{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T07:49:26Z","timestamp":1779263366116,"version":"3.51.4"},"reference-count":75,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T00:00:00Z","timestamp":1625788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The work was carried out within the framework of the state task","award":["0030-2019- 0009"],"award-info":[{"award-number":["0030-2019- 0009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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\u2013frequency 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\u2013frequency 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\u2013frequency 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\u2019s disease and healthy volunteers. It is demonstrated that new regularities useful for the high-accuracy diagnosis of Parkinson\u2019s disease can be revealed using the method of analyzing the wave train electrical activity and AUC diagrams.<\/jats:p>","DOI":"10.3390\/s21144700","type":"journal-article","created":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T10:50:38Z","timestamp":1625827838000},"page":"4700","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson\u2019s Disease Investigation"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1960-7289","authenticated-orcid":false,"given":"Olga Sergeevna","family":"Sushkova","sequence":"first","affiliation":[{"name":"Kotel\u2019nikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya 11-7, 125009 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexei Alexandrovich","family":"Morozov","sequence":"additional","affiliation":[{"name":"Kotel\u2019nikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya 11-7, 125009 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandra Vasilievna","family":"Gabova","sequence":"additional","affiliation":[{"name":"Institute of Higher Nervous Activity and Neurophysiology of RAS, Butlerova 5A, 117485 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexei Vyacheslavovich","family":"Karabanov","sequence":"additional","affiliation":[{"name":"FSBI \u201cResearch Center of Neurology\u201d, Volokolamskoe Shosse 80, 125367 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2704-6282","authenticated-orcid":false,"given":"Sergey Nikolaevich","family":"Illarioshkin","sequence":"additional","affiliation":[{"name":"FSBI \u201cResearch Center of Neurology\u201d, Volokolamskoe Shosse 80, 125367 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,9]]},"reference":[{"key":"ref_1","first-page":"187","article-title":"EMGs spectral analysis method for the objective diagnosis of different clinical forms of Parkinson\u2019s disease","volume":"36","author":"Andreeva","year":"1996","journal-title":"J. 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