IMAGE PROCESSING IN FREQUENCY DOMAIN USING MATLAB
Author(s):
kokilavani c, G.Sandra Karunya, A.Mathan Gopi, D.Rajinigirinath
ISSN:
2349-6002
Cite This Article:
IMAGE PROCESSING IN FREQUENCY DOMAIN USING MATLAB International Journal of Innovative Research in Technology(www.ijirt.org) ,ISSN: 2349-6002 ,Volume 5 ,Issue 12 ,Page(s):262-268 ,May 2019 ,Available :IJIRT148074_PAPER.pdf
Keywords:
ECG signal quality assessment, ECG noise detection and classification, muscle artifacts, baseline wander, ECG denoising
Abstract
Objective: image processing in frequency and classification of noises can play a vital role in the development of robust unsu-pervised electrocardiogram (ECG) analysis systems. This paper proposes a novel unified framework for automatic detection, localization and classification of single and combined ECG noises. Methods: The proposed framework consists of the modified ensemble empirical mode decomposition (CEEMD), the short-term temporal feature extraction, and the decision rules based noise detection and classification. In the proposed framework, ECG signals are first decomposed using the modified CEEMD algorithm for discriminating the ECG components from the noises and artifacts. Then, the short-term temporal features such as maximum absolute amplitude, number of zerocrossings, and local maximum peak amplitude of the autocorrelation function are computed from the extracted high-frequency (HF) and low-frequency (LF) signals. Finally, a decision rule-based algorithm is presented for detecting the presence of noises and classifying the processed ECG signals into six signal groups: noise-free ECG, ECG+BW, ECG+MA, ECG+PLI, ECG+BW+PLI, and ECG+BW+MA. Results: The proposed framework is rigorously evaluated on five benchmark ECG databases and the real-time ECG signals. The proposed framework achieves an average sensitivity of 99.12%, specificity of 98.56%, and overall accuracy of 98.90% in detecting the presence of noises. Classification results show that the framework achieves an average sensitivity, positive predicticvity, and classification accuracy of 98.93%, 98.39% and 97.38%, respectively. Conclusion: The proposed framework not only achieves better noise detection and classification rates than the current state-of-the-art methods but also accurately localizes short bursts of noises with low endpoint delineation errors. Significance: Extensive studies on benchmark databases demonstrate that the proposed framework is more suitable for reducing false alarm rates and selecting appropriate noise-specific denoising techniques in automated ECG analysis applications.
Article Details
Unique Paper ID: 148074

Publication Volume & Issue: Volume 5, Issue 12

Page(s): 262 - 268
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