Recognition of cardiac abnormalities from synchronized ECG and PCG signals.

Phonocardiogram signals (PCG) and electrocardiogram signals (PCG) have been used separately for decades to diagnose heart abnormalities. Combining these two synchronous signals is expected to enhance the diagnosis for better medical management of patients. This paper's objective is to highlight the performance comparison between the diagnosis of heart abnormalities based only on PCG recordings and that based on synchronized PCG and ECG recordings. For evaluating the classification results, we have used the ROC curve and four performance measures: Accuracy, Area Under Curve (AUC), sensitivity, and specificity.

Discriminating Between Patients With Parkinson's and Neurological Diseases Using Cepstral Analysis.

In this paper, we wanted to discriminate between two groups of patients (patients who suffer from Parkinson's disease and patients who suffer from other neurological disorders). We collected a variety of voice samples from 50 subjects using different recording devices in different conditions. Subsequently, we analyzed and extracted features from these samples using three different Cepstral techniques; Mel frequency cepstral coefficients (MFCC), perceptual linear prediction (PLP), and ReAlitive SpecTrAl PLP (RASTA-PLP). For classification we used leave one subject out validation scheme along with five different supervised learning classifiers. The best obtained result was 90% using the first 11 coefficients of the PLP and linear SVM kernels.

A preliminary study on improving the recognition of esophageal speech using a hybrid system based on statistical voice conversion.

In this paper, we propose a hybrid system based on a modified statistical GMM voice conversion algorithm for improving the recognition of esophageal speech. This hybrid system aims to compensate for the distorted information present in the esophageal acoustic features by using a voice conversion method. The esophageal speech is converted into a "target" laryngeal speech using an iterative statistical estimation of a transformation function. We did not apply a speech synthesizer for reconstructing the converted speech signal, given that the converted Mel cepstral vectors are used directly as input of our speech recognition system. Furthermore the feature vectors are linearly transformed by the HLDA (heteroscedastic linear discriminant analysis) method to reduce their size in a smaller space having good discriminative properties. The experimental results demonstrate that our proposed system provides an improvement of the phone recognition accuracy with an absolute increase of 3.40 % when compared with the phone recognition accuracy obtained with neither HLDA nor voice conversion.

Unsupervised approach data analysis based on fuzzy possibilistic clustering: application to medical image MRI.

The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM) to initialize the parameters of possibilistic c-means (PCM), in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images.