Temat: Parkinson’s - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Analysis of hand and face images for the purpose of engineering support for Parkinsons disease diagnosis
Analiza obrazów dłoni i twarzy na potrzeby inżynierskiego wsparcia diagnostyki choroby Parkinsona
Autorzy:: Białek, Kamila
Potulska-Chromik, Anna
Jakubowski, Jacek
Nojszewska, Monika
Kostera-Pruszczyk, Anna
Powiązania:: https://bibliotekanauki.pl/articles/2171782.pdf
Data publikacji:: 2022
Wydawca:: Uniwersytet Technologiczno-Humanistyczny im. Kazimierza Pułaskiego w Radomiu
Tematy:: image processing
medical diagnosis
Parkinson’s disease
przetwarzanie obrazów
diagnoza medyczna
choroba Parkinsona
Opis:: Engineering support in the field of recognizing Parkinson's disease against the background of other diseases, its progression and monitoring the effectiveness of drugs is currently widely implementedas part of work devoted to the use of recording and analysis devices equipped with sensors of movement parameters attached to the patient's body, e.g. accelerometers and gyroscopes. This material touches on an alternative approach, in which the concept of using techniques for processing selected image data obtained during a clinical examination evaluating a patient using the unified UPDRS number scale is proposed. The research was conducted on a material that corresponded to selected components of the scale and included images of faces recorded in the visible light range and images of the outer surfaces of the hand recorded with a thermal imaging camera.This was aimed at assessing the possibility of differentiating personsin terms of detecting Parkinson's disease on the basis of registered modalities. Thus, tasks aimed at developing characteristics important in the binary classification process were carried out. The assessment of features was made in a modality-dependent manner based on available tools in the field of statistics and machine learning.
Wsparcie inżynierskie w zakresie rozpoznawania choroby Parkinsona na tle innych chorób, jej progresji oraz monitorowania skuteczności leków jest obecnie szeroko realizowane w ramach prac poświęconych wykorzystaniu urządzeń rejestrujących i analizujących wyposażonych w sensory parametrów ruchu przymocowanych do ciała pacjenta, np. akcelerometry i żyroskopy. W prezentowanej pracy przedstawiono alternatywne podejście, w którym proponuje się koncepcję wykorzystania technik przetwarzania wybranych danych obrazowych uzyskanych podczas badania klinicznego oceniającego pacjenta za pomocą ujednoliconej skali liczbowej UPDRS. Badania przeprowadzono na materiale, który odpowiadał wybranym składowym skali i obejmował obrazy twarzy utrwalone w zakresie światła widzialnego oraz obrazy zewnętrznych powierzchni dłoni rejestrowane kamerą termowizyjną. Wykonane badania miały na celu ocenę możliwości różnicowania osób pod względem wykrywania choroby Parkinsona na podstawie zarejestrowanych metod. W ten sposób zrealizowano zadania mające na celu opracowanie cech istotnych w procesie klasyfikacji binarnej. Ocena cech została dokonana w sposób zależny od modalności w oparciu o dostępne narzędzia z zakresu statystyki i uczenia maszynowego.
Źródło:: Journal of Automation, Electronics and Electrical Engineering; 2022, 4, 1; 13--20
2658-2058
2719-2954
Pojawia się w:: Journal of Automation, Electronics and Electrical Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Deep learning vs feature engineering in the assessment of voice signals for diagnosis in Parkinson’s disease
Autorzy:: Majda-Zdancewicz, Ewelina
Potulska-Chromik, Anna
Jakubowski, Jacek
Nojszewska, Monika
Kostera-Pruszczyk, Anna
Powiązania:: https://bibliotekanauki.pl/articles/2173626.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: voice processing
Parkinson’s disease
non-linear analysis
convolutional network
przetwarzanie głosu
choroba Parkinsona
analiza nieliniowa
sieci konwolucyjne
Opis:: Voice acoustic analysis can be a valuable and objective tool supporting the diagnosis of many neurodegenerative diseases, especially in times of distant medical examination during the pandemic. The article compares the application of selected signal processing methods and machine learning algorithms for the taxonomy of acquired speech signals representing the vowel a with prolonged phonation in patients with Parkinson’s disease and healthy subjects. The study was conducted using three different feature engineering techniques for the generation of speech signal features as well as the deep learning approach based on the processing of images involving spectrograms of different time and frequency resolutions. The research utilized real recordings acquired in the Department of Neurology at the Medical University of Warsaw, Poland. The discriminatory ability of feature vectors was evaluated using the SVM technique. The spectrograms were processed by the popular AlexNet convolutional neural network adopted to the binary classification task according to the strategy of transfer learning. The results of numerical experiments have shown different efficiencies of the examined approaches; however, the sensitivity of the best test based on the selected features proposed with respect to biological grounds of voice articulation reached the value of 97% with the specificity no worse than 93%. The results could be further slightly improved thanks to the combination of the selected deep learning and feature engineering algorithms in one stacked ensemble model.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e137347
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Deep learning vs feature engineering in the assessment of voice signals for diagnosis in Parkinson’s disease
Autorzy:: Majda-Zdancewicz, Ewelina
Potulska-Chromik, Anna
Jakubowski, Jacek
Nojszewska, Monika
Kostera-Pruszczyk, Anna
Powiązania:: https://bibliotekanauki.pl/articles/2090742.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: voice processing
Parkinson’s disease
non-linear analysis
convolutional network
przetwarzanie głosu
choroba Parkinsona
analiza nieliniowa
sieci konwolucyjne
Opis:: Voice acoustic analysis can be a valuable and objective tool supporting the diagnosis of many neurodegenerative diseases, especially in times of distant medical examination during the pandemic. The article compares the application of selected signal processing methods and machine learning algorithms for the taxonomy of acquired speech signals representing the vowel a with prolonged phonation in patients with Parkinson’s disease and healthy subjects. The study was conducted using three different feature engineering techniques for the generation of speech signal features as well as the deep learning approach based on the processing of images involving spectrograms of different time and frequency resolutions. The research utilized real recordings acquired in the Department of Neurology at the Medical University of Warsaw, Poland. The discriminatory ability of feature vectors was evaluated using the SVM technique. The spectrograms were processed by the popular AlexNet convolutional neural network adopted to the binary classification task according to the strategy of transfer learning. The results of numerical experiments have shown different efficiencies of the examined approaches; however, the sensitivity of the best test based on the selected features proposed with respect to biological grounds of voice articulation reached the value of 97% with the specificity no worse than 93%. The results could be further slightly improved thanks to the combination of the selected deep learning and feature engineering algorithms in one stacked ensemble model.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e137347, 1--10
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Application of imaging techniques to objectify the Finger Tapping test used in the diagnosis of Parkinsons disease
Autorzy:: Jakubowski, Jacek
Potulska-Chromik, Anna
Chmielińska, Jolanta
Nojszewska, Monika
Kostera-Pruszczyk, Anna
Powiązania:: https://bibliotekanauki.pl/articles/2204532.pdf
Data publikacji:: 2023
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: image processing
medical diagnosis
Parkinson’s disease
finger tapping test
przetwarzanie obrazu
diagnoza medyczna
choroba Parkinsona
test stukania palcem
Opis:: Finger tapping is one of the standard tests for Parkinson's disease diagnosis performed to assess the motor function of patients' upper limbs. In clinical practice, the assessment of the patient's ability to perform the test is carried out visually and largely depends on the experience of clinicians. This article presents the results of research devoted to the objectification of this test. The methodology was based on the proposed measurement method consisting in frame processing of the video stream recorded during the test to determine the time series representing the distance between the index finger and the thumb. Analysis of the resulting signals was carried out in order to determine the characteristic features that were then used in the process of distinguishing patients with Parkinson's disease from healthy cases using methods of machine learning. The research was conducted with the participation of 21 patients with Parkinson's disease and 21 healthy subjects. The results indicate that it is possible to obtain the sensitivity and specificity of the proposed method at the level of approx. 80 %. However, the patients were in the so-called ON phase when symptoms are reduced due to medication, which was a much greater challenge compared to analyzing signals with clearly visible symptoms as reported in related works.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 2; art. no. e144886
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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