The knowledge of transitions between regular, laminar or chaotic behaviors is essential to understand the underlying mechanisms behind complex systems. While several linear approaches are often insufficient to describe such processes, there are several nonlinear methods that, however, require rather long time observations. To overcome these difficulties, we propose measures of complexity based on vertical structures in recurrence plots and apply them to the logistic map as well as to heart-rate-variability data. For the logistic map these measures enable us not only to detect transitions between chaotic and periodic states, but also to identify laminar states, i.e., chaos-chaos transitions. The traditional recurrence quantification analysis fails to detect the latter transitions. Applying our measures to the heart-rate-variability data, we are able to detect and quantify the laminar phases before a life-threatening cardiac arrhythmia occurs thereby facilitating a prediction of such an event. Our findings could be of importance for the therapy of malignant cardiac arrhythmias.

In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The individual risk for this sudden cardiac death cannot be defined precisely by common available, noninvasive diagnostic tools like Holter monitoring, highly amplified ECG and traditional linear analysis of heart rate variability (HRV). Therefore, we apply some rather unconventional methods of nonlinear dynamics to analyze the HRV. Especially, some complexity measures that are based on symbolic dynamics as well as a new measure, the renormalized entropy, detect some abnormalities in the HRV of several patients who have been classified in the low risk group by traditional methods. A combination of these complexity measures with the parameters in the frequency domain seems to be a promising way to get a more precise definition of the individual risk. These findings have to be validated by a representative number of patients. (c) 1995 American Institute of Physics.

The methods of NLD describe complex rhythm fluctuations and separate structures of non-linear behavior in the heart rate time series more successfully than classical methods of time and frequency domains. This leads to an improved discrimination between a normal (healthy persons) and an abnormal (high risk patients) type of heart beat generation. Some patients with an unknown risk exhibit similar patterns to high risk patients and this suggests a hidden high risk. The methods of symbolic dynamics and renormalized entropy were particularly useful measures for classifying the dynamics of HRV.

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This article is available online at http://www.jlr.org Cytochrome P450 (CYP) enzymes catalyze the formation of biologically active epoxy-and hydroxy-metabolites of long-chain PUFAs ( 1 ). Traditionally, and in line with the prevalence of n-6 PUFAs in the "Western diet", arachidonic acid (AA) (20:4 n-6) has been considered as the main precursor and the corresponding metabolites were categorized as a subclass of eicosanoids ( 2 ). CYP-eicosanoid formation is also known as the "third branch of the AA cascade," complementary to the previously discovered cyclooxygenase (COX)-and lipoxygenase (LOX)-initiated pathways of prostanoid and leukotriene formation ( 3, 4 ).Physiologically important AA-derived CYP-eicosanoids include a set of regio-and stereoisomeric epoxyeicosatrienoic acids (EETs) and 20-HETE ( 2, 5 ). EETs and 20-HETE play partially opposing roles in the regulation of vascular, renal, and cardiac function ( 6-9 ). The contribution of EETs to cardiovascular function is infl uenced by the soluble epoxide hydrolase (sEH) that metabolizes EETs to less potent dihydroxyeicosatrienoic acids (DHETs) ( 10 ). Imbalances in CYP-eicosanoid formation are linked to the development of endothelial dysfunction and hypertension; ischemia-induced injury of the heart, kidney and brain; infl ammatory disorders; and atherosclerosis (11)(12)(13)(14)(15)(16)(17).Recent studies demonstrated that the same CYP isoforms that epoxidize or hydroxylate AA, also effi ciently metabolize

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The fetal ECG derived from abdominal leads provides an alternative to standard means of fetal monitoring. Furthermore, it permits long-term and ambulant recordings, which expands the range diagnostic possibilities for evaluating the fetal health state. However, due to the temporal and spectral overlap of maternal and fetal signals, the usage of abdominal leads imposes the need for elaborated signal processing routines.In this work a modular combination of processing techniques is presented. Its core consists of two maternal ECG estimation techniques, namely the extended Kalman smoother (EKS) and template adaption (TA) in combination with an innovative detection algorithm. Our detection method employs principles of evolutionary computing to detect fetal peaks by considering the periodicity and morphological characteristics of the fetal signal. In a postprocessing phase, single channel detections are combined by means of kernel density estimation and heart rate correction.The described methodology was presented during the Computing in Cardiology Challenge 2013. The entry was the winner of the closed-source events with average scores for events 4/5 with 15.1/3.32 (TA) and 69.5/4.58 (EKS) on training set-A and 20.4/4.57 (TA) and 219/7.69 (EKS) on test set-B, respectively. Using our own clinical data (24 subjects each 20 min recordings) and statistical measures beyond the Challenge's scoring system, we further validated the proposed method. For our clinical data we obtained an average detection rate of 82.8% (TA) and 83.4% (EKS). The achieved results show that the proposed methods are able produce reliable fetal heart rate estimates from a restricted number of abdominal leads.