A Visibility Graph Approach for Multi-stage Classification of Parkinson's Disease Using Multimodal Data

Parkinson's disease (PD) is a neurodegenerative disorder characterized by several motor symptoms such as resting tremor, muscular rigidity, slowness of movement and different speech impairments. PD is a kind of singular, multi-system disorder that gradually worsen over the time. In this researc...

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Veröffentlicht in:IEEE access 2024-01, Vol.12, p.1-1
Hauptverfasser: Deepa Raj, K, Jyothish Lal, G., Gopalakrishnan, E. A., Sowmya, V., Orozco-Arroyave, Juan Rafael
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Orozco-Arroyave, Juan Rafael
description Parkinson's disease (PD) is a neurodegenerative disorder characterized by several motor symptoms such as resting tremor, muscular rigidity, slowness of movement and different speech impairments. PD is a kind of singular, multi-system disorder that gradually worsen over the time. In this research, we classify the neurological state of the patients with Parkinson's disease (PwPD) according to the third section of the Movement Disorders Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS-III) using multimodal bio-signal data. As PD advances from low to advanced state, PwPD finds it difficult in their speech production and irregularities in their gait patterns. Monitoring the chaotic nature of time series data corresponding to speech and gait biomarkers can provide insights into the progression of the condition across different stages. This work for the first time analyze PD in a complex system perspective while representing the biomarkers as complex networks. The time-series corresponding to speech and gait signals are represented separately, as a complex network using the visibility graph algorithm. The characterization of the different stages of PD is explored for each modalities using different network features. Performance evaluation shows that the results obtained using the multimodal configuration of speech and gait left foot signals outperform the state-of the-art method. Moreover, performance comparison with the unimodal counterparts proves the need for multimodal assessment of PD severity. The configuration 'speech and gait left foot' outperforms (in terms of accuracy) that of the unimodal by 32% in speech, 3% in gait left foot, 19% in gait right foot, and 3% in gait both feet.
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subjects Accuracy
Algorithms
Biomarkers
Complex Network
Complex networks
Complex systems
Configurations
Feature extraction
Gait
Gait recognition
Gait signal
Graph
Graphical representations
Motors
Multi-modal Data
Multimodal sensors
Network Features
Parkinson's disease
Performance evaluation
Signs and symptoms
Speech
Speech signal
Task analysis
Time series
Visibility
Visibility Network
title A Visibility Graph Approach for Multi-stage Classification of Parkinson's Disease Using Multimodal Data
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