Semiautomatic detection of cardiac diseases employing dual tree complex wavelet transform

Electrocardiogram (ECG) contains lot of information which can be utilized for a mechanism to detect cardiac abnormalities. The ECG signal is too sensitive to various types of noises as it is of low frequency and has weak amplitude, these noises reduce the diagnostic accuracy and may lead to the incorrect decision of the clinician. So, denoising of ECG signal is an essential requirement for an accurate detection of Heart disease. In this paper, a Dual-Tree Complex Wavelet Transform technique (DTCWT) is presented to denoise the noisy ECG signal and to extract the Principal features followed by implementation of Peak Detection Algorithm. The performance is evaluated on the basis of performance metrics and an increase in SNR is achieved using the technique. With the proposed technique, calculated heart rate is in consensus with the gold standard of the various bench mark databases used and accurate heart disease was determined

Removal of electromyography noise from ECG for high performance biomedical systems

This paper presents the review of the biomedical system which consists of an energy source, signal processing, signal conditioning and signal transmission. These blocks are designed by various optimization techniques to achieve high operating speed, compressed area and minimum energy consumption. These techniques are mainly divided in to four aspects: (a) increasing the longevity of device using energy harvesting approaches; (b) reducing the delay to enhance the operating frequency; (c) reducing the data storage using data compression; (d) increasing the data rate transmission with reduced power consumption. This review paper briefly summarizes the various techniques and device performance achieved by these techniques. To attain these high performance systems input played a vital role. This paper also presents the different low pass IIR filter approximation method techniques to remove Electromyography noise from ECG input signal. For this purpose, we have taken MIT-BIH Arrhythmia database. We have calculated signal to noise ratio and power spectral density. On comparing their performance parameters of different low pass IIR filters, Elliptic filter has found best suited to remove this type of noise

Improved methods for analyzing MRI brain images

Image segmentation is a part of image processing for region or object extraction from the background area. Owing to the complex background, contrast of the infected portion, low intensity difference values, intricate inner body parts etc.; the problem of region extraction in segmentation is very challenging. Among various image segmentation techniques, thresholding is one of the simplest techniques, in which the region of interest is extracted from the background by comparing the pixel values with the threshold value. The threshold value is obtained from histogram of the image. The technique presented in the paper involves graph cut method in which the initial centroids are automatically selected by exploiting the symmetrical nature of the MRI images. The results obtained by the thresholding technique in this research work shows that any abnormality can be localized easily in horizontal divided MRI brain image rather than in vertical divided MRI image. Graph cut results show better segmentation than thresholding technique which is justified by PSNR and SSIM values