Parkinson's Disease Detection - An Interpretable Approach to Temporal Audio Classification

Shah, Raj; Dave, Bhavi; Parekh, Nirali; Srivastava, Kriti

doi:10.1109/gcat55367.2022.9971881

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…However, a notable limitation of this study was that the researchers did not ensure that the training and validation sets were speaker-independent, which could potentially introduce biases and may limit the generalizability of the models’ performance [ 29 ]. Similarly, Shah et al employed a CNN-based model that analyzed 1 s speech chunks transformed into log-scaled mel spectrograms (LMS) for detecting PD from vowel phonations of /a/ and /i/, achieving 90.32% accuracy [ 30 ]. Another study employed a MobileNet CNN model with various types of spectrograms as input.…”

Section: Introductionmentioning

confidence: 99%

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

Malekroodi,

Madusanka,

Lee

et al. 2024

Bioengineering

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Speech impairments often emerge as one of the primary indicators of Parkinson’s disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification.

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Section: Introductionmentioning

confidence: 99%

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

Malekroodi,

Madusanka,

Lee

et al. 2024

Bioengineering

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Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing Homes

Zhou

Xiaoli³

et al. 2023

Electronics

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The intensification of population aging has brought pressure on public medical care. In order to reduce this pressure, we combined the image classification method with computer vision and used audio data that is easy to collect in nursing homes. Based on MelGAN, transfer learning, and Vision Transformer, we propose an application called Risevi (A Disease Risk Prediction Model Based on Vision Transformer), a disease risk prediction model for nursing homes. We first design a sample generation method based on MelGAN, then refer to the Mel frequency cepstral coefficient and the Wav2vec2 model to design the sample feature extraction method, perform floating-point operations on the tensor of the extracted features, and then convert it into a waveform. We then design a sample feature classification method based on transfer learning and Vision Transformer. Finally, we obtain the Risevi model. In this paper, we use public datasets and subject data as sample data. The experimental results show that the Risevi model has achieved an accuracy rate of 98.5%, a precision rate of 96.38%, a recall rate of 98.17%, and an F1 score of 97.15%. The experimental results show that the Risevi model can provide practical support for reducing public medical pressure.

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Parkinson's Disease Detection - An Interpretable Approach to Temporal Audio Classification

Cited by 2 publications

References 19 publications

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

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

Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing Homes

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