{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T19:54:43Z","timestamp":1775073283746,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,3,28]],"date-time":"2019-03-28T00:00:00Z","timestamp":1553731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"BUPT Excellent Ph.D. Students Foundation","award":["00"],"award-info":[{"award-number":["00"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["6170204"],"award-info":[{"award-number":["6170204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Congestive heart failure (CHF) refers to the inadequate blood filling function of the ventricular pump and it may cause an insufficient heart discharge volume that fails to meet the needs of body metabolism. Heart rate variability (HRV) based on the RR interval is a proven effective predictor of CHF. Short-term HRV has been used widely in many healthcare applications to monitor patients\u2019 health, especially in combination with mobile phones and smart watches. Inspired by the inception module from GoogLeNet, we combined long short-term memory (LSTM) and an Inception module for CHF detection. Five open-source databases were used for training and testing, and three RR segment length types (N = 500, 1000 and 2000) were used for the comparison with other studies. With blindfold validation, the proposed method achieved 99.22%, 98.85% and 98.92% accuracy using the Beth Israel Deaconess Medical Center (BIDMC) CHF, normal sinus rhythm (NSR) and the Fantasia database (FD) databases and 82.51%, 86.68% and 87.55% accuracy using the NSR-RR and CHF-RR databases, with N = 500, 1000 and 2000 length RR interval segments, respectively. Our end-to-end system can help clinicians to detect CHF using short-term assessment of the heartbeat. It can be installed in healthcare applications to monitor the status of human heart.<\/jats:p>","DOI":"10.3390\/s19071502","type":"journal-article","created":{"date-parts":[[2019,3,29]],"date-time":"2019-03-29T03:38:52Z","timestamp":1553830732000},"page":"1502","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Detection of Congestive Heart Failure Based on LSTM-Based Deep Network via Short-Term RR Intervals"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9346-6250","authenticated-orcid":false,"given":"Ludi","family":"Wang","sequence":"first","affiliation":[{"name":"Automation School, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoguang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Automation School, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1016\/j.jacc.2017.04.025","article-title":"2017 ACC\/AHA\/HFSA Focused Update of the 2013 ACCF\/AHA Guideline for the Management of Heart Failure","volume":"70","author":"Yancy","year":"2017","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1007\/s11897-014-0220-x","article-title":"Understanding the epidemic of heart failure: Past, present, and future","volume":"11","author":"Dunlay","year":"2014","journal-title":"Curr. Heart Fail. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1161\/HHF.0b013e318291329a","article-title":"Forecasting the impact of heart failure in the United States: A policy statement from the American Heart Association. Circulation","volume":"6","author":"Heidenreich","year":"2013","journal-title":"Heart Fail."},{"key":"ref_4","first-page":"PCC.13r01511","article-title":"Diagnosis and treatment of depression in patients with congestive heart failure: A review of the literature","volume":"15","author":"Rustad","year":"2013","journal-title":"Prim. Care Companion CNS Disord."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1161\/01.HYP.0000217141.20163.23","article-title":"Electrocardiographic QRS duration and the risk of congestive heart failure: The Framingham Heart Study","volume":"47","author":"Dhingra","year":"2006","journal-title":"Hypertension"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1161\/CIRCEP.112.970541","article-title":"Predictive value of beat-to-beat QT variability index across the continuum of left ventricular dysfunction: Competing risks of noncardiac or cardiovascular death and sudden or nonsudden cardiac death","volume":"5","author":"Tereshchenko","year":"2012","journal-title":"Circ. Arrhythm. Electrophysiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1161\/01.CIR.98.15.1510","article-title":"Prospective study of heart rate variability and mortality in chronic heart failure: Results of the United Kingdom heart failure evaluation and assessment of risk trial (UK-heart)","volume":"98","author":"Nolan","year":"1998","journal-title":"Circulation"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/0735-1097(91)90602-6","article-title":"Parasympathetic withdrawal is an integral component of autonomic imbalance in congestive heart failure: Demonstration in human subjects and verification in a paced canine model of ventricular failure","volume":"18","author":"Binkley","year":"1991","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1016\/j.compbiomed.2012.06.005","article-title":"Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability","volume":"42","author":"Yu","year":"2012","journal-title":"Comput. Boil. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/0735-1097(94)90737-4","article-title":"Complex heart rate variability and serum norepinephrine levels in patients with advanced heart failure","volume":"23","author":"Woo","year":"1994","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1063\/1.166141","article-title":"Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series","volume":"5","author":"Peng","year":"1995","journal-title":"Chaos"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, W., Zheng, L., Li, K., Wang, Q., Liu, G., and Jiang, Q. (2016). A Novel and Effective Method for Congestive Heart Failure Detection and Quantification Using Dynamic Heart Rate Variability Measurement. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0165304"},{"key":"ref_13","first-page":"3369","article-title":"A CHF detection method based on deep learning with RR intervals","volume":"2017","author":"Wenhui","year":"2017","journal-title":"Conf. Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1007\/s10877-013-9473-2","article-title":"Heart rate variability indices for very short-term (30 beat) analysis. Part 2: Validation","volume":"27","author":"Smith","year":"2013","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"H319","DOI":"10.1152\/ajpheart.00561.2010","article-title":"Accurate estimation of entropy in very short physiological time series: The problem of atrial fibrillation detection in implanted ventricular devices","volume":"300","author":"Lake","year":"2011","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.compbiomed.2018.07.001","article-title":"Automated detection of atrial fibrillation using long short-term memory network with RR interval signals","volume":"102","author":"Faust","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Thakre, T.P., and Smith, M.L. (2006). Loss of lag-response curvilinearity of indices of heart rate variability in congestive heart failure. BMC Cardiovasc. Disord., 6.","DOI":"10.1186\/1471-2261-6-27"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.compbiomed.2012.11.005","article-title":"Analysis of heart rate variability using fuzzy measure entropy","volume":"43","author":"Liu","year":"2013","journal-title":"Comput. Boil. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6270","DOI":"10.3390\/e17096270","article-title":"Determination of Sample Entropy and Fuzzy Measure Entropy Parameters for Distinguishing Congestive Heart Failure from Normal Sinus Rhythm Subjects","volume":"17","author":"Zhao","year":"2015","journal-title":"Entropy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"251","DOI":"10.3390\/e19060251","article-title":"Multiscale Entropy Analysis of the Differential RR Interval Time Series Signal and Its Application in Detecting Congestive Heart Failure","volume":"19","author":"Liu","year":"2017","journal-title":"Entropy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"346","DOI":"10.5370\/JEET.2017.12.1.346","article-title":"Automatic Detection of Congestive Heart Failure and Atrial Fibrillation with Short RR Interval Time Series","volume":"12","author":"Yoon","year":"2017","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/S0735-1097(86)80478-8","article-title":"Survival of patients with severe congestive heart failure treated with oral milrinone","volume":"7","author":"Baim","year":"1986","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lee, I., Kim, D., Kang, S., and Lee, S. (2017, January 22\u201329). Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.115"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_27","unstructured":"Yanjie, D., Yisheng, L., and Fei-Yue, W. (2016, January 1\u20134). Travel time prediction with LSTM neural network. Proceedings of the IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Deng, L., Li, J., Huang, J., Yao, K., Yu, D., Seide, F., Seltzer, M., Zweig, G., He, X., and Williams, J. (2013, January 26\u201331). Recent advances in deep learning for speech research at Microsoft. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6639345"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Potes, C., Parvaneh, S., Rahman, A., and Conroy, B. (2016, January 11\u201314). Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds. Proceedings of the Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.182-399"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1089\/tmj.2017.0250","article-title":"Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals","volume":"24","author":"Hwang","year":"2018","journal-title":"Telemed. J. e-Health"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2095","DOI":"10.1109\/TSMC.2017.2705582","article-title":"Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients","volume":"48","author":"Pourbabaee","year":"2018","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Wei, L., Yangqing, J., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation Applied to Handwritten Zip Code Recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_34","first-page":"R1078","article-title":"Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics","volume":"271","author":"Iyengar","year":"1996","journal-title":"Am. J. Physiol."},{"key":"ref_35","unstructured":"Min, L., Chen, Q., and Yan, S. (arXiv, 2013). Network in Network, arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_37","unstructured":"Kingma, D., and Ba, J. (arXiv, 2014). Adam: A Method for Stochastic Optimization, arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kumar, M., Pachori, R.B., and Acharya, U.R. (2017). Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals. Entropy, 19.","DOI":"10.3390\/e19030092"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/7\/1502\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:41:07Z","timestamp":1760186467000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/7\/1502"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,28]]},"references-count":38,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["s19071502"],"URL":"https:\/\/doi.org\/10.3390\/s19071502","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,28]]}}}