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. 2022 Aug 26:1-21.
doi: 10.1007/s12652-022-04342-6. Online ahead of print.

A generic optimization and learning framework for Parkinson disease via speech and handwritten records

Nada R Yousif  1 , Hossam Magdy Balaha  1 , Amira Y Haikal  1 , Eman M El-Gendy  1
Affiliations

A generic optimization and learning framework for Parkinson disease via speech and handwritten records

Nada R Yousif et al. J Ambient Intell Humaniz Comput. .

Abstract

Parkinson's disease (PD) is a neurodegenerative disorder with slow progression whose symptoms can be identified at late stages. Early diagnosis and treatment of PD can help to relieve the symptoms and delay progression. However, this is very challenging due to the similarities between the symptoms of PD and other diseases. The current study proposes a generic framework for the diagnosis of PD using handwritten images and (or) speech signals. For the handwriting images, 8 pre-trained convolutional neural networks (CNN) via transfer learning tuned by Aquila Optimizer were trained on the NewHandPD dataset to diagnose PD. For the speech signals, features from the MDVR-KCL dataset are extracted numerically using 16 feature extraction algorithms and fed to 4 different machine learning algorithms tuned by Grid Search algorithm, and graphically using 5 different techniques and fed to the 8 pretrained CNN structures. The authors propose a new technique in extracting the features from the voice dataset based on the segmentation of variable speech-signal-segment-durations, i.e., the use of different durations in the segmentation phase. Using the proposed technique, 5 datasets with 281 numerical features are generated. Results from different experiments are collected and recorded. For the NewHandPD dataset, the best-reported metric is 99.75% using the VGG19 structure. For the MDVR-KCL dataset, the best-reported metrics are 99.94% using the KNN and SVM ML algorithms and the combined numerical features; and 100% using the combined the mel-specgram graphical features and VGG19 structure. These results are better than other state-of-the-art researches.

Keywords: Feature extraction; Hyperparameters optimization; Machine learning (ML); Parkinson disease (PD); Speech segmentation; Transfer learning (TL); Voice segmentation.

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Figures

Fig. 1
Fig. 1
The Parkinson diseases learning and optimization framework
Fig. 2
Fig. 2
Samples from the NewHandPD dataset classes
Fig. 3
Fig. 3
Presentation of the proposed voice records segmentation approach
Fig. 4
Fig. 4
Sample graphs for each technique for the 60-second-segment-duration
Fig. 5
Fig. 5
Parkinson disease (PD) patient diagnosis
Fig. 6
Fig. 6
The NewHandPD experiments summarization
Fig. 7
Fig. 7
The MDVR-KCL experiments summarization
See this image and copyright information in PMC

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