Showing papers in "Biomedical Signal Processing and Control in 2014"

TL;DR: Improvements are presented on this last technique, obtaining components with less noise and more physical meaning in the empirical mode decomposition of non-stationary signals that may stem from nonlinear systems.

TL;DR: A comparative review of the two conventional methods, electrocardiogram (ECG) and photoplethysmography (PPG), and the novel methods of non-contact measuring of HR with capacitively coupled ECG, Doppler radar, optical vibrocardiography, thermal imaging, RGB camera and HR from speech.

TL;DR: A survey of the published literature in dealing with denoising methods in MR images is presented and the popular approaches are classified into different groups and an overview of various methods is provided.

TL;DR: A new method for electroencephalogram (EEG) signal classification based on fractional-order calculus, termed fractional linear prediction (FLP), is used to model ictal and seizure-free EEG signals.

TL;DR: Experimental results show that the best average classification accuracy of this algorithm can reach 99.125% with the theta rhythm of EEG signals.

TL;DR: Signal processing techniques are categorised in tabular format based on their application in intensively researched sleep areas such as sleep staging, transient pattern detection and sleep disordered breathing diagnosis.

TL;DR: In this survey, EEG inverse problem is discussed with its primary to most developed and recent solutions, the introduction to the field along with the categorization of different solutions and the relative advantages and limitations for each method are discussed.

TL;DR: A novel automated glaucoma diagnosis system using higher order spectra (HOS) cumulants extracted from Radon transform (RT) applied on digital fundus images that can detect the early glauca stage and the three classes with an average accuracy of 92.65%, sensitivity and specificity of 92% using NB classifier.

TL;DR: The proposed HSAD method accurately determines boundaries of major acoustic events of the PCG signal with signal-to-noise ratio of 5 dB and is suitable for real-time wireless cardiac health monitoring and electronic stethoscope devices.

TL;DR: A prospective review of wavelet-based ECG compression methods and their performances based upon findings obtained from various experiments conducted using both clean and noisy ECG signals is presented.

TL;DR: The meaning of cepstral peak prominence (CPP) is very similar to that of the first rahmonic and some insights are provided on its dependence with fundamental frequency and vocal tract resonances.

TL;DR: The proposed automated diagnosis system for automatic detection of the normal, AF and AFL beats of ECG provides high reliability to be used by clinicians and can be extended for detection of other abnormalities of heart and to other physiological signals.

TL;DR: A new classification method is presented to classify ECG signals more precisely based on dynamical model of the ECG signal and the probability of arrhythmia detection is increased, and this algorithm can become a useful means in laboratories.

TL;DR: It is shown that MPEG-7 part-4 audio low-level features can do very well in detecting pathological voices, as well as binary classifying the pathologies.

TL;DR: In this article, a fast block sparse Bayesian learning (BSBL) algorithm was proposed to reconstruct original signals from real-world fetal ECG signals and epilepsy EEG signals in order to reduce on-chip energy consumption and extend sensor life.

TL;DR: It is shown that the finger's joint angles can be continuously estimated well while the wrist was conducting different static motions simultaneously, and the proposed switching regime is effective for continuous estimation of the finger joint angles under different static wrist motions from EMG.

TL;DR: A framework on wavelet-based nonlinear features and extreme learning machine (ELM) for the seizure detection achieves not only a high detection accuracy but also a very fast learning speed, which makes the further development of the automatic seizure detection system feasible.

TL;DR: The proposed methodology for the optimization of surface EMG (sEMG)-based hand gesture classification is effective to implement a human–computer interaction device for both healthy subjects and transradial amputees and shows that RMS-WA/ANN is the best feature vector/classifier pair for the PCA approach.

TL;DR: A set of 12 novel features that should reflect respiratory depth and volume are proposed that can help improve deep sleep detection to more extent and calibrating the respiratory effort signals by means of body movements and performing subject-specific feature normalization can ultimately yield enhanced classification performance.

TL;DR: The results demonstrate the effectiveness of applying EEG features with machine learning techniques to classify the each emotional state difference of PD patients compared to healthy controls, and offer a promising approach for detection of emotional impairments associated with other neurological disorders.

TL;DR: A multi-condition training scheme was explored to improve the robustness of sEMG pattern recognition for hand and wrist motions by reducing the average classification error from 18.73% to 8.20% and a novel classifier, conditional Gaussian mixture model (CGMM) was proposed and yielded a lower classification error than LDA.

TL;DR: This work focused on the study of four distinct applications of robotic technology to health care, named Robotic Assisted Surgery, Robotics in Rehabilitation, Prosthetics and Companion Robotic Systems, and proposed a general modularization approach.

TL;DR: A novel feature extraction method, using the short-time Fourier transform ranking (STFT-ranking) feature, was employed to determine multichannel EMG signals, which included time-domain and frequency-domain features and was determined to offer more satisfactory performance than the other features tested for motion pattern recognition.

TL;DR: These existing algorithms for non-invasive detection and elicitation of fECG in terms of their performance and capabilities with respect to standard databases available worldwide are reviewed.

TL;DR: The proposes an efficient scheme to extract features in phase space by exploiting the theoretical results derived in nonlinear dynamics for motor imagery tasks recognition to improve the classification results in the BCI.

TL;DR: The results obtained in this study imply that targeting muscles that are involved in the rotation of the forearm could improve the performance of myoelectric control systems that include both wrist rotation and opening/closing of a terminal device.

TL;DR: A fast and fully automatic scheme based on iterative weighted averaging and adaptive curvature threshold is proposed in this study to facilitate accurate lung segmentation for inclusion of juxtapleural nodules and pulmonary vessels and ensure the smoothness of the lung boundary.

TL;DR: An attempt has been made to improve and preserve the inter-regions edges by effectively removing the noise without blurring and hence, to extract the breast tissues from infrared images using level sets based on improved edge information.

TL;DR: It is claimed that the proposed voice pathology classification tool can be employed for application of early detection of laryngeal pathology and for assessment of vocal improvement following voice therapy in clinical setting with low complexity.

TL;DR: A new approach combining ICA and Auto-Regressive eXogenous ( ARX) (ICA-ARX) is proposed for a more robust removal of ocular artifact, and its potential to be used in the EEG related studies.