Analysis of neuromuscular disorders using statistical and entropy metrics on surface EMG

This paper introduces the surface electromyogram (EMG) classification system based on statistical and entropy metrics. The system is intended for diagnostic use and enables classification of examined subject as normal, myopathic or neuropathic, regarding to the acquired EMG signals. 39 subjects in total participated in the experiment, 19 normal, 11 myopathic and 9 neuropathic. Surface EMG was recorded using 4-channel surface electrodes on the biceps brachii muscle at isometric voluntary contractions. The recording time was only 5 seconds long to avoid muscle fatigue, and contractions at fiveforce levels were performed, i.e. 10, 30, 50, 70 and 100 % of maximal voluntary contraction. The feature extraction routine deployed the wavelet transform and calculation of the Shannon entropy across all the scales in order to obtain a feature set for each subject. Subjects were classified regarding the extracted features using three machine learning techniques, i.e. decision trees, support vector machines and ensembles of support vector machines. Four 2-class classifications and a 3-class classification were performed. The scored classification rates were the following: 64+-11% for normal/abnormal, 74+-7% for normal/myopathic, 79+-8% for normal/neuropathic, 49+-20% for myopathic/neuropathic, and 63+-8% for normal/myopathic/neuropathic

Central settlements in Slovenia in 2016

This article presents central settlements in Slovenia and their main characteristics in 2016. We defined central settlements based on services of general interest and the population of an individual settlement, and developed the analysis further by using competitiveness indicators. We defined 360 central settlements at six levels of centrality, among which the significance of Ljubljana as a national center of international importance and the significance of intermunicipal, local, and rural centers are increasing. The significance of certain regional centers at the second and third levels of centrality is decreasing. The level of services of general interest supplied to Slovenian territory is relatively appropriate, but it should be improved by promoting competitiveness, especially in centers of national and regional importance

MODELLING ACTIVITY INDEX WITH HIGHER-ORDER STATISTICS FOR EVALUATION OF IMPULSE SOURCES IN CONVOLUTIVE MIXTURES

V doktorski disertaciji se ukvarjamo z vrednotenjem redkih impulznih izvorov v linearnih konvolutivnih mešanicah, tj. z ocenjevanjem njihovega števila, dolžin njihovih impulznih odzivov in njihovih medsebojnih prekrivanj. V ta namen razvijemo statistične modele indeksa aktivnosti, in sicer modele povprečja, variance in avtokovariančnega zaporedja, s pomočjo katerih lahko ocenimo dolžino sistemskih odzivov in število aktivnih impulznih izvorov v opazovanih signalnih mešanicah. Začnemo s pregledom obstoječega stanja na področju ocenjevanja števila izvorov in dolžine sistemskih odzivov. Nato predstavimo model konvolutivnih mešanic odzivov redkih impulznih izvorov. Na kratko predstavimo še dekompozicijo površinskih EMG, metodo kompenzacije konvolutivnih jeder in indeks aktivnosti. Pri modeliranju indeksa aktivnosti se osredotočimo ločeno na prispevke izvorov in šuma, dodanega signalom. Lastnosti razvitih modelov uporabimo pri ocenjevanju dolžine sistemskih odzivov in števila izvorov, za kar razvijemo dva postopka. Prvi temelji na modelu variance indeksa aktivnosti in s pomočjo redukcije iskalnega prostora ocenjuje tako dolžino odzivov kot tudi število izvorov. Drugi postopek je kombiniran in temelji na modelu avtokovariančnega zaporedja indeksa aktivnosti, s katerim ocenimo dolžine sistemskih odzivov. Ko so dolžine odzivov ocenjene, lahko ocenimo število izvorov s pomočjo metod za ocenjevanje števila izvorov v multiplikativnih mešanicah. Drugi pristop se je izkazal za boljšega. V nadaljevanju predstavimo še možnosti nadgradnje indeksa aktivnosti s statistikami 3. in 4. reda ter probleme, ki pri tem nastanejo. Razvite modele nato preverimo na umetnih signalih z naključnimi sistemskimi odzivi in na umetnih površinskih elektromiogramih.In this doctoral thesis, we address the evaluation of sparse impulse sources in convolutive signal mixtures. We develop statistical models of activity index, namely model of activity index average, variance and autocovariance sequence. The models can be used for the estimation of the system response lengths and the number of active impulse sources in the observed convolutive mixtures. We begin with an overview of the state of the art in the field of estimation of the number of sources and the length of system responses. Next, a model of convolutive mixtures of sparse impulse sources is introduced, which means a basis for all the derived activity index models. Next, a decomposition of surface EMG and the convolution kernel compensation (CKC) method are briefly presented. This is because activity index was introduced together with this decomposition method and it represents an essential part of CKC. The main part of this work is devoted to modelling the activity index. When doing this, we focus on the contributions of sources and noise separately. Developed models can be used for the estimation of system response lengths and the number of active impulse sources. For this purpose we developed two methods. The first one is based on the model of activity index variance and estimates the length of system responses and the number of sources by a reduction of search space. The second approach is based on the autocovariance sequence of activity index, which also estimates the length of the system responses. When the length of the system responses is estimated, the number of sources can be derived by using methods that rely on the system eigenvalues. The second approach has proved to be better. In the sequel, the possibilities for upgrading the activity index with higher-order statistics are presented. We address third- and fourth-order statistics and problems that appear when using them to extend the basic form of activity index. The developed models are verified on synthetic convolutive mixtures of sparse impulse sources