Speech signals-based Parkinson’s disease diagnosis using hybrid autoencoder-LSTM models

Parkinson’s disease (PD) is a neurodegenerative disorder that occurs as a result of a decrease in the chemical called dopamine in the brain. There is no definitive treatment for PD, but some medications used to control symptoms in the early stages have a critical effect on the progression of the dis...

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Veröffentlicht in:Computers in biology and medicine Jg. 193; S. 110334
Hauptverfasser: Tekindor, Ayşe Nur, Akman Aydın, Eda
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Ltd 01.07.2025
Schlagworte:
Aged
Autoencoder
Deep learning
Humans
Internal Medicine
Long short-term memory
Neural Networks, Computer
Other
Parkinson Disease - diagnosis
Parkinson Disease - physiopathology
Parkinson’s disease
Signal Processing, Computer-Assisted
Speech - physiology
Speech analysis
Deep learning
Long short-term memory
Speech analysis
Autoencoder
Parkinson’s disease
ISSN:0010-4825, 1879-0534, 1879-0534
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Zusammenfassung:Parkinson’s disease (PD) is a neurodegenerative disorder that occurs as a result of a decrease in the chemical called dopamine in the brain. There is no definitive treatment for PD, but some medications used to control symptoms in the early stages have a critical effect on the progression of the disease. Approximately 90% of patients with PD have vocal problems, and although voice disorders seen in the early stages are not apparent in the patient’s speech, they can be detected by acoustic analysis. In this study, a decision support system was proposed for the diagnosis of PD utilizing the feature extraction power of autoencoder (AE) & long short-term memory (LSTM) models by using speech signals as input data. Firstly, simple (SAE), convolutional (CAE), and recurrent (RAE) AE models were created for the ablation analysis. Then, the effect of hybridization and deepening of these models with LSTM layers on the classification performance was observed. Within the scope of the study, RAE achieved the highest accuracy among the base models while CAE & LSTM hybrid model provided the highest performance among all models with 95.79% accuracy for PD diagnosis based on audio signals. It was concluded that hybridization of the AE and LSTM models significantly improved the performance of simple and convolutional AE, and deepening the network to a certain extent improves the classification performance according to the type of AE. •Diagnosability of PD based on sound signals with 1D autoencoders.•93.53% accuracy with RAE model.•95.79% accuracy with CAE and LSTM hybrid model.•Highest accuracy with CAE based hybrid models among all hybrid models.•Improved performance and more balanced results with deepened hybrid models.
Bibliographie:ObjectType-Article-1
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2025.110334