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| Abstract: | Electrocardiogram gives the information regarding the health of the patients by monitoring the bioelectric potentials generated by the sinoatrial node in the heart. These signals can be collected by using electrodes suitably placed on the body of a patient. The normal human ECG lie in the frequency range of 0.05-100 Hz and the most useful information is contained in the range of 0.5-45 Hz. Even though a large amount of work has already been done in the field of ECG classification, no classification system has made an attempt in identifying the isolated abnormalities which pose a silent threat to patients. An adaptive filtering technique for denoising the ECG which is based on Genetic Algorithm (GA) tuned Sign-Data Least Mean Square (SD-LMS) algorithm is proposed. This algorithm gave an average signal to noise ratio improvement of 10.75 dB for baseline wander and 24.26 dB for power line interference. It is seen that the step size ‘μ’ optimized with GA helps in obtaining better SNR value without causing any damage to the information content in the ECG. A new wavelet for automatic classification of arrhythmias from electrocardiogram is proposed. This new wavelet is formed as a sum of shifted Gaussians so that it resembles a normal ECG. This shape has been chosen with the aim of extracting maximum information from the ECG under analysis. The classification performance was studied using the most commonly used database, the MIT-BIH Arrhythmia database. The shifted and summed Gaussian wavelet was then optimized using GA. The optimum wavelet for classification was obtained after several runs of the GA algorithm. The ECG class labeling was done according to the Association for the Advancement of Medical Instrumentation (AAMI). The wavelet scales corresponding to the different frequency levels giving maximum classification performance were identified by selecting finer scales. Probabilistic Neural Network classifier was used for classification purpose. The proposed classification system offered better results than that reported in literature by giving an overall sensitivity of 97.01% for Normal beats, 75.20% for Supraventricular beats and 93.06% for Ventricular beats. As mentioned above this technique could exclusively identify some of the isolated abnormalities present in the patient records. |
| URI: | http://dyuthi.cusat.ac.in/purl/5143 |
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| Dyuthi-T2177.pdf | (4.847Mb) |
| Abstract: | Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions |
| Description: | International conference on Communication and Signal Processing, April 3-5, 2013, India |
| URI: | http://dyuthi.cusat.ac.in/purl/4231 |
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| Wavelet based I ... ispectral Brain Tissue.pdf | (825.4Kb) |
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