Prediction of Parkinson’s Disease Severity Based on Gait Signals Using a Neural Network and the Fast Fourier Transform

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

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1 Application of neural network to detect freezing of gait in patients with Parkinson’s disease

Soft Computing ◽

10.1515/9783110628616-001 ◽

2020 ◽

pp. 1-12

Keyword(s):

Neural Network ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Freezing Of Gait

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High-throughput behavioral screen in C. elegans reveals Parkinson’s disease drug candidates

Communications Biology ◽

10.1038/s42003-021-01731-z ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Salman Sohrabi ◽

Danielle E. Mor ◽

Rachel Kaletsky ◽

William Keyes ◽

Coleen T. Murphy

Keyword(s):

Neural Network ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

High Throughput ◽

Potential Candidate ◽

Automated Scoring ◽

C Elegans ◽

Drug Candidates ◽

Branched Chain ◽

Approved Drugs

AbstractWe recently linked branched-chain amino acid transferase 1 (BCAT1) dysfunction with the movement disorder Parkinson’s disease (PD), and found that RNAi-mediated knockdown of neuronal bcat-1 in C. elegans causes abnormal spasm-like ‘curling’ behavior with age. Here we report the development of a machine learning-based workflow and its application to the discovery of potentially new therapeutics for PD. In addition to simplifying quantification and maintaining a low data overhead, our simple segment-train-quantify platform enables fully automated scoring of image stills upon training of a convolutional neural network. We have trained a highly reliable neural network for the detection and classification of worm postures in order to carry out high-throughput curling analysis without the need for user intervention or post-inspection. In a proof-of-concept screen of 50 FDA-approved drugs, enasidenib, ethosuximide, metformin, and nitisinone were identified as candidates for potential late-in-life intervention in PD. These findings point to the utility of our high-throughput platform for automated scoring of worm postures and in particular, the discovery of potential candidate treatments for PD.

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Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images

Biomedicines ◽

10.3390/biomedicines9010012 ◽

2020 ◽

Vol 9 (1) ◽

pp. 12

Author(s):

Chung-Yao Chien ◽

Szu-Wei Hsu ◽

Tsung-Lin Lee ◽

Pi-Shan Sung ◽

Chou-Ching Lin

Keyword(s):

Neural Network ◽

Parkinson’S Disease ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Parkinson's Disease ◽

Dopamine Transporter ◽

Support Vector ◽

Dat Spect ◽

Ann Classifier ◽

Spect Images

Background: The challenge of differentiating, at an early stage, Parkinson’s disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson’s disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson’s disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson’s disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson’s disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.

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