Estrella Everss

A systematic Review of the Literature on Home Monitoring for Patients with Heart Failure

We conducted a systematic review of the literature for assessing the value of home monitoring for heart failure (HF) patients. The abstracts of 383 articles were read. We excluded those in which either no home monitoring was done or only the technical aspects of the telemedicine application were described. Forty-two studies met the selection criteria. We classified the results into feasibility (technical and institutional) and impact (on the clinical process, on patient health, on accessibility and acceptability of the health system, and on the economy). Evaluating the articles showed that home monitoring in HF patients is viable, given that: (1) it appears to be technically effective for following the patient remotely; (2) it appears to be easy to use, and it is widely accepted by patients and health professionals; and (3) it appears to be economically viable. Furthermore, home monitoring of HF patients has been shown to have a positive impact on: (1) the clinical process, supported by a significant improvement of patient follow-up by adjustment of treatment, diet or behaviour, as well as hospital readmissions and emergency visits reduction; (2) the patients health, supported by a relevant improvement in quality of life, a reduction of days in hospital, and a decrease in mortality; and (3) costs resulting from the use of health resources.

Nonparametric Model Comparison and Uncertainty Evaluation for Signal Strength Indoor Location

Indoor location (IL) using received signal strength (RSS) is receiving much attention, mainly due to its ease of use in deployed IEEE 802.11b (Wi-Fi) wireless networks. Fingerprinting is the most widely used technique. It consists of estimating position by comparison of a set of RSS measurements, made by the mobile device, with a database of RSS measurements whose locations are known. However, the most convenient data structure to be used and the actual performance of the proposed fingerprinting algorithms are still controversial. In addition, the statistical distribution of indoor RSS is not easy to characterize. Therefore, we propose here the use of nonparametric statistical procedures for diagnosis of the fingerprinting model, specifically: 1) A nonparametric statistical test, based on paired bootstrap resampling, for comparison of different fingerprinting models and 2) new accuracy measurements (the uncertainty area and its bias) which take into account the complex nature of the fingerprinting output. The bootstrap comparison test and the accuracy measurements are used for RSS-IL in our Wi-Fi network, showing relevant information relating to the different fingerprinting schemes that can be used.

Heart Rate Turbulence Denoising Using Support Vector Machines

Heart rate turbulence (HRT) is a transient acceleration and subsequent deceleration of the heart rate after a premature ventricular complex (PVC), and it has been shown to be a strong risk stratification criterion in patients with cardiac disease. In order to reduce the noise level of the HRT signal, conventional measurements of HRT use a patient-averaged template of post-PVC tachogram (PPT), hence providing with long-term HRT indexes. We hypothesize that the reduction of the noise level at each isolated PPT, using signal processing techniques, will allow us to estimate short-term HRT indexes. Accordingly, its application could be extended to patients with reduced number of available PPT. In this paper, several HRT denoising procedures are proposed and tested, with special attention to support vector machine (SVM) estimation, as this is a robust algorithm that allows us to deal with few available time samples in the PPT. Pacing-stimulated HRT during electrophysiological study are used as a low-noise gold standard. Measurements in a 24-h Holter patient database reveal a significant reduction in the bias and the variance of HRT measurements. We conclude that SVM denoising yields short-term HRT measurements and improves the signal-to-noise level of long-term HRT measurements.

Fundamental Frequency and Regularity of Cardiac Electrograms With Fourier Organization Analysis

Dominant frequency analysis (DFA) and organization analysis (OA) of cardiac electrograms (EGMs) aims to establish clinical targets for cardiac arrhythmia ablation. However, these previous spectral descriptions of the EGM have often discarded relevant information in the spectrum, such as the harmonic structure or the spectral envelope. We propose a fully automated algorithm for estimating the spectral features in EGM recordings This approach, called Fourier OA (FOA), accounts jointly for the organization and periodicity in the EGM, in terms of the fundamental frequency instead of dominant frequency. In order to compare the performance of FOA and DFA-OA approaches, we analyzed simulated EGM, obtained in a computer model, as well as two databases of implantable defibrillator-stored EGM. FOA parameters improved the organization measurements with respect to OA, and averaged cycle length and regularity indexes were more accurate when related to the fundamental (instead of dominant) frequency, as estimated by the algorithm (p <; 0.05 comparing f0 estimated by DFA and by FOA). FOA yields a more detailed and robust spectral description of EGM compared to DFA and OA parameters.

Spectral analysis of intracardiac electrograms during induced and spontaneous ventricular fibrillation in humans

AIMS Very limited data are available on the differences between spontaneous and induced episodes of ventricular fibrillation (VF) in humans. The aim of the study was to compare the spectral characteristics of the electrical signal recorded by an implantable cardioverter defibrillator (ICD) during both types of episodes. METHODS AND RESULTS Thirteen ICD patients with at least one spontaneous and one induced VF recorded by the device were included in the study. A spectral representation was obtained for the first 3 s of the intracardiac unipolar electrogram during VF. The dominant frequency (f(d)), the peak power at f(d), an organization index (OI), a bandwidth measurement, and an estimate of the correlation with a sinusoidal wave (leakage) were estimated for each episode. The f(d) was higher in induced episodes (4.75 +/- 0.57 vs. 3.95 +/- 0.59 Hz for the spontaneous episodes, P = 0.002), as well as the degree of organization assessed by the OI, bandwidth, and leakage parameters. CONCLUSION Clinical and induced VF episodes in humans have different spectral characteristics. Changes in the electrophysiological substrate or in the location of the arrhythmia wavefront at onset could play a role to explain the observed differences.

Implantable Defibrillator Electrograms and Origin of Left Ventricular Impulses: An Analysis of Regionalization Ability and Visual Spatial Resolution

ICD Electrograms and Origin of Impulses. Introduction: The implantable cardioverter‐defibrillator (ICD) electrogram (EG) is a documentation of ventricular tachycardia. We prospectively analyzed EGs from ICD electrodes located at the right ventricle apex to establish (1) ability to regionalize origin of left ventricle (LV) impulses, and (2) spatial resolution to distinguish between paced sites. Methods and Results: LV electro‐anatomic maps were generated in 15 patients. ICD‐EGs were recorded during pacing from 22 ± 10 LV sites. Voltage of far‐field EG deflections (initial, peak, final) and time intervals between far‐field and bipolar EGs were measured. Blinded visual analysis was used for spatial resolution. Initial deflections were more negative and initial/peak ratios were larger for lateral versus septal and superior versus inferior sites. Time intervals were shorter for apical versus basal and septal versus lateral sites. Best predictive cutoff values were voltage of initial deflection <–1.24 mV, and initial/peak ratio >0.45 for a lateral site, voltage of final deflection <–0.30 for an inferior site, and time interval <80 milliseconds for an apical site. In a subsequent group of 9 patients, these values predicted correctly paced site location in 54–75% and tachycardia exit site in 60–100%. Recognition of paced sites as different by EG inspection was 91% accurate. Sensitivity increased with distance (0.96 if ≥ 2 cm vs 0.84 if < 2 cm, P  <  0.001) and with presence of low‐voltage tissue between sites (0.94 vs 0.88, P  <  0.001). Conclusions: Standard ICD‐EG analysis can help regionalize LV sites of impulse formation. It can accurately distinguish between 2 sites of impulse formation if they are ≥2 cm apart. (J Cardiovasc Electrophysiol, Vol. 23, pp. 506‐514, May 2012)

Effects of the Location of Myocardial Infarction on the Spectral Characteristics of Ventricular Fibrillation

Background: The location of the myocardial infarction (MI) might modify the spectral characteristics of ventricular fibrillation (VF) in humans.

Analysis of physiological meaning of detrended Fluctuation Analysis in Heart Rate Variability using a lumped parameter model

Chaos and fractal based measurements, such as detrended fluctuation analysis (DFA), have been widely used for quantifying the heart rate variability (HRV) for cardiac risk stratification purposes. However, the physiological meaning of these measurements is not clear. Given that existing lumped parameter models contain a detailed physiological description of several of the circulatory system regulation processes, we hypothesize that controlled changes in these processes will highlight the physiological basis in DFA indices. We used a detailed lumped parameter model of HRV, introduced earlier. Ten signals were generated in different physiological conditions. DFA coefficients alpha1, alpha2, and the Hurst exponent, were calculated. A clear disruption point was always observed. Modifications in sympatho-vagal activity yielded significant changes in alpha1 when compared to basal, but not in alpha2 or Hurst exponent. Modifications in non-nervous system mediated changes yielded significant differences only for peripheral resistance and heart period, only in alpha1. In conclusion, the analysis of the effect of changes in the regulatory system on the HRV chaotic/fractal indices can be analyzed using detailed lumped parameter models.

Spectral analysis of sustained and non-sustained ventricular fibrillation in patients with an implantable cardioverter-defibrillator.

The mechanisms responsible for the maintenance and termination of ventricular fibrillation (VF) are poorly understood. The aim of this study was to compare the spectral characteristics of the electrical signal during sustained and non-sustained VF in patients with an implantable cardioverter-defibrillator. The study included 51 patients who had had at least one episode of sustained VF (i.e., duration >5 s and requiring shock administration) and non-sustained VF (i.e., duration >3 s and spontaneously terminated) that were recorded by the device set in a unipolar configuration. Spectral analysis of the first 3 s of each episode was performed. The dominant frequency was higher in sustained VF (4.6+/-0.7 Hz) than in non-sustained VF (4.3+/-0.6 Hz; P=.01), while the other parameters were similar. Although the spectral characteristics of sustained and non-sustained VF were similar, differences were observed during the first 3 s that could be used in algorithms for the early detection of non-sustained VF.

A Review on Recent Patents in Digital Processing for Cardiac Electric Signals (II): Advanced Systems and Applications

Digital signal processing algorithms for cardiac recordings have been paid much attention in recently disclosed patents. In this second part of our review of the state-of-art patents, systems for sudden cardiac death prediction, as well as for apnea analysis, are summarized. Advanced digital signal processing algorithms for cardiac electric signals are specifically reviewed, including independent component decompositions, and nonlinear methods (chaos, fractals, and entropies). Finally, systems aiming to solve the inverse problem in electrocardiography are presented. Concluding remarks on these systems and on the whole review are discussed. In the companion paper (1), a compilation of recent patents on digital processing algorithms for cardiac signals analysis has been presented. This review included systems for basic feature extraction of the electric signals recorded from electrodes in the skin (called electrocardiogram, ECG), or from electrode systems in catheters placed inside the heart (called electrograms, EGM). Systems for analyzing cardiac arrhythmias in different applications have also been assembled therein. In this paper, we include relevant higher-level systems that use digital processing algorithms in advanced applications related with electrocardiology and with cardiac electrophysiology environments. Specifically, the problem of accurate sudden cardiac death (SCD) prediction (2,3) has been targeted by a large number of disclosed systems. Also, apnea analysis (4) has been addressed in the recently disclosed patents. Special attention is paid here to advanced signal processing algorithms, such as blind source separation techniques or nonlinear analysis procedures including chaos, fractals and entropy principled algorithms, which have been disclosed as a part of advanced application systems. Finally, the inverse problem in electrocardiography (5), consisting of creating an anatomically detailed image of the electrical underlying cardiac activity from a diversity of electric signals, continues to be a field in which disclosed advanced systems aim to give to the clinician the best view of the cardiac electric activity. The structure of this second part of the review is as follows. Section 2 deals with patents devoted to SCD risk stratification and prediction. Section 3 briefly deals with