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Graphical representation and variability quantification of handwriting signals: New tools for Parkinson’s disease detection

Dehghanpur Deharab Elham, Ghaderyan Peyvand

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

158--172

EN

A reliable computer-aided method for Parkinson’s disease (PD) detection can slow down its progression and improve the life quality of patients. In this study, a new non-invasive and cost-effective method based on the online analysis of handwriting signals has been proposed. First, the dynamic handwriting signals have been converted into two graphical representations of the variability rate. Then, two new feasible features, including area of the analytic signal representation and area of the second-order difference plot, have been used to quantify the variability rate of handwriting signals. A statistical test and support vector machine classifier have been applied in a comparative study to test the impact of each variability feature, writing task, and time sequence on the detection performance, separately. The obtained results on PaHaW database with 35 Parkinson’s disease patients and 36 healthy controls have shown that the proposed method of handwriting variability feature extraction has effective performance and the capability for the PD detection. It has achieved an average sensitivity of 86.26% with only two types of features, providing a trade-off between the performance, the computational complexity, and interpretability of the motor patterns from the point of view of clinicians and neuropsychologists. Xcoordinate time-series and writing a sentence can achieve superior accuracy and robustness in the presence of individual differences. The experimental results have demonstrated that extracting the variability features that used graphical representations of the global changes in oscillatory mode has the ability to clinically describe the pathological dynamics of the handwriting signals for the PD identification.

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EEG based alcoholism detection by oscillatory modes decomposition second order difference plots and machine learning

Salankar Nilima, Qaisar Saeed Mian, Pławiak Paweł, Tadeusiewicz Ryszard, Hammad Mohamed

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

173--186

EN

The excessive drinking of alcohol can disrupt the neural system. This can be observed by properly analysing the Electroencephalogram (EEG) signals. However, the EEG is a signal of complex nature. Therefore, an accurate categorization between alcoholic (A) and nonalcoholic (NA) subjects, while using a short time EEG recording, is a challenging task. In this paper a novel hybridization of the oscillatory modes decomposition, features mining based on the Second Order Difference Plots (SODPs) of oscillatory modes, and machine learning algorithms is devised for an effective identification of alcoholism. The Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are used to respectively decompose the considered EEG signals in Intrinsic Mode Functions (IMFs) and Modes. Onward, the SODPs, derived from first six IMFs and Modes, are considered. Features of SODPs are mined. To reduce the dimension of features set and computational complexity of the classification model, the pertinent features selection is made on the basis of Wilcoxon statistical test. Three features with p-values (p) of < 0.05 are selected from each intended SODP and these are the Central Tendency Measure (CTM), area and mean. These features are used for the discrimination between A and NA classes. In order to determine a suitable EEG signal segment length for the intended application, experiments are performed by considering features extracted from three different length time windows. The classification is carried out by using the Least Square Support Vector Machine (LS-SVM), Multilayer perceptron neural network (MLPNN), K-Nearest Neighbour (KNN) and Random Forest (RF) algorithms. The applicability is tested by using the UCI-KDD EEG dataset. The results are noteworthy for MLPNN with 99.89% and 99.45% accuracies for EMD and VMD respectively for 8-second window.

3

Detekcja uszkodzeń przekładni zębatych z wykorzystaniem dekompozycji empirycznej

Majkut L., Olszewski R., Marczuk R.

Logistyka

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2010

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nr 6

PL

W pracy przedstawiono detekcję uszkodzeń zębów przekładni zębatej z wykorzystaniem dekompozycji empirycznej oraz transformacji cepstralnej. Dekompozycja empiryczna jest metodą analizy sygnału poprzez rozłożenie go na zbiór ortogonalnych tzw. Intrinsic Mode Functions. Pozwala również na wyznaczenie tzw. częstości chwilowych. Zastosowanie analizy cepstralnej do "sygnału" częstości chwilowych pozwoliło na uzyskanie informacji czy przekładnia jest uszkodzona, natomiast "intensywność" tzn. wielkość uszkodzenia badano analizując wartość współczynników cepstralnych.

EN

This paper presents detection of gears faults using Empirical Mode Decomposition algorithm. EMD is a way to decompose a signal into so-called Intrinsic Mode Functions (IMF), and obtain instantaneous frequency data. Cepstral analysis of instantaneous frequency was used for obtaining information about gear health, cepstral coefficients allowed to determine the fault intensity.