Nonlinear analysis of scalp EEGs from normal and brain tumour subjects.

Measurement of features from the chaos theory or as popularly known, the concept of nonlinear dynamics, as indicatives of several pathological conditions and cognition states using the electroencephalography (EEG) signal is very popular. In this paper, the analysis of scalp EEG signals of normal subjects and brain tumour patients using the nonlinear dynamic features has been presented. The nonlinear dynamic features that represent the dimensional and waveform complexities of the signal being analyzed have been considered. The statistical analysis of the selected nonlinear dynamic features has been presented. The results show that the nonlinear dynamic features significantly discriminate the brain tumour group from the normal group.

An epoch based methodology to denoise magnetocardiogram (MCG) signals and its application to measurements on subjects with implanted devices.

Magnetocardiograms (MCG) provide clinically useful diagnostic information in a variety of cardiac dysfunctions. Low frequency baseline drifts and high frequency noise are inevitably present in routine MCG even for those measured inside magnetically shielded rooms. These interferences sometimes exceed subtle cardiac features in MCG recorded on subjects with implanted devices like cardiac pacemakers; this makes interpretation of cardiac magnetic fields difficult. The present study proposes a correlation-based beat-by-beat approach and principal component analysis to eliminate drifts and high frequency noise respectively; the approach is suitable for denoising both single and multi-channel MCG data. The methodology is critically evaluated on simulated noisy measurements using a 37 channel MCG system, when objects such as implantable permanent pacemaker and stainless-steel wire are sequentially kept externally on the chests of five healthy subjects. By characterizing the noise introduced by each of these objects, the deterioration in the quality of MCG and its subsequent restoration by using the proposed method is assessed. The performance of the proposed method is also compared with other conventional denoising techniques namely, bandpass filters, wavelets and ensemble empirical mode decomposition. The proposed method not only exhibits least distortion, but also preserves the beat-by-beat dynamics of cardiac time series. The method has also been illustrated on actual MCG measurements on two subjects with implanted pacemaker which highlight the ability of the proposed method for denoising MCG in general and during extremely noisy measurement situations.

Fuzzy-based classification of breast lesions using ultrasound echography and elastography.

Common breast lesions have different elasticity properties. Segmentation of contours of breast lesions from elastography and B mode images by incorporating variational level set method is involved in the proposed work. After segmentation, strain and shape features, such as differences in area, perimeter, and contour and width to height difference and solidity, as well as texture features like contrast, entropy, standard deviation, dissimilarity, homogeneity and energy, are estimated. A nonlinear fuzzy inference system is applied for classifying the breast lesions as benign cyst, benign solid mass, or malignant solid mass. Detection of malignant solid masses is our primary objective. A classification accuracy of 83% is obtained. One hundred percent sensitivity is reported. It can be concluded that the proposed fuzzy-based classification technique can be used as an aid for the automated detection of breast lesions.