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Applicability of artificial intelligence in smart healthcare systems for automatic detection of Parkinson’s Disease

Pallathadka Harikumar, Padminivalli V. S.J.R.K., Vasavi M., Nancy P., Naved Mohd, Kumar Harish, Ray Samrat

Computer Assisted Methods in Engineering and Science

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2024

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Vol. 31, no. 2

175--185

EN

Parkinson’s disease is associated with memory loss, anxiety, and depression in the brain. Problems such as poor balance and difficulty during walking can be observed in addition to symptoms of impaired posture and rigidity. The field dedicated to making computers capable of learning autonomously, without having to be explicitly programmed, is known as machine learning. An approach to the diagnosis of Parkinson’s disease, which is based on artificial intelligence, is discussed in this article. The input for this system is provided through photographic examples of Parkinson’s disease patient handwriting. Received photos are preprocessed using the relief feature option to begin the process. This is helpful in the process of selecting characteristics for the identification of Parkinson’s disease. After that, the linear discriminant analysis (LDA) algorithm is employed to reduce the dimensions, bringing down the total number of dimensions that are present in the input data. The photos are then classified via radial basis function-support vector machine (SVM-RBF), k-nearest neighbors (KNN), and naive Bayes algorithms, respectively.

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Detekcja błędów segmentacji sygnału fotopletyzmograficznego

Bołtrukiewicz M.

Pomiary Automatyka Kontrola

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2014

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R. 60, nr 8

541--543

PL

Tematyka pracy związana jest z analizą zmienności rytmu serca, a dotyczy w szczególności detekcji błędów powstających podczas segmentacji procedury wyznaczającej zbiór okresów przebiegu. W artykule omówiono i zilustrowano podstawowe przyczyny błędów segmentacji. Zaproponowano dwa algorytmy detekcyjne wykorzystujące statystyczne przedziały tolerancji, które następnie przetestowano i oceniono przy użyciu posiadanego zbioru 5-minutowych przebiegów sygnału fotopletyzmograficznego.

EN

The paper concerns the detection of segmentation errors in a photoplethysmographic signal (PPG). In the paper, the causes of segmentation errors are considered. The technical causes are presented in Figs. 1 and 2 while the biological causes are shown in Fig. 3. Two algorithms of detection of errors are proposed. Both algorithms use statistical tolerance ranges, which are described by Eq. 1. The principles of operation of these algorithms are given in Eqs. 2 and 3. In the study the efficiency of these algorithms was evaluated using the factor of errors defined by Eq. 4. For both algorithms the sensitivity (SE), specificity (SP) and positive prediction value (PPV) and negative prediction value (NPV) were calculated, too. In the experiments real photoplethysmographic signals were analyzed. Time duration of each signal was equal to 5 min. The coefficients of errors obtained for both algorithms are presented in Fig. 4. The comparison of the sensitivity and the positive prediction value is shown in Fig. 5. The causes of differences between the obtained values of the coefficients are considered. The possibility of improvement of SE and PPV is also analyzed.

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Walidacja metod badawczych

Salata A.

Laboratorium - Przegląd Ogólnopolski

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2009

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nr 5

16--19

PL

Artykuł przedstawia wymagania dotyczące walidacji metod badawczych, definicje cech charakterystycznych metod badawczych oraz sposoby przedstawiania dowodów na spełnienie wymagań dotyczących konkretnego zastosowania metody badawczej.

EN

The article presents requirements concerning validation of research methods, definitions of characteristic features of research methods, as well as ways of presenting evidence of fulfilling the requirements concerning a specific use of a research method.

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Walidacja i niepewność metod analitycznych w laboratorium badawczym.

Pietrzak A.

Barwniki, Środki Pomocnicze

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2006

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nr 1-2

43-58

PL

Jednym z wymagań normy PN-EN ISO/IEC 17025 dotyczącej kompetencji laboratoriów badawczych jest walidacja wykorzystywanych metod badawczych oraz szacowanie niepewności pomiaru. W artykule przedstawiono sposób określania niepewności zgodnie z Eurachem Citac 2000.

EN

One of reguirement of the norm PN-EN ISO/IEC 17025 for the competence of testing laboratories is validation methods of measuring and estimation uncertainty of measurement Measurement method of uncertainty estimation according to Eurachem Citac 2000 have been presented.