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Abstract:
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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. |