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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: | In this paper an attempt has been made to determine the number of Premature Ventricular Contraction (PVC) cycles accurately from a given Electrocardiogram (ECG) using a wavelet constructed from multiple Gaussian functions. It is difficult to assess the ECGs of patients who are continuously monitored over a long period of time. Hence the proposed method of classification will be helpful to doctors to determine the severity of PVC in a patient. Principal Component Analysis (PCA) and a simple classifier have been used in addition to the specially developed wavelet transform. The proposed wavelet has been designed using multiple Gaussian functions which when summed up looks similar to that of a normal ECG. The number of Gaussians used depends on the number of peaks present in a normal ECG. The developed wavelet satisfied all the properties of a traditional continuous wavelet. The new wavelet was optimized using genetic algorithm (GA). ECG records from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database have been used for validation. Out of the 8694 ECG cycles used for evaluation, the classification algorithm responded with an accuracy of 97.77%. In order to compare the performance of the new wavelet, classification was also performed using the standard wavelets like morlet, meyer, bior3.9, db5, db3, sym3 and haar. The new wavelet outperforms the rest |
| Description: | Power, Signals, Controls and Computation (EPSCICON), 2012 International Conference on,pp 1-5 |
| URI: | http://dyuthi.cusat.ac.in/purl/4526 |
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| Towards the dev ... for ECG classification.pdf | (387.0Kb) |
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