A Comparison of Classification Methods for Telediagnosis of Parkinson's Disease

Parkinson’s disease (PD) is a progressive and chronic nervous system disease that impairs the ability of speech, gait, and complex muscle-and-nerve actions. Early diagnosis of PD is quite important for alleviating the symptoms. Cost effective and convenient telemedicine technology helps to distinguish the patients with PD from healthy people using variations of dysphonia, gait or motor skills. In this study, a novel telemedicine technology was developed to detect PD remotely using dysphonia features. Feature transformation and several machine learning (ML) methods with 2-, 5- and 10-fold cross-validations were implemented on the vocal features. It was observed that the combination of principal component analysis (PCA) as a feature transformation (FT) and k-nearest neighbor (k-NN) as a classifier with 10-fold cross-validation has the best accuracy as 99.1%. All ML processes were applied to the prerecorded PD dataset using a newly created program named ParkDet 2.0. Additionally, the blind test interface was created on the ParkDet so that users could detect new patients with PD in future. Clinicians or medical technicians, without any knowledge of ML, will be able to use the blind test interface to detect PD at a clinic or remote location utilizing internet as a telemedicine application

KOMPLEKSOWE METODY UCZENIA MASZYNOWEGO I UCZENIA GŁĘBOKIEGO DO KLASYFIKACJI CHOROBY PARKINSONA I OCENY JEJ NASILENIA

In this study, we aimed to adopt a comprehensive approach to categorize and assess the severity of Parkinson's disease by leveraging techniques from both machine learning and deep learning. We thoroughly evaluated the effectiveness of various models, including XGBoost, Random Forest, Multi-Layer Perceptron (MLP), and Recurrent Neural Network (RNN), utilizing classification metrics. We generated detailed reports to facilitate a comprehensive comparative analysis of these models. Notably, XGBoost demonstrated the highest precision at 97.4%. Additionally, we took a step further by developing a Gated Recurrent Unit (GRU) model with the purpose of combining predictions from alternative models. We assessed its ability to predict the severity of the ailment. To quantify the precision levels of the models in disease classification, we calculated severity percentages. Furthermore, we created a Receiver Operating Characteristic (ROC) curve for the GRU model, simplifying the evaluation of its capability to distinguish among various severity levels. This comprehensive approach contributes to a more accurate and detailed understanding of Parkinson's disease severity assessment.W tym badaniu naszym celem było przyjęcie kompleksowego podejścia do kategoryzacji i oceny ciężkości choroby Parkinsona poprzez wykorzystanie technik zarówno uczenia maszynowego, jak i głębokiego uczenia. Dokładnie oceniliśmy skuteczność różnych modeli, w tym XGBoost, Random Forest, Multi-Layer Perceptron (MLP) i Recurrent Neural Network (RNN), wykorzystując wskaźniki klasyfikacji. Wygenerowaliśmy szczegółowe raporty, aby ułatwić kompleksową analizę porównawczą tych modeli. Warto zauważyć, że XGBoost wykazał najwyższą precyzję na poziomie 97,4%. Ponadto poszliśmy o krok dalej, opracowując model Gated Recurrent Unit (GRU) w celu połączenia przewidywań z alternatywnych modeli. Oceniliśmy jego zdolność do przewidywania nasilenia dolegliwości. Aby określić ilościowo poziomy dokładności modeli w klasyfikacji chorób, obliczyliśmy wartości procentowe nasilenia. Ponadto stworzyliśmy krzywą charakterystyki operacyjnej odbiornika (ROC) dla modelu GRU, upraszczając ocenę jego zdolności do rozróżniania różnych poziomów nasilenia. To kompleksowe podejście przyczynia się do dokładniejszego i bardziej szczegółowego zrozumienia oceny ciężkości choroby Parkinsona

CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK

In our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough of the chemical to support the body's motor functions. The main symptoms of this illness are eyesight, excretion activity, speech, and mobility issues, followed by depression, anxiety, sleep issues, and panic attacks. The main aim of this research is to develop a workable clinical decision-making framework that aids the physician in diagnosing patients with PD influence. In this research, we proposed a technique to classify Parkinson’s disease by MRI brain images. Initially, normalize the input data using the min-max normalization method and then remove noise from input images using a median filter. Then utilizing the Binary Dragonfly Algorithm to select the features. Furthermore, to segment the diseased part from MRI brain images using the technique Dense-UNet. Then, classify the disease as if it’s Parkinson’s disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with Enhanced Whale Optimization Algorithm (EWOA) to get better classification accuracy. Here, we use the public Parkinson’s Progression Marker Initiative (PPMI) dataset for Parkinson’s MRI images. The accuracy, sensitivity, specificity, and precision metrics will be utilized with manually gathered data to assess the efficacy of the proposed methodology

Investigating Voice as a Biomarker for Leucine-Rich Repeat Kinase 2-Associated Parkinson's Disease

We investigate the potential association between leucine-rich repeat kinase 2 (LRRK2) mutations and voice. Sustained phonations ('aaah' sounds) were recorded from 7 individuals with LRRK2-associated Parkinson's disease (PD), 17 participants with idiopathic PD (iPD), 20 non-manifesting LRRK2-mutation carriers, 25 related non-carriers, and 26 controls. In distinguishing LRRK2-associated PD and iPD, the mean sensitivity was 95.4% (SD 17.8%) and mean specificity was 89.6% (SD 26.5%). Voice features for non-manifesting carriers, related non-carriers, and controls were much less discriminatory. Vocal deficits in LRRK2-associated PD may be different than those in iPD. These preliminary results warrant longitudinal analyses and replication in larger cohorts

Assessing Parkinson’s Disease at Scale Using Telephone-Recorded Speech:Insights from the Parkinson’s Voice Initiative

Numerous studies have reported on the high accuracy of using voice tasks for the remote detection and monitoring of Parkinson’s Disease (PD). Most of these studies, however, report findings on a small number of voice recordings, often collected under acoustically controlled conditions, and therefore cannot scale at large without specialized equipment. In this study, we aimed to evaluate the potential of using voice as a population-based PD screening tool in resource-constrained settings. Using the standard telephone network, we processed 11,942 sustained vowel /a/ phonations from a US-English cohort comprising 1078 PD and 5453 control participants. We characterized each phonation using 304 dysphonia measures to quantify a range of vocal impairments. Given that this is a highly unbalanced problem, we used the following strategy: we selected a balanced subset (n = 3000 samples) for training and testing using 10-fold cross-validation (CV), and the remaining (unbalanced held-out dataset, n = 8942) samples for further model validation. Using robust feature selection methods we selected 27 dysphonia measures to present into a radial-basis-function support vector machine and demonstrated differentiation of PD participants from controls with 67.43% sensitivity and 67.25% specificity. These findings could help pave the way forward toward the development of an inexpensive, remote, and reliable diagnostic support tool for PD using voice as a digital biomarker

An algorithm for Parkinson's disease speech classification based on isolated words analysis

Introduction Automatic assessment of speech impairment is a cutting edge topic in Parkinson's disease (PD). Language disorders are known to occur several years earlier than typical motor symptoms, thus speech analysis may contribute to the early diagnosis of the disease. Moreover, the remote monitoring of dysphonia could allow achieving an effective follow-up of PD clinical condition, possibly performed in the home environment. Methods In this work, we performed a multi-level analysis, progressively combining features extracted from the entire signal, the voiced segments, and the on-set/off-set regions, leading to a total number of 126 features. Furthermore, we compared the performance of early and late feature fusion schemes, aiming to identify the best model configuration and taking advantage of having 25 isolated words pronounced by each subject. We employed data from the PC-GITA database (50 healthy controls and 50 PD patients) for validation and testing. Results We implemented an optimized k-Nearest Neighbours model for the binary classification of PD patients versus healthy controls. We achieved an accuracy of 99.4% in 10-fold cross-validation and 94.3% in testing on the PC-GITA database (average value of male and female subjects). Conclusion The promising performance yielded by our model confirms the feasibility of automatic assessment of PD using voice recordings. Moreover, a post-hoc analysis of the most relevant features discloses the option of voice processing using a simple smartphone application

Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD