TPat: Transition pattern feature extraction based Parkinson’s disorder detection using FNIRS signals

• Published in: Applied acoustics Vol. 228; p. 110307
• Main Authors: Tuncer, Turker, Tasci, Irem, Tasci, Burak, Hajiyeva, Rena, Tuncer, Ilknur, Dogan, Sengul
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.01.2025
• Subjects: Channel Transformer; Connectome; CWNCA; Graph; PD detection; TPat; XFE; PD detection; CWNCA; Graph; XFE; TPat; Channel Transformer; Connectome
• ISSN: 0003-682X
• DOI: 10.1016/j.apacoust.2024.110307

•A new feature extraction function, called Transition Pattern (TPat), has been used.•We have presented a new explainable feature engineering (XFE) model by deploying the TPat feature extractor.•A public Parkinson’s Disease (PD) fNIRS signal dataset has been utilized.•The connectome diagrams of PD patients have been extracted.•The proposed XFE model achieved 94.16% accuracy with LOSO and 100% with 10-fold cross-validations respectively. Parkinson’s Disease (PD) is one of the most commonly observed neurodegenerative disorders worldwide. Many researchers have utilized machine learning (ML) models to detect PD and understand its underlying causes automatically. In this research, our primary objective is to automatically detect PD and extract meaningful results using the proposed ML model. In this study, an FNIRS dataset collected from PD patients and control participants under three conditions—(i) rest, (ii) walking, and (iii) finger tapping—was utilized. A new explainable feature engineering (XFE) model was proposed to detect PD and automatically extract meaningful information under these conditions. The XFE model consists of four main phases: (i) feature extraction using the proposed channel transformation and transition pattern (TPat), (ii) feature selection employing cumulative weighted neighborhood component analysis (CWNCA), (iii) classification using the k-nearest neighbors (kNN) classifier, and (iv) channel network extraction to obtain explainable results. The suggested TPat-based XFE model was applied to the FNIRS dataset. This dataset included three distinct cases. Our model achieved over 94% classification accuracy using leave-one-subject-out cross-validation (LOSO CV) and 100% classification accuracy using 10-fold cross-validation. Additionally, channel transitions for each case were identified and discussed. Based on the results and findings, the proposed model demonstrated high accuracy in FNIRS signal classification and provided explainable results. In this regard, the presented TPat-based XFE model contributed significantly to both ML and neuroscience.