A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease Investigation
A statistical method for exploratory data analysis based on 2D and 3D area under curve (AUC) diagrams was developed. The method was designed to analyze electroencephalogram (EEG), electromyogram (EMG), and tremorogram data collected from patients with Parkinson’s disease. The idea of the method of w...
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doaj-1f6efe0ae9c4431781886b1452384c5c2021-07-23T14:05:22ZengMDPI AGSensors1424-82202021-07-01214700470010.3390/s21144700A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease InvestigationOlga Sergeevna Sushkova0Alexei Alexandrovich Morozov1Alexandra Vasilievna Gabova2Alexei Vyacheslavovich Karabanov3Sergey Nikolaevich Illarioshkin4Kotel’nikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya 11-7, 125009 Moscow, RussiaKotel’nikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya 11-7, 125009 Moscow, RussiaInstitute of Higher Nervous Activity and Neurophysiology of RAS, Butlerova 5A, 117485 Moscow, RussiaFSBI “Research Center of Neurology”, Volokolamskoe Shosse 80, 125367 Moscow, RussiaFSBI “Research Center of Neurology”, Volokolamskoe Shosse 80, 125367 Moscow, RussiaA statistical method for exploratory data analysis based on 2D and 3D area under curve (AUC) diagrams was developed. The method was designed to analyze electroencephalogram (EEG), electromyogram (EMG), and tremorogram data collected from patients with Parkinson’s disease. The idea of the method of wave train electrical activity analysis is that we consider the biomedical signal as a combination of the wave trains. The wave train is the increase in the power spectral density of the signal localized in time, frequency, and space. We detect the wave trains as the local maxima in the wavelet spectrograms. We do not consider wave trains as a special kind of signal. The wave train analysis method is different from standard signal analysis methods such as Fourier analysis and wavelet analysis in the following way. Existing methods for analyzing EEG, EMG, and tremor signals, such as wavelet analysis, focus on local time–frequency changes in the signal and therefore do not reveal the generalized properties of the signal. Other methods such as standard Fourier analysis ignore the local time–frequency changes in the characteristics of the signal and, consequently, lose a large amount of information that existed in the signal. The method of wave train electrical activity analysis resolves the contradiction between these two approaches because it addresses the generalized characteristics of the biomedical signal based on local time–frequency changes in the signal. We investigate the following wave train parameters: wave train central frequency, wave train maximal power spectral density, wave train duration in periods, and wave train bandwidth. We have developed special graphical diagrams, named AUC diagrams, to determine what wave trains are characteristic of neurodegenerative diseases. In this paper, we consider the following types of AUC diagrams: 2D and 3D diagrams. The technique of working with AUC diagrams is illustrated by examples of analysis of EMG in patients with Parkinson’s disease and healthy volunteers. It is demonstrated that new regularities useful for the high-accuracy diagnosis of Parkinson’s disease can be revealed using the method of analyzing the wave train electrical activity and AUC diagrams.https://www.mdpi.com/1424-8220/21/14/4700electromyogramEMGexploratory data analysiswave train electrical activity analysis methodwave trainswavelets |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Olga Sergeevna Sushkova Alexei Alexandrovich Morozov Alexandra Vasilievna Gabova Alexei Vyacheslavovich Karabanov Sergey Nikolaevich Illarioshkin |
spellingShingle |
Olga Sergeevna Sushkova Alexei Alexandrovich Morozov Alexandra Vasilievna Gabova Alexei Vyacheslavovich Karabanov Sergey Nikolaevich Illarioshkin A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease Investigation Sensors electromyogram EMG exploratory data analysis wave train electrical activity analysis method wave trains wavelets |
author_facet |
Olga Sergeevna Sushkova Alexei Alexandrovich Morozov Alexandra Vasilievna Gabova Alexei Vyacheslavovich Karabanov Sergey Nikolaevich Illarioshkin |
author_sort |
Olga Sergeevna Sushkova |
title |
A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease Investigation |
title_short |
A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease Investigation |
title_full |
A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease Investigation |
title_fullStr |
A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease Investigation |
title_full_unstemmed |
A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease Investigation |
title_sort |
statistical method for exploratory data analysis based on 2d and 3d area under curve diagrams: parkinson’s disease investigation |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-07-01 |
description |
A statistical method for exploratory data analysis based on 2D and 3D area under curve (AUC) diagrams was developed. The method was designed to analyze electroencephalogram (EEG), electromyogram (EMG), and tremorogram data collected from patients with Parkinson’s disease. The idea of the method of wave train electrical activity analysis is that we consider the biomedical signal as a combination of the wave trains. The wave train is the increase in the power spectral density of the signal localized in time, frequency, and space. We detect the wave trains as the local maxima in the wavelet spectrograms. We do not consider wave trains as a special kind of signal. The wave train analysis method is different from standard signal analysis methods such as Fourier analysis and wavelet analysis in the following way. Existing methods for analyzing EEG, EMG, and tremor signals, such as wavelet analysis, focus on local time–frequency changes in the signal and therefore do not reveal the generalized properties of the signal. Other methods such as standard Fourier analysis ignore the local time–frequency changes in the characteristics of the signal and, consequently, lose a large amount of information that existed in the signal. The method of wave train electrical activity analysis resolves the contradiction between these two approaches because it addresses the generalized characteristics of the biomedical signal based on local time–frequency changes in the signal. We investigate the following wave train parameters: wave train central frequency, wave train maximal power spectral density, wave train duration in periods, and wave train bandwidth. We have developed special graphical diagrams, named AUC diagrams, to determine what wave trains are characteristic of neurodegenerative diseases. In this paper, we consider the following types of AUC diagrams: 2D and 3D diagrams. The technique of working with AUC diagrams is illustrated by examples of analysis of EMG in patients with Parkinson’s disease and healthy volunteers. It is demonstrated that new regularities useful for the high-accuracy diagnosis of Parkinson’s disease can be revealed using the method of analyzing the wave train electrical activity and AUC diagrams. |
topic |
electromyogram EMG exploratory data analysis wave train electrical activity analysis method wave trains wavelets |
url |
https://www.mdpi.com/1424-8220/21/14/4700 |
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