Skip to main content
Log in

RETRACTED ARTICLE: A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

This article was retracted on 13 September 2022

This article has been updated

Abstract

Electrocardiographic (ECG) signals often consist of unwanted noises and speckles. In order to remove the noises, various image processing filters are used in various studies. In this paper, FIR and IIR filters are initially used to remove the linear and nonlinear delay present in the input ECG signal. In addition, filters are used to remove unwanted frequency components from the input ECG signal. Linear Discriminant Analysis (LDA) is used to reduce the features present in the input ECG signal. Support Vector Machines (SVM) is widely used for pattern recognition. However, traditional SVM method does not applicable to compute different characteristics of the features of data sets. In this paper, we use SVM model with a weighted kernel function method to classify more features from the input ECG signal. SVM model with a weighted kernel function method is significantly identifies the Q wave, R wave and S wave in the input ECG signal to classify the heartbeat level such as Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Premature Ventricular Contraction (PVC) and Premature Atrial Contractions (PACs). The performance of the proposed Linear Discriminant Analysis (LDA) with enhanced kernel based Support Vector Machine (SVM) method is comparatively analyzed with other machine learning approaches such as Linear Discriminant Analysis (LDA) with multilayer perceptron (MLP), Linear Discriminant Analysis (LDA) with Support Vector Machine (SVM), and Principal Component Analysis (PCA) with Support Vector Machine (SVM). The calculated RMSE, MAPE, MAE, R2 and Q2 for the proposed Linear Discriminant Analysis (LDA) with enhanced kernel based Support Vector Machine (SVM) method is low when compared with other approaches such as LDA with MLP, and PCA with SVM and LDA with SVM. Finally, Sensitivity, Specificity and Mean Square Error (MSE) are calculated to prove the effectiveness of the proposed Linear Discriminant Analysis (LDA) with an enhanced kernel based Support Vector Machine (SVM) method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Change history

References

  1. Ahmed A, Hargrave AR, Nohara Y, Kai E, Ripon ZH, Nakashima N (2014) Targeting morbidity in unreached communities using portable health clinic system. IEICE Trans Commun 97(1):540–545

    Article  Google Scholar 

  2. Çatak FÖ, Balaban ME (2013). A MapReduce based distributed SVM algorithm for binary classification. Turkish Journal of Electrical Engineering & Computer Science. https://arxiv.org/abs/1312.4108

  3. Hong L, Wan Y, Jain A (1998) Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans Pattern Anal Mach Intell 20(8):777–789

    Article  Google Scholar 

  4. Hossain MS (2015) Cloud-supported cyber-physical localization framework for patients monitoring. IEEE Syst J 99:1–10

    Google Scholar 

  5. Hossain MS, Muhammad G (2014) Cloud-based collaborative media service framework for healthcare. Int J Distrib Sens Netw 10:1–11

    Google Scholar 

  6. Katsaggelos AK, Kleihorst RP, Efstratiadis SN, Lagendijk RL (1991). Adaptive image sequence noise filtering methods. In Visual Communications,'91, Boston (pp. 716–727). International Society for Optics and Photonics

  7. Kaur PD, Chan I (2014) Cloud based intelligent system for delivering health care as a service. Comput Methods Prog Biomed 113(1):346–359

    Article  Google Scholar 

  8. Kumar PM, Gandhi UD (2017). A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases. Computers & Electrical Engineering

  9. Lopez D, Gunasekaran M (2015). Assessment of Vaccination Strategies Using Fuzzy Multi-criteria Decision Making. In Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO-2015) (pp. 195–208). Springer

  10. Lopez D, Manogaran G (2016). Big data architecture for climate change and disease dynamics, The human element of big data: issues, analytics, and performance. CRC Press, USA, pp. 303–343

  11. Lopez D, Sekaran G (2016) Climate change and disease dynamics-A Big Data perspective. Int J Infect Dis 45:23–24

    Article  Google Scholar 

  12. Lopez D, Gunasekaran M, Murugan BS, Kaur H, Abbas KM (2014). Spatial Big Data analytics of influenza epidemic in Vellore, India. In Big Data (Big Data), 2014 I.E. International Conference on (pp. 19–24). IEEE

  13. Lopez D, Manogaran G, Jagan J (2017) Modelling the H1N1 influenza using mathematical and neural network approaches. Biomed Res 28(8):1–5

    Google Scholar 

  14. Mamun KAA, Alhussein M, Sailunaz K, Islam MS (2016). Cloud based framework for Parkinson’s disease diagnosis and monitoring system for remote healthcare applications. Future Gener. Comput. Syst., in press

  15. Manogaran G, Lopez D (2016) Health Data Analytics using Scalable Logistic Regression with Stochastic Gradient Descent. International Journal of Advanced Intelligence Paradigms 8(2):1–15

    Google Scholar 

  16. Manogaran G, Lopez D (2016) Health Data Analytics using Scalable Logistic Regression with Stochastic Gradient Descent. International Journal of Advanced Intelligence Paradigms 9:1–15

    Google Scholar 

  17. Manogaran G, Lopez D (2017) Spatial Cumulative Sum Algorithm with Big Data Analytics for Climate Change Detection. Computers & Electrical Engineering 60(2):1–25

    Google Scholar 

  18. Manogaran G, Lopez D (2017) Disease Surveillance System for Big Climate Data Processing and Dengue Transmission. International Journal of Ambient Computing and Intelligence (IJACI) 8(2):88–105

    Article  Google Scholar 

  19. Manogaran G, Lopez D (2017) Spatial cumulative sum algorithm with big data analytics for climate change detection. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2017.04.006

  20. Manogaran G, Lopez D (2017) A Gaussian process based big data processing framework in cluster computing environment. Clust Comput:1–16

  21. Manogaran G, Thota C, Kumar MV (2016) MetaCloudDataStorage Architecture for Big Data Security in Cloud Computing. Procedia Computer Science 87:128–133

    Article  Google Scholar 

  22. Manogaran G, Thota C, Lopez D, Vijayakumar V, Abbas KM, Sundarsekar R (2017). Big data knowledge system in healthcare. In: Internet of things and big data technologies for next generation healthcare. Springer, USA, pp. 133–157

  23. Manogaran G, Lopez D, Thota C, Abbas KM, Pyne S, Sundarasekar R (2017). Big data analytics in healthcare Internet of Things. In Innovative Healthcare Systems for the 21st Century (pp. 263–284). Springer International Publishing

  24. Manogaran G, Thota C, Lopez D (2018). Human-Computer Interaction With Big Data Analytics. In HCI Challenges and Privacy Preservation in Big Data Security (pp. 1–22). IGI Global

  25. Mirzapour F, Ghassemian H (2016) Multiscale Gaussian Derivative Functions for Hyperspectral Image Feature Extraction. IEEE Geosci Remote Sens Lett 13(4):525–529

    Article  Google Scholar 

  26. Muhammad G (2015) Automatic speech recognition using interlaced derivative pattern for cloud based healthcare system. Cluster Comput 18(2):795–780

    Article  Google Scholar 

  27. Osher S, Rudin LI (1990) Feature-oriented image enhancement using shock filters. SIAM J Numer Anal 27(4):919–940

    Article  Google Scholar 

  28. Parekh M, Saleena B (2015) Designing a cloud based framework for healthcare system and applying clustering techniques for region wise diagnosis. Proc Comput Sci 50:537–542

    Article  Google Scholar 

  29. Priyan MK, Devi GU (2017). Energy efficient node selection algorithm based on node performance index and random waypoint mobility model in internet of vehicles. Cluster Computing, 1–15

  30. Rana J, Bajpayee A (2015) HealthCare monitoring and alerting system using cloud computing. Int J Recent Innov Trends Comput Commun 3(2):102–105

    Google Scholar 

  31. Rebentrost P, Mohseni M, Lloyd S (2014) Quantum support vector machine for big data classification. Phys Rev Lett 113(13):130503

    Article  Google Scholar 

  32. Reddy PB, Reddy KK, Reddy PA (2017) Associate-Image Filtering Method with Enhanced De-noising Feature for Road Detection in Disaster Management. Transactions on Machine Learning and Artificial Intelligence 4(6):50

    Google Scholar 

  33. Shin D, Shin D, Shin D. (2016) Development of emotion recognition interface using complex EEG/ECG bio-signal for interactive contents. Multimedia Tools and Applications, 1–22

  34. Song YT, Hong S, Pak J (2015) Empowering patients using cloud based personal health record system. In: Software engineering, artificial intelligence, networking and parallel/distributed computing (SNPD), 2015 16th IEEE/ACIS International Conference, pp. 1–6

  35. Sun T, Shu C, Li F, Yu H, Ma L, Fang Y (2009). An efficient hierarchical clustering method for large datasets with map-reduce. In Parallel and Distributed Computing, Applications and Technologies, 2009 International Conference on (pp. 494–499). IEEE. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 1–3 June 2015, Takamatsu, pp. 1–6

  36. Thota C, Manogaran G, Lopez D, Vijayakumar V (2017). Big Data Security Framework for Distributed Cloud Data Centers. In Cybersecurity Breaches and Issues Surrounding Online Threat Protection (pp. 288–310). IGI Global

  37. Thota C, Sundarasekar R, Manogaran G, Varatharajan R, Priyan MK (2018). Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System. In Exploring the Convergence of Big Data and the Internet of Things (pp. 141–154). IGI Global

  38. Varatharajan R, Manogaran G, Priyan MK, Sundarasekar R (2017). Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Cluster Computing, 1–10

  39. Varatharajan R, Manogaran G, Priyan MK, Balaş VE, Barna C (2017). Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. Multimedia Tools and Applications, 1–21

  40. Varatharajan R, Vasanth K, Gunasekaran M, Priyan M, Gao XZ (2017). An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images. Computers & Electrical Engineering

  41. Wang H, Zhang W, Yu N (2016) Protecting patient confidential information based on ECG reversible data hiding. Multimedia Tools and Applications 75(21):13733–13747

    Article  Google Scholar 

  42. Wen J, Chang XW (2017) Success probability of the Babai estimators for box-constrained integer linear models. IEEE Trans Inf Theory 63(1):631–648

    Article  MathSciNet  Google Scholar 

  43. Wen J, Zhou Z, Wang J, Tang X, Mo Q (2016) A sharp condition for exact support recovery of sparse signals with orthogonal matching pursuit. IEEE Trans Signal Process 65:1370–1382

    Article  Google Scholar 

  44. Yadava M, Kumar P, Saini R, Roy PP, Dogra DP (2017) Analysis of EEG signals and its application to neuromarketing. Multimedia Tools and Applications, 1–25

  45. Zainuddin Z, Lai KH, Ong P (2016) An enhanced harmony search based algorithm for feature selection: Applications in epileptic seizure detection and prediction. Comput. Electr. Eng. 53:143–162

    Article  Google Scholar 

  46. Zhang Y, Qi R, Zeng Y (2017). Forward-backward pursuit method for distributed compressed sensing. Multimedia Tools and Applications 76(20):20587–20608

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. K. Priyan.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Varatharajan, R., Manogaran, G. & Priyan, M.K. RETRACTED ARTICLE: A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimed Tools Appl 77, 10195–10215 (2018). https://doi.org/10.1007/s11042-017-5318-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5318-1

Keywords

Navigation