/ajplegacy.1939.127.1.1","article-title":"Studies on the estimation of cardiac output in man, and of abnormalities in cardiac function, from the heart\u2019s recoil and the blood\u2019s impacts; the ballistocardiogram","volume":"127","author":"Starr","year":"1939","journal-title":"Am. J. Physiol. Leg. Content."},{"key":"ref_26","first-page":"533","article-title":"Certain molar movements of the human body produced by the circulation of the blood","volume":"11","author":"Gordon","year":"1877","journal-title":"J. Anat. Physiol."},{"key":"ref_27","first-page":"55","article-title":"Seismocardiography\u2014a new method in the study of functional conditions of the heart","volume":"33","author":"Bozhenko","year":"1961","journal-title":"Ter. Arkh."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1378\/chest.100.4.991","article-title":"Seismocardiography for monitoring changes in left ventricular function during ischemia","volume":"100","author":"Salerno","year":"1991","journal-title":"Chest J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s13755-019-0071-7","article-title":"Ballistocardiogram signal processing: A review","volume":"7","author":"Sadek","year":"2019","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1414","DOI":"10.1109\/JBHI.2014.2361732","article-title":"Ballistocardiography and seismocardiography: A review of recent advances","volume":"19","author":"Inan","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"64","DOI":"10.3390\/vibration2010005","article-title":"Recent Advances in Seismocardiography","volume":"2","author":"Taebi","year":"2019","journal-title":"Vibration"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1007\/s11760-018-1372-z","article-title":"Nonintrusive heart rate measurement using ballistocardiogram signals: A comparative study","volume":"13","author":"Sadek","year":"2019","journal-title":"Signal Image Video P."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Leonhardt, S., Leicht, L., and Teichmann, D. (2018). Unobtrusive Vital Sign Monitoring in Automotive Environments\u2014A Review. Sensors, 18.","DOI":"10.3390\/s18093080"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Huysmans, D., Borz\u00e9e, P., Testelmans, D., Buyse, B., Willemen, T., Van Huffel, S., and Varon, C. (2019). Evaluation of a Commercial Ballistocardiography Sensor for Sleep Apnea Screening and Sleep Monitoring. Sensors, 19.","DOI":"10.3390\/s19092133"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"13175","DOI":"10.1038\/s41598-017-13138-0","article-title":"Unobtrusive nocturnal heartbeat monitoring by a ballistocardiographic sensor in patients with sleep disordered breathing","volume":"7","author":"Zink","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhou, X., Zhao, W., Liu, F., Ni, H., and Yu, Z. (2017). Assessing the severity of sleep apnea syndrome based on ballistocardiogram. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0175351"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TITB.2012.2225067","article-title":"Automatic detection of atrial fibrillation in cardiac vibration signals","volume":"17","author":"Diesel","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/JBHI.2017.2688473","article-title":"Atrial fibrillation detection via accelerometer and gyroscope of a smartphone","volume":"22","author":"Lahdenoja","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1109\/JBHI.2019.2927165","article-title":"A feasible feature extraction method for atrial fibrillation detection from BCG","volume":"24","author":"Wen","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_40","first-page":"671","article-title":"Using piezoelectric sensor for continuous-contact-free monitoring of heart and respiration rates in real-life hospital settings","volume":"40","author":"Klap","year":"2013","journal-title":"Comput. Cardiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2906","DOI":"10.1109\/TBME.2019.2897952","article-title":"Cardiovascular function and ballistocardiogram: A relationship interpreted via mathematical modeling","volume":"66","author":"Guidoboni","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Pino, E.J., Larsen, C., Chavez, J., and Aqueveque, P. (2016, January 16\u201320). Non-invasive BCG monitoring for non-traditional settings. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591795"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1109\/PROC.1975.10036","article-title":"Adaptive noise cancelling: Principles and application","volume":"63","author":"Widrow","year":"1975","journal-title":"Proc. IEEE"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TBME.2009.2018831","article-title":"Adaptive cancellation of floor vibrations in standing ballistocardiogram measurements using a seismic sensor as a noise reference","volume":"57","author":"Inan","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1109\/TBME.2019.2935619","article-title":"Classification of decompensated heart failure from clinical and home ballistocardiography","volume":"67","author":"Aydemir","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"105626","DOI":"10.1016\/j.cmpb.2020.105626","article-title":"Performance evaluation of the spectral autocorrelation function and autoregressive models for automated sleep apnea detection using single-lead ECG signal","volume":"195","author":"Zarei","year":"2020","journal-title":"Comput. Meth. Prog. Biomed."},{"key":"ref_47","unstructured":"Aubert, A.E., Denys, B.G., Ector, H., and De Geest, H. (1983, January 12\u201315). Fibrillation recognition using autocorrelation analysis. Proceedings of the 1983 Computing in Cardiology, San Francisco, CA, USA."},{"key":"ref_48","unstructured":"Box, G.E.P., Jenkins, G.M., and Reinsel, G.C. (1994). Time Series Analysis: Forecasting and Control, Prentice Hall."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1109\/78.492555","article-title":"Localization of the complex spectrum: The S transform","volume":"44","author":"Stockwell","year":"1996","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.ymssp.2018.07.039","article-title":"Parameterised time-frequency analysis methods and their engineering applications: A review of recent advances","volume":"119","author":"Yang","year":"2019","journal-title":"Mech. Syst. Signal Pr."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Lydon, K., Su, B.Y., Rosales, L., Enayati, M., Ho, K.C., Rantz, M., and Skubic, M. (2015, January 25\u201329). Robust heartbeat detection from in-home ballistocardiogram signals of older adults using a bed sensor. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7320047"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.neucom.2018.03.067","article-title":"Time series feature extraction on basis of scalable hypothesis tests (tsfresh\u2013A Python package)","volume":"307","author":"Maximilian","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Takalo-Mattila, J., Kiljander, J., and Soininen, J.-P. (2018, January 29\u201331). Inter-patient ECG classification using deep convolutional neural networks. Proceedings of the 2018 21st Euromicro Conference on Digital System Design (DSD), Prague, Czech Republic.","DOI":"10.1109\/DSD.2018.00077"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","article-title":"Learning from class-imbalanced data: Review of methods and applications","volume":"73","author":"Guo","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s10916-016-0441-5","article-title":"Detection of shockable ventricular arrhythmia using variational mode decomposition","volume":"40","author":"Tripathy","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_56","first-page":"87","article-title":"Application of statistical features and multilayer neural network to automatic diagnosis of arrhythmia by ECG signals","volume":"25","author":"Slama","year":"2018","journal-title":"Metrol. Meas. Syst."},{"key":"ref_57","first-page":"49","article-title":"Machine learning approach to recognize ventricular arrhythmias using VMD based features. Multidim","volume":"31","author":"Mohanty","year":"2020","journal-title":"Syst. Sign. Process."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s13246-020-00964-2","article-title":"Detection of ventricular arrhythmia using hybrid time\u2013frequency-based features and deep neural network","volume":"44","author":"Sabut","year":"2021","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_59","unstructured":"Lai, D., Fan, X., Zhang, Y., and Chen, W. (2020). Intelligent and efficient detection of life-threatening ventricular arrhythmias in short segments of surface ECG signals. IEEE Sens. J., 1."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Alonzo, L.M.B., and Co, H.S. (December, January 29). Ensemble empirical mode decomposition of photoplethysmogram signals for assessment of ventricular fibrillation. Proceedings of the 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Baguio City, Philippines.","DOI":"10.1109\/HNICEM.2018.8666241"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/10\/3524\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:03:47Z","timestamp":1760162627000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/10\/3524"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,19]]},"references-count":60,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["s21103524"],"URL":"https:\/\/doi.org\/10.3390\/s21103524","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,19]]}}}