Leveraging Deep Learning for Fine-Grained Categorization of Parkinson’s Disease Progression Levels through Analysis of Vocal Acoustic Patterns

Malekroodi, Hadi Sedigh; Madusanka, Nuwan; Lee, Byeong-il; Yi, Myunggi

doi:10.3390/bioengineering11030295

Cited by 4 publications

(1 citation statement)

References 54 publications

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“…In order to enhance the accuracy of feature representation and the results of classification, authors in Malekroodi et al ( 2024 ) have implemented three separate component selection procedures. These strategies enable each of the 23 features to identify and pick the top 10 most effective features.…”

Section: Related Workmentioning

confidence: 99%

SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson's disease detection

Kim,

Krichen,

Ojo

et al. 2024

Front. Comput. Neurosci.

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Parkinson's disease (PD) is a globally significant health challenge, necessitating accurate and timely diagnostic methods to facilitate effective treatment and intervention. In recent years, self-supervised deep representation pattern learning (SS-DRPL) has emerged as a promising approach for extracting valuable representations from data, offering the potential to enhance the efficiency of voice-based PD detection. This research study focuses on investigating the utilization of SS-DRPL in conjunction with deep learning algorithms for voice-based PD classification. This study encompasses a comprehensive evaluation aimed at assessing the accuracy of various predictive models, particularly deep learning methods when combined with SS-DRPL. Two deep learning architectures, namely hybrid Long Short-Term Memory and Recurrent Neural Networks (LSTM-RNN) and Deep Neural Networks (DNN), are employed and compared in terms of their ability to detect voice-based PD cases accurately. Additionally, several traditional machine learning models are also included to establish a baseline for comparison. The findings of the study reveal that the incorporation of SS-DRPL leads to improved model performance across all experimental setups. Notably, the LSTM-RNN architecture augmented with SS-DRPL achieves the highest F1-score of 0.94, indicating its superior ability to detect PD cases using voice-based data effectively. This outcome underscores the efficacy of SS-DRPL in enabling deep learning models to learn intricate patterns and correlations within the data, thereby facilitating more accurate PD classification.

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Section: Related Workmentioning

confidence: 99%

SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson's disease detection

Kim,

Krichen,

Ojo

et al. 2024

Front. Comput. Neurosci.

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Transformer-based transfer learning on self-reported voice recordings for Parkinson’s disease diagnosis

Tougui,

Zakroum,

Karrakchou

et al. 2024

Sci Rep

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Deep learning (DL) techniques are becoming more popular for diagnosing Parkinson’s disease (PD) because they offer non-invasive and easily accessible tools. By using advanced data analysis, these methods improve early detection and diagnosis, which is crucial for managing the disease effectively. This study explores end-to-end DL architectures, such as convolutional neural networks and transformers, for diagnosing PD using self-reported voice data collected via smartphones in everyday settings. Transfer learning was applied by starting with models pre-trained on large datasets from the image and the audio domains and then fine-tuning them on the mPower voice data. The Transformer model pre-trained on the voice data performed the best, achieving an average AUC of and an average AUPRC of , outperforming models trained from scratch. To the best of our knowledge, this is the first use of a Transformer model for audio data in PD diagnosis, using this dataset. We achieved better results than previous studies, whether they focused solely on the voice or incorporated multiple modalities, by relying only on the voice as a biomarker. These results show that using self-reported voice data with state-of-the-art DL architectures can significantly improve PD prediction and diagnosis, potentially leading to better patient outcomes.

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Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples

Rahmatallah,

Kemp,

Iyer

et al. 2024

Preprint

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Machine learning approaches including deep learning models have shown promising performance in the automatic detection of Parkinson’s disease. These approaches rely on different types of data with voice recordings being the most used due to the convenient and non-invasive nature of data acquisition. Our group has successfully developed a novel approach that uses convolutional neural network with transfer learning to analyze spectrogram images of the sustained vowel /a/ to identify people with Parkinson’s disease. We tested this approach by collecting a dataset of voice recordings via telephone lines, which have limited bandwidth. This study builds upon our prior results in two major ways: First, we tested the performance of our approach on a larger voice dataset recorded using smartphones with wide bandwidth. Our results show comparable performance between two datasets generated using different recording platforms where we report differences in most important features resulting from the limited bandwidth of telephonic lines. Second, we compared the classification performance achieved using linear-scale and mel-scale spectrogram images and showed a small but statistically significant gain using mel-scale spectrograms. The convolutional neural network with transfer learning approach showed superior performance against conventional machine learning methods that collapse measurements across time to generate feature vectors.

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Leveraging Deep Learning for Fine-Grained Categorization of Parkinson’s Disease Progression Levels through Analysis of Vocal Acoustic Patterns

Cited by 4 publications

References 54 publications

SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson's disease detection

SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson's disease detection

Transformer-based transfer learning on self-reported voice recordings for Parkinson’s disease diagnosis

Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples

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