Diagnosing Parkinson’s Disease Using Multimodal Physiological Signals

Guo, Guoxin; Wang, Shujie; Wang, Shuaibin; Zhou, Zhiyu; Pei, Guangying; Yan, Tianyi

doi:10.1007/978-981-16-1288-6_9

Cited by 7 publications

(3 citation statements)

References 30 publications

(33 reference statements)

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“…It is mentioned that Huntington's disease will interfere with the recognition of negative emotions, especially anger, disgust, and fear [54]- [56]. Patients with affective decline who attack their brains best identify their emotions through physiological signals, including electroencephalography (EEG), electrocardiogram (ECG), photoplethysmography (PPG), and respiratory (RA), to classify PD and healthy control (HC) [57]. This is in line with other research that showed weaknesses in facial emotion recognition in these patients due to impairments in facial emotion mimicking [58].…”

Section: Research Topic In Multimodal Emotion Recognitionmentioning

confidence: 99%

A Systematic Literature Review of Multimodal Emotion Recognition

Rahayu,

Muharrom,

Windiarti

et al. 2023

SJI

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Purpose: This literature review aims to identify Multimodal Emotion Recognition (MER) in depth and breadth by analysing the topics, trends, modalities, and other supporting sources discussed in research over the years and between 2010 and 2022. Based on the screening analysis, a total of 14,533 articles were analysed to achieve this goal.Methods: This research was conducted in 3 (three) phases, including Planning, Conducting and Reporting. The first step was defining the research objectives by searching for systematic reviews with similar topics to this study, then reviewing them to develop research questions and systematic review protocols for this study. The second stage is to collect articles according to a pre-determined protocol, selecting the articles obtained and then conducting an analysis of the filtered articles in order to answer the research questions. The final stage is to summarize the results of the analysis so new findings from this research can be reported. Result: In general, the focus of MER research can be categorised into two issues, namely the object background and the source or modality of emotion recognition. When looking at the object background, most of the 55% to support emotion recognition with a health background, especially brain function decline, 34% based on age, 10% based on gender, 1% data collection situation and a small portion of less than 1% related to ethnic culture. In terms of the source of emotion recognition, research is divided into electromagnetic signals, voice signals, text, photo/video and the development of wearable devices. Based on the above results, there are at least 7 scientific fields that discuss MER research, namely health, psychology, electronics, grammar, communication, socio-culture and computer science.Novelty: MER research has the potential to develop further. There are still many areas that have received less attention, while the ecosystem that uses them has grown massively. Emotion recognition modalities are numerous and diverse, but research is still focused on validating the emotions of each modality, rather than exploring the strengths of each modality to improve the quality of recognition results.

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Section: Research Topic In Multimodal Emotion Recognitionmentioning

confidence: 99%

A Systematic Literature Review of Multimodal Emotion Recognition

Rahayu,

Muharrom,

Windiarti

et al. 2023

SJI

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“…Biofeedback therapy using SMR and ECG and RSP as feedback signals can effectively improve the motor symptoms of PD [26][27]39], while ECG, PPG, and RSP signals can improve nonmotor symptoms such as cognition and depression [40][41][42]. The weight parameters of EEG, ECG, PPG, and RSP signals were obtained from the research of Guo et al [43]. Brain Sci.…”

Section: Biofeedback Protocolmentioning

confidence: 99%

Multimodal biofeedback for Parkinson’s disease motor and nonmotor symptoms

Shi

Ding

Han

et al. 2023

Brain Science Advances

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Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor retardation, myotonia, quiescent tremor, and postural gait abnormality, as well as nonmotor symptoms such as anxiety and depression. Biofeedback improves motor and nonmotor functions of patients by regulating abnormal electroencephalogram (EEG), electrocardiogram (ECG), photoplethysmography (PPG), electromyography (EMG), respiration (RSP), or other physiological signals. Given that multimodal signals are closely related to PD states, the clinical effect of multimodal biofeedback on patients with PD is worth exploring. Twenty-one patients with PD in Beijing Rehabilitation Hospital were enrolled and divided into three groups: multimodal (EEG, ECG, PPG, and RSP feedback signal), EEG (EEG feedback signal), and sham (random feedback signal), and they received biofeedback training five times in two weeks. The combined clinical scale and multimodal signal analysis results revealed that the EEG group significantly improved motor symptoms and increased Berg balance scale scores by regulating β band activity; the multimodal group significantly improved nonmotor symptoms and reduced Hamilton rating scale for depression scores by improving θ band activity. Our preliminary results revealed that multimodal biofeedback can improve the clinical symptoms of PD, but the regulation effect on motor symptoms is weaker than that of EEG biofeedback.

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“…This was reflected in [65], which classified PD patients in the ON state vs. OFF state of medication, and in those studies that evaluated cognitive impairment [32,34,45,47,53]. Consequently, those articles that did not specify the clinical setting of the patients [37,40,42,43,46,51,53,55,56,[58][59][60][61][62][64][65][66][68][69][70], nor the status of the medication in the patients [32,33,35,41,50,54,63,71] were excluded from the evaluation process of the current state of diagnosis of PD, because they did not allow the objective evaluation of the results of the models used. These exclusions, based on quality criteria, resulted in 15 admissible articles.…”

Section: Quality Of the Datasetmentioning

confidence: 99%

Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review

2022

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Background: Parkinson’s disease (PD) affects 7–10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 ± 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.

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Diagnosing Parkinson’s Disease Using Multimodal Physiological Signals

Cited by 7 publications

References 30 publications

A Systematic Literature Review of Multimodal Emotion Recognition

A Systematic Literature Review of Multimodal Emotion Recognition

Multimodal biofeedback for Parkinson’s disease motor and nonmotor symptoms

Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review

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