Showing papers in "Biomedical Signal Processing and Control in 2015"

TL;DR: HRV resulted significantly depressed during mental stress, showing a reduced variability and less chaotic behaviour, and the method proposed to transform and then meta-analyze the HRV measures can be applied to other fields where HRV proved to be clinically significant.

TL;DR: The major benefits and challenges of myoelectric interfaces are evaluated and recommendations are given, for example, for electrode placement, sampling rate, segmentation, and classifiers.

TL;DR: Various methods of glucose monitoring are reviewed and overall emphasis is laid on the development of NIRS (near-infrared spectroscopy) based non-invasive glucose monitoring.

TL;DR: The proposed framework for classification of EMG signals using multiscale principal component analysis (MSPCA) for de-noising, discrete wavelet transform (DWT) for feature extraction and decision tree algorithms for classification can be used to support clinicians for diagnosis of neuromuscular disorders.

TL;DR: The 5-fold cross validation results showed that the “WTA-KSVM + PSOTVAC” performed best over the OASIS benchmark dataset, with overall accuracy of 81.5% among all proposed nine classifiers.

TL;DR: One-dimensional local binary pattern (1D-LBP) based features are used for classification of seizure and seizure-free electroencephalogram (EEG) signals with a classification accuracy of 98.33%.

TL;DR: A final comparison between the results obtained with the developed technique and results adopted by Polat and coworkers using Fourier analysis with the same database is given to show the effectiveness of this technique for seizure detection.

TL;DR: A novel automated glaucoma diagnosis method using various features extracted from Gabor transform applied on digital fundus images and a GRI developed using principal components to classify the two classes using just one number is proposed.

TL;DR: Experimental results demonstrate that the proposed method does not only produce better results by successfully fusing the different CT and MR images, but also ensures an improvement in the various quantitative parameters as compared to other existing methods.

TL;DR: The proposed algorithm provides a patient specific detection of AF using a simple classifier, and can be leveraged as a tool to detect AF onsets/offsets over short AF episodes even when a patient's heart rate is controlled.

TL;DR: A registration framework based on speed up robust feature (SURF) detector, PIIFD and robust point matching, called SURF–PIIFD–RPM, which outperforms existing algorithms, and it is quite robust to outliers.

TL;DR: Novel weighted spectral features based on Local Hu moments based on Hu moments are presented, which can evaluate the degree how the energy is concentrated to the center of energy gravity of local region of spectrogram and can significantly vary with the speech emotion types.

TL;DR: An algorithm is presented to combine ensemble empirical mode decomposition (EEMD) with spectrum subtraction (SS) to track HR changes during subjects’ physical activities to improve accuracy and efficiency in heart rate monitoring.

TL;DR: The method presented eliminates the phase distortion while offering a better compromise between signal denoising and signal information retention than conventional filtering methods.

TL;DR: A new method has been proposed by combining two techniques: a canonical correlation analysis (CCA) followed by a stationary wavelet transform (SWT) to remove EMG artifacts and a second-order blind identification (SOBI) technique followed by SWT to remove EOG artifacts.

TL;DR: The concept of reinforcement learning (RL) is used to develop a closed-loop anesthesia controller using the bispectral index (BIS) as a control variable while concurrently accounting for mean arterial pressure (MAP) to regulate the BIS and MAP within a desired range.

TL;DR: A novel algorithm for the detection of fixations and smooth pursuit movements in high-speed eye-tracking data is proposed, which uses a three-stage procedure to divide the intersaccadic intervals into a sequence of fixation and Smooth pursuit events.

TL;DR: Initial results indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor.

TL;DR: A comparative analysis of different signal processing techniques for each BCI system stage concerning steady state visually evoked potentials (SSVEP), which includes feature extraction performed by different spectral methods, leads to a representative and helpful comparative overview of robustness and efficiency of classical strategies.

TL;DR: A new complexity measure of time series by combining ordinal patterns and Lempel-Ziv complexity (LZC) for quantifying the dynamical changes of EEG, which suggests that PLZC is a potential nonlinear method for characterizing the changes in EEG signal.

TL;DR: This work presents a new method to detect the QRS signal in a simple way with minimal computational cost and resource needs using a novel non-linear filter.

TL;DR: An OD segmentation scheme to infer how the performance of the well-known gradient vector flow (GVF) model compares with nine popular/recent ACM algorithms by supplying them with the initial OD contour derived from the circular Hough transform is designed.

TL;DR: A new method combining ICA and wavelet neural networking (WNN) is proposed, where WNN is applied to the contaminated ICs, correcting the OA and thus lowering the data lost.

TL;DR: This study investigated the classification capability of different gait statistical features extracted from gait rhythm signals and found the highest accuracy rate for discriminating between groups of NDD patients and healthy control subjects was 96.83%.

TL;DR: Findings showed that males and females walk at the same comfortable speed, despite the significantly lower height and higher cadence detected in females, indicating a propensity of females for a more complex recruitment of TA, GL and VL during walking, compared to males.

TL;DR: An attempt has been made to use curvelet transforms which permit identifying the coefficients that store the crucial information about diagnosis in ECG steganography to validate that coefficients around zero are ideal for watermarking to minimize deterioration and there is no loss in the data retrieved.

TL;DR: The improved method can effectively reduce the dimensions of multidimensional time series for clinical data through the combination of the Kozachenko–Leonenko (K–L) information entropy estimation method for feature extraction based on mutual information and the feature selection algorithm based on class separability.

TL;DR: This study generates noisy test signals by adding a noise source such as random Gaussian and scalp-recorded background noise into the original motor imagery based EEG signals and compares the classification performance of the SRC and support vector machine (SVM).

TL;DR: The proposed hybrid strategy could effectively enhance the performance of the SSVEP-based mental spelling system by simultaneously using the information of eye-gaze direction detected by a low-cost webcam without calibration.

TL;DR: A method to automatically identify cough segments from the pediatric sound recordings is developed based on extracting mathematical features such as non-Gaussianity, Shannon entropy, and cepstral coefficients to describe cough characteristics and achieved sensitivity, specificity, and Cohen's Kappa levels of 93, 98%, and 0.65.