A Simulation Study on the Pacing and Driving of the Biological Pacemaker.

The research on the biological pacemaker has been very active in recent years. And turning nonautomatic ventricular cells into pacemaking cells is believed to hold the key to making a biological pacemaker. In the study, the inward-rectifier K+ current (I K1) is depressed to induce the automaticity of the ventricular myocyte, and then, the effects of the other membrane ion currents on the automaticity are analyzed. It is discovered that the L-type calcium current (I CaL) plays a major part in the rapid depolarization of the action potential (AP). A small enough I CaL would lead to the failure of the automaticity of the ventricular myocyte. Meanwhile, the background sodium current (I bNa), the background calcium current (I bCa), and the Na+/Ca2+ exchanger current (I NaCa) contribute significantly to the slow depolarization, indicating that these currents are the main supplementary power of the pacing induced by depressing I K1, while in the 2D simulation, we find that the weak electrical coupling plays a more important role in the driving of a biological pacemaker.

Evoked Hemodynamic Response Estimation to Auditory Stimulus Using Recursive Least Squares Adaptive Filtering with Multidistance Measurement of Near-Infrared Spectroscopy.

The performance of functional near-infrared spectroscopy (fNIRS) is sometimes degraded by the interference caused by the physical or the systemic physiological activities. Several interferences presented during fNIRS recordings are mainly induced by cardiac pulse, breathing, and spontaneous physiological low-frequency oscillations. In previous work, we introduced a multidistance measurement to reduce physiological interference based on recursive least squares (RLS) adaptive filtering. Monte Carlo simulations have been implemented to evaluate the performance of RLS adaptive filtering. However, its suitability and performance on human data still remain to be evaluated. Here, we address the issue of how to detect evoked hemodynamic response to auditory stimulus using RLS adaptive filtering method. A multidistance probe based on continuous wave fNIRS is devised to achieve the fNIRS measurement and further study the brain functional activation. This study verifies our previous findings that RLS adaptive filtering is an effective method to suppress global interference and also provides a practical way for real-time detecting brain activity based on multidistance measurement.

Depth Attenuation Degree Based Visualization for Cardiac Ischemic Electrophysiological Feature Exploration.

Although heart researches and acquirement of clinical and experimental data are progressively open to public use, cardiac biophysical functions are still not well understood. Due to the complex and fine structures of the heart, cardiac electrophysiological features of interest may be occluded when there is a necessity to demonstrate cardiac electrophysiological behaviors. To investigate cardiac abnormal electrophysiological features under the pathological condition, in this paper, we implement a human cardiac ischemic model and acquire the electrophysiological data of excitation propagation. A visualization framework is then proposed which integrates a novel depth weighted optic attenuation model into the pathological electrophysiological model. The hidden feature of interest in pathological tissue can be revealed from sophisticated overlapping biophysical information. Experiment results verify the effectiveness of the proposed method for intuitively exploring and inspecting cardiac electrophysiological activities, which is fundamental in analyzing and explaining biophysical mechanisms of cardiac functions for doctors and medical staff.

The virtual heart as a platform for screening drug cardiotoxicity.

To predict the safety of a drug at an early stage in its development is a major challenge as there is a lack of in vitro heart models that correlate data from preclinical toxicity screening assays with clinical results. A biophysically detailed computer model of the heart, the virtual heart, provides a powerful tool for simulating drug-ion channel interactions and cardiac functions during normal and disease conditions and, therefore, provides a powerful platform for drug cardiotoxicity screening. In this article, we first review recent progress in the development of theory on drug-ion channel interactions and mathematical modelling. Then we propose a family of biomarkers that can quantitatively characterize the actions of a drug on the electrical activity of the heart at multi-physical scales including cellular and tissue levels. We also conducted some simulations to demonstrate the application of the virtual heart to assess the pro-arrhythmic effects of cisapride and amiodarone. Using the model we investigated the mechanisms responsible for the differences between the two drugs on pro-arrhythmogenesis, even though both prolong the QT interval of ECGs. Several challenges for further development of a virtual heart as a platform for screening drug cardiotoxicity are discussed.

Morphology variability analysis of wrist pulse waveform for assessment of arteriosclerosis status.

In this paper, approximate entropy (ApEn) is applied to study the variability of pulse waveform for assessing coronary arteriosclerosis status. Having analyzed the wrist pulse waveforms taken from both normal subjects and the patients suffering from coronary arteriosclerosis (CA) disorders, we find that pulse morphology variability (PMV) is more efficient than pulse interval variability (PIV) in assessing the conditions of human coronary artery. Usually, the PMVs of the healthy are higher than those of the patients with CA diseases, and the PMVs of patients with CA diseases have more high frequency components than those of the healthy subjects. That is to say, the CA disease also has influence on vascular tone. The effect of changes in cardiac performance due to CA disease can be reflected through the PMV. The experiment demonstrates that the specificity and sensitivity of the PMV's spectral energy ratio for clinical diagnosis of cardiovascular system is 80% and 97%, respectively.

Computerized feature quantification of sublingual veins from color sublingual images.

Characteristics of tongue pose the most important information for Traditional Chinese Medicine diagnosis. So far, extensive studies have been made on extracting tongue surface features, but rarely refer to sublingual vein that is also diagnostically important. This paper focuses on establishing a feature quantification framework for the inspection of sublingual veins, composed of two parts: the segmentation of sublingual veins and the feature quantification of them. Pixel-based sublingual vein segmentation algorithm and adaptive sublingual vein segmentation algorithm for color sublingual images with visible contrast and low contrast are proposed respectively. The experiments prove that the proposed algorithms perform well on the segmentation of sublingual veins from color sublingual images with both visible contrast and low contrast. A chromatic system in conformity with diagnostic standard of tongue diagnosis is established to characterize the chromatic feature of sublingual veins. Experimental results reveal that the breadth and chromatic features quantified by the proposed framework are properly consistent with the diagnostic standard summarized by tongue diagnosis.

Computerized diagnosis from tongue appearance using quantitative feature classification.

This study investigates relationships between diseases and the appearance of the human tongue in terms of quantitative features. The experimental samples are digital tongue images captured from three groups of candidates: one group in normal health, one suffering with appendicitis, and a third suffering with pancreatitis. For the purposes of diagnostic classification, we first extract chromatic and textural measurements from original tongue images. A feature selection procedure then identifies the measures most relevant to the classifications, based on which of the three tongue image categories are clearly separated. This study validates the use of tongue inspection by means of quantitative feature classification in medical diagnosis.

The bi-elliptical deformable contour and its application to automated tongue segmentation in Chinese medicine.

Automated tongue image segmentation, in Chinese medicine, is difficult due to two special factors: 1) there are many pathological details on the surface of the tongue, which have a large influence on edge extraction; 2) the shapes of the tongue bodies captured from various persons (with different diseases) are quite different, so they are impossible to describe properly using a predefined deformable template. To address these problems, in this paper, we propose an original technique that is based on a combination of a bi-elliptical deformable template (BEDT) and an active contour model, namely the bi-elliptical deformable contour (BEDC). The BEDT captures gross shape features by using the steepest decent method on its energy function in the parameter space. The BEDC is derived from the BEDT by substituting template forces for classical internal forces, and can deform to fit local details. Our algorithm features fully automatic interpretation of tongue images and a consistent combination of global and local controls via the template force. We apply the BEDC to a large set of clinical tongue images and present experimental results.

Computerized tongue diagnosis based on Bayesian networks.

Tongue diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experience-based nature, traditional tongue diagnosis has a very limited-application in clinical medicine. Moreover, traditional tongue diagnosis is always concerned with the identification of syndromes rather than with the connection between tongue abnormal appearances and diseases. This is not well understood in Western medicine, thus greatly obstruct its wider use in the world. In this paper, we present a novel computerized tongue inspection method aiming to address these problems. First, two kinds of quantitative features, chromatic and textural measures, are extracted from tongue images by using popular digital image processing techniques. Then, Bayesian networks are employed to model the relationship between these quantitative features and diseases. The effectiveness of the method is tested on a group of 455 patients affected by 13 common diseases as well as other 70 healthy volunteers, and the diagnostic results predicted by the previously trained Bayesian network classifiers are reported.