Smart Medical Evacuation Support System for the Military.

Medical support in crisis situations is a major challenge. Efficient implementation of the medical evacuation process especially in operations with limited human resources that may occur during armed conflicts can limit the loss of these resources. Proper evacuation of wounded soldiers from the battlefield can increase the chances of their survival and rapid return to further military operations.

Relationship between the Central and Regional Pulse Wave Velocity in the Assessment of Arterial Stiffness Depending on Gender in the Geriatric Population.

Artery stiffness is a risk factor for cardiovascular disease (CVD). The measurement of pulse wave velocity (PWV) between the carotid artery and the femoral artery (cfPWV) is considered the gold standard in the assessment of arterial stiffness. A relationship between cfPWV and regional PWV has not been established.

Decision Support System Proposal for Medical Evacuations in Military Operations.

The area of military operations is a big challenge for medical support. A particularly important factor that allows medical services to react quickly in the case of mass casualties is the ability to rapidly evacuation of wounded soldiers from a battlefield. To meet this requirement, an effective medical evacuation system is essential.

Automatic Speaker Recognition System Based on Gaussian Mixture Models, Cepstral Analysis, and Genetic Selection of Distinctive Features.

This article presents the Automatic Speaker Recognition System (ASR System), which successfully resolves problems such as identification within an open set of speakers and the verification of speakers in difficult recording conditions similar to telephone transmission conditions. The article provides complete information on the architecture of the various internal processing modules of the ASR System. The speaker recognition system proposed in the article, has been compared very closely to other competing systems, achieving improved speaker identification and verification results, on known certified voice dataset.

Evaluation of motor unit potential wavelet analysis in the electrodiagnosis of neuromuscular disorders.

Introduction: Electrophysiological studies of human motor units can use various electromyographic techniques. Together with the development of new techniques for analysis and processing of bioelectric signals, motor unit action potential (MUAP) wavelet analysis represents an important change in the development of electromyographic techniques.

Methods: The proposed approach involves isolating single MUAPs, computing their scalograms, taking the maximum values of the scalograms in 5 selected scales, and averaging across MUAPs to give a single five-dimensional feature vector per muscle.

Multiresolution MUAPs decomposition and SVM-based analysis in the classification of neuromuscular disorders.

This paper describes a new method for the classification of neuromuscular disorders based on the analysis of scalograms determined by the Symlet 4 wavelet technique. The approach involves isolating single motor unit action potentials (MUAPs), computing their scalograms, taking the maximum values of the scalograms in five selected scales, and averaging across MUAPs to give a single 5-dimensional feature vector per subject. After SVM analysis, the vector is reduced to a single decision parameter, called the Wavelet Index, allowing the subject to be assigned to one of three groups: myogenic, neurogenic or normal.

Wavelet analysis for Support Vector Machine classification of motor unit action potentials.

The paper presents a new method for neuromuscular disorders diagnosis based on analysis of scalograms determined by the Symlet 4 wavelets technique. Obtained results served for extraction of five features, which, after SVM analysis, were reduced to a single decision parameter allowing assigning the investigated cases to one of three groups: myogenic, neurogenic or normal. Software implementation of the method permitted to create a diagnostic tool for EMG investigation aid.

Automated diagnostic method supporting EMG examination.

A method of diagnosing neural-muscular diseases via Fourier spectral analysis is presented in the paper. Application of this analysis allowed for obtaining a series of spectral features. This resulted in a selection of a discriminant ensuring the best sensitivity of the new method, better than the QEMG method used in the clinical practice.

Linear discriminant analysis of MUAP scalograms.

The paper presents a new approach to the computer aided diagnostic systems for the needs of quantitative electromyography. The approach is based on the analysis of wavelet scalograms of the motor unit action potentials calculated on the basis of 4th order Symlet wavelet. The scalograms provide the vector consisting of six features describing the state of a muscle that can be reduced to the two features with use of Linear Discriminant Analysis.