PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network
Parkinson’s disease (PD) is a progressive degenerative brain disease that worsens with age, causing areas of the brain to weaken. Vocal dysfunction often emerges as one of the earliest and most prominent indicators of Parkinson’s disease, with a significant number of patients exhibiting vocal impair...
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MDPI AG
2025-02-01
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| author | Munira Islam Khadija Akter Md. Azad Hossain M. Ali Akber Dewan |
| author_facet | Munira Islam Khadija Akter Md. Azad Hossain M. Ali Akber Dewan |
| author_sort | Munira Islam |
| collection | DOAJ |
| description | Parkinson’s disease (PD) is a progressive degenerative brain disease that worsens with age, causing areas of the brain to weaken. Vocal dysfunction often emerges as one of the earliest and most prominent indicators of Parkinson’s disease, with a significant number of patients exhibiting vocal impairments during the initial stages of the illness. In view of this, to facilitate the diagnosis of Parkinson’s disease through the analysis of these vocal characteristics, this study focuses on exerting a combination of mel spectrogram and MFCC as spectral features. This study adopts Italian raw audio data to establish an efficient detection framework specifically designed to classify the vocal data into two distinct categories: healthy individuals and patients diagnosed with Parkinson’s disease. To this end, the study proposes a hybrid model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the detection of Parkinson’s disease. Certainly, CNNs are employed to extract spatial features from the extracted spectro-temporal characteristics of vocal data, while LSTMs capture temporal dependencies, accelerating a comprehensive analysis of the development of vocal patterns over time. Additionally, the merging of a multi-head attention mechanism significantly enhances the model’s ability to concentrate on essential details, hence improving its overall performance. This unified method aims to enhance the detection of subtle vocal changes associated with Parkinson’s, enhancing overall diagnostic accuracy. The findings declare that this model achieves a noteworthy accuracy of 99.00% for the Parkinson’s disease detection process. |
| format | Article |
| id | doaj-art-0cf0db041d1d43aa82c80fb749288eae |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-0cf0db041d1d43aa82c80fb749288eae2025-08-20T03:11:21ZengMDPI AGInformation2078-24892025-02-0116213510.3390/info16020135PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural NetworkMunira Islam0Khadija Akter1Md. Azad Hossain2M. Ali Akber Dewan3Department of Electronics and Telecommunication Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, BangladeshDepartment of Electronics and Telecommunication Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, BangladeshDepartment of Electronics and Telecommunication Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, BangladeshSchool of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Athabasca, AB T9S 3A3, CanadaParkinson’s disease (PD) is a progressive degenerative brain disease that worsens with age, causing areas of the brain to weaken. Vocal dysfunction often emerges as one of the earliest and most prominent indicators of Parkinson’s disease, with a significant number of patients exhibiting vocal impairments during the initial stages of the illness. In view of this, to facilitate the diagnosis of Parkinson’s disease through the analysis of these vocal characteristics, this study focuses on exerting a combination of mel spectrogram and MFCC as spectral features. This study adopts Italian raw audio data to establish an efficient detection framework specifically designed to classify the vocal data into two distinct categories: healthy individuals and patients diagnosed with Parkinson’s disease. To this end, the study proposes a hybrid model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the detection of Parkinson’s disease. Certainly, CNNs are employed to extract spatial features from the extracted spectro-temporal characteristics of vocal data, while LSTMs capture temporal dependencies, accelerating a comprehensive analysis of the development of vocal patterns over time. Additionally, the merging of a multi-head attention mechanism significantly enhances the model’s ability to concentrate on essential details, hence improving its overall performance. This unified method aims to enhance the detection of subtle vocal changes associated with Parkinson’s, enhancing overall diagnostic accuracy. The findings declare that this model achieves a noteworthy accuracy of 99.00% for the Parkinson’s disease detection process.https://www.mdpi.com/2078-2489/16/2/135Parkinson diseasedegenerativespectral featureshybrid modelmulti-head attention |
| spellingShingle | Munira Islam Khadija Akter Md. Azad Hossain M. Ali Akber Dewan PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network Information Parkinson disease degenerative spectral features hybrid model multi-head attention |
| title | PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network |
| title_full | PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network |
| title_fullStr | PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network |
| title_full_unstemmed | PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network |
| title_short | PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network |
| title_sort | pd net parkinson s disease detection through fusion of two spectral features using attention based hybrid deep neural network |
| topic | Parkinson disease degenerative spectral features hybrid model multi-head attention |
| url | https://www.mdpi.com/2078-2489/16/2/135 |
| work_keys_str_mv | AT muniraislam pdnetparkinsonsdiseasedetectionthroughfusionoftwospectralfeaturesusingattentionbasedhybriddeepneuralnetwork AT khadijaakter pdnetparkinsonsdiseasedetectionthroughfusionoftwospectralfeaturesusingattentionbasedhybriddeepneuralnetwork AT mdazadhossain pdnetparkinsonsdiseasedetectionthroughfusionoftwospectralfeaturesusingattentionbasedhybriddeepneuralnetwork AT maliakberdewan pdnetparkinsonsdiseasedetectionthroughfusionoftwospectralfeaturesusingattentionbasedhybriddeepneuralnetwork |