A. Dobrowolski

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. The software implementation of the method described above created a tool supporting electromyographic (EMG) examinations. The method is characterized by a high probability for the accurate diagnosis of muscle state. The method produced 5 misclassifications out of 800 examined cases (total error of 0.6%).

Multifrequency microwave thermograph for biomedical applications

This paper presents problems related to thermal radiation of human bodies in microwave range with respect to diagnosis of breast carcinoma. A mathematical model of thermal radiation transfer through tissues is introduced and methods of measurement of temperature, depth and size of a heat source, by means of multifrequency microwave thermograph are described. Theoretical considerations are supplemented by presentation of experimental results.

Spectral Analysis of Motor Unit Action Potentials

The statistical processing of electromyographic signal examination performed in the time domain ensures mostly correct classification of pathology; however, because of an ambiguity of most temporal parameter definitions, a diagnosis can include a significant error that strongly depends on the neurologists experience. Then, selected temporal parameters are determined for each run, and their mean values are calculated. In the final stage, these mean values are compared with a standard and, including additional clinical information, a diagnosis is given. An inconvenience of this procedure is high time consumption that arises from the necessity of determination of many parameters. Additionally, an ambiguity in determination of basic temporal parameters can cause doubts when parameters found by the physician are compared with standard parameters determined in other research centers. In this paper, we present a definition for spectral discriminant that directly enables a unique diagnosis to be made. An essential advantage of the suggested discriminant is a precise and algorithmically realized definition that enables an objective comparison of examination results obtained by physicians with different experiences or working in different research centers. A suggestion of the standard for selected muscle based on a population of 70 healthy cases is presented in the Results section.

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. After Support Vector Machine analysis, the feature vector is reduced to a single decision parameter that allows the subject to be assigned to 1 of 3 groups: myogenic, healthy, or neurogenic. The software is available as freeware. Results: MUAP wavelet analysis yielded consistent results for the diagnostic index and muscle classification, with only 7 incorrect classifications out of a total of 1,015 samples. Conclusions: This proposed approach provides a sensitive and reliable method for evaluating and characterizing MUAPs. Muscle Nerve 46: 63–69, 2012

Estimation of deep-seated profile of temperature distribution inside biological tissues by means of multifrequency microwave thermograph

This paper presents problems related to thermal radiation of human bodies in the microwave range in relation to diagnosis of breast carcinoma. A mathematical model of thermal radiation transmission through tissues is introduced and methods of measurement of temperature and the depth and size of a heat source, by means of multifrequency microwave thermography, are described. Theoretical considerations are supplemented by results of experiments.

Estimation of internal distribution of temperature inside biological tissues by means of multifrequency microwave thermograph

The paper presents problems connected with thermal radiation of human bodies in the microwave range in respect of diagnosis of breast carcinoma. A mathematical model of transmission of thermal radiation through tissues is introduced and methods of measurement of temperature, depth and size of heat source, by means of multifrequency microwave thermography, are described. Theoretical considerations are supplemented with presentation of the results of experiments.

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. Consequently, the healthy, myogenic and neurogenic cases can be successfully classified using the linear methods.

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. Software implementation of the spectral discriminant enabled creation of a computer diagnostic tool supporting EMG examination. The method is fully automated.

Diagnosis of Muscle Condition on the Basis of MUP Spectral Analysis

Electromyography (EMG) is a functional examination that plays a fundamental role in the diagnosis of neuromuscular disorders. The method allows for distinction between records of a healthy muscle and a changed muscle as well as for determination of whether pathological changes are of primary myopathic or neuropathic character. The statistical processing of electromyographic signal examination performed in the time domain ensures mostly correct classification of pathology; however, because of an ambiguity of most temporal parameter definitions, a diagnosis can include a significant error that strongly depends on the neurologists experience. Then, selected temporal parameters are determined for each run, and their mean values are calculated. In the final stage, these mean values are compared with a standard and, including additional clinical information, a diagnosis is given. An inconvenience of this procedure is high time consumption that arises from, among other things, the necessity of determination of many parameters. Additionally, an ambiguity in determination of basic temporal parameters can cause doubts when parameters found by the physician are compared with standard parameters determined in other research centers. In this paper, we present a definition for single-point spectral discriminant that directly enables a unique diagnosis to be made. An essential advantage of the suggested discriminant is a precise and algorithmically realized definition that enables an objective comparison of examination results obtained by physicians with different experiences or working in different research centers. Therefore, the definition fulfills a fundamental criterion for the parameter used for preparation of a standard. A suggestion of the standard for selected muscle based on a population of 70 healthy cases is presented in the Results section.

Spectral analysis of visual evoked potentials

The paper presents a conception of classification method of Visual Evoked Potentials (VEP) to physiological or pathological case based on power spectral parameters. The authors have verified their concept through a series of numerical experiments performed using a dedicated application. As a result of experiments, the final method provided only 4 cases of wrong classification among training data (6%) and 13 among the testing data (43%).