Abstract
Severe cardiovascular diseases (CVD) are the leading cause of death worldwide. In the emergency scenario, reliable electrocardiography (ECG) is critical for the rapid diagnosis and management of acute CVD. Deep learning (DL) is the most important leading technology for automatic computer-aided ECG detection of cardiovascular disorders. This work proposed a ResNet-50 model that classifies healthy people and patients with four types of CVD based on ECG abnormalities. The ECG signals were decomposed using a uniform cosine modulated filter bank (UCMFB) that helps in the easy identification of irregular and regular heartbeats. The study was performed on four different types of ECG databases specifically for short segmented (i.e., 2sec and 5sec) and long segmented (5min and 8min) time frames, and these sub-signals are converted into 2-D images using wavelet transform packet (WTP). The extensive tests result in the identification of AF, CHF, HT, and NSR classes with an accuracy, recall, precision, and F1-score of 99.93%, 99.96%, 99.89%, and 99.95%, respectively for multi-class classification. The proposed approach undergoes different fold cross-validation techniques and has achieved high classification accuracy when compared with different state-of-the-art models, demonstrating the superiority of our system over previous systems. It is discovered that the proposed technique achieves decayed computational complexity; thus, it is recommended for categorization challenges. The suggested approach has the potential to gain essential clinical acceptability and be used for ECG prioritisation of CVD detection in clinics and out-of-hospital situations.
Similar content being viewed by others
References
Ibrahim L, Mesinovic M, Yang K W, and Eid M A 2020 Explainable prediction of acute myocardial infarction using machine learning and shapley values. IEEE Access, 8, 210410–210417
Laslett L J, Alagona P, Clark B A, Drozda J P, Saldivar F, Wilson S R and ... Hart M 2012 The worldwide environment of cardiovascular disease: prevalence, diagnosis, therapy, and policy issues: a report from the American College of Cardiology. Journal of the American College of Cardiology, 60(25S), S1–S49
Tabassum N and Ahmad F 2011 Role of natural herbs in the treatment of hypertension. Pharmacognosy reviews, 5(9): 30
Chockalingam A 2008 World Hypertension Day and global awareness. Canadian Journal of Cardiology, 24(6), 441–444
Baker-Smith C M, Flinn S K, Flynn J T, Kaelber D C, Blowey D and Carroll A E, ... Others 2018 Diagnosis, evaluation, and management of high blood pressure in children and adolescents. Pediatrics, 142(3)
Lackland D T and Weber M A 2015 Global burden of cardiovascular disease and stroke: hypertension at the core. Canadian Journal of Cardiology, 31(5), 569–571
Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A and Böhm M ... Others 2014 2013 ESH/ESC practice guidelines for the management of arterial hypertension: ESH-ESC the task force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Blood pressure, 23(1): 3–16
Mann S J 2009 The clinical spectrum of labile hypertension: a management dilemma. The Journal of Clinical Hypertension, 11(9): 491–497
Banegas J R, Ruilope L M, de la Sierra A, de la Cruz J J, Gorostidi M, Segura J and Williams B 2014 High prevalence of masked uncontrolled hypertension in people with treated hypertension. European heart journal, 35(46): 3304–3312
Holt-Lunstad J, Jones B Q and Birmingham W 2009 The influence of close relationships on nocturnal blood pressure dipping. International Journal of Psychophysiology, 71(3): 211–217
Cooperrider D L and Srivastva S 2013 A Contemporary Commentary on Appreciative Inquiry in Organizational LifeAppreciative Inquiry in Organizational Life Cooperrider D and Srivastva S 1987). Appreciative inquiry in organizational life. In: R Woodman and W Pasmore (Eds.), Research in organizational change and development, Vol. 1, pp. 129–169. Organizational generativity: The appreciative inquiry summit and a scholarship of transformation. Emerald Group Publishing Limited
Carey R M and Whelton P K & Committee* 2017 Acc/aha Hypertension Guideline Writing. (2018). Prevention, detection, evaluation, and management of high blood pressure in adults: synopsis of the 2017 American College of Cardiology/American Heart Association Hypertension Guideline. Annals of internal medicine, 168(5): 351—358
Kaptoge S, Pennells L, De Bacquer D, Cooney M T, Kavousi M , Stevens G and... Others 2019 World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. The Lancet Global Health, 7(10): e1332–e1345
Ponikowski P, Voors A A, Anker S D, Bueno H, Cleland J G F , Coats A J S and... Others 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Kardiologia Polska (Polish Heart Journal), 74(10): 1037—1147
Furberg C D, Psaty B M, Manolio T A, Gardin J M, Smith V E, Rautaharju P M and ... Others 1994 Prevalence of atrial fibrillation in elderly subjects (the Cardiovascular Health Study). The American journal of cardiology, 74(3): 236—241
Wolf P A, Abbott R D and Kannel W B 1991 Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. Stroke, 22(8): 983—988
Members A F, Camm A J, Lip G Y H, De Caterina R, Savelieva I, Atar D and ... Others 2012 focused update of the ESC Guidelines for the management of atrial fibrillation: an update of the 2010 ESC Guidelines for the management of atrial fibrillation Developed with the special contribution of the European Heart Rhythm Association. European heart journal, 33(21): 2719—2747
Hijazi Z, Oldgren J, Siegbahn A, Granger C B and Wallentin L 2013 Biomarkers in atrial fibrillation: a clinical review. European heart journal, 34(20): 1475—1480
Faezipour M, Saeed A, Bulusu S C, Nourani M, Minn H and Tamil L 2010 A patient-adaptive profiling scheme for ECG beat classification. IEEE transactions on information technology in biomedicine, 14(5): 1153—1165
Kishi T 2012 Heart failure as an autonomic nervous system dysfunction. Journal of cardiology, 59(2): 117—122
Lee I, Kim D, Kang S and Lee S 2017 Ensemble deep learning for skeleton-based action recognition using temporal sliding lstm networks. Proceedings of the IEEE international conference on computer vision, 1012—1020
LeCun Y, Bengio Y and Hinton G 2015 Deep learning. Nature, 521(7553): 436—444
Hinton G E, Osindero S and Teh Y-W 2006 A fast learning algorithm for deep belief nets. Neural computation, 18(7): 1527—1554
Schmidhuber J 2015 Deep learning in neural networks: An overview. Neural networks, 61: 85—117
Moody G B and Mark R G 1990 The MIT-BIH arrhythmia database on CD-ROM and software for use with it. In: [1990] Proceedings Computers in Cardiology (pp. 185-188). IEEE
Goldberger A L, Amaral L A, Glass L, Hausdorff J M, Ivanov P C, Mark R G, Mietus J E, Moody G B, Peng C K and H E Stanley 2000 PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), e215-e220
Pecchia L, Melillo P, Sansone M and Bracale M 2010 Discrimination power of short-term heart rate variability measures for CHF assessment. IEEE Transactions on Information Technology in biomedicine, 15(1): 40—46
Liu G, Wang L, Wang Q, Zhou G, Wang Y and Jiang Q 2014 A new approach to detect congestive heart failure using short-term heart rate variability measures. PloS one, 9(4): e93399
Cornforth D J and Jelinek H F 2016 Detection of congestive heart failure using Renyi entropy. 2016 Computing in Cardiology Conference (CinC), 669-672. IEEE
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(11), e0165304
Masetic Z and Subasi A 2016 Congestive heart failure detection using random forest classifier. Computer methods and programs in biomedicine, 130: 54—64
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(3): 92
Wang Y, Wei S, Zhang S, Zhang Y, Zhao L, Liu C and Murray A 2018 Comparison of time-domain, frequency-domain and non-linear analysis for distinguishing congestive heart failure patients from normal sinus rhythm subjects. Biomedical Signal Processing and Control, 42: 30—36
Hu B, Wei S, Wei D, Zhao L, Zhu G and Liu C 2019 Multiple time scales analysis for identifying congestive heart failure based on heart rate variability. IEEE Access, 7: 17862—17871
Isler Y, Narin A, Ozer M and Perc M 2019 Multi-stage classification of congestive heart failure based on short-term heart rate variability. Chaos, Solitons & Fractals, 118: 145—151
Melillo P, De Luca N, Bracale M and Pecchia L 2013 Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability. IEEE journal of biomedical and health informatics, 17(3): 727—733
Shahbazi F and Asl B M 2015 Generalized discriminant analysis for congestive heart failure risk assessment based on long-term heart rate variability. Computer methods and programs in biomedicine, 122(2): 191—198
Afkhami R G, Azarnia G and Tinati M A 2016 Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals. Pattern Recognition Letters, 70: 45—51
Costa M D, Davis R B and Goldberger A L 2017 Heart rate fragmentation: a new approach to the analysis of cardiac interbeat interval dynamics. Frontiers in Physiology, 8: 255
Wu Z, Ding X and Zhang G 2016 A novel method for classification of ECG arrhythmias using deep belief networks. International Journal of Computational Intelligence and Applications, 15(04): 1650021
Acharya U R, Oh S L, Hagiwara Y, Tan J H, Adam M, Gertych A and San Tan R 2017 A deep convolutional neural network model to classify heartbeats. Computers in biology and medicine, 89: 389—396
Al Rahhal M M, Bazi Y, AlHichri H, Alajlan N, Melgani F and Yager R R 2016 Deep learning approach for active classification of electrocardiogram signals. Information Sciences, 345: 340–354
Chen W, Liu G, Su S, Jiang Q and Nguyen H 2017 A CHF detection method based on deep learning with RR intervals. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 3369—3372. IEEE
Potes C, Parvaneh S, Rahman A and Conroy B 2016 Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds. 2016 computing in cardiology conference (CinC), 621—624. IEEE
Hwang B, You J, Vaessen T, Myin-Germeys I, Park C and Zhang B T 2018 Deep ECGNet: An optimal deep learning framework for monitoring mental stress using ultra short-term ECG signals. TELEMEDICINE and e-HEALTH, Deep convolutional neural networks and learning ECG features for screening paroxysmal atrial fibrillation patients. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(12): 2095—2104
Altan G Diagnosis of coronary artery disease using deep belief networks. European journal of engineering and natural sciences, 2(1): 29—36
Jamshidi M, Lalbakhsh A, Talla J, Peroutka Z, Hadjilooei F, Lalbakhsh P, M Jamshidi, L La Spada, M Mirmozafari, M Dehghani et al 2020 Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment. IEEE Access, 8: 109581–109595
Azizpour H, Razavian A S, Sullivan J, Maki A and Carlsson S 2015 Factors of transferability for a generic convnet representation. IEEE transactions on pattern analysis and machine intelligence, 38(9): 1790—1802
Krizhevsky A, Sutskever I and Hinton G E 2012 Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25
Simonyan K and Zisserman A 2014 Very deep convolutional networks for large-scale image recognition. arXiv preprintarXiv:1409.1556
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D and ... Rabinovich A 2015 Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, 1–9
Kumar Ashnil, Kim J, Lyndon D, Fulham M and Feng D 2016 An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE journal of biomedical and health informatics, 21(1): 31–40
Salama M S, Eltrass A S and Elkamchouchi H M 2018 An improved approach for computer-aided diagnosis of breast cancer in digital mammography. 2018 IEEE international symposium on medical measurements and applications (MeMeA), 1–5. IEEE
Eltrass A S and Salama M S 2020 Fully automated scheme for computer-aided detection and breast cancer diagnosis using digitised mammograms. IET Image Processing, 14(3): 495—505
Ribas Ripoll V J, Wojdel A, Romero E, Ramos P and Brugada J 2016 ECG assessment based on neural networks with pretraining. Applied Soft Computing, 49: 399—406 https://doi.org/10.1016/j.asoc.2016.08.013
Li Y, Zhang Y, Zhao L, Zhang Y, Liu C, Zhang L and... He Z 2018 Combining Convolutional Neural Network and Distance Distribution Matrix for Identification of Congestive Heart Failure. IEEE Access, 6: 39734—39744. https://doi.org/10.1109/ACCESS.2018.2855420
Kaouter K, Mohamed T, Sofiene D, Abbas D and Fouad M 2019 Full training convolutional neural network for ECG signals classification. AIP Conference Proceedings, 2190(1): 020055. https://doi.org/10.1063/1.5138541
Acharya U R, Fujita H, Oh S L, Hagiwara Y, Tan J H, Adam, M and Tan R S 2019 Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals. Applied Intelligence, 49(1): 16–27
Wang L, Zhou W, Chang Q, Chen J and Zhou X 2019 Deep Ensemble Detection of Congestive Heart Failure Using Short-Term RR Intervals. IEEE Access, 7: 69559—69574. https://doi.org/10.1109/ACCESS.2019.2912226
Happy S L, Mohanty R and Routray A 2017 An effective feature selection method based on pair-wise feature proximity for high dimensional low sample size data. 2017 25th European Signal Processing Conference (EUSIPCO), 1574-1578. IEEE
Duda R O, Hart P E and Stork D G 2000 Pattern Classification. John Wiley & Sons. Inc. , New York, 5
Kumar Aman and Sunkaria R K 2022 Design of uniform cosine modulated filter bank using IACOR-LS and its application in baseline wander removal from ECG signal. AEU-International Journal of Electronics and Communications, 154198
Çınar A and Tuncer S A 2021 Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks. Computer methods in biomechanics and biomedical engineering, 24(2): 203—214
Kumari C U, Murthy A S D, Prasanna B L, Reddy M P P and Panigrahy A K 2021 An automated detection of heart arrhythmias using machine learning technique: SVM. Materials Today: Proceedings, 45: 1393—1398
Nahak S and Saha G 2020 A fusion based classification of normal, arrhythmia and congestive heart failure in ECG. 2020 National Conference on Communications (NCC), 1–6. IEEE
Krak I, Stelia O, Pashko A, Efremov M and Khorozov O 2020 Electrocardiogram classification using wavelet transformations. 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), 930—933. IEEE
Kumari C U, Ankita R, Pavani T, Vignesh N A, Varma N T, Manzar M A and Reethika A 2020 Heart rhythm abnormality detection and classification using machine learning technique. 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), 580—584. IEEE
Eltrass A S, Tayel M B and Ammar A I 2022 Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures. Neural Computing and Applications, 1–21
Degirmenci M, Ozdemir M A, Izci E and Akan A 2021 Arrhythmic heartbeat classification using 2d convolutional neural networks. Irbm
Yamashita R, Nishio M, Do R K G and Togashi K 2018 Convolutional neural networks: an overview and application in radiology. Insights into imaging, 9(4): 611–629
Lu L, Wang X, Carneiro G and Yang L Eds 2019 Deep learning and convolutional neural networks for medical imaging and clinical informatics (pp. 69-91). Berlin/Heidelberg, Germany: Springer
Thakur S and Kumar A 2021 X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN). Biomedical Signal Processing and Control, 69: 102920
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mohan Rao, B., Kumar, A. Detection of heart arrhythmia based on UCMFB and deep learning technique. Sādhanā 47, 252 (2022). https://doi.org/10.1007/s12046-022-02027-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12046-022-02027-6