Your browser doesn't support javascript.
loading
FLP: Factor lattice pattern-based automated detection of Parkinson's disease and specific language impairment using recorded speech.
Tuncer, Turker; Dogan, Sengul; Baygin, Mehmet; Barua, Prabal Datta; Palmer, Elizabeth Emma; March, Sonja; Ciaccio, Edward J; Tan, Ru-San; Acharya, U Rajendra.
Afiliación
  • Tuncer T; Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey. Electronic address: turkertuncer@firat.edu.tr.
  • Dogan S; Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey. Electronic address: sdogan@firat.edu.tr.
  • Baygin M; Department of Computer Engineering, Faculty of Engineering and Architecture, Erzurum Technical University, Erzurum, Turkey. Electronic address: mehmet.baygin@erzurum.edu.tr.
  • Barua PD; School of Business (Information System), University of Southern Queensland, Australia. Electronic address: Prabal.Barua@usq.edu.au.
  • Palmer EE; Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick, 2031, Australia; School of Women's and Children's Health, University of New South Wales, Randwick, 2031, Australia. Electronic address: elizabeth.palmer@unsw.edu.au.
  • March S; School of Psychology and Counselling and Centre for Health Research, University of Southern Queensland, Springfield, Australia. Electronic address: Sonja.March@unisq.edu.au.
  • Ciaccio EJ; Department of Medicine, Columbia University Irving Medical Center, USA. Electronic address: ciaccio@columbia.edu.
  • Tan RS; Department of Cardiology, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore.
  • Acharya UR; School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia; Centre for Health Research, University of Southern Queensland, Australia. Electronic address: Rajendra.Acharya@usq.edu.au.
Comput Biol Med ; 173: 108280, 2024 May.
Article en En | MEDLINE | ID: mdl-38547655
ABSTRACT

BACKGROUND:

Timely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures. MATERIALS AND

METHODS:

In this research, we have introduced an advanced feature engineering model incorporating a novel feature extraction function, the Factor Lattice Pattern (FLP), which is a quantum-inspired method and uses a superposition-like mechanism, making it dynamic in nature. The FLP encompasses eight distinct patterns, from which the most appropriate pattern was discerned based on the data structure. Through the implementation of the FLP, we automatically extracted signal-specific textural features. Additionally, we developed a new feature engineering model to assess the efficacy of the FLP. This model is self-organizing, producing nine potential results and subsequently choosing the optimal one. Our speech classification framework consists of (1) feature extraction using the proposed FLP and a statistical feature extractor; (2) feature selection employing iterative neighborhood component analysis and an intersection-based feature selector; (3) classification via support vector machine and k-nearest neighbors; and (4) outcome determination using combinational majority voting to select the most favorable results.

RESULTS:

To validate the classification capabilities of our proposed feature engineering model, designed to automatically detect PD and SLI, we employed three speech datasets of PD and SLI patients. Our presented FLP-centric model achieved classification accuracy of more than 95% and 99.79% for all PD and SLI datasets, respectively.

CONCLUSIONS:

Our results indicate that the proposed model is an accurate alternative to deep learning models in classifying neurological conditions using speech signals.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Trastorno Específico del Lenguaje Límite: Child / Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Trastorno Específico del Lenguaje Límite: Child / Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos