Parkinson’s Disease Detection by Using Feature Selection and Sparse Representation

Mohamadzadeh, Sajad; Pasban, Sadegh; Zeraatkar-Moghadam, Javad; Shafiei, Ali

doi:10.1007/s40846-021-00626-y

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…Various conventional techniques used for analyzing the performance of the developed model are FS‐ANFIS, 1 LSTM, 25 fuzzy KNN + case‐based reasoning classifier, 15 and sparse representation 29 …”

Section: Resultsmentioning

confidence: 99%

“…However, the training dataset lacked permanent activity data, causing the permanent data in the validation set to be incorrectly categorized as FOG. Mohamadzadeh et al 29 developed a sparse representation for detecting PD. By using 10 restrictions for each sample, the new technique reduced storage constraints while also improving the precision of PD diagnosis.…”

Section: Literature Surveymentioning

confidence: 99%

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Parkinson's disease detection using sea lion shuffled shepherd optimization assisted deep maxout network

Sundaram¹,

Velliangiri

2022

Concurrency and Computation

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Summary When compared to a cloud‐based application, fog computing allows the doctor to make better decisions in an emergency and also helps secure personal data with less latency. Parkinson's disease (PD) is a kind of brain illness, which causes stiffness, shaking, trouble in walking, talking, and so on. There is various detection techniques have been invented by researchers previously, but less accuracy is a major drawback. The main purpose of this research is the effective PD detection by the progression of optimized deep maxout network (DMN), namely, sea lion shuffled shepherd optimization (SLnSSOA)‐based DMN. Here, the developed method involves fog nodes, blockchain (BC), cloud, medical analyzer, and so on. The input data, which comes from the cloud is sent to the preprocessing phase, where data normalization is used to process it. Additionally, SLnSSOA, a combination of the sea lion optimization algorithm (SLnO) and the shuffled shepherd optimization (SSOA), is used to classify diseases using DMN, where the parameters are trained using SLnSSOA. Moreover, this technique provides an improved classification outcome. The developed method attains maximum testing accuracy of 0.908, sensitivity of 0.930, and specificity of 0.875.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Parkinson's disease detection using sea lion shuffled shepherd optimization assisted deep maxout network

Sundaram¹,

Velliangiri

2022

Concurrency and Computation

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Section: Introductionmentioning

confidence: 99%

“…As some acoustic features are quantified using the similar signal processing tools, feature correlation analysis and selection procedures could be considered to reduce the feature dimensions and prevent the unnecessary computation costs [ 25 , 26 ]. Mohamadzadeh et al [ 26 ] utilized a sparse representation technique to select the distinct features, and then categorized the vocal patterns of PD patients using an approximate message passing classifier. The vocal features may also help develop different effective computer-aided diagnostic tools that are capable of providing high sensitivity and specificity results for the detection of the symptomatic speech changes of PD patients [ 25 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Dysphonic Voices in Parkinson’s Disease with Semi-Supervised Competitive Learning Algorithm

et al. 2022

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This article proposes a novel semi-supervised competitive learning (SSCL) algorithm for vocal pattern classifications in Parkinson’s disease (PD). The acoustic parameters of voice records were grouped into the families of jitter, shimmer, harmonic-to-noise, frequency, and nonlinear measures, respectively. The linear correlations were computed within each acoustic parameter family. According to the correlation matrix results, the jitter, shimmer, and harmonic-to-noise parameters presented as highly correlated in terms of Pearson’s correlation coefficients. Then, the principal component analysis (PCA) technique was implemented to eliminate the redundant dimensions of the acoustic parameters for each family. The Mann–Whitney–Wilcoxon hypothesis test was used to evaluate the significant difference of the PCA-projected features between the healthy subjects and PD patients. Eight dominant PCA-projected features were selected based on the eigenvalue threshold criterion and the statistical significance level (p < 0.05) of the hypothesis test. The SSCL algorithm proposed in this paper included the procedures of the competitive prototype seed selection, K-means optimization, and the nearest neighbor classifications. The pattern classification experimental results showed that the proposed SSCL method can provide the excellent diagnostic performances in terms of accuracy (0.838), recall (0.825), specificity (0.85), precision (0.846), F-score (0.835), Matthews correlation coefficient (0.675), area under the receiver operating characteristic curve (0.939), and Kappa coefficient (0.675), which were consistently better than those results of conventional KNN or SVM classifiers.

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“…Thanks to clinical needs and the rapid development of deep learning, CNN is widely used in the evaluation and diagnosis of PD. The research is focused on the assessment of motor disorders, pathological analysis and early diagnosis of PD [6][7][8]. Dyskinesia is the core symptom of Parkinson's disease, so this paper focuses on the related research of motor dysfunction in PD.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Evaluation Method for Parkinson's Dyskinesia Using Finger Tapping Video for Small Samples

Zhu

Kang

Cai

et al. 2022

Preprint

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PurposeThe assessment of dyskinesia in Parkinson's disease (PD) based on Artificial Intelligence technology is a significant and challenging task. At present, doctors usually use MDS-UPDRS scale to assess the severity of patients. This method is time-consuming and laborious, and there are subjective differences. The evaluation method based on sensor equipment is also widely used, but this method is expensive and needs professional guidance, which is not suitable for remote evaluation and patient self-examination. In addition, it is difficult to collect patient data in medical research, so it is of great significance to find an objective and automatic assessment method for Parkinson's dyskinesia based on small samples.MethodsIn this study, we design an automatic evaluation method combining manual features and convolutional neural network (CNN), which is suitable for small sample classification. Based on the finger tapping video of Parkinson's patients, we use the pose estimation model to obtain the action skeleton information and calculate the feature data. We then use the 5-folds cross validation training model to achieve optimum trade-of between bias and variance, and finally make multi-class prediction through fully connected network (FCN). ResultsOur proposed method achieves the current optimal accuracy of 79.7% in this research. We have compared with the latest methods of related research, and our method is superior to them in terms of accuracy, number of parameters and FLOPs. ConclusionThe method in this paper does not require patients to wear sensor devices, and has obvious advantages in remote clinical evaluation. At the same time, the method of using motion feature data to train CNN model obtains the optimal accuracy, effectively solves the problem of difficult data acquisition in medicine, and provides a new idea for small sample classification.

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Parkinson’s Disease Detection by Using Feature Selection and Sparse Representation

Cited by 7 publications

References 27 publications

Parkinson's disease detection using sea lion shuffled shepherd optimization assisted deep maxout network

Parkinson's disease detection using sea lion shuffled shepherd optimization assisted deep maxout network

Classification of Dysphonic Voices in Parkinson’s Disease with Semi-Supervised Competitive Learning Algorithm

An Automatic Evaluation Method for Parkinson's Dyskinesia Using Finger Tapping Video for Small Samples

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