Combined Principal Component Analysis and Compression of 12-Lead Electrocardiogram signal using Singular Value Decomposition

International Journal of Computer Trends and Technology (IJCTT)          
© 2017 by IJCTT Journal
Volume-45 Number-1
Year of Publication : 2017
Authors : Dr. Mridul Kumar Mathur, Gyan Prakash Bissa


Dr. Mridul Kumar Mathur, Gyan Prakash Bissa; "Combined Principal Component Analysis and Compression of 12-Lead Electrocardiogram signal using Singular Value Decomposition". International Journal of Computer Trends and Technology (IJCTT) V45(1):4-9, March 2017. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Electrocardiogram (ECG) is the bioelectric signals generated during the cardiac cycle. ECG shows the actual functioning of the heart and various heart diseases can be diagnosed after analyzing ECG signals. In addition to that ECG signals are required to be compressed for cost-effective storage and fast transmission over low bandwidth channels to the remote locations. Principal Component Analysis identifies the most important features of ECG signals and ECG signal can be compressed using these Principal Components. Principal Components with high variance can be retained and low variance can be discarded. Thus the ECG signal can be compressed. Multiple leads of ECG signal can depict the clear picture of the heart therefore in this paper singular value decomposition (SVD) is used to obtained combined Principal Components of twelve lead Physikalisch-Technische Bundesanstalt (PTB) Data Base ECG signal. Along with this ECG signal is also compressed and reconstructed with the help of first few Eigen Vectors and quantity of compression is measured by Compression Ratio (CR) and reconstructed signal quality is measured by Percentage Root Mean Square (PRD) difference.

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Compression Ratio (CR), Percentage Root Mean Square Difference (PRD) difference ,Eigen Value; Eigen Vector; Principal Components analysis;