Detection of T-wave beat-by-beat variations prior to ventricular arrhythmias onset in ICD-stored intracardiac electrograms: the Endocardial T-Wave Alternans Study (ETWAS).

BACKGROUND: The aim of the Endocardial T-Wave Alternans Study was to prospectively assess the presence of T-wave alternans (TWA) or beat-to-beat repolarization changes on implantable cardioverter-defibrillator (ICD)-stored electrograms (EGMs) immediately preceding the onset of spontaneous ventricular tachycardia (VT) or fibrillation (VF). METHODS: Thirty-seven VT/VF episodes were compared to 116 baseline reference EGMs from the same 57 patients. A Bayesian model was used to estimate the T-wave waveform in each cardiac beat and a set of 10 parameters was selected to segment each detected T wave. Beat-by-beat differences in each T-wave parameter were computed using the absolute value of the difference between each beat and the following one. Fisher criterion was used for determining the most discriminant T-wave parameters, then top-M ranked parameters yielding a normalized cumulative Fisher score > 95% were selected, and analysis was applied on these selected parameters. Simulated TWA EGMs were used to validate the algorithm. RESULTS: In the simulation study, TWA was detectable even in the case of the smallest simulated alternans of 25 µV. In 13 of the 37 episodes (35%) occurring in nine of 16 patients, significant larger beat-to-beat variations before arrhythmia onset were detected compared to their respective references (median one positive episode per patient). Parameters including the T-wave apex amplitude seem the more discriminant parameters. CONCLUSIONS: Detection of beat-by-beat repolarization variations in ICD-stored EGMs is feasible in a significant subset of cases and may be used for predicting the onset of ventricular arrhythmias.

Hyperspectral image compression: adapting SPIHT and EZW to anisotropic 3-D wavelet coding.

Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties.

End-to-end signal processing from the embedded body sensor to the medical end user through QoS-less public communication channels: the U-R-SAFE experience.

The URSafe project (IST-2001-33352) aims at creating a telemedicine care environment for the elderly and convalescent. The idea is to provide a portable device which monitors autonomously different biomedical signals and is able to send an alarm to a medical center if an abnormality is detected. In the initial version of URSafe, three sensors where included in the platform: electrocardiogram (ECG), oxygen rate (SpO2) and Fall Detector. This paper presents the signal processing algorithms involved in the project. These are at two levels: ECG analysis and signal transmission.

Simulated prosthetic vision: improving text accessibility with retinal prostheses.

Image processing can improve significantly the every-day life of blind people wearing current and upcoming retinal prostheses relying on an external camera. We propose to use a real-time text localization algorithm to improve text accessibility. An augmented text-specific rendering based on automatic text localization has been developed. It has been evaluated in comparison to the classical rendering through a Simulated Prosthetic Vision (SPV) experiment with 16 subjects. Subjects were able to detect text in natural scenes much faster and further with the augmented rendering compared to the control rendering. Our results show that current and next generation of low resolution retinal prostheses may benefit from real-time text detection algorithms.

T-wave alternans detection using a Bayesian approach and a Gibbs sampler.

The problem of detecting T-wave alternans (TWA) in ECG signals has received considerable attention in the biomedical community. This paper introduces a Bayesian model for the T waves contained in ECG signals. A block Gibbs sampler was recently studied to estimate the parameters of this Bayesian model (including wave locations, amplitudes and shapes). This paper shows that the samples generated by this Gibbs sampler can be used efficiently for TWA detection via different statistical tests constructed from odd and even T-wave amplitude samples. The proposed algorithm is evaluated on real ECG signals subjected to synthetic TWA and compared with two classical algorithms.

P- and T-wave delineation in ECG signals using a Bayesian approach and a partially collapsed Gibbs sampler.

Detection and delineation of P- and T-waves are important issues in the analysis and interpretation of electrocardiogram (ECG) signals. This paper addresses this problem by using Bayesian inference to represent a priori relationships among ECG wave components. Based on the recently introduced partially collapsed Gibbs sampler principle, the wave delineation and estimation are conducted simultaneously by using a Bayesian algorithm combined with a Markov chain Monte Carlo method. This method exploits the strong local dependency of ECG signals. The proposed strategy is evaluated on the annotated QT database and compared to other classical algorithms. An important feature of this paper is that it allows not only for the detection of P- and T-wave peaks and boundaries, but also for the accurate estimation of waveforms for each analysis window. This can be useful for some ECG analysis that require wave morphology information.

Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery.

This paper studies a new Bayesian unmixing algorithm for hyperspectral images. Each pixel of the image is modeled as a linear combination of so-called endmembers. These endmembers are supposed to be random in order to model uncertainties regarding their knowledge. More precisely, we model endmembers as Gaussian vectors whose means have been determined using an endmember extraction algorithm such as the famous N-finder (N-FINDR) or Vertex Component Analysis (VCA) algorithms. This paper proposes to estimate the mixture coefficients (referred to as abundances) using a Bayesian algorithm. Suitable priors are assigned to the abundances in order to satisfy positivity and additivity constraints whereas conjugate priors are chosen for the remaining parameters. A hybrid Gibbs sampler is then constructed to generate abundance and variance samples distributed according to the joint posterior of the abundances and noise variances. The performance of the proposed methodology is evaluated by comparison with other unmixing algorithms on synthetic and real images.

OURSES: a telemedicine project for rural areas in france. Telemonitoring of elderly people.

Several telemedicine applications are proposed within the frame of OURSES project, French acronym for Offer of Rural Use of Services by Satellite, providing services for elderly people. The main objective of this project is to show the interest of using satellites as a complement to terrestrial technologies, in areas where telecommunication infrastructure is lacking or incomplete. This paper describes one of these applications: an ECG monitoring system. This telemonitoring system allows, thanks to a wireless wearable sensor, to detect possible cardiac problems of elderly people. ECG signals are analyzed through signal processing algorithms and if some abnormal condition is detected, an alarm is raised and sent via satellite to the physician's office. The corresponding physician is able to access at any time the recorded ECG signals, whenever he is willing to, in the presence of an alarm or not. This allows a constant monitoring of the elderly people. Tests realized in a real environment have demonstrated the feasibility and the interest of this application.

Lost sample recovering of ECG signals in e-health applications.

This paper shows the interest of an interpolation method based on parametric modeling to retrieve missing samples in ECG signals. This problem occurs more and more with the emergence of telemedicine applications. The different links (fixed access network (PSTN), mobile access network (GSM/GPRS and future UMTS) or satellite interfacing (DVB-RCS technology)) involved in e-health applications are liable to induce errors on the transmitted data. These errors/losses can occur anytime and anywhere (according to the channel availability, memory overflows, protocols, etc) during a transmission process. Therefore the recovering of missing samples for biomedical signals is of great interest. The method used in this paper is based on a left-sided and right-sided autoregressive model, i.e., the interpolation algorithm uses the samples before and after the sequence of missing samples. An objective measure is used to assess the method performance. Results show that this interpolation method represents a really suitable technique to ECG signal reconstruction in a possible corrupted transmission.