Diverse analysis techniques have been used to comprehend the regulation by the autonomic nervous system (ANS) of the cardiovascular system when a human being faces a stressor. Recently, however, the complete ensemble empirical mode decomposition (EMD) with adaptive noise (CEEMDAN) allows analyzing nonstationary signals in a nonlinear and time- variant way. Consequently, CEEMDAN may provide a means to obtain clues about ANS regulation in health and disease.  https://www.selleckchem.com/products/ver155008.html  In this study, we analyze the average Hilbert-Huang spectrum (HHS) of cardiovascular variability signals by CEEMDAN during a head-up tilt test (HUTT) in 12 healthy female subjects and 18 orthostatic intolerance female patients. Beat-to-beat intervals (BBI) as well as systolic (SYS) blood pressure variability time series were analyzed. In addition, instantaneous amplitudes and frequencies of specific intrinsic mode functions (IMF) were investigated separately to define the influence of the disease on ANS regulation. Female groups demonstrated statistical differences in the high-frequency band of BBI but higher differences for the high and low-frequency bands of SYS from the mechanical transition of HUTT.Clinical Relevance- A relevant outcome of the study is the average HHS of healthy female subjects along HUTT. This HHS may be used as reference to help diagnose OI when HHS of the cardiovascular variability signals of any subject deviates from the normal course.Over a third of patients suffering from epilepsy continue to live with recurrent disabling seizures and would greatly benefit from personalized seizure forecasting. While electroencephalography (EEG) remains most popular for studying subject-specific epileptic precursors, dysfunctions of the autonomous nervous system, notably cardiac activity measured in heart rate variability (HRV), have also been associated with epileptic seizures. This work proposes an unsupervised clustering technique which aims to automatically identify preictal HRV changes in 9 patients who underwent simultaneous electrocardiography (ECG) and intracranial EEG presurgical monitoring at the University of Montreal Hospital Center. A 2-class k-means clustering combined with a quantitative preictal HRV change detection technique were adopted in a subject- and seizure-specific manner. Results indicate inter and intra-patient variability in preictal HRV changes (between 3.5 and 6.5 min before seizure onset) and a statistically significant negative correlation between the time of change in HRV state and the duration of seizures (p less then 0.05). The presented findings show promise for new avenues of research regarding multimodal seizure prediction and unsupervised preictal time assessment.Clinical Relevance- This study proposed an unsupervised technique for quantitatively identifying preictal HRV changes which can be eventually used to implement an ECG-based seizure forecasting algorithm.In this paper, a deep learning framework for detection and classification of EMG signals for diagnosis of neuromuscular disorders is proposed employing cross wavelet transform. Cross wavelet transform which is a modification of continuous wavelet transform is an important tool to analyze any non-stationary signal in time scale and in time-frequency frame. To this end, EMG signals of healthy, myopathy and Amyotrophic lateral sclerosis disorders were procured from an online existing database. A healthy EMG signal was chosen as reference and cross wavelet transform of the rest of the healthy as well as the disease EMG signals was done with the reference. From the resulting cross wavelet spectrum images of EMG signals, a convolution neural network (CNN) based automated deep feature extraction technique was implemented. The extracted deep features were further subjected to feature ranking employing one way analysis of variance (ANOVA) test. The extracted deep features with high degree of statistical significance were fed to several benchmark machine learning classifiers for the purpose of discrimination of EMG signals. Two binary classification problems are addressed in this paper and it has been observed that the highest mean classification accuracy of 100% is achieved using the statistically significant extracted deep features. The proposed method can be implemented for real-time detection of neuromuscular disorders.The nonstationarity measure of surface Electromyography (sEMG) signals provide an index for muscle fatigue conditions. In this paper, a new framework has been proposed for the analysis of sEMG signal using Instantaneous Spectral Centroid (ISC). The novelty of the proposed work is use of topological signal processing method to quantify the nonstationarity of sEMG signal. For this, the signals are recorded from the biceps brachii muscles of 25 healthy subjects in isometric contraction. The analytical signals corresponding to nonfatigue and fatigue segments are computed using Hilbert Transform. Further, topological features such as center of gravity (CoG), triangular area function (TAF) and ISC are calculated from the geometrical representation of a transformed signal. The result indicates the increase of TAF in fatigue condition and the significant right shift of CoG in x-axis for 80% of subjects. Importantly, the ISC estimate is decreased by 17% upon fatiguing for 84% of subjects. The obtained results show statistical significance with p less then 0.05. It is observed that the shape parameters are varied in accordance with the changes observed in global characteristics of sEMG signals during muscle fatigue. The preliminary results show that the topological features are able to quantify the nonstationarity in sEMG signal. Therefore, the proposed method can be used as a fatigue index for diagnosing various neuromuscular disorders.Clinical Relevance-This method can be used to establish metrics of muscle fatigue for the benefit of physicians especially in the field of fitness, sports, pre and post-surgery surveillance and rehabilitation.This study investigates the applicability of Electromyography (EMG) signal classification algorithms with relatively low training time to control prosthetic devices. The perceived quality of control depends on many factors, such as the 1) accuracy of the algorithm, 2) the complexity of the control, and 3) the ability to compensate for the error. The high granularity of control in the time domain reduces the perceived effect of error but also limits the classification accuracy. This work aims to find the borderline for the accuracy of algorithms to be selected as a control strategy for hand prosthetic devices and thus shorten the gap between laboratory devices and commercially available devices. In particular, we compared five classification algorithms and selected one for real-time testing. The results from a test conducted on four subjects showed that the EMG-based control strategy has comparable performances with an IMU-based controller.