Showing papers in "Biomedical Signal Processing and Control in 2012"

TL;DR: This work proposes a methodology for the automatic detection of normal, pre-ictal, and ictal conditions from recorded EEG signals and shows that the Fuzzy classifier was able to differentiate the three classes with a high accuracy of 98.1%.

TL;DR: The proposed method to perform windowing in the EMD domain in order to reduce the noise from the initial IMFs instead of discarding them completely thus preserving the QRS complex and yielding a relatively cleaner ECG signal.

TL;DR: This paper demonstrates that the proposed preprocessor with a Shannon energy envelope (SEE) estimator is better able to detect R-peaks than other well-known methods in case of noisy or pathological signals.

TL;DR: The proposed methodology successfully rejected a good percentage of artefacts and noise, while preserving almost all the cerebral activity, and presents a very good improvement compared with recorded raw EEG: 96% of the EEGs are easier to interpret.

TL;DR: A novel approach for generating the wavelet that best represents the ECG beats in terms of discrimina- tion capability is proposed, which makes use of the polyphase representation of the wavelets filter bank and formulates the design problem within a particle swarm optimization (PSO) framework.

TL;DR: This paper classified these techniques for ultrasound enhancement into two groups: preprocessing and post-processing, analyzed their benefits and limitations, and presented beliefs about where ultrasound research could be directed to, in order to improve its effectiveness and broaden its applications.

TL;DR: It is proven that the addition of nonlinear features improves accuracy of classification and the process of FHR evaluation can become more objective and may enable clinicians to focus on additional non-cardiotocography parameters influencing the fetus during delivery.

TL;DR: This study proposes a LASSO model using the linear regression between electroencephalogram recordings and the standard square-wave signals of different frequencies to recognize SSVEP without the training stage and can assist to reduce the recording time without sacrificing the classification accuracy.

TL;DR: A new automatic system for liver segmentation in abdominal MRI images that utilizes MLP neural networks and watershed algorithm and uses trained neural networks to extract features of the liver region.

TL;DR: This work proposes that for a given workload state, the values of HRV vary in a sub-range of a Gaussian distribution, and describes methods to monitor a HRV signal in real-time for change points based upon sub-Gaussian fitting.

TL;DR: An extensive study in identification of different voice disorders which their origin is in the vocal folds shows that entropy features in the sixth level of WPT decomposition is the most optimum algorithm that leads to the recognition rate of 100% and AUC of 100%.

TL;DR: During and after phonation into a straw, the midsagittal area of the vocal tract increased and the velar closure improved and the overall SPL and the SPL of the speaker's formant cluster increased.

TL;DR: To suppress clusters of murmurs and noise, weighted density function by atom energy is further proposed to improve the segmentation of heart sounds.

TL;DR: Two different approaches to ventilation management are discussed and the impact on optimal PEEP and the use of model-based methods are discussed, which can potentially be used to help with clinical decision making with regard to PEEP.

TL;DR: Results obtained show that detection accuracies are comparable for obese and lean subjects, and suggest feasibility of food intake detection based on swallowing sounds and potential of the proposed methodology for automatic monitoring of ingestive behavior.

TL;DR: Non-linear analysis of HRV is superior compared to time and frequency methods and non-linear parameters namely, correlation dimension (CD), approximate entropy (ApEn), sample entropy (SampEn) and recurrence plot properties (REC and DET), are clinically significant.

TL;DR: The results showed that the proposed adaptive fuzzy model based on ANFIS and DEACS algorithm is applicable for the operator functional state assessment.

TL;DR: In this article, an extensive comparison of advanced denoising algorithms specifically designed for both signal-dependent noise (AAS, BM3Dc, HHM, TLS) and independent additive noise (AV, BM 3D, K-SVD) was presented.

TL;DR: The results suggest that a combination of CBC and haemoglobin typing analysis with a naive Bayes classifier or a multilayer perceptron is highly suitable for automatic thalassaemia screening.

TL;DR: The results demonstrate that the robust control strategy can deliver propofol to yield consistent and acceptable closed-loop induction and maintenance phase responses over wide-ranging PK and PD differences, whereas its PID control counterpart exhibits limitations in performance.

TL;DR: It is concluded that the nonlinear features exhibit only a minor influence on the overall accuracy in discerning different arrhythmias, and a simple combination of time domain features is shown to be comparable to the more informed combinations, with only 1–4% worse results on average than the three best ones.

TL;DR: A semi-supervised approach for liver segmentation from computed tomography (CT) scans, which is based on the graph cut model integrated with domain knowledge, which shows effectiveness and efficiency.

TL;DR: Experimental results show that the proposed CD based preprocessing scheme improves the compression performance of the predictors significantly and the magnitude of the prediction error decreases leading to less number of bits for transmission.

TL;DR: This work is developing a method to automatically identify, measure and highlight selected qualitative features in infant cry recordings, which starts with the automatic discovery of infant cry units and ends with the process implementation.

TL;DR: A patient-specific algorithm for possible seizure warning using machine learning classification of 34 algorithmic features derived from EEG–ECG recordings is proposed, which enables a quantitative way to identify “pro-ictal” states with a high risk of seizure generation.

TL;DR: The feasibility of applying the continuous emotion models approach to annotation of emotional speech is demonstrated and ways to take advantage of this kind of annotation to improve the automatic classification of basic emotions are explored.

TL;DR: Up to this limit, the tools presented here for jitter estimation can give a valid support to clinicians also in term of reproducibility of results and time saving and could be better suited for perturbation measure in strongly irregular voice signals.

TL;DR: Improved acoustic programs using more reliable algorithms could validly transgress the traditional limit of 5% if they demonstrate the correspondence of their computations with the true jitter values, made possible by synthesizers generating artificial deviant voices that cannot be distinguished from true dysphonia.

TL;DR: The VKG-Analyser is presented, a new tool for measuring and tracking quantitative parameters from VKG images, which can define reference values for normal and pathological cases, providing a valid support for diagnosis and evaluation of surgical effectiveness.

TL;DR: The best results are obtained using the pre-processed spectra without quantification as input for the classifiers and it is confirmed that Support Vector Machine are more efficient than Multilayer Perceptron in processing high dimensional data.