Classification of ecg data for predictive analysis to assist in medical decisions

A. R. Chitupe; S. A. Joshi

Authors

Chitupe AR Department of Computer Engineering, Pune Institute of Computer Technology, Maharashtra, India.
Joshi SA Department of Computer Engineering, Pune Institute of Computer Technology, Maharashtra, India.

Keywords:

ECG, QRS Complex, Data mining, KNN

Abstract

In recent years due to physical and mental stress in the working environments the cases of medical diagnosis using ECG are increasing up-bounds. The critical decisions in diagnosis referring to the normal ECG or indicative dysfunctions of the heart results into overlapped data values causing ambiguities. This research paper performs analytical processing and related mining to classify normal and abnormalities of the ECG. The ECG is a graphical representation generated due to polarities of the weak electrical signals generated in certain defined timely manner. With reference to time an ECG is used to measure the rate and regularity of heartbeats, as well as some special behaviour of the patient. ECG can be used to investigate heart abnormalities. With increased number of patients and reported diseases, it is becoming mandatory of maintaining medical databases and effective classification method for mining the effective relation between causes.

This paper investigates the results of KNN (K-Nearest Neighbour) algorithm to find relation between geometric parameters like area and behavioural parameters of ECG especially in pregnancy cases.

References

A.R. Chitupe et al, “Data Classification Algorithm Using K-Nearest Neighbor Method Applied to ECG Data”, in International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE), September2013.

Urszula Markowska et al, "Mining of an electrocardiogram", in XXI Autumn Meeting of Polish Information Processing Society Conference Proceedings pp. 169–175, 2005.

Yun-Chi Yeh et al, “QRS complexes detection for ECG signal: The Difference Operation Method”, in Journal of computer methods and programs in biomedicine 91, 2008.

F Chiarugi et al, “Adaptive Threshold QRS Detector with Best Channel Selection Based on a Noise Rating System”, in Journal of Computers in Cardiology pp157−160,2007.

Mohamed Elgendi et al, “ Improved QRS Detection Algorithm using Dynamic Thresholds”, in International Journal of Hybrid Information Technology Vol. 2, No. 1, January, 2009

Jiapu pan et al, “A Real-Time QRS Detection Algorithm”, in IEEE Transactions on Biomedical Engineering Vol. 32, No. 3, March 1983.

M.Sabarimalai Manikandan et al, “A novel method for detecting R peaks in electrocardiogram (ECG) signal”, in Journal of Biomedical Signal Processing and Control, March 2011.

Chia-Hung Lin et al, “Multiple Cardiac Arrhythmia Recognition Using Adaptive Wavelet Network”, in proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September pp1-4, 2005.

V.S. Chouhan et al, “Delineation of QRS-complex, P and T-wave in 12-lead ECG”, in International Journal of Computer Science and Network Security, VOL.8 No.4, April 2008.

A. Dallali et al, “ Integration of HRV, WT and Neural Networks for ECG Arrhythmias Classification”, in ARPN Journal of Engineering and Applied Sciences, VOL. 6, NO. 5, May 2011.

S. S. Mehta et al, “Comparative Study of QRS Detection in Single Lead and 12-Lead ECG Based on Entropy And Combined Entropy Criteria Using Support Vector Machine”, in Journal of Theoretical and Applied Information Technology, 2007.

K.V.L.Narayana et al, “Noise removal using adaptive noise cancelling, analysis of ECG using MATLAB”, in International Journal of Engineering Science and Technology, Vol. 3 No. 4, Apr 2011.

B.Madasamy et al, "General Framework for Biomedical Knowledge With Data Mining Techniques", in International Journal of Computer Trends and Technology, Vol. 4 No. 5, May 2013

"Medication Error-Risk Prevention in Infusion Therapy", study by B. Braun Medical Pvt. Ltd.

ECG database from PHYSIONET, "http://physionet.org/physiobank/database/".

DAISY ECG database. " http://homes.esat.kuleuven.be/~smc/daisy/daisydata.html".

Anjli Maroo et al, "Pregnancy and Heart Disease", in disease Management Project , January 2009