Explainable hypergraphs for gait based Parkinson classification

Sharing multicenter imaging datasets can be advantageous to increase data diversity and size but may lead to spurious correlations between site-related biological and non-biological image features and target labels, which machine learning (ML) models may exploit as shortcuts. To date, studies analyzing how and if deep learning models may use such effects as a shortcut are scarce. Thus, the aim of this work was to investigate if site-related effects are encoded in the feature space of an established deep learning model designed for Parkinson's disease (PD) classification based on T1-weighted MRI datasets. Therefore, all layers of the PD classifier were frozen, except for the last layer of the network, which was replaced by a linear layer that was exclusively re-trained to predict three potential bias types (biological sex, scanner type, and originating site). Our findings based on a large database consisting of 1880 MRI scans collected across 41 centers show that the feature space of the established PD model (74% accuracy) can be used to classify sex (75% accuracy), scanner type (79% accuracy), and site location (71% accuracy) with high accuracies despite this information never being explicitly provided to the PD model during original training. Overall, the results of this study suggest that trained image-based classifiers may use unwanted shortcuts that are not meaningful for the actual clinical task at hand. This finding may explain why many image-based deep learning models do not perform well when applied to data from centers not contributing to the training set.

Parkinson's disease (PD) classification plays a crucial role in medical diagnosis and patient management. Identifying Parkinson's disease at an early stage can lead to more effective treatment and improved patient outcomes. However, existing methods for Parkinson's disease classification face several limitations. The foremost limitation is the need for accurate and reliable diagnostic tools, as misdiagnosis can lead to inappropriate treatments and unnecessary stress for patients. Thus, a hybrid deep learning model is introduced in this research. The proposed model involves the utilization of EEG signals obtained from a publicly available dataset. Key features are extracted from the EEG signals using a bandpass filter, and every feature is associated with specific brainwave frequencies and cognitive states. The feature mapping and classification are executed through the Chaotic Chebyshev Zebra optimization- based Residual GhostNet (CCZO_Residual_GhostNet). This hybrid classifier, Residual GhostNet, combines ResNet-152 with GhostNet, enhancing classification precision. Furthermore, the CCZO algorithm optimizes the loss function, introducing elements of chaos and Chebyshev mapping to improve classification accuracy. The assessment based on accuracy, sensitivity, specificity, and F-score acquired 98.76%, 98.59%, 98.95%, and 99%, respectively.

Application and use of deep learning algorithms for different healthcare applications is gaining interest at a steady pace. However, use of such algorithms can prove to be challenging as they require large amounts of training data that capture different possible variations. This makes it difficult to use them in a clinical setting since in most health applications researchers often have to work with limited data. Less data can cause the deep learning model to over-fit. In this paper, we ask how can we use data from a different environment, different use-case, with widely differing data distributions. We exemplify this use case by using single-sensor accelerometer data from healthy subjects performing activities of daily living - ADLs (source dataset), to extract features relevant to multi-sensor accelerometer gait data (target dataset) for Parkinson's disease classification. We train the pre-trained model using the source dataset and use it as a feature extractor. We show that the features extracted for the target dataset can be used to train an effective classification model. Our pretrained source model consists of a convolutional autoencoder, and the target classification model is a simple multi-layer perceptron model. We explore two different pre-trained source models, trained using different activity groups, and analyze the influence the choice of pre-trained model has over the task of Parkinson's disease classification.

Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder and the early diagnosis is crucial for managing symptoms and slowing disease progression. This paper proposes a framework named Federated Learning Enabled Waterwheel Shuffled Shepherd Optimization-based Efficient-Fuzzy Deep Maxout Network (FedL_WSSO based Eff-FDMNet) for PD detection and classification. In local training model, the input image from the database “Image and Data Archive (IDA)” is given for preprocessing that is performed using Gaussian filter. Consequently, image augmentation takes place and feature extraction is conducted. These processes are executed for every input image. Therefore, the collected outputs of images are used for PD detection using Shepard Convolutional Neural Network Fuzzy Zeiler and Fergus Net (ShCNN-Fuzzy-ZFNet). Then, PD classification is accomplished using Eff-FDMNet, which is trained using WSSO. At last, based on CAViaR, local updation and aggregation are changed in server. The developed method obtained highest accuracy as 0.927, mean average precision as 0.905, lowest false positive rate (FPR) as 0.082, loss as 0.073, Mean Squared Error (MSE) as 0.213, and Root Mean Squared Error (RMSE) as 0.461. The high accuracy and low error rates indicate that the potent framework can enhance patient outcomes by enabling more reliable and personalized diagnosis.

Parkinson's disease classification using gait analysis via deterministic learning

Parkinson's disease (PD) is a chronic neurodegenerative disorder characterized by progressive loss of dopaminergic neurons in substantia nigra, resulting in both motor impairments and cognitive decline. Traditional PD classification methods are expert-dependent and time-intensive, while existing deep learning (DL) models often suffer from inconsistent accuracy, limited interpretability, and inability to fully capture PD's clinical heterogeneity. This study proposes a novel framework Enhanced EfficientNet-Extended Multimodal PD Classification with Hybrid Particle Swarm and Grey Wolf Optimizer (EEFN-XM-PDC-HybPS-GWO) to overcome these challenges. The model integrates T1-weighted MRI, DaTscan images, and gait scores from NTUA and PhysioNet repository respectively. Denoising is achieved via Multiscale Attention Variational Autoencoders (MSA-VAE), and critical regions are segmented using Semantic Invariant Multi-View Clustering (SIMVC). The Enhanced EfficientNet-Extended Multimodal (EEFN-XM) model extracts and fuses image and gait features, while HybPS-GWO optimizes classification weights. The system classifies subjects into early-stage PD, advanced-stage PD, and healthy controls (HCs). Ablation analysis confirms the hybrid optimizer's contribution to performance gains. The proposed model achieved 99.2% accuracy with stratified 5-fold cross-validation, outperforming DMFEN-PDC, MMT-CA-PDC, and LSTM-PDD-GS by 7.3%, 15.97%, and 10.43%, respectively, and reduced execution time by 33.33%. EEFN-XM-PDC-HybPS-GWO demonstrates superior accuracy, computational efficiency, and clinical relevance, particularly in early-stage diagnosis and PD classification.

Parkinson's disease (PD) is a chronic neurodegenerative disorder characterized by motor impairments and various other symptoms. Early and accurate classification of PD patients is crucial for timely intervention and personalized treatment. Inertial measurement units (IMUs) have emerged as a promising tool for gathering movement data and aiding in PD classification. This paper proposes a Convolutional Wavelet Neural Network (CWNN) approach for PD classification using IMU data. CWNNs have emerged as effective models for sensor data classification. The objective is to determine the optimal combination of wavelet transform and IMU data type that yields the highest classification accuracy for PD. The proposed CWNN architecture integrates convolutional neural networks and wavelet neural networks to capture spatial and temporal dependencies in IMU data. Different wavelet functions, such as Morlet, Mexican Hat, and Gaussian, are employed in the continuous wavelet transform (CWT) step. The CWNN is trained and evaluated using various combinations of accelerometer data, gyroscope data, and fusion data. Extensive experiments are conducted using a comprehensive dataset of IMU data collected from individuals with and without PD. The performance of the proposed CWNN is evaluated in terms of classification accuracy, precision, recall, and F1-score. The results demonstrate the impact of different wavelet functions and IMU data types on PD classification performance, revealing that the combination of Morlet wavelet function and IMU data fusion achieves the highest accuracy. The findings highlight the significance of combining CWT with IMU data fusion for PD classification using CWNNs. The integration of CWT-based feature extraction and the fusion of IMU data from multiple sensors enhance the representation of PD-related patterns, leading to improved classification accuracy. This research provides valuable insights into the potential of CWT and IMU data fusion for advancing PD classification models, enabling more accurate and reliable diagnosis.

Supervised machine learning based gait classification system for early detection and stage classification of Parkinson’s disease

INTRODUCTION: Parkinson's Disease (PD) is a progressive neurological disorder affecting a significant portion of the global population, leading to profound impacts on daily life and imposing substantial burdens on healthcare systems. Early identification and precise classification are crucial for effectively managing this disease. This research investigates the potential of deep learning techniques in facilitating early recognition and accurate classification of PD.
 OBJECTIVES: The primary objective of this study is to leverage advanced deep learning techniques for the early detection and precise classification of Parkinson's Disease. By utilizing a rich dataset comprising speech signal features extracted from 3000 PD patients, including Time Frequency Features, Mel Frequency Cepstral Coefficients (MFCCs), Wavelet Transform based Features, Vocal Fold Features, and TWQT features, this research aims to evaluate the performance of various deep learning models in PD classification.
 METHODS: The dataset containing diverse speech signal features from PD patients' recordings serves as the foundation for training and evaluating five different deep learning models: ResNet50, VGG16, Inception v2, AlexNet, and VGG19. Each model undergoes training and assessment to determine its capability in accurately classifying PD patients. Performance metrics such as accuracy are employed to evaluate the models' effectiveness.
 RESULTS: The results demonstrate promising potential, with overall accuracies ranging from 89% to 95% across the different deep learning models. Notably, AlexNet emerges as the top-performing model, achieving an accuracy of 95% and demonstrating balanced performance in accurately identifying both true and false PD cases.
 CONCLUSION: This research highlights the significant potential of deep learning in facilitating the early detection and classification of Parkinson's Disease. Leveraging speech signal features offers a non-invasive and cost-effective approach to PD assessment. The findings contribute to the growing body of evidence supporting the integration of artificial intelligence in healthcare, particularly in the realm of neurodegenerative disorders. Further exploration into the application of deep learning in this domain holds promise for advancing PD diagnosis and management.

Parkinson's disease (PD) is disabling disease that affects the quality of life. It belimps due to the death of cells that produce dopamine's in the substantia nigra part of the central nervous system (CNS) which affects the human body. People who have Parkinson's disease feel difficulty in doing activities like speaking, writing, and walking. In the recent past, speech, gait and EEG signals have been investigated for the detection of PD. However, speech analysis is the most considered technique to be used. Researches have shown that 90% of the people who suffer from Parkinson's disease have speech disorders. With the increase in the severity of the disease, the patient's voice gets more and more deteriorated. The non-invasive treatments for voice analysis are available that helps in ameliorating the life quality of a patient. Thus, for building the telemonitoring and telediagnosis models for prediction, the speech analysis has been tremendously increased. The proper interpretation of speech signals is one of the important classification problems for Parkinson's disease diagnosis. The main purpose of this paper is to contemplate the survey work of the machine learning techniques and deep learning procedures used for Parkinson's disease classification. Deep learning and machine learning techniques have been used as a part of the discovery for the efficient classification of PD. The various classification models like support vector machines, naive Bayes, deep neural networks, decision tree and random forest are effectively employed for classification purposes. The analysis of results of different research works showed that both machine learning and deep learning algorithms have shown promising future and therefore paving a better way for the detection of Parkinson's disease at its earlier stages. The classification accuracy achieved by the machine learning classifier. Among deep learning approaches, the deep neural network has achieved the best accuracy of 99.49%. The results obtained from different works suggest that artificial intelligence is becoming a powerful learning tool that has much to offer to data scientists as well as neurologists. In general the learning methods are adding value to decision-making problems especially in the field of medical diagnosis.

For the diagnosis and early detection of Parkinson's disease, a noninvasive method based on observed abnormal motor signs is desired. Therefore, in this paper, a 10-layered 1-d convolutional neural network (CNN) and novel-residual-network-type 1-d CNN were introduced for Parkinson's disease classification using vocal feature datasets. The resulting residual network provided a good classification result with an accuracy of 0.888, F-measure of 0.928, and MCC of 0.692.

Diagnosis is the key step forward to cure a disease. Deep learning is becoming popular as a tool for usage in medical diagnosis. The existing literature using deep learning for the diagnosis of Parkinson’s disease (PD) by transfer learning of MRI data was limited to the AlexNet architecture. The present work aims to inculcate commonly used deep learning architectures using transfer learning for effective diagnosis of PD using MRI data. The best three performing models are selected based on the standard metric called F1-score. An ensemble model is proposed based on the maximum probability across all the selected models for PD classification. The approach mainly focuses on the effective diagnosis of PD. The performance of the proposed ensemble approach is validated using the standard metrics known as F1-score and classification accuracy. Among the commonly used deep learning architectures, it was found that VGG19 is better than the existing state-of-the-art, which is AlexNet. The proposed ensemble approach applied on top three commonly used deep learning models led to the improvement in accuracy to 0.978. The false positive (FP) and false negative (FN) were reduced significantly by the proposed ensemble approach.KeywordsDeep learningTransfer learningEnsemble methodParkinson’s diseaseCNN

MRI classification of progressive supranuclear palsy, Parkinson disease and controls using deep learning and machine learning algorithms for the identification of regions and tracts of interest as potential biomarkers.

Parkinson's disease is a neurodegenerative disorder affecting motor abilities, with a prevalence of 329 cases per 100,000 individuals. Early diagnosis is crucial to prevent complications. This study classifies Parkinson's disease using the Extreme Gradient Boosting (XGBoost) algorithm with hyperparameter tuning via Grid Search and Random Search. The dataset from Kaggle consists of 2105 records from 2024 and includes 32 clinical and demographic features such as age, gender, BMI, medical history, and Parkinson's symptoms. The XGBoost method effectively manages large and complex data and reduces. Tuning was performed with 5-fold cross-validation for result validity. After tuning with Grid Search, the model achieved 93.35% accuracy in 44 minutes 51 seconds, with optimal parameters gamma=5, max depth=3, learning rate=0.3, n estimators=100, and subsample=0.7. Meanwhile, Random Search with 50 iterations achieved 93.97% accuracy in 3 minutes 4 seconds with optimal parameters gamma=5, max depth=3, learning rate=0.262, n estimators=58, and subsample=0.631. Random Search also shows better time efficiency than Grid Search, although with relatively similar accuracy. The results of this study confirm that hyperparameter tuning using Random Search not only produces competitive accuracy performance but also minimizes computation time, making it a more optimal choice for Parkinson's disease classification.