Multi-label speech feature selection for Parkinson’s Disease subtype recognition using graph model

• Published in: Computers in biology and medicine Vol. 185; p. 109566
• Main Authors: Ji, Wei, Fu, Yuchen, Zheng, Huifen, Li, Yun
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.02.2025; Elsevier Limited
• Subjects: Aged; Algorithms; Cognitive tasks; Dysphagia; Feature extraction; Feature selection; Female; Graphical representations; Humans; Internal Medicine; Labels; Male; Middle Aged; Movement disorders; Multi-label feature selection; Neurodegenerative diseases; Other; Parkinson Disease - classification; Parkinson Disease - diagnosis; Parkinson Disease - physiopathology; Parkinson's disease; Parkinson’s Disease subtype recognition; Signal Processing, Computer-Assisted; Speech; Speech - physiology; Speech recognition; Speech signal processing; Tremor; Parkinson’s Disease subtype recognition; Speech signal processing; Multi-label feature selection
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.109566

Parkinson’s Disease (PD) is the second-most common neurodegenerative disorder. There is a certain pathological connection between PD and dysphonia. Speech signals have been successfully used to identify PD and predict its severity. Moreover, PD has several subtypes, such as tremor, freezing of gait and dysphagia. The recognition of subtypes is of great significance for the diagnosis and treatment of PD. In this paper, we consider PD subtype recognition as a multi-label learning task and try to simultaneously recognize these subtypes using speech signals. In the proposed recognition framework, multiple types of speech data are collected, such as/a/,/pa-ka-la/, etc., and different speech features are extracted from different types of speech data. The features are concatenated as the representation of speech data. Especially, a multi-label speech feature selection algorithm based on graph structure is proposed to choose the key features and followed by a multi-label classifier for PD subtype recognition. The speech samples of 70 PD patients are collected as speech corpus. Experimental results show that the proposed multi-label feature selection method can obtain higher recognition performance than other classical ones in most cases. •The multiple types of Parkinson’s Disease (PD) speech data are collected.•Multiple PD subtype recognition based on speech.•Consider PD subtype recognition as multi-label learning paradigm.•A multi-label PD speech feature selection algorithm is presented.