Machine Learning Approaches for Detecting Parkinson’s Disease from EEG Analysis: A Systematic Review

Abstract

Diagnosis of Parkinson’s disease (PD) is mainly based on motor symptoms and can be supported by imaging techniques such as the single photon emission computed tomography (SPECT) or M-iodobenzyl-guanidine cardiac scintiscan (MIBG), which are expensive and not always available. In this review, we analyzed studies that used machine learning (ML) techniques to diagnose PD through resting state or motor activation electroencephalography (EEG) tests. Methods: The review process was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. All publications previous to May 2020 were included, and their main characteristics and results were assessed and documented. Results: Nine studies were included. Seven used resting state EEG and two motor activation EEG. Subsymbolic models were used in 83.3% of studies. The accuracy for PD classification was 62–99.62%. There was no standard cleaning protocol for the EEG and a great heterogeneity in the characteristics that were extracted from the EEG. However, spectral characteristics predominated. Conclusions: Both the features introduced into the model and its architecture were essential for a good performance in predicting the classification. On the contrary, the cleaning protocol of the EEG, is highly heterogeneous among the different studies and did not influence the results. The use of ML techniques in EEG for neurodegenerative disorders classification is a recent and growing field.

Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges

“…The utilization of biosignals along with ML techniques has grown in recent years, and has been applied to several different diseases. Related to PD, various studies show the importance of those techniques for identifying PD using electroencephalography (EEG) [ 36 ] or for diagnosing and assessing PD using inertial sensors or video signals [ 37 ]. There are other diseases being assessed that utilize the same approach; for example, identifying atrial fibrillation using an electrocardiogram (ECG) and applying ML techniques to identify potential alterations [ 38 ], or diagnosing Alzheimer’s disease using the ML algorithms by processing sensor movement data from patients [ 39 , 40 ].…”

Artificial Intelligence in Translational Medicine

“…Similarly, comparable procedures, extracting specific features from available data, allowing the development of ML-based models for Parkinson's disease (PD). In fact, as reported for AD, several studies highlighted that through ML-based approaches applied to PD [279], it is possible to predict the progression of the disorder by employing serum cytokines [280], MRI [281], and walking tests [282]; to estimate the state of PD, employing longitudinal data [283]; to rate the main symptoms (resting tremor and bradykinesia) [284]; and to produce a correct diagnosis from EEG analysis [285,286] and from voice datasets [287,288].…”

On the Use of Machine Learning for Classifying Auditory Brainstem Responses: A Scoping Review

“…To assess the quality of the twelve targeted studies, the reporting items from the checklist described in Table 1 from [45] were compared to the content of the publications. This checklist requested information about the study's nature, objectives, rationale, data collection setup, machine learning models and algorithms, theoretical claims, datasets, validation metrics, experimental results, clinical applications, limitations, and unexpected results.…”