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1
Content available remote Diagnosis of amyotrophic lateral sclerosis (ALS) disorders based on electromyogram (EMG) signal analysis and feature selection
Mokdad Aicha, Debbal Sidi Mohammed El Amine, Meziani Fadia
Polish Journal of Medical Physics and Engineering
|
2020
|
Vol. 26, Iss. 3
155--160
EN
Electromyogram signal (EMG) provides an important source of information for the diagnosis of neuromuscular disorders. In this study, we proposed two methods of analysis which concern the bispectrum and continuous wavelet transform (CWT) of the EMG signal then a comparison is made to select which one is the most suitable to identify an abnormality in biceps brachii muscle in the main purpose is to assess the pathological severity in bifrequency and time-frequency analysis applying respectively bispectrum and CWT. Then four time and frequency features are extracted and three popular machine learning algorithms are implemented to differentiate neuropathy and healthy conditions of the selected muscle. The performance of these time and frequency features are compared using support vector machine (SVM), linear discriminate analysis (LDA) and K-Nearest Neighbor (KNN) classifier performance. The results obtained showed that the SVM classifier yielded the best performance with an accuracy of 95.8%, precision of 92.59% and specificity of 92%. followed by respectively KNN and LDA classifier that achieved respectively an accuracy of 92% and 91.5%, precision of 92% and 85.4%, and specificity of 92% and 83%.
2
Content available remote A novel multi-class approach for early-stage prediction of sudden cardiac death
Devi Reeta, Tyagi Hitender Kumar, Kumar Dinesh
Biocybernetics and Biomedical Engineering
|
2019
|
Vol. 39, no. 3
586--598
EN
Sudden cardiac death (SCD) is a complex issue that may occur in population groups with either known or unknown cardiovascular disease (CVD). Given the complex nature of SCD, the discovery of a suitable biomarker will prove essential in identifying individuals at risk of SCD, while discriminating it from patients with other cardiac pathologies as well as healthy individuals. Thus, this study aimed to develop an efficient approach to support a better comprehension of heart rate variability (HRV) as a predictive biomarker to identify SCD patients at an early stage. The present study proposed a novel multi-class classification approach using signal processing methods of HRV to predict SCD 10 min before its occurrence. The developed algorithm was qualitatively and quantitatively analyzed in terms of discriminating SCD patients from patients of heart failure and normal people. A total of 51 HRV signals of all three classes obtained from PhysioBank were processed to extract 32 features in each subject. The optimal feature selection was performed by a hybrid approach of sequential feature selection-random under sampling boosting algorithms. Multi-class classifiers, namely decision tree, support vector machine, and k-nearest neighbors were used for classification. An average classification accuracy of SCD prediction 10 min before occurrence was obtained as 83.33%. Therefore, this study suggests a new efficient approach for the early-stage prediction of SCD that is considerably different from that reported in the literature to date. However, to generalize the findings, the algorithm needs to be tested for a larger population group.
3
Content available Application of the wavelet and neural technologies for processing of signals obtained during railway tracks diagnostics by the magnetic flux leakage method
Nichoga V., Vashchyshyn L.
Biuletyn Wojskowej Akademii Technicznej
|
2017
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Vol. 66, nr 4
195--201
EN
In this article, the approach for detecting a transverse crack in the rail head via ANN with CWT and application created on its basis are presented. The ways of further development of the ANN for improving its work accuracy and the possibility of identification of other types of defects are also presented.
PL
W artykule rozpatrzono sposób ujawnienia poprzecznego pęknięcia w głowicy szyny kolejowej metodą ciągłej transformacji falkowej (CTF) oraz metodą sztucznej sieci neuronowej (SSN). Przedstawiono program stosowany do analizy sygnałów defektoskopijnych. Zaproponowano sposoby dalszego rozwoju SSN w celu poprawy dokładności jego pracy i możliwości zidentyfikowania innych rodzajów wad.
4
Content available remote Using of continuous wavelet transform for de-noising signals accompanying electrical treeing in epoxy resins
Dobrzycki A., Mikulski S.
Przegląd Elektrotechniczny
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2016
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R. 92, nr 4
26--29
EN
The following paper presents application of methods of noise reduction in acoustic emission signals, accompanying phenomenon electrical treeing of solid dielectric such as epoxy resin, based on time-frequency signal analysis. For signal estimation was applied method of soft and hard thresholding described by Donoho and Johnson. All calculations ware obtained with use of Matlab software, especially Wavelet Toolbox.
PL
W artykule zaprezentowanie zastosowanie metod redukcji szumu w sygnałach emisji akustycznej towarzyszących zjawisku drzewienia dielektryków stałych, w szczególności żywic epoksydowych, opartych na falkowej analizie sygnałow. Opisany został algorytm usuwania szumów z sygnału m. in. algorytm miękkiego oraz twardego progowania stworzonych przez Donoho i Johnsona. Wszystkie obliczenia wykonano w Matlabie z wykorzystaniem dodatku Wavelet Toolbox.
5
Content available EBMBDT : Effective Block Matching Based Denoising Technique using Dual Tree Complex Wavelet Transform
Selvi M.
Machine Graphics and Vision
|
2014
|
Vol. 23, No. 3/4
23--41
EN
In processing and investigation of digital image denoising of images is hence very important. In this paper, we propose a Hybrid denoising technique by using Dual Tree Complex Wavelet Transform (DTCWT) and Block Matching Algorithm (BMA). DTCWT and BMA is a method to identify the noisy pixel information and remove the noise in the image. The noisy image is given as input at first. Then, bring together the comparable image blocks into the load. Afterwards Complex Wavelet Transform (CWT) is applied to each block in the group. The analytic filters are made use of by CWT, i.e. their real and imaginary parts from the Hilbert Transform (HT) pair, defending magnitude-phase representation, shift invariance, and no aliasing. After that, adaptive thresholding is applied to enhance the image in which the denoising result is visually far superior. The proposed method has been compared with our previous denoising technique with Gaussian and salt-pepper noise. From the results, we can conclude that the proposed de-noising technique have shown better values in the performance analysis.
6
Content available Continuous wavelet transform of railway track defectoscopic signals in the MATLAB wavelet toolbox
Nichoga V., Vashchyshyn L.
Biuletyn Wojskowej Akademii Technicznej
|
2014
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Vol. 63, nr 4
21-27
EN
Rail networks across the world are getting busier with trains travelling at higher speeds and carrying more passengers and heavier axle loads than ever before. The combination of these factors has put considerable pressure on the existing infrastructure, leading to increased demands in inspection and maintenance of rail assets [1]. Nowadays, rails are systematically inspected for internal and surface defects using various non-destructive evaluation (NDE) techniques, the most common of which are ultrasonic and magnetic flux leakage (MFL) methods. The article is focused on the analysis of defectoscopic signals received using the magnetic wagon-defectoscope of Lviv Railway (MFL method) by the continuous wavelet transform (CWT).
PL
Sieci kolejowe na całym świecie są coraz bardziej zatłoczone, przy coraz wyższych prędkościach pociągów i przewożą coraz więcej pasażerów przy większym nacisku na osi kół niż dotychczas. Połączenie tych czynników oznacza poważne zagrożenie dla istniejącej infrastruktury, co prowadzi do zwiększonego zapotrzebowania na ilość inspekcji i na koszty utrzymania aparatury kolejowej [1]. Obecnie, szyny są systematycznie sprawdzane pod kątem uszkodzeń wewnętrznych i powierzchniowych za pomocą różnych metod badań nieniszczących. Najbardziej upowszechnione z nich to metody ultradźwiękowe i magnetodynamiczna (Magnetic Flux Leakage Rail Inspection – MFL). Artykuł skupia się na analizie sygnałów defektoskopowych otrzymanych przy użyciu wagonu defektoskopii magnetycznej Kolei Lwowskiej (elektromagnetyczna metoda badań nieniszczących) wykorzystującej ciągłą transformatę falkową.
7
Content available remote Modern method of processing signals from dangerous defects in the high-speed magnetic rails flaw detection
Nichoga V., Prudyus I., Vashchshyn L.
Prace Naukowe Politechniki Warszawskiej. Transport
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2013
|
z. 98
483--490
EN
In this paper analysis of defectoscopic signal using the modern digital signal processing tool - continuous wavelet transform (CWT) is described. The main criteria in the railway tracks flaws detection by CWT are proposed.
PL
W artykule opisano analizę sygnału defektoskopicznego za pośrednictwem nowoczesnej metody cyfrowego przetwarzania sygnału - ciągła transformata falkowa. Zaproponowano główne kryteria wykrywania wad w szynach kolejowych przez CWT (continuous wavelet transform).
8
Content available Automatic prolongation recognition in disordered speech using CWT and Kohonen network
Codello I., Kuniszyk-Jóźkowiak W., Smołka E., Kobus A.
Journal of Medical Informatics & Technologies
|
2012
|
Vol. 20
137--144
EN
Automatic disorder recognition in speech can be very helpful for the therapist while monitoring therapy progress of the patients with disordered speech. In this article we focus on prolongations. We analyze the signal using Continuous Wavelet Transform with 18 bark scales, we divide the result into vectors (using windowing) and then we pass such vectors into Kohonen network. Quite large search analysis was performed (5 variables were checked) during which, recognition above 90% was achieved. All the analysis was performed and the results were obtained using the authors' program - "WaveBlaster". It is very important that the recognition ratio above 90% was obtained by a fully automatic algorithm (without a teacher) from the continuous speech. The presented problem is part of our research aimed at creating an automatic prolongation recognition system.
9
Content available Disordered sound repetition recognition in continuous speech using CWT and Kohonen network
Codello I., Kuniszyk-Jóźkowiak W., Smołka E., Kobus A.
Journal of Medical Informatics & Technologies
|
2011
|
Vol. 17
123--130
EN
Automatic disorders recognition in speech can be very helpful for therapist while monitoring therapy progress of patients with disordered speech. This article is focused on sound repetitions. The signal is analyzed using Continuous Wavelet Transform with 16 bark scales, the result is divided into vectors and passed into Kohonen network. Finally, the Kohonen winning neuron result is put on the 3-layer perceptron. The recognition ratio was increased by about 20% by adding a modification into the Kohonen network training process as well as into CWT computation algorithm. All the analysis was performed and the results were obtained using the authors' program ”WaveBlaster“, The problem presented in this article is a part of our research work aimed at creating an automatic disordered speech recognition system.
10
Wykorzystanie transformaty falkowej do diagnostyki przekładni zębatych
Majkut L., Olszewski R., Marczuk R.
Logistyka
|
2010
|
nr 6
PL
W pracy przedstawiono model zazębienia przekładni zębatej oraz analizę uzyskanych sygnałów z wykorzystaniem ciągłej i dyskretnej transformaty falkowej. Analiza falkowa pozwala ustalić, czy przekładnia jest uszkodzona oraz pozwala wyznaczyć wskaźniki będące miarą uszkodzenia.
EN
In this work model of gear drives is presented. As a tool for analysis of vibration signals discrete and continuous Wavelet Transform is used. This analysis is helpful in determining whether the gear drive is damaged and allow designate failure modes.
11
Content available remote Metody diagnozowania stanu technicznego amortyzatorów zabudowanych w samochodach osobowych
Gardulski J.
Zeszyty Naukowe. Transport / Politechnika Śląska
|
2006
|
z. 63
307-314
PL
W artykule przedstawiono nową metodę diagnozowania amortyzatorów samochodów osobowych. Zaproponowano metodykę obliczania estymatorów diagnostycznych z przetworzonych za pomocą transformat STFT, CWT, WVD sygnałów drganiowych. Wyznaczone miary stanu technicznego amortyzatora wykazały czułość na analizowane uszkodzenia.
EN
The paper presents new method of passenger cars shock-absorbers diagnosis. It were proposed technical condition estimators based on STFT, CWT, WVD analysis of vibration signals analytical algorithm. Determined shock-absorber technical condition measures show simulations defects sensitivity.
12
Content available remote Klasyfikator neuronowy typu RBF uczony za pomocą deskryptorów uszkodzeń zębów kół otrzymanych przy użyciu analizy CWT
Czech P.
Zeszyty Naukowe. Transport / Politechnika Śląska
|
2007
|
z. 61
107-112
PL
W opracowaniu przedstawiono wyniki eksperymentu mającego na celu wykorzystanie sztucznych sieci neuronowych typu RBF do diagnozowania stopnia pęknięcia w stopie zęba koła. Do nauki sieci radialnych wykorzystywano deskryptory wyznaczone na podstawie rozkładów uzyskanych z ciągłej transformaty falkowej. Badania oparto na zidentyfikowanym modelu przekładni zębatej pracującej w układzie napędowym
EN
The work presents results of an experiment that employs the artificial neuronal network in the task of identification of the degree of tooth root cracking. In the experiment was used continuous wavelets analysis (CWT) and RBF neural network. This experiment was based on a simulation experiment
13
Content available remote Klasyfikator neuronowy SVM oparty na ciągłej transformacie falkowej
Łazarz B., Madej H., Czech P.
Zeszyty Naukowe. Transport / Politechnika Śląska
|
2004
|
z. 56
123-120
PL
W opracowaniu przedstawiono wyniki eksperymentu, którego celem było zastosowanie sieci neuronowej typu SVM w zadaniu klasyfikacji stopnia pęknięcia podstawy zęba. Klasyfikator neuronowy oparto na danych wejściowych uzyskanych z ciągłej analizy falkowej.
EN
The work presents results of an experiment that employs the artificial neuronal network in the task of identification of the degree of tooth root cracking. In the experiment was used continuous wavelets transform (CWT) and SVM neural network.
14
Content available remote Application of theory of wavelets for analysis of EKG signal
Ciepłucha M., Jóźwik K.
Mechanics and Mechanical Engineering
|
2000
|
Vol. 4, nr 1
31--38
EN
Application of Wavelet Theory for detection of P-wave, reduction of floating izoelectric line, monitoring of changes in EKG signal and compression of EKG signal is presented. Signals of biologic origin, like EKG signal, are wideband signals and methods such as Short Time Fourier Transform (STFT), utilizing narrowband basis functions, are not suitable for representation of these signals. Wavelet Theory, which provides for wideband representation of signals, uses a special analysis window, called mother wavelet, to analyze local spectral information of the EKG signal. The mother wavelet is either compressed or dilated to give multiresolution signal representation. Results of application of Continues Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT) shows good efficiency in P-wave detection in noisy EKG signal and benefits of multiresolution analysis in eliminating unwanted frequency components, monitoring of EKG changes and reduction of redundant samples in e EKG signal.
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