Showing papers in "Biomedical Signal Processing and Control in 2017"

TL;DR: Results indicate that the proposed model has the potential to obtain a reliable classification of motor imagery EEG signals, and can thus be used as a practical system for controlling a wheelchair.

TL;DR: The DT and RF classifiers developed in this work predicted early transplant rejection with accuracy of 85%, thus offering an accurate decision support tool for doctors tasked with predicting outcomes of kidney transplantation in advance of the clinical intervention.

TL;DR: A critical review of EEG artifact removal approaches is presented, their applicability to daily-life EEG-BCI applications is discussed, and some directions and guidelines for upcoming research in this topic are given.

TL;DR: Specific facial cues, derived from eye activity, mouth activity, head movements and camera based heart activity achieve good accuracy and are suitable as discriminative indicators of stress and anxiety.

TL;DR: Results show that the raised method has better performance, compared with the state-of-the-art automated heartbeat classification systems.

TL;DR: There is potential for improvement and optimization in the seizure prediction framework, and new databases, higher sampling frequencies, adequate preprocessing, electrode selection, and machine-learning considerations are all elements of the prediction scheme that should be assessed to achieve more realistic, better-than-chance performances.

TL;DR: A new diagnostic approach for analysis and classification of seizure and seizure-free EEG signals that uses single feature to diagnose the epilepsy accurately and demonstrates significant values of classification accuracy, sensitivity, specificity and Matthew's correlation coefficient.

TL;DR: This study suggests that LNDP and 1D-LGP could be effective feature extraction techniques for the classification of epileptic EEG signals.

TL;DR: A novel method for detecting normal, interictal and epileptic signals using wavelet-based envelope analysis (EA) neural network ensemble (NNE) and the discrete wavelet transform (DWT) in combination with EA method is developed to extract significant features from the EEG signals.

TL;DR: A critical review of digital camera based heart rate estimating method on facial skin is presented, which showed the reliability of the state of the art methods and provided direction to improve for situations involving illumination variance and motion variance.

TL;DR: A proposed machine learning (ML) scheme was tested and validated with resting-state EEG data involving 33 MDD patients and 30 healthy controls and proved suitable as clinical diagnostic tools for MDD.

TL;DR: FODPSO-DSD-MSA accuracy is high and processing time is low, it may be a promising automated glioma diagnosis system in clinical milieu.

TL;DR: A fusion method of variational mode decomposition (VMD) and autoregression (AR) based quadratic feature extraction was proposed for feature extraction and the random forest classifier was employed to hand with three-classification task.

TL;DR: An adaptive Stacked Denoising AutoEncoder is developed to tackling such cross-session MW classification task in which the weights of the shallow hidden neurons could be adaptively updated during the testing procedure, and results indicate a higher performance of the adaptive SDAE in dealing with the cross- session EEG features.

TL;DR: The proposed method is a good candidate for differentiating between healthy and Parkinson's disease individuals, and shows promise in the context of telemedicine applications and tracking of the disease’s symptoms via inexpensive, widely available hardware.

TL;DR: The developed methodology can be used in mass cardiac screening and can aid cardiologists in performing diagnosis as well as improve the classification accuracy up to fourth level of decomposition.

TL;DR: An integrated index called Coronary Artery Disease Index (CADI) is formulated and developed for automated characterization of normal and ECG signals with CAD condition using a single number that works efficiently to discriminate normal and CAD ECG classes for the any dataset with priory knowledge of the database.

TL;DR: An acute lymphoblastic leukemia detection strategy from the microscopic images is proposed, which achieves 96.29% segmentation accuracy and classification accuracy of 99.004% and 96% for nucleus and cytoplasm respectively.

TL;DR: An arrhythmia classification method implemented on a Digital Signal Processing (DSP) platform intended for on-line, real-time ambulatory operation to classify eight heartbeat conditions is presented and suggests that the method and prototype presented may be suitable for being implemented on wearable sensing applications auxiliary for on theline,real-time diagnosis.

TL;DR: Improved Binary Gravitation Search Algorithm (IBGSA) is used to automatically detect the effective electroencephalography (EEG) channels in left or right hand classification and confirms that automatically detecting effective channels can enhance the practical implementation of BCI based systems and reduce the complexity.

TL;DR: Experimental results show, the proposed Optimum Spectrum Mask Fusion (OSMF) technique pageantries the improved results than other conventional pixel based fusion techniques.

TL;DR: The FMCW wide-band radar is a reliable, robust, and harmless tool for continuous and timely monitoring of cardiac and respiratory rates for multi-human targets, which has a potential to be applied in wards or home healthcare.

TL;DR: A novel method for detection and localization of myocardial infarction (MI) from the reduced MECG tensor, employing the mode-n singular values (MSVs) and the normalized multiscale wavelet energy (NMWE) of each subband tensor to be accurate in detecting and localizing MI.

TL;DR: A scaled spectrogram and partial least squares regression (PLSR) based method was proposed for the classification of PCG signals and the results are compared to those obtained using the best methods in the challenge, thereby proving the effectiveness of the method.

TL;DR: A new algorithm built on wavelets transforms and mathematical morphology for detecting the optic disc and the tubular characteristic of the blood vessels is explored to segment the retinal veins and arteries.

TL;DR: Experimental results show that the IDP based features give higher accuracy than that using other related features in all the three databases, and the accuracies using cross-databases are also high using theIDP features.

TL;DR: The experimental results showed that the proposed method outperforms other methods in terms of both visual perception and objective evaluation metrics.

TL;DR: In this review study, a plethora of challenges and opportunities are covered with respect to wearable device design and the inherent possibilities for biosignal analysis and interpretation and a comparison of some vital biosignals obtained from wearables and clinical equivalents are attempted.

TL;DR: A novel method based on the dual-tree complex wavelet transform (DT-CWT), in this study, is proposed to develop a reliable diagnosis method for the epileptic EEG detection and will be a potential method for practical applications extended to the development of a real-time brain monitoring system.

TL;DR: An overview of the existing standards of fetal monitoring is provided and a comprehensive survey on Fetal Phonocardiography is provided with focus on trends in data collection, signal processing techniques and synthesis models that have been developed to date.