Analysis of vertical eye movements in Parkinson’s disease and its potential for diagnosis

Applied Intelligence - It is well known that eye movements are highly affected by Parkinson’s disease. The majority of studies related to effects of Parkinson’s disease on eye movements...     

Auxiliary diagnosis of heterogeneous data of Parkinson’s disease based on improved convolution neural network

Multimedia Tools and Applications - Parkinson’s disease (PD) is a kind of nervous system degenerative disease frequently occurring in the elderly over sixty years old. With the development of...     

Multiclass classification of Parkinson’s disease using cepstral analysis

This paper addressees the problem of an early diagnosis of PD (Parkinson’s disease) by the classification of characteristic features of person’s voice knowing that 90% of the people with PD suffer from speech disorders. We collected 375 voice samples from healthy and people suffer from PD. We extracted from each voice sample features using the MFCC and PLP Cepstral techniques. All the features are analyzed and selected by feature selection algorithms to classify the subjects in 4 classes according to UPDRS (unified Parkinson’s disease Rating Scale) score. The advantage of our approach is the resulting and the simplicity of the technique used, so it could also extended for other voice pathologies. We used as classifier the discriminant analysis for the results obtained in previous multiclass classification works. We obtained accuracy up to 87.6% for discrimination between PD patients in 3 different stages and healthy control using MFCC along with the LLBFS algorithm.     

Speech-based solution to Parkinson’s disease management

Parkinson’s disease (PD) is a neurological disorder marked by decreased dopamine levels in the brain. Persons suffering from PD, exhibits vocal symptoms such as dysphonia and dysarthria. Speech impairments in PD are grouped together and called as hypokinetic dysarthria. Traditional PD management is based on a patient’s clinical history and through physical examination as there are currently no known biomarkers for its diagnosis. Automatic analysis techniques aid clinicians in diagnosis and monitoring patients using speech and provide frequent, cost effective and objective assessment. This paper presents pilot experiment to detect presence of dysarthria in speech and detect level of severity based on deep learning approach. Automated feature extraction and classification using convolutional neural network shows 77.48% accuracy on test samples of TORGO database with five fold validation. Using transfer learning, system performance is further analyzed for gender specific performance as well as in detection of severity of disease.

     

Analysis of multiple types of voice recordings in cepstral domain using MFCC for discriminating between patients with Parkinson’s disease and healthy people

In this study, we wanted to discriminate between two groups of people. The database used in this study contains 20 patients with Parkinson’s disease (PD) and 20 healthy people. Three types of sustained vowels (/a/, /o/ and /u/) were recorded from each participant and then the analyses were done on these voice samples. The technique used in this study is to extract voiceprint from each voice samples by using mel frequency cepstral coefficients (MFCCs). The extracted MFCC were compressed by calculating their average value in order to extract the voiceprint from each voice recording. Subsequently, a classification method was performed using leave one subject out (LOSO) validation scheme along with support vector machines (SVMs). We also used an independent test to validate our results by using another database which contains 28 PD patients. Based on the research result, the best obtained classification accuracy using LOSO on the first dataset was 82.50 % using MLP kernel of SVM on sustained vowel /u/. And the maximum classification accuracy using the independent test was 100 % using sustained vowel /a/ with polynomial kernel of the SVM and with MLP kernel of the SVM. This result was also achieved using sustained vowel /o/ with polynomial kernel of the SVM.     

Statistical methods for analysis of Parkinson’s disease gait pattern and classification

Multimedia Tools and Applications - Understanding the human gait and extracting intrinsic feature helps to classify walking patterns of Parkinson disease patients. The measurement of time series...     

Addressing voice recording replications for Parkinson’s disease detection

A clinical expert system has been developed for detection of Parkinson’s Disease (PD). The system extracts features from voice recordings and considers an advanced statistical approach for pattern recognition. The significance of the work lies on the development and use of a novel subject-based Bayesian approach to account for the dependent nature of the data in a replicated measure-based design. The ideas under this approach are conceptually simple and easy-to-implement by using Gibbs sampling. Available information could be included in the model through the prior distribution. In order to assess the performance of the proposed system, a voice recording replication-based experiment has been specifically conducted to discriminate healthy people from people suffering PD. The experiment involved 80 subjects, half of them affected by PD. The proposed system is able to discriminate acceptably well healthy people from people with PD in spite that the experiment has a reduced number of subjects.     

Fuzzy recurrence plot-based analysis of dynamic and static spiral tests of Parkinson’s disease patients

Neural Computing and Applications - Parkinson’s disease (PD) is a chronic and progressive neurological illness affecting millions of people in the world. The cure for PD is not available....     

Multiclass classification of Parkinson’s disease using different classifiers and LLBFS feature selection algorithm

Since approximately 90% of the people with PD (Parkinson’s disease) suffer from speech disorders including disorders of laryngeal, respiratory and articulatory function, using voice analysis disease can be diagnosed remotely at an early stage with more reliability and in an economic way. All previous works are done to distinguish healthy people from people with Parkinson’s disease (PWP). In this paper, we propose to go further by multiclass classification with three classes of Parkinson stages and healthy control. So we have used 40 features dataset, all the features are analyzed and 9 features are selected to classify PWP subjects in four classes, based on unified Parkinson’s disease Rating Scale (UPDRS). Various classifiers are used and their comparison is done to find out which one gives the best results. Results show that the subspace discriminant reach more than 93% overall classification accuracy.     

Machine learning Ensemble for the Parkinson’s disease using protein sequences

Multimedia Tools and Applications - The most challenging issue in diagnosing and treating neurological disorders is gene identification that causes the disease. Classification of the genes that...     

A novel PBL-McRBFN-RFE approach for identification of critical brain regions responsible for Parkinson’s disease

In this paper, we present a novel approach for the identification of critical brain regions responsible for Parkinson’s disease (PD) based on magnetic resonance images (MRI) using meta-cognitive radial basis function network (McRBFN) classifier with Recursive Feature Elimination (RFE). The McRBFN classifier uses voxel based morphometric (VBM) features extracted from MRI and employs a projection based learning (PBL) algorithm. The meta-cognitive learning present in PBL-McRBFN helps in selecting proper samples to learn based on its current knowledge and evolve the architecture automatically. Since, the classifier developed using PBL-McRBFN is efficient, we propose recursive feature elimination approach (called PBL-McRBFN-RFE) to identify most relevant brain regions responsible for PD prediction.The study has been conducted using the Parkinson’s Progression Markers Initiative (PPMI) data set. First, we conducted the study on PD prediction using the PBL-McRBFN classifier on the PPMI data set. We have also compared the results of the PBL-McRBFN classifier with the support vector machine (SVM) classifier. The study results clearly show that the PBL-McRBFN classifier produces better generalization performance on PD prediction. Finally, we use RFE approach with PBL-McRBFN to identify the brain regions responsible for PD. The PBL-McRBFN-RFE selected features indicate that the loss of gray matter in the superior temporal gyrus region may be responsible for the onset of PD, and is consistent with the earlier findings from medical research studies.     

An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach

In this paper, we present an effective and efficient diagnosis system using fuzzy k-nearest neighbor (FKNN) for Parkinson’s disease (PD) diagnosis. The proposed FKNN-based system is compared with the support vector machines (SVM) based approaches. In order to further improve the diagnosis accuracy for detection of PD, the principle component analysis was employed to construct the most discriminative new feature sets on which the optimal FKNN model was constructed. The effectiveness of the proposed system has been rigorously estimated on a PD data set in terms of classification accuracy, sensitivity, specificity and the area under the receiver operating characteristic (ROC) curve (AUC). Experimental results have demonstrated that the FKNN-based system greatly outperforms SVM-based approaches and other methods in the literature. The best classification accuracy (96.07%) obtained by the FKNN-based system using a 10-fold cross validation method can ensure a reliable diagnostic model for detection of PD. Promisingly, the proposed system might serve as a new candidate of powerful tools for diagnosing PD with excellent performance.     

Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson’s disease

Neural Computing and Applications - Speech diagnosis of Parkinson’s disease (PD) as a non-invasive and simple diagnosis method is particularly worth exploring. However, the number of samples...     

General Theory of security and a study of hacker’s behavior in big data era

Big data brings great value as well as a lot of network security problems, which makes the hacker possess more and more attack strategies. This paper precisely describes the static form of hackers, and proposes the best dynamic hackers attack tactics under certain assumptions. When the proportion of the hacker’s resource input is its static probability distribution value, the hacker income reaches maximum. In particular, on the premise of uniform ratio of input and output, if the entropy of hacker reduces 1 bit, the hacker income will be double. Furthermore, this paper studies the optimal combination of hacker attacks and proposes a logarithmic optimal combination attack strategy that the hacker attacks several systems simultaneously. This strategy not only can maximize the hacker’s overall income, but also can maximize the income of each round attack. We find that the input-output ratio of each system will not change at the end of this round attack when hacker adopts the logarithmic optimal combination strategy, and find the growth rate of additional hacker income does not exceed the mutual information between the input-output ratio of the attacked system and the inedge information if an attacker can get some inedge information through other ways. Moreover, there is an optimum attack growth rate of hackers if time-varying attacked system is a stationary stochastic process. We can conclude that, in Big Data era, the more information the hacker gets, the more hacker income.