Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 5 Articles
Background: Heart rate variability (HRV) has been widely used in the non-invasive\nevaluation of cardiovascular function. Recent studies have also attached great importance\nto the cardiac diastolic period variability (DPV) examination. Short-term variability\nmeasurement (e.g., 5 min) has drawn increasing attention in clinical practice, since\nit is able to provide almost immediate measurement results and enables the real-time\nmonitoring of cardiovascular function. However, it is still a contemporary challenge to\nrobustly estimate the HRV and DPV parameters based on short-term recordings.\nMethods: In this study, a refined fuzzy entropy (rFuzzyEn) was developed by substituting\na piecewise fuzzy membership function for the Gaussian function in conventional\nfuzzy entropy (FuzzyEn) measure. Its stability and robustness against additive noise\ncompared with sample entropy (SampEn) and FuzzyEn, were examined by two wellaccepted\nsimulation modelsââ?¬â?the 1/f ? noise and the Logistic attractor. The rFuzzyEn\nwas further applied to evaluate clinical short-term (5 min) HRV and DPV of the patients\nwith coronary artery stenosis and healthy volunteers.\nResults: Simulation results showed smaller fluctuations in the rFuzzyEn than in\nSampEn and FuzzyEn values when the data length was decreasing. Besides, rFuzzyEn\ncould distinguish the simulation models with different amount of additive noise even\nwhen the percentage of additive noise reached 60%, but neither SampEn nor FuzzyEn\nshowed comparable performance. Clinical HRV analysis did not indicate any significant\ndifferences between the patients with coronary artery disease and the healthy volunteers\nin all the three mentioned entropy measures (all p > 0.20). But clinical DPV analysis\nshowed that the patient group had a significantly higher rFuzzyEn (p < 0.01) than\nthe healthy group. However, no or less significant difference was observed between\nthe two groups in either SampEn (p = 0.14) or FuzzyEn (p = 0.05).\nConclusions: Our proposed r Fuzzy En outperformed conventional SampEn and\nFuzzyEn in terms of both stability and robustness against additive noise, particularly\nwhen the data set was relatively short. Analysis of DPV using rFuzzyEn may provide\nmore valuable information to assess the cardiovascular states than the other entropy\nmeasures and has a potential for clinical application....
This commentary is intended to find possible explanations for the low impact of computational\nmodeling on pain research. We discuss the main strategies that have been\nused in building computational models for the study of pain. The analysis suggests\nthat traditional models lack biological plausibility at some levels, they do not provide\nclinically relevant results, and they cannot capture the stochastic character of neural\ndynamics. On this basis, we provide some suggestions that may be useful in building\ncomputational models of pain with a wider range of applications....
Background: Fast and accurate quality estimation of the electrocardiogram (ECG) signal\nis a relevant research topic that has attracted considerable interest in the scientific\ncommunity, particularly due to its impact on tele-medicine monitoring systems, where\nthe ECG is collected by untrained technicians. In recent years, a number of studies have\naddressed this topic, showing poor performance in discriminating between clinically\nacceptable and unacceptable ECG records.\nMethods: This paper presents a novel, simple and accurate algorithm to estimate the\nquality of the 12-lead ECG by exploiting the structure of the cross-covariance matrix\namong different leads. Ideally, ECG signals from different leads should be highly correlated\nsince they capture the same electrical activation process of the heart. However, in\nthe presence of noise or artifacts the covariance among these signals will be affected.\nEigenvalues of the ECG signals covariance matrix are fed into three different supervised\nbinary classifiers.\nResults and conclusion: The performance of these classifiers were evaluated using\nPhysioNet/CinC Challenge 2011 data. Our best quality classifier achieved an accuracy\nof 0.898 in the test set, while having a complexity well below the results of contestants\nwho participated in the Challenge, thus making it suitable for implementation in current\ncellular devices....
Electrogastrographic examination (EGG) is a noninvasive method for an investigation\nof a stomach slow wave propagation. The typical range of frequency for EGG signal is\nfrom 0.015 to 0.15 Hz or (0.015ââ?¬â??0.3 Hz) and the signal usually is captured with sampling\nfrequency not exceeding 4 Hz. In this paper a new approach of method for recording\nthe EGG signals with high sampling frequency (200 Hz) is proposed. High sampling\nfrequency allows collection of signal, which includes not only EGG component but\nalso signal from other organs of the digestive system such as the duodenum, colon as\nwell as signal connected with respiratory movements and finally electrocardiographic\nsignal (ECG). The presented method allows improve the quality of analysis of EGG\nsignals by better suppress respiratory disturbance and extract new components from\nhigh sampling electrogastrographic signals (HSEGG) obtained from abdomen surface.\nThe source of the required new signal components can be inner organs such as the\nduodenum and colon. One of the main problems that appear during analysis the EGG\nsignals and extracting signal components from inner organs is how to suppress the\nrespiratory components. In this work an adaptive filtering method that requires a reference\nsignal is proposed. In the present research, the respiratory component is obtained\nfrom non standard ECG (NSECG) signal. For purposes of this paper non standard ECG\n(namely NSECG) is used, because ECG signal was recorded by other than the standard\nelectrodes placement on the surface of the abdomen. The electrocardiographic\nderived respiration signal (EDR) is extracted using the phenomena of QRS complexes\namplitude modulation by respiratory movements. The main idea of extracting the EDR\nsignal from electrocardiographic signal is to obtain the modulating signal. Adaptive\nfiltering is done in the discrete cosine transform domain. Next the resampled HSEGG\nsignal with attenuated respiratory components is low pass filtered and as a result the\nextended electro gastro graphic signals, included EGG signal and components from\nother inner organs of digestive system is obtained. One of additional features of the\nproposed method is a possibility to obtain simultaneously recorded signals, such as:\nnon-standard derivation of ECG, heart rate variability signal, respiratory signal, and EGG\nsignal that allow investigating mutual interferences among internal human systems....
Background: Fuzzy connectedness method has shown its effectiveness for fuzzy\nobject extraction in recent years. However, two problems may occur when applying it\nto hepatic vessel segmentation task. One is the excessive computational cost, and the\nother is the difficulty of choosing a proper threshold value for final segmentation.\nMethods: In this paper, an accelerated strategy based on a lookup table was presented\nfirst which can reduce the connectivity scene calculation time and achieve a\nspeed-up factor of above 2. When the computing of the fuzzy connectedness relations\nis finished, a threshold is needed to generate the final result. Currently the threshold\nis preset by users. Since different thresholds may produce different outcomes, how to\ndetermine a proper threshold is crucial. According to our analysis of the hepatic vessel\nstructure, a watershed-like method was used to find the optimal threshold. Meanwhile,\nby using Ostu algorithm to calculate the parameters for affinity relations and assigning\nthe seed with the mean value, it is able to reduce the influence on the segmentation\nresult caused by the location of the seed and enhance the robustness of fuzzy connectedness\nmethod.\nResults: Experiments based on four different datasets demonstrate the efficiency of\nthe lookup table strategy. These experiments also show that an adaptive threshold\nfound by watershed-like method can always generate correct segmentation results\nof hepatic vessels. Comparing to a refined region-growing algorithm that has been\nwidely used for hepatic vessel segmentation, fuzzy connectedness method has advantages\nin detecting vascular edge and generating more than one vessel system through\nthe weak connectivity of the vessel ends.\nConclusions: An improved algorithm based on fuzzy connectedness method is proposed.\nThis algorithm has improved the performance of fuzzy connectedness method\nin hepatic vessel segmentation...
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