Entropy-Based Machine Learning Model for Fast Diagnosis and Monitoring of Parkinson’s Disease

Abstract

This study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson’s disease (PD) using rest-state EEG signals (rs-EEG) from 20 PD subjects and 20 normal control (NC) subjects at a sampling rate of 128 Hz. Based on the comparative analysis of the effectiveness of entropy calculation methods, fuzzy entropy showed the best results in diagnosing and monitoring PD using rs-EEG, with classification accuracy (ARKF) of ~99.9%. The most important frequency range of rs-EEG for PD-based diagnostics lies in the range of 0–4 Hz, and the most informative signals were mainly received from the right hemisphere of the head. It was also found that ARKF significantly decreased as the length of rs-EEG segments decreased from 1000 to 150 samples. Using a procedure for selecting the most informative features, it was possible to reduce the computational costs of classification by 11 times, while maintaining an ARKF ~99.9%. The proposed method can be used in the healthcare internet of things (H-IoT), where low-performance edge devices can implement ML sensors to enhance human resilience to PD.

Exploring the Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps

“…The fact that FuzzyEn turned out to be more effective in classifying short (N = 300) time series than NNetEn confirmed the results of our work on the classification of EEG signals [1]. Moreover, experimental work [1,3] showed that FuzzyEn was the most effective compared to the other entropies, such as SampEn, SVDEn, and PermEn. However, individual pairs of time series can be better classified by NNetEn; this was also confirmed in the EEG experiment [1], where one channel performed better when using NNetEn as a feature.…”

“…The classification of time series based on entropy analysis and machine learning (ML) is a trending task in the study of nonlinear signals in the fields of finance, biology, and medicine, for example, in EEG classification in diagnosing Alzheimer's disease [1,2] and Parkinson's disease [3][4][5][6]. The creation of Brain-Computer Interfacing (BCI) [7] enables the classification of the movements of body parts according to EEG signals.…”

“…There are many types of entropies, which in turn have several customizable parameters; for example, sample entropy (SampEn) [12], cosine similarity entropy (CoSiEn) [13], singular value decomposition entropy (SVDEn) [14], fuzzy entropy (FuzzyEn) [3,[15][16][17][18], permutation entropy (PermEn) [19], etc. A promising research direction is the development of new types of entropies or modifications of known entropy types [20,21].…”

“…The effectiveness of using FuzzyEn and NNetEn was shown on EEG signals in diagnosing Alzheimer's disease [1]. The effectiveness of fuzzy entropy in diagnosing Parkinson's disease was also demonstrated in the paper by Belyaev et al [3]. It was experimentally shown that FuzzyEn has an advantage over other entropies when classifying EEG signals with several elements in a time series of ~100-1000.…”

“…As a result, the authors of this work had the idea of studying whether or not the efficiency of FuzzyEn is universal and applicable to a series of different dynamics. In this paper, the authors analyzed two entropies, FuzzyEn and NNetEn, with the most effective settings taken from the works [1,3], and conducted a test on artificially created databases based on chaotic mappings. In addition, we explored the option of preprocessing time series using HVG transformation.…”