A systematic approach to diagnose Parkinson’s disease through kinematic features extracted from handwritten drawings

Lamba, Rohit; Gulati, Tarun; Al‐Dhlan, Kawther A.; Jain, Anurag

doi:10.1007/s40860-021-00130-9

Cited by 37 publications

(21 citation statements)

References 27 publications

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“…Lamba [15] proposed a kinematic feature extraction method from the handwritten document for the detection of parkinson's disease. The synthetic minority oversampling technique (SMOTE) method was applied to handle the imbalanced dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The SVM has a lower performance in handling the imbalanced data for the classification. The proposed NDR-R2CNN method has an accuracy of The proposed NDR-R2CNN model is evaluated in the dysgraphia classification and compared with existing methods [11][12][13][14][15]. Various existing methods were applied for the classification of dysgraphia and achieved considerable performance.…”

Section: Quantitative Analysismentioning

confidence: 99%

“…The proposed NDR-R2CNN model is compared with existing methods such as TestGraphia [11], CNN [12], Partial Correlation Regression Model [13], PCA + Clustering [14], and SMOTE + SVM [15], as shown in Table 3. The result shows that the proposed NDR-R2CNN has higher performance compared to existing methods.…”

Section: Quantitative Analysismentioning

confidence: 99%

“…Machine learning is a International Journal of Intelligent Engineering and Systems, Vol. 15 common approach for image and signal classification due to its efficiency. Machine learning is applied for the classification of handwriting text line images and speech signals [8].…”

Section: Introductionmentioning

confidence: 99%

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Dysgraphia Classification based on the Non-Discrimination Regularization in Rotational Region Convolutional Neural Network

2022

IJIES

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Dysgraphia is a handwriting disorder and the classification of dysgraphia in children's handwritten images helps to identify the dysgraphia patient effectively and also prevents low self-esteem. Traditional methods of dysgraphia classification based on experts manually classify the dysgraphia that requires more cost and time. Few researches apply the machine learning and deep learning techniques for the classification of dysgraphia and existing models have the limitations of overfitting problems in the training process. In this research, the Non-Discrimination Regularization in Rotational Region Convolutional Neural Network (NDR-R2CNN) is proposed to improve the efficiency of dysgraphia classification. The balancing parameters are introduced in the loss function to balance the class in the training and eliminate the features to reduce the overfitting problem. The collected children's handwriting data were used to evaluate the performance of the proposed NDR-R2CNN model. The proposed NDR-R2CNN model has the advantages of effective feature analysis and non-discrimination word analysis. The NDR-R2CNN model has the accuracy of 98.2 % and the SMOTE+SVM method has 90.2 % accuracy in Dysgraphia classification. The result shows that the NDR-R2CNN model has 98.2 % accuracy and the existing CNN has the accuracy of 94.2 %.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Quantitative Analysismentioning

confidence: 99%

Section: Quantitative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dysgraphia Classification based on the Non-Discrimination Regularization in Rotational Region Convolutional Neural Network

2022

IJIES

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“…Lamba et al [32] analyzed basic temporal (e.g., duration) and kinematic (e.g., velocity, acceleration, jerk) measures in 62 PD patients and 15 HC (enrolled in the frame of the Irvine (UCI) Parkinson's disease spiral drawings dataset). Due to high imbalance, the synthetic minority-oversampling technique was employed to balance the cohort.…”

Section: Diaz Et Almentioning

confidence: 99%

Exploiting Spectral and Cepstral Handwriting Features on Diagnosing Parkinson’s Disease

et al. 2021

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Diagnosing of Parkinson's disease based on hand drawing analysis using Bi‐Directional LSTM equipped with fuzzy inferential soft‐max classifier

Ramzani,

Yadollahzadeh‐Tabari,

GolesorkhtabarAmiri

et al. 2023

Int J Imaging Syst Tech

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Parkinson's Disease (PD) is a brain‐related disease that eventually causes disability and disrupts a person's normal life. Most physicians and researchers try to diagnose it quickly and treat it on time. In recent years, computer science and the field of artificial intelligence (machine learning) have helped researchers find a way to detect the disease early. This article proposes a method that diagnoses Parkinson's disease by analyzing the hand drawing shaped by the individuals using Bi‐Directional Long Short‐Term Memory (Bi‐LSTM) neural network. In addition, this paper proposes a Fuzzy Inferential Classifier for the Dense layer, which classifies the output of LSTM Blocks to the associated classes by modifying the Soft‐max function. Our decision to propose this classifier was because, in some cases, hand‐drawing data related to people with Parkinson's disease have no significant difference with the healthy subjects for the distinguishing and are often very similar to each other. A standard dataset has been used in this paper, which includes spirals drawn test (including static spiral, dynamic spiral, and stability specific point) by a group of healthy people and people with Parkinson's disease. The proposed method has reached 97%, 98.5%, and 100% accuracy rates for the three mentioned spiral tests with a smoother training loss and accuracy plots. In addition, the results outperform state‐of‐the‐art research conducted on this dataset and show at least more than 2.5% improvement in the accuracy rate in comparison.

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A systematic approach to diagnose Parkinson’s disease through kinematic features extracted from handwritten drawings

Cited by 37 publications

References 27 publications

Dysgraphia Classification based on the Non-Discrimination Regularization in Rotational Region Convolutional Neural Network

Dysgraphia Classification based on the Non-Discrimination Regularization in Rotational Region Convolutional Neural Network

Exploiting Spectral and Cepstral Handwriting Features on Diagnosing Parkinson’s Disease

Diagnosing of Parkinson's disease based on hand drawing analysis using Bi‐Directional LSTM equipped with fuzzy inferential soft‐max classifier

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