Abstract
One of the fatal diseases in the world is heart disease. Every year, millions of people die of cardiovascular diseases. However, one can decrease the mortality rates if the heart disease was detected and treated early. Usually, people do an electrocardiogram (ECG) test to know about the well-being of their heart. Some kind of irregular functioning and illness in the heart can be found in an ECG test. When the heart malfunctions or if there is any improper beating of the heart, then it results in arrhythmia. There are several types of arrhythmia and some of them are fatal. The process to identify the correct type of arrhythmia is quite difficult and effort-taking process. Even the small changes in the ECG relate to another kind of arrhythmia. It takes experience and patience to recognize the type of arrhythmia accurately. Therefore, deep learning techniques should be employed to analyze the test. Machine learning that involves many levels of processing is known as deep learning. From computer vision to natural language processing, there’s a lot to learn. It has been used in various applications. This method is receiving more popularity because of extreme accuracy, provided the numerous amount of data. The interesting feature is that it analyses the examples and distinguishes the classes and levels automatically. This study is regarding arrhythmia prediction in ECG and the attention it deserves in deep learning community. Providing CNN model, we are going to elaborate the process of detecting cardiac arrhythmia using ECG dataset in this study. The model is executed by rendering CNN with cardiac arrhythmia recognition database. Purpose: About one-third of the world’s population is affected by arrhythmia. Hence, the development of new and successful methodologies is highly in demand in the field of arrhythmia prediction. Further, the need of a cost-effective wearable monitoring gadget to identify the condition of arrhythmia is highly recommended. It assures the trouble-free environment for those who are affected. Observations: Various research papers that were written bases on arrhythmia prediction using machine learning techniques. Additionally, there are also new advancements all over Internet regarding deep learning-based strategies. These strategies can bring an immense change in cardiac arrhythmia prediction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Annam JR et al. (2020) Classification of ECG heartbeat arrhythmia: a review. Proc Comput Sci
Aslam M, Jaisharma K (Jan 2021) Hierarchical random forest formation with nonlinear regression model for cardiovascular diseases prediction. In: International conference on computer communication and informatics (ICCCI)
Yuepeng L, Mengfei Z, Zezhong F, Yinghan C (Dec 2020) Heart disease prediction based on random forest and LSTM. In: 2nd international conference on information technology and computer application (ITCA)
Sah RD, patro SP, Padhy N, Salimath N (Mar 2021) Diabetics patients analysis using deep learning and gradient boosted trees. In: 8th International conference on computing for sustainable global development (INDIACom)
Swapna G et al. (2018) Automated detection of cardiac arrhythmia using deep learning techniques. In: International conference on computational intelligence and data science (ICCIDS)
Qaddoum K, Alazzam A, Al Azawi R (2020) A deep neural network heartbeat classification approach for arrhythmia detection. In: Seventh international conference on information technology trends (ITT)
Kavitha M, Gnaneswar G, Dinesh R, Rohith Sai Y, Sai Suraj R (Jan 2021) Heart disease prediction using hybrid machine learning model. In: 6th International conference on inventive computation technologies (ICICT)
Srinidhi H, S GM, Kiran MHM, Srinivasa KG (2021) A comparative study and analysis of LSTM deep neural networks for heartbeats classification. Health Technol 11:663–671. Djerioui M, Brik Y, Bilal A (Sep 2020) Heart disease prediction using MLP and LSTM models. In: International conference on electrical engineering (ICEE)
Shadab H, Susmith B, Shadab A, Suaib M (May 2021) Novel deep learning architecture for heart disease prediction using convolutional neural network. Mach Lear
Sujatha P, Mahalakshmi K (Oct 2020) Performance evaluation of supervised machine learning algorithms in prediction of heart disease. In: IEEE International conference for innovation in technology (INOCON)
Fitriyani NL, Muhammad S, Ganjar A, Rhee J (July 2020) HDPM: An effective heart disease prediction model for a clinical decision support system. IEEE Access 8:133034–133050
Sarah S, Yasmeen A, Alsahali S, Asalam N (2020) Automated prediction of coronary artery disease using random forest and naïve bayes. In: International conference on advanced computer science and information systems (ICACSIS)
Djerioui M, Brik Y, Bilal A (Sep 2020) Heart disease prediction using MLP and LSTM models. In: International conference on electrical engineering (ICEE), Pranav M, Ankita D, Suganya G, Premalatha M (2020) Cognitive approach for heart disease prediction using machine learning. In: International conference on emerging trends in information technology and engineering (ic-ETITE)
El H, Boujraf S, Chaoui NEH, Maaroufi M (Sept 2020) A clinical support system for prediction of heart disease using machine learning techniques. In: 5th International conference on advanced technologies for signal and image processing (ATSIP)
Sharma V, Rasool A, Hajela G (2020) Prediction of heart disease using DNN. In: Second international conference on inventive research in computing applications (ICIRCA)
Toomula S, Paulraj D, Bose J, Bikku T, Sivabalaselvamani D (2022) IoT and wearables for detection of COVID-19 diagnosis using fusion-based feature extraction with multikernel extreme learning machine. In Wearable Telemedicine Technology for the Healthcare Industry (pp. 137-152). Academic Press.
Adeen P, Sondhi (February 2021) Random forest based heart disease prediction. Int J Sci Res (IJSR) 10(2)
Ambekar S, Phalnikar R (2018) Disease risk prediction by using convolutional neural network. In: Fourth international conference on computing communication control and automation (ICCUBEA)
Nanthini K, Dr Tamilarasi A, Dr Pyingkodi M, Dishanthi M, Kaviya SM, Aslam Mohideen P (2022) Epileptic seizure detection and prediction using deep learning technique, International Conference on Computer Communication and Informatics (ICCCI)
Sivabalaselvamani D, Selvakarthi D, Rahunathan L, Eswari SN, Pavithraa M, Sridhar M (2021 January) Investigation on heart disease using machine learning algorithms. In 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp 1–6). IEEE
Sivabalaselvamani D, Selvakarthi D, Rahunathan L, Gayathri G, Baskar MM (2021 December) Survey on improving health care system by implementing an air ambulance system with the support of drones. In 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp 878–883). IEEE
Hemalatha S, Tamilarasi A, Kavitha T, Sivabalaselvamani D, Raj MK (2022 January) A crossbreed framework for heart disease prediction using SVM and rough set techniques. In 2022 International Conference on Computer Communication and Informatics (ICCCI) (pp 1–5). IEEE
Sathesh A (2019) Performance analysis of granular computing model in soft computing paradigm for monitoring of fetal echocardiography. J Soft Comput Paradigm (JSCP) 1(01):14–23
Shakya S, Joby PP (2021) Heart disease prediction using fog computing based wireless body sensor networks (WSNs). IRO J Sustain Wirel Syst 3(1):49–58
Nanthini K, Preethi S, Venkateshwaran S (2020) Heart disease prediction using machine learning algorithms. Int J Adv Sci Technol
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nanthini, K., Sivabalaselvamani, D., Chitra, K., Mohideen, P.A., Raja, R.D. (2023). Cardiac Arrhythmia Detection and Prediction Using Deep Learning Technique. In: Bindhu, V., Tavares, J.M.R.S., Vuppalapati, C. (eds) Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems . Lecture Notes in Electrical Engineering, vol 977. Springer, Singapore. https://doi.org/10.1007/978-981-19-7753-4_75
Download citation
DOI: https://doi.org/10.1007/978-981-19-7753-4_75
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-7752-7
Online ISBN: 978-981-19-7753-4
eBook Packages: EngineeringEngineering (R0)