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BackgroundHeart Failure (HF) is a common reason for hospitalization. Admissions might be prevented by early detection of and intervention for decompensation. Conventionally, changes in weight, a possible measure of fluid accumulation, have been used to detect deterioration. Transthoracic impedance may be a more sensitive and accurate measure of fluid accumulation.ObjectiveIn this study, we review previously proposed predictive algorithms using body weight and noninvasive transthoracic bio-impedance (NITTI) to predict HF decompensations.MethodsWe monitored 91 patients with chronic HF for an average of 10 months using a weight scale and a wearable bio-impedance vest. Three algorithms were tested using either simple rule-of-thumb differences (RoT), moving averages (MACD), or cumulative sums (CUSUM).ResultsAlgorithms using NITTI in the 2 weeks preceding decompensation predicted events (P<.001); however, using weight alone did not. Cross-validation showed that NITTI improved sensitivity of all algorithms tested and that trend algorithms provided the best performance for either measurement (Weight-MACD: 33%, NITTI-CUSUM: 60%) in contrast to the simpler rules-of-thumb (Weight-RoT: 20%, NITTI-RoT: 33%) as proposed in HF guidelines.ConclusionsNITTI measurements decrease before decompensations, and combined with trend algorithms, improve the detection of HF decompensation over current guideline rules; however, many alerts are not associated with clinically overt decompensation.

A new unsupervised and low complexity method for detection of S1 and S2 components of heart sound without the ECG reference is described The most reliable and invariant feature applied in current state-of-the-art of unsupervised heart sound segmentation algorithms is implicitly or explicitly the S1-S2 interval regularity. However; this criterion is inherently prone to noise influence and does not appropriately tackle the heart sound segmentation of arrhythmic cases. A solution based upon a high frequency marker; which is extracted from heart sound using the fast wavelet decomposition, is proposed in order to estimate instantaneous heart rate. This marker is physiologically motivated by the accentuated pressure differences found across heart valves, both in native and prosthetic valves, which leads to distinct high frequency signatures of the valve closing sounds. The algorithm has been validated with heart sound samples collected from patients with mechanical and bio prosthetic heart valve implants in different locations, as well as with patients with native valves. This approach exhibits high sensitivity and specificity without being dependent on the valve type nor their implant position. Further more, it exhibits invariance with respect to normal sinus rhythm (NSR) arrhythmias and sound recording location.

MyHeart is a so-called Integrated Project of the European Union aiming to develop intelligent systems for the prevention and monitoring of cardiovascular diseases. The project develops smart electronic and textile systems and appropriate services that empower the users to take control of their own health status.

This paper aims to assess the predictive value of physiological data daily collected in a telemonitoring study in the early detection of heart failure (HF) decompensation events. The main hypothesis is that physiological time series with similar progression (trends) may have prognostic value in future clinical states (decompensation or normal condition). The strategy is composed of two main steps: a trend similarity analysis and a predictive procedure. The similarity scheme combines the Haar wavelet decomposition, in which signals are represented as linear combinations of a set of orthogonal bases, with the Karhunen-Loève transform, that allows the selection of the reduced set of bases that capture the fundamental behavior of the time series. The prediction process assumes that future evolution of current condition can be inferred from the progression of past physiological time series. Therefore, founded on the trend similarity measure, a set of time series presenting a progression similar to the current condition is identified in the historical dataset, which is then employed, through a nearest neighbor approach, in the current prediction. The strategy is evaluated using physiological data resulting from the myHeart telemonitoring study, namely blood pressure, respiration rate, heart rate, and body weight collected from 41 patients (15 decompensation events and 26 normal conditions). The obtained results suggest, in general, that the physiological data have predictive value, and in particular, that the proposed scheme is particularly appropriate to address the early detection of HF decompensation.

Abstract

Wireless local area networks (WLANs) designed as wireless ATM systems to extend the services of fixed ATM networks to mobile users appear best suited to provide a guaranteed quality of service (QoS) for wireless IP networks. HiperLAN/2 is an ETSI/BRAN standard providing convergence layers for both IP and ATM classes of service. Besides a description of HiperLAN/2 and its Home Environment Extension, the performance for IP traffic flows is presented from analysis and from simulating a prototype implementation. Coexistence with the IEEE 802.11a WLAN is discussed and the ability of HiperLAN/2 to guarantee QoS even when coexisting is analyzed.Ad hoc networking of HiperLAN/2 is analyzed and two possible extensions of the system are introduced and their performance evaluated, namely, adaptive antennas and wireless base stations.

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