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Cardiac magnetic field mapping (CMFM) is a noninvasive method to determine cardiac electrical activity. We analysed the utility of CMFM for the detection of patients with coronary artery disease (CAD) without subjecting them to stress. We studied 59 healthy control subjects and 101 patients with CAD without previous myocardial infarction (MI). The heart's magnetic field was recorded over the anterior chest wall using a multichannel magnetic measurement system with axial second-order gradiometers. The evaluation of CMFM was based on comparison of the 'ideal' group mean maps of young healthy subjects and maps of examined individuals. Three measures of similarity were considered: Kullback-Leibler (KL) entropy, normalized residual magnetic field strength and deviations in the magnetic field map orientation. The mean values of these parameters during the depolarization and repolarization were used for further classification with the help of logistic regression. The feature set based on the KL-entropy demonstrated the best classification results (sensitivity/specificity of 85/80%), followed by the residual feature (85/75%) and the magnetic field orientation feature (80/73%) sets. The forward stepwise technique was applied to select the best set of features from the combined feature set. Two parameters were selected, namely the KL-entropy for the repolarization period and the residual parameter for the depolarization period. The classification based on these parameters demonstrated a sensitivity of 88% and a specificity of 88% for the distinction of CAD patients from the control subjects. The area under the receiver operator curve was 94%. Hence, we suggest that CMFM evaluation based on KL-entropy is a promising technique to identify patients with CAD. [ABSTRACT FROM AUTHOR]

Preeclampsia is one of the main sources of morbidity in pregnancy with a high mortality rate without a known ultimate cause. Using a corpus including 927 measurements of pregnant women with and without different hypertonic diseases at multiple stages of pregnancy, we utilised a new approach to analyse cardiorespiratory coordination. Since the recording quality of respiratory effort was limited due to a high signal to noise ratio, we applied an ECG derived respiration approach to create an ersatz respiratory signal. After applying the few available recordings with a sufficiently high quality of the respiratory signal to validate the substitute, coordigrams were calculated and quantified by utilising an epsilon method. We showed significant (p  <  0.05) differences in the coordination in a matched (BMI, age, gestational week) comparison of preeclamptic and healthy subjects. Hopefully future applications of and improvements on these methods are able to create a fast and convenient prediction methodology to reduce the impact of this disease as well as help in the determination of its underlying cause.

Health is one of the most important non-material assets and thus also has an enormous influence on material values, since treating and preventing diseases is expensive. The number one cause of death worldwide today originates in cardiovascular diseases. For these reasons the aim of understanding the functions and the interactions of the cardiovascular system is and has been a major research topic throughout various disciplines for more than a hundred years. The purpose of most of today's research is to get as much information as possible with the lowest possible effort and the least discomfort for the subject or patient, e.g. via non-invasive measurements. A family of tools whose importance has been growing during the last years is known under the headline of coupling measures. The rationale for this kind of analysis is to identify the structure of interactions in a system of multiple components. Important information lies for example in the coupling direction, the coupling strength, and occurring time lags. In this work, we will, after a brief general introduction covering the development of cardiovascular time series analysis, introduce, explain and review some of the most important coupling measures and classify them according to their origin and capabilities in the light of physiological analyses. We will begin with classical correlation measures, go via Granger-causality-based tools, entropy-based techniques (e.g. momentary information transfer), nonlinear prediction measures (e.g. mutual prediction) to symbolic dynamics (e.g. symbolic coupling traces). All these methods have contributed important insights into physiological interactions like cardiorespiratory coupling, neuro-cardio-coupling and many more. Furthermore, we will cover tools to detect and analyze synchronization and coordination (e.g. synchrogram and coordigram). As a last point we will address time dependent couplings as identified using a recent approach employing ensembles of time series. The scope of this review, as opposed to various other excellent reviews like (Hlaváčková-Schindler et al Phys. Rep. 441 1-46, Kramer et al 2004 Phys. Rev. E 70 1-10, Lombardi 2000 Circulation 101 8-10, Porta et al 2000 Am. J. Physiol.: Heart and Circulatory Physiol. 279 H2558-67, Schelter et al 2006 J. Neurosci. Methods 152 210-9), is to give a broader overview over existing coupling measures and where to look to find the most appropriate tool for a given situation. The review will comprise a test of one representative of the most important coupling measure groups using a simple toy model to illustrate some essential features of the tools. At the end we will summarise the performance of each measure and offer some advice on when to use which method.

This article studies a recently introduced information-theoretic approach to detect and quantify the causal couplings in a complex cardiovascular system. In the first step a causal algorithm detects the coupling delays and in the second step the causal strength of each coupling mechanism is quantified using the recently introduced momentary information transfer. As an example, the method is applied to time series of respiration, systolic and diastolic blood pressure, and heart rate of pregnant healthy women and women suffering from pre-eclampsia. A possible explanation for the influence of heart rate on systolic blood pressure is found and some differences between healthy women and patients are discussed.

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.

During anaesthesia awareness and nociception are serious complications that may further lead to haemodynamic instability. Specific monitoring of depth of hypnosis and depth of analgesia based on heart rate variability (HRV) analysis is eligible to improve patient safety and reduce efforts in post-operative care. Consequently, in this analysis we assess the applicability of HRV parameters during surgical interventions with standardized intravenous propofol-remifentanil-anaesthesia. Peri-operative electrocardiograms were recorded from cardiovascular stable patients (ASA Score I/II, N = 32, age: 36.4 ± 11.23 a, BMI: 25.2 ± 3.16) scheduled for trauma and dentofacial surgery. HRV time- and frequency-domain parameters, measures of complexity and nonlinear dynamics were compared by analysing longitudinally distributed 300 s intervals preceding/following induction of anaesthesia (BL-I1), intubation (I1-I2) and extubation (E1-E2). Mean value (meanNN) and standard deviation (sdNN) of the heart rate are influenced in BL-I1 (p < 0.001), I1-I2 (p < 0.05) and E1-E2 (p < 0.001). The number of forbidden words of symbolic dynamics changes significantly for BL-I1 (p < 0.001) and not for I1-I2 and E1-E2 (p > 0.05). Probability of low-variability POLVAR10 is significantly altered in all comparisons (BL-I1: Δ = 0.032, p < 0.01, I1-I2: Δ = 0.12, p < 0.05, E1-E2: Δ = 0.169, p < 0.01) but especially during nociception. While standard time-domain parameters lacked selectivity, parameters of symbolic dynamics appear to be specifically influenced by changes in depth of hypnosis and nociception, respectively. However, the lack of steady-state ventilation/breathing in this study needs to be considered in future research. To be used for clinical anaesthesia monitoring our results have to be prospectively validated in clinical studies.