Advertisement

Computer-aided diagnosis of congestive heart failure using ECG signals – A review

      Highlights

      • Existing CHF detection techniques are reported.
      • Salient features of automated systems developed using deep learning and machine learning are described.
      • Several features extraction methods are presented.
      • State-of-the-art automated CHF detection techniques are discussed.

      Abstract

      The heart muscle pumps blood to vital organs, which is indispensable for human life. Congestive heart failure (CHF) is characterized by the inability of the heart to pump blood adequately throughout the body without an increase in intracardiac pressure. The symptoms include lung and peripheral congestion, leading to breathing difficulty and swollen limbs, dizziness from reduced delivery of blood to the brain, as well as arrhythmia. Coronary artery disease, myocardial infarction, and medical co-morbidities such as kidney disease, diabetes, and high blood pressure all take a toll on the heart and can impair myocardial function. CHF prevalence is growing worldwide. It afflicts millions of people globally, and is a leading cause of death. Hence, proper diagnosis, monitoring and management are imperative. The importance of an objective CHF diagnostic tool cannot be overemphasized. Standard diagnostic tests for CHF include chest X-ray, magnetic resonance imaging (MRI), nuclear imaging, echocardiography, and invasive angiography. However, these methods are costly, time-consuming, and they can be operator-dependent. Electrocardiography (ECG) is inexpensive and widely accessible, but ECG changes are typically not specific for CHF diagnosis. A properly designed computer-aided detection (CAD) system for CHF, based on the ECG, would potentially reduce subjectivity and provide quantitative assessment for informed decision-making. Herein, we review existing CAD for automatic CHF diagnosis, and highlight the development of an ECG-based CAD diagnostic system that employs deep learning algorithms to automatically detect CHF.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Physica Medica: European Journal of Medical Physics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Bui A.L.
        • Horwich T.B.
        • Fonarow G.C.
        Epidemiology and risk profile of heart failure.
        Nat Rev Cardiol. 2011; 8: 30-41https://doi.org/10.1038/nrcardio.2010.165
        • Ambrosy A.P.
        • Fonarow G.C.
        • Butler J.
        • Chioncel O.
        • Greene S.J.
        • Vaduganathan M.
        • et al.
        The global health and economic burden of hospitalizations for heart failure: lessons learned from hospitalized heart failure registries.
        J Am Coll Cardiol. 2014; 63: 1123-1133https://doi.org/10.1016/j.jacc.2013.11.053
        • Sakata Y.
        • Shimokawa H.
        Epidemiology of heart failure in Asia.
        Circ J. 2013; 77: 2209-2217https://doi.org/10.1016/j.hfc.2015.07.009
        • Acharya U.R.
        • Fujita H.
        • Lih O.S.
        • Adam M.
        • Tan J.H.
        • Chua C.K.
        Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network.
        Knowledge-Based Syst. 2017; 132: 62-71https://doi.org/10.1016/j.knosys.2017.06.003
      1. Burgess A, Shah K, Hough O, Hynynen K. HHS Public Access 2016;15:477–91. https://doi.org/10.1586/14737175.2015.1028369.Focused.

        • Hunt S.A.
        • Abraham W.T.
        • Chin M.H.
        • Feldman A.M.
        • Francis G.S.
        • Ganiats T.G.
        • et al.
        2009 focused update incorporated into the ACC/AHA 2005 guidelines for the diagnosis and management of heart failure in adults.
        J Am Coll Cardiol. 2009; 53: e1-e90https://doi.org/10.1016/j.jacc.2008.11.013
        • Inamdar A.A.
        • Inamdar A.C.
        Heart failure : diagnosis, management and utilization.
        J Clin Med. 2016; https://doi.org/10.3390/jcm5070062
        • Gladding P.A.
        • Cave A.
        • Zareian M.
        • Smith K.
        • Hussan J.
        • Homer M.L.
        • et al.
        Personalized medicine open access integrated therapeutic and diag nostic platforms for personalized cardiovascular medicine.
        J Pers Med. 2013; : 203-237https://doi.org/10.3390/jpm3030203
        • Beymer D.
        • Syeda-Mahmood T.
        Cardiac disease recognition in echocardiograms using spatio-temporal statistical models.
        Conf Proc Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Conf. 2008; 2008: 4784-4788https://doi.org/10.1109/IEMBS.2008.4650283
        • Baumert M.
        • Porta A.
        • Cichocki A.
        Biomedical signal processing: from a conceptual framework to clinical applications [Scanning the Issue].
        Proc IEEE. 2016; 104: 220-222https://doi.org/10.1109/JPROC.2015.2511359
        • Chang C.H.
        • Ko H.J.
        • Chang K.M.
        Cancellation of high-frequency noise in ECG signals using adaptive filter without external reference.
        in: Proc - 2010 3rd Int Conf Biomed Eng Informatics. BMEI, 2010: 787-790https://doi.org/10.1109/BMEI.2010.5639953
        • Martis R.J.
        • Acharya U.R.
        • Adeli H.
        Current methods in electrocardiogram characterization.
        Comput Biol Med. 2014; 48: 133-149https://doi.org/10.1016/j.compbiomed.2014.02.012
        • Rajendra Acharya U.
        • Faust O.
        • Adib Kadri N.
        • Suri J.S.
        • Yu W.
        Automated identification of normal and diabetes heart rate signals using nonlinear measures.
        Comput Biol Med. 2013; 43: 1523-1529https://doi.org/10.1016/j.compbiomed.2013.05.024
        • Goldberger A.L.
        • Amaral L.A.N.
        • Glass L.
        • Hausdorff J.M.
        • Ivanov P.Ch.
        • Mark R.G.
        • et al.
        Current Perspective.
        Circulation. 2000; 101: 215-220https://doi.org/10.1161/01.CIR.101.23.e215
        • Panda S.K.
        • Jana P.K.
        SLA-based task scheduling algorithms for heterogeneous multi-cloud environment.
        J Supercomput. 2017; 73: 2730-2762https://doi.org/10.1007/s11227-016-1952-z
        • Panda S.K.
        • Jana P.K.
        A multi-objective task scheduling algorithm for heterogeneous multi-cloud environment.
        in: 2015 Int Conf Electron Des Comput Networks Autom Verif EDCAV 2015. 2015: 82-87https://doi.org/10.1109/EDCAV.2015.7060544
        • Faust O.
        • Acharya U.R.
        • Krishnan S.M.
        • Min L.C.
        Analysis of cardiac signals using spatial filling index and time-frequency domain.
        Biomed Eng Online. 2004; 3: 1-11https://doi.org/10.1186/1475-925X-3-30
        • Pincus S.
        Approximate entropy (ApEn) as a complexity measure.
        Chaos. 1995; 5: 110-117https://doi.org/10.1063/1.166092
        • Richman J.S.
        • Moorman J.R.
        Physiological time-series analysis using approximate entropy and sample entropy.
        Am J Physiol Circ Physiol. 2017; 278: H2039-H2049https://doi.org/10.1152/ajpheart.2000.278.6.h2039
        • Anastasiadis A.
        Special issue: Tsallis entropy.
        Entropy. 2012; 14: 174-176https://doi.org/10.3390/e14020174
        • Jaynes E.T.
        Principles of Statistical Mechanics-The Information Theory Approach.
        1968
        • Ramer A.
        Concepts of fuzzy information measures on continuous domains.
        Int J Gen Syst. 1990; 17: 241-248https://doi.org/10.1080/03081079008935109
        • Al-sharhan S.
        • Karray F.
        • Gueaieb W.
        • Basir O.
        Fuzzy entropy: a brief survey. 10th.
        IEEE Int Conf Fuzzy Syst (Cat No01CH37297). 2001; 2: 1135-1139https://doi.org/10.1109/FUZZ.2001.1008855
        • Pham T.D.
        The Kolmogorov-Sinai entropy in the setting of fuzzy sets for image texture analysis and classification.
        Pattern Recogn. 2016; 53: 229-237https://doi.org/10.1016/j.patcog.2015.12.012
        • De Wu.S.
        • Wu C.W.
        • Lee K.Y.
        • Lin S.G.
        Modified multiscale entropy for short-term time series analysis.
        Phys A Stat Mech Its Appl. 2013; 392: 5865-5873https://doi.org/10.1016/j.physa.2013.07.075
        • Dostál O.
        • Vysata O.
        • Pazdera L.
        • Procházka A.
        • Kopal J.
        • Kuchyňka J.
        • et al.
        Permutation entropy and signal energy increase the accuracy of neuropathic change detection in needle EMG.
        Comput Intell Neurosci. 2018; 2018https://doi.org/10.1155/2018/5276161
        • Savare G.
        • Toscani G.
        The concavity of rényi entropy power.
        IEEE Trans Inf Theory. 2014; 60: 2687-2693https://doi.org/10.1109/TIT.2014.2309341
        • Shannon Claude E.
        A mathematical theory of communication.
        Bell Syst Tech J. 1948; 27: 623-656https://doi.org/10.2307/3611062
        • Rosso O.A.
        • Blanco S.
        • Yordanova J.
        • Kolev V.
        • Figliola A.
        • Schürmann M.
        • Başar E.
        Wavelet entropy: a new tool for analysis of short duration brain electrical signals.
        J. Neurosci Method. 2001; 105: 65-75https://doi.org/10.1016/S0165-0270(00)00356-3
        • Kolmogorov A.N.
        Three approaches to the quantitative definition of information.
        Int J Comput Math. 1968; 2: 157-168https://doi.org/10.1080/00207166808803030
        • Ziv J.
        • Lempel A.
        A universal algorithm for sequential data compression.
        IEEE Trans Inf Theory. 1977; 23: 337-343https://doi.org/10.1109/TIT.1977.1055714
        • Sweller J.
        Introduction.
        Educ Psychol Rev. 1988; 12: 257-285https://doi.org/10.1207/s15516709cog1202_4
        • Sweller J.
        • Van Merrienboer J.J.G.
        • Paas F.G.W.C.
        Cognitive architecture and instructional design.
        Educ Psychol Rev. 1998; 10: 251-296https://doi.org/10.1023/A:1022193728205
        • Brünken R.
        • Plass J.L.
        Bruenken_Plass_Leutner_EP. 2003: 1-9https://doi.org/10.1207/S15326985EP3801_7
        • Curtin P.
        • Curtin A.
        • Austin C.
        • Gennings C.
        • Tammimies K.
        • Bölte S.
        • et al.
        Recurrence quantification analysis to characterize cyclical components of environmental elemental exposures during fetal and postnatal development.
        PLoS ONE. 2017; 12e0187049
        • Pavlov A.N.
        • Pavlova O.N.
        • Kurths J.
        Determining the largest Lyapunov exponent of chaotic dynamics from sequences of interspike intervals contaminated by noise.
        Eur Phys J B. 2017; 90https://doi.org/10.1140/epjb/e2017-70439-7
        • Budescu B.
        • Cǎliman A.
        • Ivanovici M.
        The correlation dimension: a video quality measure.
        Lect Notes Inst Comput Sci Soc Telecommun Eng. 2012; 79: 55-64https://doi.org/10.1007/978-3-642-30419-4_5
        • Collis W.B.
        • White P.R.
        • Hammond J.K.
        Higher-order spectra: the bispectrum and trispectrum.
        Mech Syst Signal Process. 1998; 12: 375-394https://doi.org/10.1006/mssp.1997.0145
        • Brillinger D.R.
        An introduction to polyspectra.
        Ann Math Stat. 1965; 36: 1351-1374https://doi.org/10.1214/aoms/1177699896
        • Brillinger D.R.
        Time Series: Data Analysis and Theory.
        McGraw-Hill, New York1981
        • Acharya U.R.
        • Fujita H.
        • Sudarshan V.K.
        • Oh S.L.
        • Adam M.
        • Tan J.H.
        • et al.
        Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal.
        Knowledge-Based Syst. 2017; 132: 156-166https://doi.org/10.1016/j.knosys.2017.06.026
        • Bhurane A.A.
        • Sharma M.
        • San-Tan R.
        • Rajendra Acharya U.
        An efficient detection of congestive heart failure using frequency localized filter banks for the diagnosis with ECG signals.
        Cogn Syst Res. 2019; 55: 82-94https://doi.org/10.1016/j.cogsys.2018.12.017
        • Liao Ken Ying-Kai
        • Chiu Chuang-Chien
        • Yeh Shoou-Jeng
        A novel approach for classification of congestive heart failure using relatively short-term ECG waveforms and SVM CLASSIFIER.
        in: Proc Int MultiConference Eng Comput Sci 2015, IMECS 2015, March 18–20, 2015, Hong Kong. 2015: 18-21
        • Melillo P.
        • Fusco R.
        • Sansone M.
        • Bracale M.
        • Pecchia L.
        Discrimination power of long-term heart rate variability measures for chronic heart failure detection.
        Med Biol Eng Comput. 2011; 49: 67-74https://doi.org/10.1007/s11517-010-0728-5
        • Yu S.N.
        • Lee M.Y.
        Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability.
        Comput Biol Med. 2012; 42: 816-825https://doi.org/10.1016/j.compbiomed.2012.06.005
        • Liu G.
        • Wang L.
        • Wang Q.
        • Zhou G.M.
        • Wang Y.
        • Jiang Q.
        A new approach to detect congestive heart failure using short-term heart rate variability measures.
        PLoS ONE. 2014; 9https://doi.org/10.1371/journal.pone.0093399
        • Shahbazi F.
        • Asl B.M.
        Generalized discriminant analysis for congestive heart failure risk assessment based on long-term heart rate variability.
        Comput Methods Programs Biomed. 2015; 122: 191-198https://doi.org/10.1016/j.cmpb.2015.08.007
        • Orhan U.
        Real-time CHF detection from ECG signals using a novel discretization method.
        Comput Biol Med. 2013; 43: 1556-1562https://doi.org/10.1016/j.compbiomed.2013.07.015
        • Mašetic Z.
        • Subasi Abdulhamit
        Detection of congestive heart failures using c4. 5 decision tree.
        Southeast Eur J Soft Comput. 2013; 2https://doi.org/10.21533/scjournal.v2i2.32
        • Kamath C.
        A new approach to detect congestive heart failure using detrended fluctuation analysis of electrocardiogram signals.
        J Eng Sci Technol. 2015; 10: 145-159
        • Sudarshan V.K.
        • Acharya U.R.
        • Lih S.
        • Adam M.
        • Hong J.
        • Kuang C.
        • et al.
        Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2 s of ECG signals.
        Comput Biol Med. 2017; 83: 48-58https://doi.org/10.1016/j.compbiomed.2017.01.019
        • Acharya U.R.
        • Fujita H.
        • Oh S.L.
        • Hagiwara Y.
        • Tan J.H.
        • Adam M.
        • et al.
        Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals.
        Appl Intell. 2018; https://doi.org/10.1007/s10489-018-1179-1
        • Faust O.
        • Shenfield A.
        • Kareem M.
        • San T.R.
        • Fujita H.
        • Acharya U.R.
        Automated detection of atrial fibrillation using long short-term memory network with RR interval signals.
        Comput Biol Med. 2018; 102: 327-335https://doi.org/10.1016/j.compbiomed.2018.07.001
        • Kumar A.
        • Komaragiri R.
        • Kumar M.
        Heart rate monitoring and therapeutic devices: a wavelet transform based approach for the modeling and classification of congestive heart failure.
        ISA Trans. 2018; 79: 239-250https://doi.org/10.1016/j.isatra.2018.05.003
        • Sharma R.R.
        • Pachori R.B.
        Baseline wander and power line interference removal from ECG signals using eigenvalue decomposition.
        Biomed Signal Process Control. 2018; 45: 33-49https://doi.org/10.1016/j.bspc.2018.05.002
        • Thuraisingham R.A.
        A classification system to detect congestive heart failure using second-order difference plot of RR intervals.
        Cardiol Res Pract. 2009; 2009: 1-7https://doi.org/10.4061/2009/807379
        • Jong T.L.
        • Chang B.
        • Kuo C.D.
        Optimal timing in screening patients with congestive heart failure and healthy subjects during circadian observation.
        Ann Biomed Eng. 2011; 39: 835-849https://doi.org/10.1007/s10439-010-0180-6
        • Melillo P.
        • De Luca N.
        • Bracale M.
        • Pecchia L.
        Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability.
        IEEE J Biomed Heal Informatics. 2013; 17: 727-733https://doi.org/10.1109/JBHI.2013.2244902
        • Narin A.
        • Isler Y.
        • Ozer M.
        Investigating the performance improvement of HRV Indices in CHF using feature selection methods based on backward elimination and statistical significance.
        Comput Biol Med. 2014; 45: 72-79https://doi.org/10.1016/j.compbiomed.2013.11.016
        • Sood S.
        • Kumar M.
        • Pachori R.B.
        • Acharya U.R.
        Application of empirical mode decomposition-based features for analysis of normal and Cad heart rate signals.
        J Mech Med Biol. 2016; 16: 1640002https://doi.org/10.1142/S0219519416400029
        • Kumar M.
        • Pachori R.B.
        • Acharya U.R.
        Use of accumulated entropies for automated detection of congestive heart failure in flexible analytic wavelet transform framework based on short-term HRV signals.
        Entropy. 2017; : 19https://doi.org/10.3390/e19030092
        • Pan W.
        • He A.
        • Feng K.
        • Li Y.
        • Wu D.
        • Liu G.
        Multi-frequency components entropy as novel heart rate variability indices in congestive heart failure assessment.
        IEEE Access. 2019; https://doi.org/10.1109/access.2019.2896342
        • Sharma R.R.
        • Kumar A.
        • Pachori R.B.
        • Acharya U.R.
        Accurate automated detection of congestive heart failure using eigenvalue decomposition based features extracted from HRV signals.
        Biocybern Biomed Eng. 2018; https://doi.org/10.1016/j.bbe.2018.10.001
        • Wang L.
        • Zhou X.
        Detection of congestive heart failure based on LSTM-based deep network via short-term RR intervals.
        Sensors. 2019; 19: 1502https://doi.org/10.3390/s19071502
        • Faust O.
        • Hagiwara Y.
        • Hong T.J.
        • Lih O.S.
        • Acharya U.R.
        Deep learning for healthcare applications based on physiological signals: a review.
        Comput Methods Programs Biomed. 2018; 161: 1-13https://doi.org/10.1016/j.cmpb.2018.04.005
        • Haykin S.S.
        Neural networks: a comprehensive foundation.
        1999
      2. Gershenson C. Artificial Neural Networks for Beginners. Arxiv Prepr Cs/0308031 2003. p. 1–8.

        • Lecun Y.
        • Bengio Y.
        • Hinton G.
        Deep learning.
        Nature. 2015; 521: 436-444https://doi.org/10.1038/nature14539
        • Yıldırım Ö.
        • Pławiak P.
        • Tan R.S.
        • Acharya U.R.
        Arrhythmia detection using deep convolutional neural network with long duration ECG signals.
        Comput Biol Med. 2018; 102: 411-420https://doi.org/10.1016/j.compbiomed.2018.09.009
        • Pławiak P.
        • Acharya U.R.
        Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals.
        Neural Comput Appl. 2019; https://doi.org/10.1007/s00521-018-03980-2
        • Acharya U.R.
        • Oh S.L.
        • Hagiwara Y.
        • Tan J.H.
        • Adam M.
        • Gertych A.
        • et al.
        A deep convolutional neural network model to classify heartbeats.
        Comput Biol Med. 2017; 89: 389-396https://doi.org/10.1016/j.compbiomed.2017.08.022
        • Oh S.L.
        • Ng E.Y.K.
        • Tan R.S.
        • Acharya U.R.
        Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats.
        Comput Biol Med. 2018; 102: 278-287https://doi.org/10.1016/j.compbiomed.2018.06.002
        • Baloglu U.B.
        • Talo M.
        • Yildirim O.
        • Tan R.S.
        • Acharya U.R.
        Classification of myocardial infarction with multi-lead ECG signals and deep CNN.
        Pattern Recogn Lett. 2019; 122: 23-30https://doi.org/10.1016/j.patrec.2019.02.016
        • Acharya U.R.
        • Fujita H.
        • Sudarshan V.K.
        • Oh S.L.
        • Adam M.
        • Koh J.E.W.
        • et al.
        Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads.
        Knowledge-Based Syst. 2016; 99: 146-156https://doi.org/10.1016/j.knosys.2016.01.040
        • Fujita H.
        • Cimr D.
        Computer Aided detection for fibrillations and flutters using deep convolutional neural network.
        Inf Sci (Ny). 2019; 486: 231-239https://doi.org/10.1016/j.ins.2019.02.065
        • Acharya U.R.
        • Fujita H.
        • Lih S.
        • Raghavendra U.
        • Hong J.
        Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network.
        Futur Gener Comput Syst. 2018; 79: 952-959https://doi.org/10.1016/j.future.2017.08.039
        • Tan J.H.
        • Hagiwara Y.
        • Pang W.
        • Lim I.
        • Oh S.L.
        • Adam M.
        • et al.
        Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals.
        Comput Biol Med. 2018; 94: 19-26https://doi.org/10.1016/j.compbiomed.2017.12.023
        • Ngo L.H.
        Using a deep learning network to diagnose congestive heart failure.
        Radiology. 2018; 182341https://doi.org/10.1148/radiol.2018182341