In general, there is no perfect method for esophageal replacement under consideration of the numerous associated risks and complications. The aim of this study was to examine a new material--small intestinal submucosa (SIS)--in alloplastic esophageal replacement. We implanted tubular SIS prosthesis about 4 cm in length in the cervical esophagus of 14 piglets (weight 9-13 kg). For the first 10 days, the animals were fed parenterally, supplemented by free given water, followed by an oral feeding phase. Four weeks after surgery, the animals were sacrificed. Only 1 of the 14 animals survived the study period of 4 weeks. The other piglets had to be sacrificed prematurely because of severe esophageal stenosis. On postmortem exploration, the prosthesis could not be found either macroscopically or histologically. Sutures between the prosthesis and the cervical muscles did not improve the results. Until now, the use of alloplastic materials in esophageal replacement has failed irrespective of the kind of material. As well as in our experiments, severe stenosis had been reported in several animal studies. The reasons for this unacceptable high rate of stenosis after alloplastic esophageal replacement seem to be multifactorial. Possible solutions could be transanastomotic splints, less inert materials, the decrease of anastomotic tension by stay sutures, the use of adult stem cells, and tissue engineering.

Dilated cardiomyopathy (DCM) has an incidence of about 20100 000 new cases per annum and accounts for nearly 10 000 deaths per year in the United States. Approximately 36% of patients with dilated cardiomyopathy (DCM) suffer from cardiac death within five years after diagnosis. Currently applied methods for an early risk prediction in DCM patients are rather insufficient. The objective of this study was to investigate the suitability of short-term nonlinear methods symbolic dynamics (STSD), detrended fluctuation (DFA), and Poincare plot analysis (PPA) for risk stratification in these patients. From 91 DCM patients and 30 healthy subjects (REF), heart rate and blood pressure variability (HRV, BPV), STSD, DFA, and PPA were analyzed. Measures from BPV analysis, DFA, and PPA revealed highly significant differences (p<0.0011) discriminating REF and DCM. For risk stratification in DCM patients, four parameters from BPV analysis, STSD, and PPA revealed significant differences between low and high risk (maximum sensitivity: 90%, specificity: 90%). These results suggest that STSD and PPA are useful nonlinear methods for enhanced risk stratification in DCM patients.

Our data indicate PAD alters the HRV in cardiovascular patients. PAD should be considered in the assessment of cardiac autonomic regulation especially in risk stratification.

For the first time an index from Poincaré plot analysis of heart rate variability was able to contribute to risk stratification in patients suffering from DCM.

Electrocardiogram (ECG) particular from tiny, non Q-wave myocardial infarction may lack striking infarct pattern. Spatiotemporal correlation analysis (SCA) of multichannel magnetocardiogram (MCG) is a high-resolution "magnifying glass" to analyze homogeneity of repolarization. SCA involves full 4D spatiotemporal information to identify abnormalities as empirically done by eye in conventional ECG-but on an advanced level of analysis. We compared the discriminatory performance of SCA to ECG analysis in identifying myocardial infarction. Eleven SCA parameters were taken from signal averaged 31-channel MCG and compared to infarct indicators of ECG's in 178 subjects: 108 patients (76 males, mean age 62 years) after myocardial infarction (16-64 d) and 70 controls (36 males, mean age 46 years). SCA improves the detection of myocardial injury: in 72.5% ECG (sensitivity 68.6%, specificity 56%) and in 80.2% SCA parameters (sensitivity 72.6%, specificity 64%) separated patients from controls. SCA is applicable for the analysis of de- and repolarization of cardiac mapping data.

Recent studies reported differential information in human magnetocardiogram and in electrocardiogram. Vortex currents have been discussed as a possible source of this divergence. With the help of physical phantom experiments, we quantified the influence of active vortex currents on the strength of electric and magnetic signals, and we tested the ability of standard source localization algorithms to reconstruct vortex currents. The active vortex currents were modeled by a set of twelve single current dipoles arranged in a circle and mounted inside a phantom that resembles a human torso. Magnetic and electric data were recorded simultaneously while the dipoles were switched on stepwise one after the other. The magnetic signal strength increased continuously for an increasing number of dipoles switched on. The electric signal strength increased up to a semicircle and decreased thereafter. Source reconstruction with unconstrained focal source models performed well for a single dipole only (less than 3-mm localization error). Minimum norm source reconstruction yielded reasonable results only for a few of the dipole configurations. In conclusion active vortex currents might explain, at least in part, the difference between magnetically and electrically acquired data, but improved source models are required for their reconstruction.

Automatic detection of ectopic beats has become a thoroughly researched topic, with literature providing manifold proposals typically incorporating morphological analysis of the electrocardiogram (ECG). Although being well understood, its utilization is often neglected, especially in practical monitoring situations like online evaluation of signals acquired in wearable sensors. Continuous blood pressure estimation based on pulse wave velocity considerations is a prominent example, which depends on careful fiducial point extraction and is therefore seriously affected during periods of increased occurring extrasystoles. In the scope of this work, a novel ectopic beat discriminator with low computational complexity has been developed, which takes advantage of multimodal features derived from ECG and pulse wave relating measurements, thereby providing additional information on the underlying cardiac activity. Moreover, the blood pressure estimations’ vulnerability towards ectopic beats is closely examined on records drawn from the Physionet database as well as signals recorded in a small field study conducted in a geriatric facility for the elderly. It turns out that a reliable extrasystole identification is essential to unsupervised blood pressure estimation, having a significant impact on the overall accuracy. The proposed method further convinces by its applicability to battery driven hardware systems with limited processing power and is a favorable choice when access to multimodal signal features is given anyway.

The objectives of this study were to introduce a new type of heart-rate variability analysis improving risk stratification in patients with idiopathic dilated cardiomyopathy (DCM) and to provide additional information about impaired heart beat generation in these patients. Beat-to-beat intervals (BBI) of 30-min ECGs recorded from 91 DCM patients and 21 healthy subjects were analyzed applying the lagged segmented Poincaré plot analysis (LSPPA) method. LSPPA includes the Poincaré plot reconstruction with lags of 1-100, rotating the cloud of points, its normalized segmentation adapted to their standard deviations, and finally, a frequency-dependent clustering. The lags were combined into eight different clusters representing specific frequency bands within 0.012-1.153 Hz. Statistical differences between low- and high-risk DCM could be found within the clusters II-VIII (e.g., cluster IV: 0.033-0.038 Hz; p = 0.0002; sensitivity = 85.7 %; specificity = 71.4 %). The multivariate statistics led to a sensitivity of 92.9 %, specificity of 85.7 % and an area under the curve of 92.1 % discriminating these patient groups. We introduced the LSPPA method to investigate time correlations in BBI time series. We found that LSPPA contributes considerably to risk stratification in DCM and yields the highest discriminant power in the low and very low-frequency bands.