A novel method based on matching pursuit decomposition of gait signals for Parkinson's disease, Amyotrophic lateral sclerosis and Huntington's disease detection.

BACKGROUND AND OBJECTIVE: An accurate detection of neurodegenerative diseases (NDDs) definitely improves the life of patients and has attracted growing attention. METHODS: In this paper, a general automatic method for detection of Parkinson's disease (PD), Amyotrophic lateral sclerosis (ALS) and Huntington's disease (HD) has been proposed based on the localized time-frequency information of gait signals. The new main part of the detection method is to obtain a small set of sparse coefficients for the local representation of gait signals with appropriate time and frequency resolution. For this purpose, a hybrid feature set based on sparse matching pursuit decomposition and two sets of nonlinear and linear features has been developed. Then, principal components of the proposed feature have been analyzed using a sparse coding classifier. Results The proposed approach has achieved high average accuracy rates of 93%, 94%, and 97% for PD, ALS, and HD detection, respectively. CONCLUSIONS: The obtained results have indicated that combination of time and frequency information of the gait signals through adaptive localized window length in MP makes it more efficient in comparison with the existing time, frequency or other time-frequency gait parameters. The great potential of nonlinear sparse features for PD and HD detection and linear ones for ALS detection has also been shown.

Neurodegenerative diseases detection using distance metrics and sparse coding: A new perspective on gait symmetric features.

Gait rhythm fluctuations are of great importance for automatic neurodegenerative diseases (NDDs) detection. They provide a cost-effective and noninvasive monitoring tool in which their parameters are related to neuromuscular function. This study investigated a new solution based on a set of new symmetric features and sparse non-negative least squares (NNLS) coding classifier. Dynamic gait series warping (DGSW), Euclidean, Manhattan, Minkowski, Chebyshev, Canberra distances, and cosine function were used to quantify the amount of divergence between the left and right stride, swing, and stance intervals. The algorithm was evaluated using the gait signals of 16 healthy control subjects, 13 patients with amyotrophic lateral sclerosis (ALS), 15 patients with Parkinson's disease (PD) and 20 patients with Huntington's disease (HD). The proposed new approach using symmetric features and NNLS technique achieved outstanding accuracies of 98%, 97%, and 95% on the patients with PD, ALS, and HD, respectively. The findings also suggested that the new DGSW, cosine function, and Chebyshev distance, which are designed to dynamically, geometrically, or nonlinearly quantify the similarity between two time series, provide the discriminatory measures to describe how NDDs alter the gait symmetry. In comparison with other studies, combining symmetric features with a sparse NNLS coding classifier can improve the detection accuracy providing an efficient and cost-effective framework for the development of a NDDs detection system.

Dynamic Hilbert warping, a new measure of RR-interval signals evaluated in the cognitive load estimation.

RR interval (RRI) signals represent the time intervals between successive heart R-waves. These signals are influenced by many cognitive and psychological processes. In this study, a new technique based on the combination of empirical mode decomposition and dynamic Hilbert warping (DHW) was proposed to inference cognitive states from measured RRI signals. Moreover, a set of entropic and statistical measures was extracted to characterize the regularity and temporal distribution in the phase spectra and amplitude envelope of the analytic signals. The discriminating capability of the proposed method was studied in 45 healthy subjects. They performed an arithmetic task with five levels of difficulty. The study indicated the importance of phase information in cognitive load estimation (CLE). The new phase characteristics were able to extract hidden information from the RRI signals. The results revealed a striking decrease in DHW value with increasing load level. The entropic measures of analytic signal also showed an increasing trend as the mental load increased. Although, phase information had an ability to discriminate between more distinct levels as well as between more similar ones, amplitude information was effective only in discriminating between more distinct levels.

An efficient automatic workload estimation method based on electrodermal activity using pattern classifier combinations.

Automatic workload estimation has received much attention because of its application in error prevention, diagnosis, and treatment of neural system impairment. The development of a simple but reliable method using minimum number of psychophysiological signals is a challenge in automatic workload estimation. To address this challenge, this paper presented three different decomposition techniques (Fourier, cepstrum, and wavelet transforms) to analyze electrodermal activity (EDA). The efficiency of various statistical and entropic features was investigated and compared. To recognize different levels of an arithmetic task, the features were processed by principal component analysis and machine-learning techniques. These methods have been incorporated into a workload estimation system based on two types: feature-level and decision-level combinations. The results indicated the reliability of the method for automatic and real-time inference of psychological states. This method provided a quantitative estimation of the workload levels and a bias-free evaluation approach. The high-average accuracy of 90% and cost effective requirement were the two important attributes of the proposed workload estimation system. New entropic features were proved to be more sensitive measures for quantifying time and frequency changes in EDA. The effectiveness of these measures was also compared with conventional tonic EDA measures, demonstrating the superiority of the proposed method in achieving accurate estimation of workload levels.

An efficient seizure prediction method using KNN-based undersampling and linear frequency measures.

Seizure prediction based on analysis of electroencephalogram signals has generated considerable research interests. A reliable seizure prediction algorithm with minimal computational requirements is prominent issue for medical facilities; however, it has not been addressed correctly. In this study, an optimized novel method is proposed in order to remove computational complexity, and predict epileptic seizures clinically. It is based on the univariate linear features in eight frequency sub-bands. It also employs principal component analysis (PCA) for dimension reduction and optimal feature selection. Class unbalanced problem is tackled by K-nearest neighbor (KNN)-based undersampling combined with support vector machine (SVM) classifier. To find out the best results two types of postprocessing methods were studied. The proposed algorithm was evaluated on seizures and 434.9h of interictal data from 18 patients of Freiburg database. It predicted 100% of seizures with average false alarm rate of 0.13 per hour ranging between 0 and 0.39. Furthermore, G-Mean and F-measure were used for validation which were 0.97 and 0.90, respectively. These results confirmed the discriminative ability of the algorithm. In comparison with other studies, the proposed method improves trade-off between sensitivity and false prediction rate with linear features and low computational requirements and it can potentially be employed in implantable devices. Achieving high performance by linear features, PCA, KNN-based undersampling, and SVM demonstrates that this method can potentially be used in implantable devices.