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Automatic Detection of Heart Diseases Using Biomedical Signals: A Literature Review of Current Status and Limitations

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Advances in Information and Communication (FICC 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 439))

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Abstract

Heart diseases impact disproportionately the low-income portion of any society. The lack of a sufficient number of physicians as well as expensive diagnostic procedures brings forth the necessity of automatic preliminary detection of heart disease and symptoms. The low cost of acquiring biomedical signals, low cost of high performance computing platforms, advances in signal processing, and the rapid improvement in machine learning and deep learning make it possible for the development of automatic heart monitoring and diagnosis techniques. This can be used as medical support for practicing physicians as well as part of home health monitoring at the convenience of a patient premise in the future. Presently, research is going in the area of automatic heart disease diagnosis. However, only a very small number of reliable devices are available commercially that monitor vital signs and provide a very basic warning about heart abnormalities. Affordable automatic detection of a wide range of heart conditions poses significant challenges to overcome. This review paper focuses on the existing and emerging techniques to automatically detect heart diseases and symptoms. Finally, the current challenges which needed to be dealt with are brought forth along with some suggestive approaches to overcome existing limitations.

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References

  1. Litterini, A.J., Wilson, C.M.: Diseases of the heart. In: Physical Activity and Rehabilitation in Life-Threatening Illness, pp. 113–118. Routledge (2021)

    Google Scholar 

  2. Almustafa, K.M.: Prediction of heart disease and classifiers’ sensitivity analysis. BMC Bioinf. 21(1), 1–18 (2020)

    Google Scholar 

  3. Grimson, J., Stephens, G., Jung, B., Grimson, W., Berry, D., Pardon, S.: Sharing health-care records over the internet. IEEE Internet Comput. 5(3), 49–58 (2001)

    Google Scholar 

  4. Daniels, M., Schroeder, S.A.: Variation among physicians in use of laboratory tests II. Relation to clinical productivity and outcomes of care. Med Care 15, 482–487 (1977)

    Google Scholar 

  5. Wennberg, J.E.: Dealing with medical practice variations: a proposal for action. Health Aff. 3(2), 6–33 (1984)

    MathSciNet  Google Scholar 

  6. Stuart, P.J., Crooks, S., Porton, M.: An interventional program for diagnostic testing in the emergency department. Med. J. Aust. 177(3), 131–134 (2002)

    Google Scholar 

  7. Pölsterl, S., Conjeti, S., Navab, N., Katouzian, A.: Survival analysis for high-dimensional, heterogeneous medical data: exploring feature extraction as an alternative to feature selection. Artif. Intell. Med. 72, 1–11 (2016)

    Google Scholar 

  8. Mamun, M.M.R.K., Alouani, A.T.: Myocardial infarction detection using multi biomedical sensors, pp. 117–122 (2018)

    Google Scholar 

  9. Hu, J., Cui, X., Gong, Y., et al.: Portable microfluidic and smartphone-based devices for monitoring of cardiovascular diseases at the point of care. Biotechnol. Adv. 34(3), 305–320 (2016)

    Google Scholar 

  10. Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)

    MathSciNet  MATH  Google Scholar 

  11. Raschka, S., Mirjalili, V.: Python Machine Learning, 2nd edn. Packt Publishing Ltd., Birmingham (2017)

    Google Scholar 

  12. Wang, W., Krishnan, E.: Big data and clinicians: a review on the state of the science. JMIR Med. Inf. 2(1), e1 (2014)

    Google Scholar 

  13. Scruggs, S.B., Watson, K., Su, A.I., et al.: Harnessing the heart of big data. Circ Res. 116(7), 1115–1119 (2015)

    Google Scholar 

  14. Bello, H.C.A., et al.: Oximetry and neonatal examination for the detection of critical congenital heart disease: a systematic review and meta-analysis. F1000Research 8, 242 (2019)

    Google Scholar 

  15. Gnaneswar, B., Jebarani, M.E.: A review on prediction and diagnosis of heart failure. In: 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1–3 (2017)

    Google Scholar 

  16. Telford, L.H., Abdullahi, L.H., Ochodo, E.A., Zühlke, L.J., Engel, M.E.: Standard echocardiography versus handheld echocardiography for the detection of subclinical rheumatic heart disease: protocol for a systematic review. BMJ Open 8(2), e020140 (2018). https://doi.org/10.1136/bmjopen-2017-020140

    Article  Google Scholar 

  17. Yahaya, L., Oye, N.D., Garba, E.J.: A comprehensive review on heart disease prediction using data mining and machine learning techniques. Am. J. Artif. Intell. 4(1), 20–29 (2020)

    Google Scholar 

  18. Nabih-Ali, M., El-Dahshan, E.A., Yahia, A.S.:“A review of intelligent systems for heart sound signal analysis. J. Med. Eng. Technol. 41(7), 553–563 (2017)

    Google Scholar 

  19. Gaies, M., Anderson, J., Kipps, A., et al.: Cardiac networks united: an integrated paediatric and congenital cardiovascular research and improvement network. Cardiol. Young 29(2), 111–118 (2019)

    Google Scholar 

  20. Liberati, A., Altman, D.G., Tetzlaff, J., et al.: The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J. Clin. Epidemiol. 62(10), e1–e34 (2009)

    Google Scholar 

  21. Serruys, P.W., Morice, M., Kappetein, A.P., et al.: Percutaneous coronary intervention versus coronary-artery bypass grafting for severe coronary artery disease. N. Engl. J. Med. 360(10), 961–972 (2009)

    Google Scholar 

  22. Nasarian, E., Abdar, M., Fahami, M.A., et al.: Association between work-related features and coronary artery disease: a heterogeneous hybrid feature selection integrated with balancing approach. Pattern Recogn. Lett. 133, 33–40 (2020)

    Google Scholar 

  23. Garcia, E.V., Cooke, C.D., Folks, R.D., et al.: Diagnostic performance of an expert system for the interpretation of myocardial perfusion SPECT studies. J. Nucl. Med. 42(8), 1185–1191 (2001)

    Google Scholar 

  24. Fitzmaurice, C., Allen, C., Barber, R.M., et al.: Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: a systematic analysis for the global burden of disease study. JAMA Oncol. 3(4), 524–548 (2017)

    Google Scholar 

  25. Palaniappan, S., Awang, R.: Intelligent heart disease prediction system using data mining techniques, pp. 108–115 (2008)

    Google Scholar 

  26. Abidov, A., Bax, J.J., Hayes, S.W., et al.: Integration of automatically measured transient ischemic dilation ratio into interpretation of adenosine stress myocardial perfusion SPECT for detection of severe and extensive CAD. J. Nucl. Med. 45(12), 1999–2007 (2004)

    Google Scholar 

  27. Alizadehsani, R., Abdar, M., Roshanzamir, M., et al.: Machine learning-based coronary artery disease diagnosis: a comprehensive review. Comput. Biol. Med. 111, 103346 (2019)

    Google Scholar 

  28. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Google Scholar 

  29. Hecht-Nielsen, R.: Theory of the backpropagation neural network. In: 1989 International Joint Conference on Neural Network (IJCNN) (1989)

    Google Scholar 

  30. Avci, E.: A new intelligent diagnosis system for the heart valve diseases by using genetic-SVM classifier. Exp. Syst Appl. 36(7), 10618–10626 (2009)

    Google Scholar 

  31. Fei, S.: Diagnostic study on arrhythmia cordis based on particle swarm optimization-based support vector machine. Exp. Syst Appl. 37(10), 6748–6752 (2010)

    Google Scholar 

  32. Singh, G., ManjotKaur, E.: A review paper: decision tree algorithms for diagnosis of angioplasty and stents for heart disease treatment. Int. J. Eng. Sci. 7, 6643–6645 (2017)

    Google Scholar 

  33. Thenmozhi, K., Deepika, P.: Heart disease prediction using classification with different decision tree techniques. Int. J. Eng. Res. Gen. Sci. 2(6), 6–11 (2014)

    Google Scholar 

  34. Soni, J., Ansari, U., Sharma, D., Soni, S.: Predictive data mining for medical diagnosis: An overview of heart disease prediction. Int. J. Comput. Appl. 17(8), 43–48 (2011)

    Google Scholar 

  35. Pattekari, S.A., Parveen, A.: Prediction system for heart disease using naïve bayes. Int. J. Adv. Comput. Math. Sci. 3(3), 290–294 (2012)

    Google Scholar 

  36. Chaurasia, V., Pal, S.: Early prediction of heart diseases using data mining techniques. Carib. J. Sci. Technol. 1, 208–217 (2013)

    Google Scholar 

  37. Sciarretta, S., Palano, F., Tocci, G., Baldini, R., Volpe, M.: Antihypertensive treatment and development of heart failure in hypertension: a Bayesian network meta-analysis of studies in patients with hypertension and high cardiovascular risk. Arch. Intern. Med. 171(5), 384–394 (2011)

    Google Scholar 

  38. Dimopoulos, A.C., Nikolaidou, M., Caballero, F.F., et al.: Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk. BMC Med. Res. Methodol. 18(1), 1–11 (2018)

    Google Scholar 

  39. Kannan, R., Vasanthi, V.: Machine learning algorithms with ROC curve for predicting and diagnosing the heart disease. In: Soft Computing and Medical Bioinformatics, pp. 63–72. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0059-2_8

    Chapter  Google Scholar 

  40. Althoff, K.N., McGinnis, K.A., Wyatt, C.M., et al.: Comparison of risk and age at diagnosis of myocardial infarction, end-stage renal disease, and non-AIDS-defining cancer in HIV-infected versus uninfected adults. Clin. Infect. Dis. 60(4), 627–638 (2015)

    Google Scholar 

  41. Jen, C., Wang, C., Jiang, B.C., Chu, Y., Chen, M.: Application of classification techniques on development an early-warning system for chronic illnesses. Exp. Syst Appl. 39(10), 8852–8858 (2012)

    Google Scholar 

  42. Mamun, M.M.R.K., Alouani, A.: Using feature optimization and fuzzy logic to detect hypertensive heart diseases (2020)

    Google Scholar 

  43. Dogan, M.V., Beach, S.R., Simons, R.L., Lendasse, A., Penaluna, B., Philibert, R.A.: Blood-based biomarkers for predicting the risk for five-year incident coronary heart disease in the framingham heart study via machine learning. Genes 9(12), 641 (2018)

    Google Scholar 

  44. Yuan, Z., Lu, Y., Wang, Z., Xue, Y.: Droid-Sec: deep learning in Android malware detection, pp. 371–372 (2014)

    Google Scholar 

  45. Rath, A., Mishra, D., Panda, G.: LSTM-based cardiovascular disease detection using ECG signal. In: Mallick, P.K., Bhoi, A.K., Marques, G., Victor, H.C., de Albuquerque, (eds.) Cognitive Informatics and Soft Computing. AISC, vol. 1317, pp. 133–142. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-1056-1_12

    Chapter  Google Scholar 

  46. Nguyen, T., Nguyen, T.: Deep learning framework with ECG feature-based kernels for heart disease classification. Elektronika ir Elektrotechnika. 27(1), 48–59 (2021)

    Google Scholar 

  47. Lopes, R.R., et al.: Improving electrocardiogram-based detection of rare genetic heart disease using transfer learning: an application to phospholamban p.Arg14del mutation carriers. Comput. Biol. Med. 131, 104262 (2021)

    Google Scholar 

  48. Tyagi, A., Mehra, R.: Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA. SN Appl. Sci. 3(2), 1–14 (2021)

    Google Scholar 

  49. Yu-Sheng, S., Ding, T.-J., Chen, M.-Y.: Deep learning methods in internet of medical things for valvular heart disease screening system. IEEE Internet Things J. 8(23), 16921–16932 (2021)

    Google Scholar 

  50. Mori, H., Inai, K., Sugiyama, H., Muragaki, Y.: Diagnosing atrial septal defect from electrocardiogram with deep learning. Pediatr. Cardiol. 42(6), 1379–1387 (2021)

    Google Scholar 

  51. Xiong, P., Xue, Y., Zhang, J., et al.: Localization of myocardial infarction with multi-lead ECG based on DenseNet. Comput. Meth. Program. Biomed. 203, 106024 (2021)

    Google Scholar 

  52. Shin, D., Park, R.C., Chung, K.: Decision boundary-based anomaly detection model using improved AnoGAN from ECG data. IEEE Access 8, 108664–108674 (2020)

    Google Scholar 

  53. Song, W.: A new method for refined recognition for heart disease diagnosis based on deep learning. Information 11(12), 556 (2020)

    Google Scholar 

  54. Mamun, M.M.K., Alouani, A.: FA-1D-CNN implementation to improve diagnosis of heart disease risk level, pp. 122.1–122.9 (2020)

    Google Scholar 

  55. Baccouche, A., Garcia-Zapirain, B., Castillo Olea, C., Elmaghraby, A.: Ensemble deep learning models for heart disease classification: a case study from Mexico. Information 11(4), 207 (2020)

    Google Scholar 

  56. Ali, F., El-Sappagh, S., Islam, S.R., et al.: A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Inf. Fusion 63, 208–222 (2020)

    Google Scholar 

  57. Vullings, R.: Fetal electrocardiography and deep learning for prenatal detection of congenital heart disease, pp. 1–4 (2019)

    Google Scholar 

  58. Darmawahyuni, A., Nurmaini, S.: Deep learning with long short-term memory for enhancement myocardial infarction classification, pp. 19–23 (2019)

    Google Scholar 

  59. 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. 122, 23–30 (2019)

    Google Scholar 

  60. Al-Makhadmeh, Z., Tolba, A.: Utilizing IoT wearable medical device for heart disease prediction using higher order boltzmann model: a classification approach. Measurement 147, 106815 (2019)

    Google Scholar 

  61. Nguyen, T.-H., Nguyen, T.-N., Nguyen, T.-T.: A deep learning framework for heart disease classification in an IoTs-based system. In: Balas, V.E., Solanki, V.K., Kumar, M.R., Ahad, A.R. (eds.) A Handbook of Internet of Things in Biomedical and Cyber Physical System, pp. 217–244. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-23983-1_9

    Chapter  Google Scholar 

  62. Moody, G.B., Mark, R.G., Goldberger, A.L.: PhysioNet: a web-based resource for the study of physiologic signals. IEEE Eng. Med. Biol. 20, 70–75 (2001)

    Google Scholar 

  63. Choi, E., Bahadori, M.T., Kulas, J.A., Schuetz, A., Stewart, W.F., Sun, J.: RETAIN: an interpretable predictive model for healthcare using reverse time attention mechanism. arXiv preprint arXiv:1608.05745 (2016)

  64. Hong, S., Xiao, C., Ma, T., Li, H., Sun, J.: MINA: multilevel knowledge-guided attention for modeling electrocardiography signals. arXiv preprint arXiv:1905.11333 (2019)

  65. Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579–2605 (2008)

    MATH  Google Scholar 

  66. Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should i trust you?” explaining the predictions of any classifier, pp. 1135–1144 (2016)

    Google Scholar 

  67. Ribeiro, M.T., Singh, S., Guestrin, C.: Anchors: high-precision model-agnostic explanations, vol. 32, no. 1 (2018)

    Google Scholar 

  68. Shashikumar, S.P., Shah, A.J., Clifford, G.D., Nemati, S.: Detection of paroxysmal atrial fibrillation using attention-based bidirectional recurrent neural networks, pp. 715–723 (2018)

    Google Scholar 

  69. Strodthoff, N., Strodthoff, C.: Detecting and interpreting myocardial infarction using fully convolutional neural networks. Physiol. Meas. 40(1), 015001 (2019)

    Google Scholar 

  70. Li, R., Zhang, X., Dai, H., Zhou, B., Wang, Z.: Interpretability analysis of heartbeat classification based on heartbeat activity’s global sequence features and BiLSTM-attention neural network. IEEE Access 7, 109870–109883 (2019)

    Google Scholar 

  71. Manyika, J., Chui, M., Brown, B., et al.: Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute (2011)

    Google Scholar 

  72. Gajardo, A.I., Henríquez, F., Llancaqueo, M.: Big data, social determinants of coronary heart disease and barriers for data access. Eur. J. Prev. Cardiol. 28, 397–399 (2021)

    Google Scholar 

  73. Saluja, M.K., Agarwal, I., Rani, U., Saxena, A.: Analysis of diabetes and heart disease in big data using MapReduce framework. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds.) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1165, pp. 37–51. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5113-0_3

  74. Qaffas, A.A., Hoque, R., Almazmomi, N.: The internet of things and big data analytics for chronic disease monitoring in Saudi Arabia. Telemed. e-Health 27(1), 74–81 (2021)

    Google Scholar 

  75. Ismail, A., Abdlerazek, S., El-Henawy, I.: Big data analytics in heart diseases prediction. J. Theoret. Appl. Inf. Technol. 98(11), 15–19 (2020)

    Google Scholar 

  76. Leopold, J.A., Maron, B.A., Loscalzo, J.: The application of big data to cardiovascular disease: paths to precision medicine. J. Clin. Invest. 130(1), 29–38 (2020)

    Google Scholar 

  77. Yang, Y.: Medical multimedia big data analysis modeling based on DBN algorithm. IEEE Access 8, 16350–16361 (2020)

    Google Scholar 

  78. Zhou, C., Li, A., Zhang, Z., Zhang, Z., Haiping, Q.: A cloud-based platform for ECG monitoring and early warning using big data and artificial intelligence technologies. In: Nah, Y., Kim, C., Kim, S.-Y., Moon, Y.-S., Whang, S.E. (eds.) Database Systems for Advanced Applications. DASFAA 2020 International Workshops: BDMS, SeCoP, BDQM, GDMA, and AIDE, Jeju, South Korea, September 24–27, 2020, Proceedings, pp. 60–72. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59413-8_5

    Chapter  Google Scholar 

  79. Ed-Daoudy, A., Maalmi, K.: Real-time machine learning for early detection of heart disease using big data approach, pp. 1–5 (2019)

    Google Scholar 

  80. Nayak, S., Gourisaria, M.K., Pandey, M., Rautaray, S.S.: Comparative analysis of heart disease classification algorithms using big data analytical tool. In: Smys, S., Senjyu, T., Lafata, P. (eds.) Second International Conference on Computer Networks and Communication Technologies: ICCNCT 2019, pp. 582–588. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37051-0_65

    Chapter  Google Scholar 

  81. Anandajayam, P., Krishnakoumar, C., Vikneshvaran, S., Suryanaraynan, B.: Coronary heart disease predictive decision scheme using big data and RNN, pp. 1–6 (2019)

    Google Scholar 

  82. Nair, L.R., Shetty, S.D., Shetty, S.D.: Applying spark based machine learning model on streaming big data for health status prediction. Comput. Electr. Eng. 65, 393–399 (2018)

    Google Scholar 

  83. Salman, O.H., Zaidan, A.A., Zaidan, B.B., Naserkalid, Hashim, M.: Novel methodology for triage and prioritizing using “big data” patients with chronic heart diseases through telemedicine environmental. Int. J. Inf. Technol. Decis. Making 16(05):1211–1245 (2017)

    Google Scholar 

  84. Akhbarifar, S., Javadi, H.H.S., Rahmani, A.M., Hosseinzadeh, M.: A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment. Pers. Ubiquit. Comput., 1–17 (2020).https://doi.org/10.1007/s00779-020-01475-3

  85. Ahmed, M.R., Mahmud, S.H., Hossin, M.A., Jahan, H., Noori, S.R.H.: A cloud based four-tier architecture for early detection of heart disease with machine learning algorithms, pp. 1951–1955 (2018)

    Google Scholar 

  86. Verma, P., Sood, S.K.: Cloud-centric IoT based disease diagnosis healthcare framework. J. Parallel Distrib. Comput. 116, 27–38 (2018)

    Google Scholar 

  87. Nguyen, T.-H., Nguyen, T.-N., Nguyen, T.-T.: A deep learning framework for heart disease classification in an IoTs-based system. In: Balas, V.E., Solanki, V.K., Kumar, R., Ahad, M.A.R. (eds.) A Handbook of Internet of Things in Biomedical and Cyber Physical System. ISRL, vol. 165, pp. 217–244. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-23983-1_9

    Chapter  Google Scholar 

  88. Ganesan, M., Sivakumar, N.: IoT based heart disease prediction and diagnosis model for healthcare using machine learning models, pp. 1–5 (2019)

    Google Scholar 

  89. Kassé, B., Gueye, B., Diallo, M., Santatra, F., Elbiaze, H.: IoT based schistosomiasis monitoring for more efficient disease prediction and control model, pp. 1–6 (2019)

    Google Scholar 

  90. Parthasarathy, P., Vivekanandan, S.: A typical IoT architecture-based regular monitoring of arthritis disease using time wrapping algorithm. Int. J. Comput. Appl. 42(3), 222–232 (2020)

    Google Scholar 

  91. Banaee, H., Ahmed, M.U., Loutfi, A.: Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors 13(12), 17472–17500 (2013)

    Google Scholar 

  92. Yang, Z., Zhou, Q., Lei, L., Zheng, K., Xiang, W.: An IoT-cloud based wearable ECG monitoring system for smart healthcare. J. Med. Syst. 40(12), 1–11 (2016)

    Google Scholar 

  93. Bhatia, M., Sood, S.K.: Temporal informative analysis in smart-ICU monitoring: M-HealthCare perspective. J. Med. Syst. 40(8), 1–15 (2016)

    Google Scholar 

  94. Gope, P., Hwang, T.: BSN-care: a secure IoT-based modern healthcare system using body sensor network. IEEE Sens. J. 16(5), 1368–1376 (2015)

    Google Scholar 

  95. Serhani, M.A., El Kassabi, H.T., Ismail, H., Navaz, A.N.: ECG monitoring systems: Review, architecture, processes, and key challenges. Sensors 20(6), 1796 (2020)

    Google Scholar 

  96. Elliott, K.: Diagnosis and management of patients with atrial fibrillation. Nurs. Stan. 33(2), 43–49 (2018)

    Google Scholar 

  97. Petmezas, G., Haris, K., Stefanopoulos, L., et al.: Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets. Biomed. Sig. Process. Control 63, 102194 (2021)

    Google Scholar 

  98. Boriani, G., Palmisano, P., Malavasi, V.L., et al.: Clinical factors associated with atrial fibrillation detection on single-time point screening using a hand-held single-lead ECG device. J. Clin. Med. 10(4), 729 (2021)

    Google Scholar 

  99. Tuboly, G., Kozmann, G., Kiss, O., Merkely, B.: Atrial fibrillation detection with and without atrial activity analysis using lead-I mobile ECG technology. Biomed. Sig. Process. Control 66, 102462 (2021)

    Google Scholar 

  100. Chen, X., Cheng, Z., Wang, S., et al.: Atrial fibrillation detection based on multi-feature extraction and convolutional neural network for processing ECG signals. Comput. Meth. Program. Biomed. 202, 106009 (2021)

    Google Scholar 

  101. Zhang, X., Li, J., Cai, Z., Zhang, L., Chen, Z., Liu, C.: Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection. Med. Biol. Eng. Comput. 59(1), 165–173 (2021)

    Google Scholar 

  102. Linz, D., Hermans, A., Tieleman, R.G.: Early atrial fibrillation detection and the transition to comprehensive management. EP Europace 23(Supplement_2), ii46–ii51 (2021)

    Google Scholar 

  103. Nguyen, Q.H., Nguyen, B.P., Nguyen, T.B., Do, T.T., Mbinta, J.F., Simpson, C.R.: Stacking segment-based CNN with SVM for recognition of atrial fibrillation from single-lead ECG recordings. Biomed. Sig. Process. Control 68, 102672 (2021)

    Google Scholar 

  104. Marsili, I.A., Biasiolli, L., Masè, M., et al.: Implementation and validation of real-time algorithms for atrial fibrillation detection on a wearable ECG device. Comput. Biol. Med. 116, 103540 (2020)

    Google Scholar 

  105. Mousavi, S., Afghah, F., Acharya, U.R.: HAN-ECG: an interpretable atrial fibrillation detection model using hierarchical attention networks. Comput. Biol. Med. 127, 104057 (2020)

    Google Scholar 

  106. Wu, X., Zheng, Y., Chu, C., He, Z.: Extracting deep features from short ECG signals for early atrial fibrillation detection. Artif. Intell. Med. 109, 101896 (2020)

    Google Scholar 

  107. Jin, Y., Qin, C., Liu, J., et al.: A novel domain adaptive residual network for automatic atrial fibrillation detection. Knowl. Based Syst. 203, 106122 (2020)

    Google Scholar 

  108. Hammad, M., Alkinani, M.H., Gupta, B., Abd El-Latif, A.A.: Myocardial infarction detection based on deep neural network on imbalanced data. Multimedia Syst., 1–13 (2021). https://doi.org/10.1007/s00530-020-00728-8

  109. Rai, H.M., Chatterjee, K., Dubey, A., Srivastava, P.: Myocardial infarction detection using deep learning and ensemble technique from ECG signals. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Ganzha, M., Rodrigues, J.J.P.C. (eds.) Proceedings of Second International Conference on Computing, Communications, and Cyber-Security: IC4S 2020, pp. 717–730. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-0733-2_51

    Chapter  Google Scholar 

  110. Martin, H., Izquierdo, W., Cabrerizo, M., Cabrera, A., Adjouadi, M.: Near real-time single-beat myocardial infarction detection from single-lead electrocardiogram using long short-term memory neural network. Biomed. Sig. Process. Control 68, 102683 (2021)

    Google Scholar 

  111. Odema, M., Rashid, N., Al Faruque, M.A.: Energy-aware design methodology for myocardial infarction detection on low-power wearable devices, pp. 621–626 (2021)

    Google Scholar 

  112. Sridhar, C., et al.: Accurate detection of myocardial infarction using non linear features with ECG signals. J. Ambient. Intell. Humaniz. Comput. 12(3), 3227–3244 (2020). https://doi.org/10.1007/s12652-020-02536-4

    Article  Google Scholar 

  113. Jahmunah, V., Ng, E., San, T.R., Acharya, U.R.: Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals. Comput. Biol. Med. 134, 104457 (2021)

    Google Scholar 

  114. Han, C., Shi, L.: ML–ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG. Comput. Meth. Program. Biomed. 185, 105138 (2020)

    Google Scholar 

  115. Çınar, A., Tuncer, S.A.: Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks. Comput. Meth. Biomech. Biomed. Eng. 24(2), 203–214 (2021)

    Google Scholar 

  116. Lei, M., Li, J., Li, M., Zou, L., Yu, H.: An improved UNet model for congestive heart failure diagnosis using short-term RR intervals. Diagnostics 11(3), 534 (2021)

    Google Scholar 

  117. Xiong, J., Liang, X., Zhao, L., Lo, B., Li, J., Liu, C.: Improving accuracy of heart failure detection using data refinement. Entropy 22(5), 520 (2020)

    Google Scholar 

  118. Suganthi, S., Vijipriya, G., Madian, N.: An approach for predicting heart failure rate using IBM auto AI service, pp. 203–207 (2021)

    Google Scholar 

  119. Porumb, M., Iadanza, E., Massaro, S., Pecchia, L.: A convolutional neural network approach to detect congestive heart failure. Biomed. Sig. Process. Control 55, 101597 (2020)

    Google Scholar 

  120. Yang, W., Si, Y., Wang, D., Zhang, G., Liu, X., Li, L.: Automated intra-patient and inter-patient coronary artery disease and congestive heart failure detection using EFAP-net. Knowl. Based Syst. 201, 106083 (2020)

    Google Scholar 

  121. Li, D., Tao, Y., Zhao, J., Wu, H.: Classification of congestive heart failure from ECG segments with a multi-scale residual network. Symmetry 12(12), 2019 (2020)

    Google Scholar 

  122. Nahak, S., Saha, G.: A fusion based classification of normal, arrhythmia and congestive heart failure in ECG, pp. 1–6 (2020)

    Google Scholar 

  123. Acharya, U.R., et al.: Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals. Appl. Intell. 49(1), 16–27 (2018). https://doi.org/10.1007/s10489-018-1179-1

    Article  Google Scholar 

  124. Cutillo, C.M., Sharma, K.R., Foschini, L., Kundu, S., Mackintosh, M., Mandl, K.D.: Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency. npj Digit. Med. 3(1), 47 (2020)

    Google Scholar 

  125. Katarya, R., Srinivas, P.: Predicting heart disease at early stages using machine learning: a survey. In: 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 302–305 (2020)

    Google Scholar 

  126. Yildirim, K.S., Kantarci, A.: Time synchronization based on slow-flooding in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 25, 244–253 (2014)

    Google Scholar 

  127. Diedrichs, A.L., Tabacchi, G., Grunwaldt, G., Pecchia, M., Mercado, G., Antivilo, F.G.: Low-power wireless sensor network for frost monitoring in agriculture research. In: Proceedings of the 2014 IEEE Biennial Congress of Argentina (ARGENCON), Bariloche, Argentina, 11–13 June 2014, pp. 525–530 (2014)

    Google Scholar 

  128. Lenzen, C., Sommer, P., Wattenhofer, R.: PulseSync: an efficient and scalable clock synchronization protocol. IEEE/ACM Trans. Netw. 23, 717–727 (2015)

    Google Scholar 

  129. Masood, W., Schmidt, J.F.: Autoregressive integrated model for time synchronization in wireless sensor networks. In: Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, Cancun, Mexico, 2–6 November 2015, pp. 133–140 (2015)

    Google Scholar 

  130. Chatterjee, A., Venkateswaran, P.: An efficient statistical approach for time synchronization in wireless sensor networks. Int. J. Commun. Syst. 29, 757–768 (2016)

    Google Scholar 

  131. Masood, W., Schmidt, J.F., Brandner, G., Bettstetter, C.: DISTY: dynamic stochastic time synchronization for wireless sensor networks. IEEE Trans. Ind. Inform. 13, 1421–1429 (2017)

    Google Scholar 

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  1. Tennessee Technological University, Cookeville, TN, 38505, USA

    Mohammad Mahbubur Rahman Khan Mamun & Ali Alouani

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  1. Mohammad Mahbubur Rahman Khan Mamun
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  1. Saga University, Saga, Japan

    Kohei Arai

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Khan Mamun, M.M.R., Alouani, A. (2022). Automatic Detection of Heart Diseases Using Biomedical Signals: A Literature Review of Current Status and Limitations. In: Arai, K. (eds) Advances in Information and Communication. FICC 2022. Lecture Notes in Networks and Systems, vol 439. Springer, Cham. https://doi.org/10.1007/978-3-030-98015-3_29

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