scholarly journals Performance Analysis of various Neural Network functions for Parkinson’s disease Classification using EEG and EMG

2019 ◽  
Vol 9 (1) ◽  
pp. 3402-3406 ◽  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Mean Square Error ◽  
Mean Square ◽  
Data Set ◽  
Elapsed Time ◽  
Levenberg Marquardt ◽  
R Value

Artificial neural network (ANN) is a significant tool for classification of various types of disease using either Biosignals/images or may be any kind of physical parameters. Establishment of appropriate combination of learning, transfer function and training function is a very tedious task. Here, we compared the performance of different training parameters in feed forward neural network for differentiating of Parkinson’s disease using human brain (Electroencephalogram) and muscle signals (Electromyogram) features as the input vector. 3 different types of training algorithm with six training functions is used. They are Gradient Descent algorithms (traingd, traingdm), Conjugate Gradient algorithms (trainscg, traincgp) and Quasi-Newton algorithms (trainbfg, trainlm). Proposed work compared the mentioned algorithm in terms of mean square error, classification rate (%),R-value and the elapsed time. Study showed that trainlm (Levenberg-Marquardt) best fits for larger data set. It showed the highest accuracy rate of 100% with 0 mismatch classification with a best validation mean square error of 0.0040254 in 3 epochs with a elapsed time of 1.12 seconds. The R-value found was 0.9998 which is in nearly equals to 1. Hence, Levenberg-Marquardt can be used as a training function for the classification of any brain disorder

scholarly journals Classification of Parkinson's disease using resting-state hemodynamic signals and convolutional neural network

IBRO Reports ◽  
2019 ◽  
Vol 6 ◽  
pp. S425
Author(s):  
Shin-Young Kang ◽  
Youngwoon Choi ◽  
Seung-Ho Paik ◽  
V. Zephaniah Phillips ◽  
Beop-Min Kim
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convolutional Neural Network ◽  
Resting State

scholarly journals Multimodal Classification of Parkinson’s Disease in Home Environments with Resiliency to Missing Modalities

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4133
Author(s):  
Farnoosh Heidarivincheh ◽  
Ryan McConville ◽  
Catherine Morgan ◽  
Roisin McNaney ◽  
Alessandro Masullo ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Average Increase ◽  
Data Set ◽  
Multiple Modalities ◽  
Multimodal Classification ◽  
Human Movements ◽  
Variational Autoencoder ◽  
The Difference

Parkinson’s disease (PD) is a chronic neurodegenerative condition that affects a patient’s everyday life. Authors have proposed that a machine learning and sensor-based approach that continuously monitors patients in naturalistic settings can provide constant evaluation of PD and objectively analyse its progression. In this paper, we make progress toward such PD evaluation by presenting a multimodal deep learning approach for discriminating between people with PD and without PD. Specifically, our proposed architecture, named MCPD-Net, uses two data modalities, acquired from vision and accelerometer sensors in a home environment to train variational autoencoder (VAE) models. These are modality-specific VAEs that predict effective representations of human movements to be fused and given to a classification module. During our end-to-end training, we minimise the difference between the latent spaces corresponding to the two data modalities. This makes our method capable of dealing with missing modalities during inference. We show that our proposed multimodal method outperforms unimodal and other multimodal approaches by an average increase in F1-score of 0.25 and 0.09, respectively, on a data set with real patients. We also show that our method still outperforms other approaches by an average increase in F1-score of 0.17 when a modality is missing during inference, demonstrating the benefit of training on multiple modalities.

scholarly journals Prediction of the Bicarbonate Amount in Drinking Water in the Region of Médéa Using Artificial Neural Network Modelling

2020 ◽  
Vol 69 (11-12) ◽  
pp. 595-602
Author(s):  
Hichem Tahraoui ◽  
Abd Elmouneïm Belhadj ◽  
Adhya Eddine Hamitouche
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Drinking Water ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Mean Square ◽  
Ann Model ◽  
Network Modelling ◽  
Data Set ◽  
Artificial Neural

The region of Médéa (Algeria) located in an agricultural site requires a large amount of drinking water. For this purpose, the water analyses in question are imperative. To examine the evolution of the drinking water quality in this region, firstly, an experimental protocol was done in order to obtain a dataset by taking into account several physicochemical parameters. Secondly, the obtained data set was divided into two parts to form the artificial neural network, where 70 % of the data set was used for training, and the remaining 30 % was also divided into two equal parts: one for testing and the other for validation of the model. The intelligent model obtained was evaluated as a function of the correlation coefficient nearest to 1 and lowest mean square error (RMSE). A set of 84 data points were used in this study. Eighteen parameters in the input layer, five neurons in the hidden layer, and one parameter in the output layer were used for the ANN modelling. Levenberg Marquardt learning (LM) algorithm, logarithmic sigmoid, and linear transfer function were used, respectively, for the hidden and the output layers. The results obtained during the present study showed a correlation coefficient of <i>R</i> = 0.99276 with root mean square error RMSE = 11.52613 mg dm<sup>–3</sup>. These results show that obtained ANN model gave far better and more significant results. It is obviously more accurate since its relative error is small with a correlation coefficient close to unity. Finally, it can be concluded that obtained model can effectively predict the rate of soluble bicarbonate in drinking water in the Médéa region.

scholarly journals Parkinson disease classification: a comparative analysis on classification techniques

2020 ◽  
Vol 18 (3) ◽  
pp. 1351
Author(s):  
Nazri Mohd Nawi ◽  
Mokhairi Makhtar ◽  
Zehan Afizah Afip ◽  
Mohd Zaki Salikon
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Decision Tree ◽  
Neurological Disorders ◽  
Early Stage ◽  
Current Trend ◽  
Disease Classification ◽  
High Demand

Parkinson’s disease (PD) among Alzheimer’s and epilepsy are one of the most common neurological disorders which appreciably affect not only live of patients but also their households. According to the current trend of aging social behaviour, it is expected to see a rise of Parkinson’s disease. Even though there is no cure for PD, a proper medication at the early stage can help significantly in alleviating the symptoms. Since, the traditional method for identifying PD is rather invasive, expansive and complicated for self-use, there is a high demand for using classification method on PD detection. This paper compares the performance of Neural Network and decision tree for classifying and discriminating healthy people for people with Parkinson’s disease (PD) by distinguishing dysphonia. The simulation results demonstrate that Neural Network outperformed decision tree by giving accurate results with 87% accuracy as compared to decision tree with only 84% accuracy in determining the classification of healthy and people with Parkinson’s.

Does Dopamine Replacement Medication Affect Postural Sequence Learning in Parkinson’s Disease?

Motor Control ◽  
2015 ◽  
Vol 19 (4) ◽  
pp. 325-340 ◽  
Author(s):  
Heather Anne Hayes ◽  
Nikelle Hunsaker ◽  
Sydney Y. Schaefer ◽  
Barry Shultz ◽  
Thomas Schenkenberg ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Sequence Learning ◽  
Task Difficulty ◽  
Mean Square ◽  
Postural Tasks ◽  
Specific Learning ◽  
Usual Dosage

Deficits in sequence-specific learning (SSL) may be a product of Parkinson’s disease (PD) but this deficit could also be related to dopamine replacement. The purpose of this study was to determine whether dopamine replacement affected acquisition and retention of a standing Continuous Tracking Task in individuals with PD. SSL (difference between random/repeated Root Mean Square Error across trials) was calculated over 2 days of practice and 1 day of retention for 4 groups; 10 healthy young (HY), 10 healthy elders, 10 individuals with PD on, 9 individuals with PD off their usual dosage of dopamine replacement. Improvements in acquisition were observed for all groups; however, only the HY demonstrated retention. Therefore, age appeared to have the largest effect on SSL with no significant effect of medication. Additional research is needed to understand the influence of factors such as practice amount, task difficulty, and dopamine replacement status on SSL deficits during postural tasks.

High-accuracy Automated Diagnosis of Parkinson's Disease

2020 ◽  
Vol 16 (6) ◽  
pp. 688-694
Author(s):  
Ilker Ozsahin ◽  
Boran Sekeroglu ◽  
Pwadubashiyi Coston Pwavodi ◽  
Greta S.P. Mok
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Single Photon ◽  
Mass Difference ◽  
Photon Emission ◽  
High Accuracy ◽  
Emission Computed Tomography ◽  
Preparation Techniques

Purpose: Parkinson's disease (PD), which is the second most common neurodegenerative disease following Alzheimer’s disease, can be diagnosed clinically when about 70% of the dopaminergic neurons are lost and symptoms are noticed. Neuroimaging methods such as single photon emission computed tomography have become useful tools in vivo to assess dopamine transporters (DATs) in the striatal region. However, inter- and intra-reader variability of construing the images might result in misdiagnosis. To overcome the challenges posed by classification of the disease, image preparation techniques and a back propagation neural network (BPNN) have been proposed. The aim of this study is to show that the proposed method can be used for the classification of PD with high accuracy. Methods: In this study, we used basic image preparation techniques and a BPNN on DAT imaging datasets from the Parkinson’s Progression Markers Initiative. 1,334 PD and 212 normal control (NC) subjects were included. In the image preparation phase, adaptive histogram equalization was applied to the cropped images, followed by image binarization. Then, the mass-difference method was applied to separate the regions of interest with similar values. Finally, the binarized images were subtracted from the original images, and the average pixel per node approach was applied to the images to minimize the inputs. In the BPNN phase, 400 input neurons and 2 output neurons were used. The dataset was divided into three sets: training, validation, and test. The BPNN was trained several times in order to obtain the optimum values. Results: The use of 40 hidden neurons, a learning rate of 0.00079, and a momentum factor of 0.90 produced superior results and were applied in the final BPNN architecture. The tolerance value used was 0.80. Uniquely, we found the sensitivity, specificity, and accuracy for PD vs. NC classification to be 99.7%, 99.2%, 99.6%, respectively. To the best of our knowledge, this is the highest accuracy value achieved in the existing literature. Our method increases computational speed together with improved performance. Conclusion: We have shown that effective image processing methods and the use of BPNN can successfully be applied to PD datasets to accurately determine any abnormalities in DATs. Using the shallow neural network, this procedure requires less processing time compared to other methods, and its accuracy, sensitivity, and specificity are reliable. However, further studies are needed to establish a prediction method for the preclinical and prodromal stages of the disease.

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