Parkinson Disease Detection Using Deep Neural Networks

Innovative approaches in the medical AI are illustrated here by using deep neural networks (DNNs) for the identification of Parkinson disease (PD). Usually by using ML techniques, the Parkinson disease we identify by considering non imaging information such as handwriting sample data, voice recordings and data based on movement due to demonstration of early symptoms of this disease by these features. Our research framework is structured in such a way that it helps scholars to smell the fresh success air in all their work as we guide them. Get some of the interesting Dissertation topic ideas from our PhD experts. Here, we describe about the PD identification by considering image data like MRI.

A procedural step for the development of PD identification framework by deploying DNNs method is discussed.

  1. Problem Discussion:

            We clear about in which perspective we are going to identify Parkinson disease.

Tremor detection from accelerometer data?

Voice degradation analysis from voice recordings?

Anomalies in handwriting?

  1. Data Gathering:

Handwriting Data:  We utilized handwritten text data specifically joint or redundant patterns.

MRI Data:  Particular patterns that are important for PD detection may be presented in brain scan images.

Voice Data:  Our approach can analyze sentences or some particular sounds by utilizing patient’s voice recordings.

Movement Data:  We record movement-based data by employing wearable devices with accelerometers.

  1. Preprocessing of data:

Augmentation:  To improve the diversity of data, we performed data augmentation techniques by adding extra noises and make jittering in various data.

Normalization:  Data are normalized by us to make all the data with same dimension.

Segmentation:  We segment the data such as voice and movement to only focus on the relevant patterns

Feature Extraction:  It is carried out to retrieve only important features such as Mel-frequency cepstral coefficients (MFCCs) or chromogram from voice data and mean, variance, and frequency factors from movement data.

  1. Choosing Model:

Model for Imaging Data:  Here, we employed 2D Convolutional Neural Networks (CNNs) method.

Model for Time Series Data: Time series data are trained by utilizing methods such as 1D CNNs, Recurrent Neural Network (RNN) and Long Short-Term Networks (LSTM) in our study.

  1. 5. Training of Model:

We train the model by considering the labeled dataset i.e that dataset should be labeled as healthy or Parkinsons.

Eliminating the overfitting issue through the use of dropout, batch normalization and some regularization approaches.

  1. Evaluation:

            We evaluate our frameworks by considering several metrics such as precision, accuracy, recall, ROC-AUC and F1-score. The highest recall values indicate the low false negative cases in medical applications.

  1. Optimization:

Transfer Learning:  In our research, the pre trained models are utilized and adjusted the model that is suitable for Parkinson disease identification.

Hyper-parameter adjusting:  To find out the best parameters, we employed methods including random search and grid search.

  1. Placement:

            Based on the research field, we implement our model in an appropriate platform. For illustration: for continuous tracking, the model can be implemented in mobile applications and for regular health check-up, the model can be implemented in medical environment.

  1. Continuous Tracking and Feedback Loop:

            We analyse the efficiency of the implemented model in an actual time data and the user’s reviews are utilized to enhance the model’s performance in future.

  1. Ethical Consideration:

Security: We have to accomplish data security particularly with the patient’s private data and consent with regulations such as HIPAA or GDPR.

Transparency and Explainability:  The share-holders should have to interpret about the forecasting ethics.

Fairness and Unfairness:  We checked whether the framework is trained on the large data or not and have to be sure whether the model is not indicates unfairness on particular demographics.

Key Takeaway:

            Even, if the identification process of PD is carried out using DNNs method, it is very important to note that these frameworks have to be integrated with opinion of clinical staffs. Appropriate validation and examining processes are very significant. We have to ensure about these processes and have to implement the framework in suitable environment.

Parkinson Disease Detection using Deep Neural Networks Research Ideas

Parkinson Disease Detection using Deep Neural Networks Project Topics

1.Handwriting dynamics assessment using deep neural network for early identification of Parkinson’s disease


Parkinson’s, Neurological disorder, Brain disorder, PD identification, Transfer learning

            Parkinson disease (PD) cannot be cured, but its symptoms can be managed to delay its progression. In this paper PD is based on patient’s handwriting samples. To improve performance, we combine multiple PD datasets and used DL based algorithm to overcome the challenge of high variability handwritten material. This achieves 99.22% accuracy on combined HandPD, NewHandPD and Parkinson’s drawing datasets.

  1. GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s disease Detection Using EEG Signals


Parkinson’s disease (PD);  classificationelectroencephalogram (EEG);  deep learningCNNGabor transformspectrograms

In this paper a DL based automated PD diagnosis using EEG recordings. By using Gabor transform, EEG recordings we convert into spectrograms, and it is used to train proposed 2D-CNN model. The proposed model achieves the accuracy of 99.46% using tenfold cross validation and it automatically detect PD patient’s medication status.

  1. Parkinson’s disease detection based on multi-pattern analysis and multi-scale convolutional neural networks


Parkinson’s disease, disease detection, EEG, multi-pattern analysis, multi-scale convolutional neural networks

            In this paper a multipattern analysis based on brain activation and brain functional connectivity was performed on the brain functional activity of PD patients and PD prediction can based on the proposed MCNN. Based on the analysis of PSD and PVD significant differences in PSD and PLV between HCs and PD patients especially in b and g bands, which were effective for PD detection. MCNN shows greatest potential for PD detection.

4. The Role of Neural Network for the Detection of Parkinson’s disease: A Scoping Review


Neural networkclassification

Numerous studies were conducted to propose CAD to diagnose PD in the early stages. This paper explores and summarizes the applications of neural networks to diagnose PD. PRISMA Extension for Scoping Reviews was followed to conduct this scoping review. Biomedical voice and signal datasets were the commonly used data types to develop and validate these models. Neural networks play an integral and substantial role in combating PD.

  1. End-to-End Parkinson’s Disease Detection Using a Deep Convolutional Recurrent Network


Speech Processing, Long Short-Term Memory.

            In this paper we propose the classification of patients suffering from PD vs. healthy subjects using a 1D CNN followed by LSTM. They show how the network behaves when its input an d the kernel size in different layers are modified. In addition, they evaluate network discriminate between PD patient and healthy controls. The fusion of tasks yielded the best result in classification shows better result in different stages of disease.

  1. Early Detection of Parkinson’s disease by Neural Network Models


PD stage, IMU, gait.

            This paper we must develop neural network models to recognize PD at its early stage. Early detection of PD enables timely initiation of therapeutic management that decreases morbidity. Early-stage disease is challenging because the population has high PD prevalence and also exhibits progressive gaits. They measured their motions using IMU sensors and the IMU data were used to develop neural network models that could identify patients advanced stage.

  1. An Enhanced EEG Microstate Recognition Framework Based on Deep Neural Networks: An Application to Parkinson’s disease


EEG microstate, enhanced recognition framework, deep neural network, activated brain regions.

            In this paper our team has proposed an enhanced EEG microstate recognition framework based on deep neural network. In addition, gradient weighted class activation mapping as a visualization technique is used to activate functional brain regions of each microstate class based on particular activated brain region. The proposed EEG microstate recognition framework paves the way to revealing more effective biomarker for early PD.

  1. Real-time detection of freezing of gait in Parkinson’s disease using multi-head convolutional neural networks and a single inertial sensor


Freezing of gait, Activities of daily living, Wearable sensors, Accelerometer

          This work proposes about a robust real-time FOG detection algorithm, which is easy to implement in stand-alone devices working in non-supervised conditions. Data from three different data sets were used and they act as two independent sets. The implemented algorithm consisted of a multi-head CNN which exploits different spatial resolution and the model’s parameter reduces computational complexity and testing time.

  1. Parkinson’s disease is characterized by sub-second resting-state spatio-oscillatory patterns: A contribution from deep convolutional neural network


Resting-state oscillation, Motor cortical beta activity, Frontoparietal theta power

              In this paper we have opted to use DCNN with a minimalistic architecture design and clinically relevant network model. Their network is based on EEG from open access data set next they validate the model by applying Gradient-weighted Class Activation Mapping technique to create a localization map based on the gradients of the classification score flowing into the last convolutional layer. Their method enables both spatial and frequency aspects of the oscillations

  1. Determining the severity of Parkinson’s disease in patients using a multi task neural network


Autoencoder, Disease progress, mixed model, Regression

            In this paper DL technique is proposed with two purposes. On the other hand, it finds out whether the person has severe or non-severe PD. The UPDRS has been used for both motor and total labels, and best results obtained by using MLP that classifies and regress at the same time. Using a full DL pipeline for data preprocessing and classification has proven very promising in the field of Parkinson’s outperformance.

11.Parkinson Disease Detection Using Deep Neural Networks


CNN, Neural, ANN, Parkinson, Gait, PPMI, Neurodegenerative, UCI

Implementation plan

Step 1: Initially load the Voice Impairment dataset, which is obtained from Max Little University of Oxford, having recorded biomedical voice measurements of 91 subjects, 43 suffering from PD.

Step 2: Next, perform the VGFR Gait Analysis using CNN and reduce the size of the dataset.

Step 3: Next, based on the weight measure balancing the imbalance data’s by tuning the dataset using Optimizing part of Adam classifier

Step 4: Next, segment the signal values of voices of normal subjects and subjects suffering from PD based on ANN.

Step 5: Next, evaluate the accuracy of VGFR Spectrogram Detector for the generated spectrogram images and Voice Impairment Classifier for detailed features of speech images and classify the results by using XG Boost, Support Vector Machine, and MLP and predict the Parkinson Disease.

Step 6: The performance of these work is measured through the following performance metrics, Accuracy, Precision, Recall and F-Measure.

Software Requirement: Python – 3.9.3 and Operating System:  Windows 10(64-bit)

12.Early Detection of Parkinson Disease using Deep Neural Networks on Gait Dynamics


Parkinson Disease, Gait Analysis, Deep Learning, Dense Neural Networks, Parameter Optimization, Foot, Force, Sensor phenomena and characterization, Machine learning, Diseases, Neural networks

Implementation plan

Step 1: Initially load the data’s which is captured by sensors located in different points of the patient’s right and left foot.

Step 2: Next, construct the deep neural networks for distinguish ill subjects from healthy subjects and distinguish subjects on the base of different degrees of illness.

Step 3: Next,  to improve the training of deep feed-forward neural network use the Batch Normalization layer process.

Step 4: Next,  calculate the weight and avoid over-fitting by implementing a regularization technique.

Step 5: Next , perform the classification by using  Stochastic Gradient Descent (SGD) algorithm and Detect the Parkinson Disease .

Step 6: The performance of these work is measured through the following performance metrics, Accuracy and Validation Accuracy, Loss and Validation Loss.

Software Requirement: Python – 3.9.3 and Operating System:  Windows 10(64-bit)

 13.Parkinson Detection using VOT-MFCC Combination and Fully-Connected Deep Neural Network (FC- DNN) Classifier


Early Parkinson detection, PD, MFCC, VOT, VOT-MFCC, FC-DNN, DDK tests, Deep learning, Parkinson’s disease, Databases, Neural networks, Mel frequency cepstral coefficient

Implementation plan

Step 1: Initially load the Parkinson’s disease dataset from Spanish database.

Step 2: Next, generate DNN model based on Mel-Frequency Cepstral coefficients (MFCC) combined with Voice Onset Time (VOT) process.

Step 3: Next, based on the model perform the feature extraction process. 

Step 4: Next, detect the Parkinson’s disease by using the classifier Fully-Connected Deep Neural Network (FC-DNN) .

Step 5: The performance of this work is measured through the following performance metrics, Accuracy, Precision, Recall and F-Measure.

Software Requirement: Python – 3.11.3 and Operating System:  Windows 10(64-bit)

14.The Neuropsychology Assessment for Identifying Dementia in Parkinson’s Disease Patients using a Deep Neural Network


dementia, parkinson’s disease, multivariate data, montreal cognitive assessment, machine learning, deep learning, Deep learning, Scientific computing, Biological system modeling, Sociology, Semantics, Medical services, Brain modeling

Implementation plan

Step 1: Initially load the dataset with Parkinson disease

Step 2: Next, designing a Deep Neural Network (DNN) architecture specific for analyzing electronic health records for PD-Dementia detection  .

Step 3: Next . classifies samples using the Montreal Cognitive Assessment (MoCA) scores as a guideline. It is classified into three categories, which are No Dementia, PD-MCI, and PD-Dementia.

Step 4: Next ,  detecting dementia among PD patients based on neuropsychological assessment.

Step 5: The performance of these work is measured through the following performance metrics, Accuracy, recall, specificity and sensitivity.

Software Requirement: Python – 3.9.3 and Operating System:  Windows 10(64-bit)

15.Diagnosis of the Parkinson disease by using deep neural network classifier


Parkinson disease, deep learning, deep neural network, stacked autoencoder

Implementation plan

Step 1: Initially load two datasets Oxford Parkinson’s Disease Detection (OPD) and Parkinson Speech Dataset with Multiple Types of Sound Recordings (PSD).

Step 2: Next, construct DNN classifier, which combines the sAE network and softmax classifier.

Step 3: Next, the weights of the DNN are optimized by an appropriate optimization algorithm. Limited Memory BFGS.

Step 4: Next, the DNN classifier classify the features of the speech signals..

Step 5: Next, the output of the DNN classifier, is the labelled with PD and control group which are represented with 1, 0 respectively.

Step 6: The performance of these work is measured through the following performance metrics, Accuracy and Validation Accuracy, Loss and Validation Loss, specificity and sensitivity.

Software Requirement: Python – 3.9.3 and Operating System:  Windows 10(64-bit)

15.A New Approach to Detection of Parkinson’s Disease Using Variational Mode Decomposition Method and Deep Neural Networks


Parkinson, Electroencephalogram, Variational Mode Decomposition, Convolutional Neural Network, Deep learning, Neural networks, Brain modeling, Electroencephalography, Convolutional neural networks, medical diagnostic imaging, Diseases

Implementation plan

Step 1: Initially load the Electroencephalography (EEG) signals dataset.

Step 2: Next, generate the three different subband signals using Variational Mode Decomposition (VMD) method.

Step 3: Next, generate 1D CNN model by using-by-using EEG signals and subband signals in separately

Step 4: Next, The classification results showed that the VMD-sub band signals obtained from EEG signals were successful in diagnosing Parkinson’s.

Step 5: The performance of these work is measured through the following performance metrics, Accuracy, Precision, Recall and F-Measure.

Software Requirement: Python – 3.11.3 and Operating System:  Windows 10(64-bit)

16.GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals


Parkinson’s disease (PD); classification; electroencephalogram (EEG); deep learning; CNN; Gabor transform; spectrograms

Implementation plan

Step 1: Initially load two datasets Oxford Parkinson’s Disease Detection (OPD) and Parkinson Speech Dataset with Multiple Types of Sound Recordings (PSD).

Step 2: Next, construct DNN classifier, which combines the sAE network and softmax classifier.

Step 3: Next,  The weights of the DNN are optimized by an appropriate optimization algorithm. Limited Memory BFGS.

Step 4: Next,  the DNN classifier classify the features of the speech signals..

Step 5: Next , the output of the DNN classifier, is the labelled with PD and control group which are represented with 1, 0 respectively.

Step 6: The performance of these work is measured through the following performance metrics, Accuracy and Validation Accuracy, Loss and Validation Loss, specificity and sensitivity.

Software Requirement: Python – 3.9.3 and Operating System:  Windows 10(64-bit)

17.Predicting Parkinson’s Disease using Latent Information extracted from Deep Neural Networks


            latent variable information, deep convolutional and recurrent neural networks, transfer learning and domain adaptation, modified loss function, prediction, Parkinson’s disease, MRI, DaT Scan data

Implementation plan

Step 1: Initially load the dataset with MRI and DaT Scan data..

Step 2: Next, Extract the Latent Variables from Trained DNN based on Fully Connected (FC) layers, using neurons with a ReLU activation function..

Step 3: Next,  if add new data into dataset , then Retraining of Deep Neural Networks with Annotated Latent Variables.

Step 4: Next, , based on extraction of latent variables from a trained DNN, and use of cluster centroids for prediction and adaptation of a Parkinson’s diagnosis system by using k-means clustering

Step 5 .Next, computing the Euclidean distances of the corresponding extracted latent variables from the 5 cluster centroids and by classifying them to the closest centroid by using k-Nearest Neighbour classification of deep neural network based on the classification Predicting Parkinson’s Disease.

Step 6: The performance of these work is measured through the following performance metrics, Accuracy, Precision, Recall,F1 Score (%)  and ROC cure.

Software Requirement: Python – 3.11.3 and Operating System:  Windows 10(64-bit)

18.Real-Time Voluntary Motion Prediction and Parkinson’s Tremor Reduction Using Deep Neural Networks


Parkinson’s disease, pathological tremor, deep neural networks, tremor prediction, voluntary motion prediction, tremor estimation.

Implementation plan

Step 1: Initially load the IMUs sensor data with 18 subjects with PD participated, which are recorded at the Wearable Biomechatronics Laboratory at Western University.

Step 2: Next, based on the data’s, the PD tremor can be classified into three types of tremors, resting, postural, and action tremor and extract and differentiate the ground truth voluntary motion from the action tremor.

Step 3: Next, CNNs learn to map the internal features extracted from sequences of observations to follow a motion sequence.

Step 4: Next, predict the Voluntary Motion and Parkinson’s based on 1D-CNN-MLP.

Step 5: The performance of these work is measured through the following performance metrics, computational power, memory storage, Computation time, PSNR and Confusion Matrix.

Software Requirement: Python – 3.11.3 and Operating System:  Windows 10(64-bit)

19.Assessment of Parkinson’s Disease Based on Deep Neural Networks


Deep neural networks, Parkinson’s disease, medical data analysis

Implementation plan

Step 1: Initially creating a database composed of about 100 patients with Parkinson’s and 40 subjects with non-Parkinson’s disease, which includes MRIs, DaT Scans and clinical data for each subject.

Step 2: Next , train the dataset by balance the dataset by applied various augmentation technique duplicating the latter category.

Step 3: Next, perform the transfer learning process for weight balancing by using ResNet-50 for the MRI triplets and DaT scan images.

Step 4: Next . We concatenated the outputs of these two ResNet substructures at the input of the first FC layer

Step 5: Next apply deep CNN-RNN network for detect the MRI sequence of triplets of frames based on the frames predict the Parkinson disease.

Step 6: The performance of these work is measured through the following performance metrics, Accuracy, Precision, Recall, ROC curves and F-Score.

Software Requirement: Python – 3.11.4 and Operating System:  Windows 10(64-bit)

            When implementing these ideas, it’s essential to remember that while deep learning models can aid in diagnosis, we use in conjunction with clinical diagnoses and not as standalone diagnostic tools. Collaborating with medical professionals and neurologists will provide us with valuable insights and validation for the developed models

            We accomplished this identification of PD through the use of DNNs and it also offers early curing and tracking the stage of disease. For that, various research scopes are discussed:

  1. Speech Recognition:

                Feature extraction:  We trained the neural network framework to examine the voice data for symptoms identification because the voice of the person may be impacted by PD and it can modify the voice tone.

Speech pattern:  While analyzing the continuous speech, the sudden stuck and other defectiveness are identified by us.

  1. Identification of Facial Expression:

               Facial Masking Identification: A person’s facial expressions can be decreased by the effect of PD. So, we can identify these anomalies by constructing effective frameworks.

  1. Medical Imaging:

              Examining DaTscan and MRI:  Brain scans also show the symptoms of PD and these abnormalities can be identified by employing CNNs method.

Functional Imaging:  We analyze the dopamine generation region and detect the anomalies through examining the PET and SPECT scans.

  1. Drug Utilization Monitoring:

                 Symptoms Progress Monitoring: We employed deep learning frameworks to analyze the patients by considering specific drug factors and also examine the utilization of drugs in symptoms minimization.

  1. Gait Analysis:

               Footstep length and Speed: Defectiveness in person’s walking is caused due to the PD. Therefore, the walking patterns are examined to identify this defectiveness through the utilization of videos

                Identification of Balance and Posture: We can recognize the balance problems and stooped pattern of humans that are considered as a most common PD symptom.

  1. Handwriting Analysis:

                   Micrographia identification: We identified the Parkinson’s disease by examining the defectiveness in handwriting named micrographia that the handwriting pattern seems cramped and small.

                 Tremor Analysis: Tremor is detected by us by extracting various features such as improper pen pressure or inappropriate line patterns.

  1. Data Challenges:

          Gathering of Data: We can utilize normal datasets for research projects or can use data from clinical organizations.

            Data security: The patient’s health related data must be confidential and have to be managed with high range of security and have to follow the rules.

  1. Tremor Identification with Wearable Sensors:

             Augmenting Data:  Gathering of real time PD tremor data is complicated task. So, to enlarge the dataset for training, we augmented the data by following various steps.

            Examining Movement Data:  Movement related data can be gathered using wearable sensors such as smartwatches. We can identify the defects and tremors by examining these data.

  1. Application for Patient’s Self-Assessment:

             Mobile-related Tests: Patients can carry out the self-assessment by building an efficient mobile application and they can evaluate the findings by employing backend neural network method and offers the reviews also.

  1. Combination with other modalities:

             Multimodal Diagnosis: We carried out a more precise and efficient diagnosis by integrating various data such as speech, handwriting, clinical imaging and gait.

              Time Series Analysis:  We can know about the disease’s consequence by consider the increasing case of diseases through examining the patient’s time series data.

                 An important point is that we should have to utilize DL based framework by integrating it with medical diagnoses instead of using it as a single diagnostic tool. This combination of DL model with some clinical staffs and professionals assists to authenticate the constructed framework.  

                Implementation service is an interactive one. It is energetic as our team keeps in touch with the latest developments and receives every day training so they apply the evolving techniques and algorithms correctly. We also guide our scholars about the tools and techniques that we have used in detail, which in turn proves to be highly beneficial to them for their career.


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