Optimized CNN-based denoising strategy for enhancing longitudinal monitoring of heart failure.

Cardiac vibration signal analysis emerges as a remarkable tool for the diagnosis of heart conditions. Our recent study shows the feasibility of the longitudinal monitoring of chronic heart diseases, particularly heart failure, using a gastric fundus implant. However, cardiac vibration data, captured from the implant, positioned at the gastric fundus, can be highly affected by different noises and artefacts.

Wavelet-Based Dual-Task Network.

In image processing, wavelet transform (WT) offers multiscale image decomposition, generating a blend of low-resolution approximation images and high-resolution detail components. Drawing parallels to this concept, we view feature maps in convolutional neural networks (CNNs) as a similar mix, but uniquely within the channel domain. Inspired by multitask learning (MTL) principles, we propose a wavelet-based dual-task (WDT) framework.

Analysis of Cardiac Vibration Signals Acquired From a Novel Implant Placed on the Gastric Fundus.

The analysis of cardiac vibration signals has been shown as an interesting tool for the follow-up of chronic pathologies involving the cardiovascular system, such as heart failure (HF). However, methods to obtain high-quality, real-world and longitudinal data, that do not require the involvement of the patient to correctly and regularly acquire these signals, remain to be developed. Implantable systems may be a solution to this observability challenge.

Fractional Wavelet-Based Generative Scattering Networks.

Generative adversarial networks and variational autoencoders (VAEs) provide impressive image generation from Gaussian white noise, but both are difficult to train, since they need a generator (or encoder) and a discriminator (or decoder) to be trained simultaneously, which can easily lead to unstable training. To solve or alleviate these synchronous training problems of generative adversarial networks (GANs) and VAEs, researchers recently proposed generative scattering networks (GSNs), which use wavelet scattering networks (ScatNets) as the encoder to obtain features (or ScatNet embeddings) and convolutional neural networks (CNNs) as the decoder to generate an image. The advantage of GSNs is that the parameters of ScatNets do not need to be learned, while the disadvantage of GSNs is that their ability to obtain representations of ScatNets is slightly weaker than that of CNNs.

Quantification of Neural Conduction Block on the Rat Sciatic Nerve based on EMG Response.

Neural conduction block performed by balanced-charge kilohertz frequency alternating currents (KHFAC) has been identified as a potential technique for therapy delivery in different clinical setups. The underlying mechanisms that contribute to this phenomenon have been studied through computational models and animal experiments. However, the optimal stimulation parameters to achieve axonal conduction block are difficult to define, since they depend on the species, the nerve being targeted, as well as the technical and experimental setup.

Fractional Wavelet Scattering Network and Applications.

Objective: This study introduces a fractional wavelet scattering network (FrScatNet), which is a generalized translation invariant version of the classical wavelet scattering network.

Methods: In our approach, the FrScatNet is constructed based on the fractional wavelet transform (FRWT). The fractional scattering coefficients are iteratively computed using FRWTs and modulus operators.

Kinesthetic stimulation for obstructive sleep apnea syndrome: An "on-off" proof of concept trial.

Obstructive sleep apnea (OSA) occurs when the upper airway narrows or collapses due to the loss of upper airway muscle activation at sleep onset. This study investigated the effectiveness of triggered kinesthetic stimulation in patients with OSA. This proof-of-concept, open-label, multicenter prospective study was conducted on 24 patients with severe OSA.

Reliable gradient search directions for kurtosis-based deflationary ICA: Application to physiological signal processing.

Efficient gradient search directions for the optimisation of the kurtosis-based deflationary RobustICA algorithm in the case of real-valued data are proposed in this paper. The proposed scheme employs, in the gradient-like algorithm typically used to optimise the considered kurtosis-based objective function, search directions computed from a more reliable approximation of the negentropy than the kurtosis. The proposed scheme inherits the exact line search of the conventional RobustICA for which a good convergence property through a given direction is guaranteed.

Early diagnosis of NAFLD-NASH transition using mid infrared spectroscopy.

Non-alcoholic fatty liver disease (NAFLD) is defined as an excessive accumulation of fat in the liver in the absence of excessive drinking of alcohol. Initially considered as benign and self-limited, NAFLD may progress to the malignant stage of non-alcoholic steatohepatitis (NASH) characterized by degenerate hepatocellular ballooning and lobular inflammation. NASH can lead to hepatic fibrosis and ultimately to cirrhosis and hepatocellular carcinoma.

Low cost and efficient kurtosis-based deflationary ICA method: application to MRS sources separation problem.

Improving the execution time and the numerical complexity of the well-known kurtosis-based maximization method, the RobustICA, is investigated in this paper. A Newton-based scheme is proposed and compared to the conventional RobustICA method. A new implementation using the nonlinear Conjugate Gradient one is investigated also.

Localization of spatially distributed brain sources after a tensor-based preprocessing of interictal epileptic EEG data.

This paper addresses the localization of spatially distributed sources from interictal epileptic electroencephalographic data after a tensor-based preprocessing. Justifying the Canonical Polyadic (CP) model of the space-time-frequency and space-time-wave-vector tensors is not an easy task when two or more extended sources have to be localized. On the other hand, the occurrence of several amplitude modulated spikes originating from the same epileptic region can be used to build a space-time-spike tensor from the EEG data.

Feasibility of blind source separation methods for the denoising of dense-array EEG.

High-density electroencephalographic recordings have recently been proved to bring useful information during the pre-surgical evaluation of patients suffering from drug-resistant epilepsy. However, these recordings can be particularly obscured by noise and artifacts. This paper focuses on the denoising of dense-array EEG data (e.

Hippocampal effective synchronization values are not pre-seizure indicator without considering the state of the onset channels.

In this paper, a model-based approach is presented to quantify the effective synchrony between hippocampal areas from depth-EEG signals. This approach is based on the parameter identification procedure of a realistic Multi-Source/Multi-Channel (MSMC) hippocampal model that simulates the function of different areas of hippocampus. In the model it is supposed that the observed signals recorded using intracranial electrodes are generated by some hidden neuronal sources, according to some parameters.

A Novel Classification Method for Prediction of Rectal Bleeding in Prostate Cancer Radiotherapy Based on a Semi-Nonnegative ICA of 3D Planned Dose Distributions.

The understanding of dose/side-effects relationships in prostate cancer radiotherapy is crucial to define appropriate individual's constraints for the therapy planning. Most of the existing methods to predict side-effects do not fully exploit the rich spatial information conveyed by the three-dimensional planned dose distributions. We propose a new classification method for three-dimensional individuals' doses, based on a new semi-nonnegative ICA algorithm to identify patients at risk of presenting rectal bleeding from a population treated for prostate cancer.

Analysis of endocardial acceleration during intraoperative optimization of cardiac resynchronization therapy.

Cardiac resynchronization therapy (CRT) is the therapy of choice for selected patients suffering from drug-refractory congestive heart failure and presenting an interventricular desynchronization. CRT is delivered by an implantable biventricular pacemaker, which stimulates the right atrium and both ventricles at specific timings. The optimization and personalization of this therapy requires to quantify both the electrical and the mechanical cardiac functions during the intraoperative and postoperative phases.

Analysis of the behavior of a seizure neural mass model using describing functions.

Neural mass models are computational nonlinear models that simulate the activity of a population of neurons as an average neuron, in such a way that different inhibitory post-synaptic potential and excitatory post-synaptic potential signals could be reproduced. These models have been developed either to simulate the recognized neural mechanisms or to predict some physiological facts that are not easy to realize naturally. The role of the excitatory and inhibitory activity variation in seizure genesis has been proved, but it is not evident how these activities influence appearance of seizure like signals.

Optical spectroscopy techniques can accurately distinguish benign and malignant renal tumours.

Unlabelled: WHAT'S KNOWN ON THE SUBJECT? AND WHAT DOES THE STUDY ADD?: There is little known about optical spectroscopy techniques ability to evaluate renal tumours. This study shows for the first time the ability of Raman and optical reflectance spectroscopy to distinguish benign and malignant renal tumours in an ex vivo environment. We plan to develop this optical assistance in the operating room in the near future.

Optimal algorithm switching for the estimation of systole period from cardiac microacceleration signals (SonR).

Previous studies have shown that cardiac microacceleration signals, recorded either cutaneously, or embedded into the tip of an endocardial pacing lead, provide meaningful information to characterize the cardiac mechanical function. This information may be useful to personalize and optimize the cardiac resynchronization therapy, delivered by a biventricular pacemaker, for patients suffering from chronic heart failure (HF). This paper focuses on the improvement of a previously proposed method for the estimation of the systole period from a signal acquired with a cardiac microaccelerometer (SonR sensor, Sorin CRM SAS, France).

A dynamical model for generating synthetic Phonocardiogram signals.

In this paper we introduce a dynamical model for Phonocardiogram (PCG) signal which is capable of generating realistic synthetic PCG signals. This model is based on PCG morphology and consists of three ordinary differential equations and can represent various morphologies of normal PCG signals. Beat-to-beat variation in PCG morphology is significant so model parameters vary from beat to beat.

Turning Tangent Empirical Mode Decomposition: A Framework for Mono- and Multivariate Signals.

A novel Empirical Mode Decomposition (EMD) algorithm, called 2T-EMD, for both mono- and multivariate signals is proposed in this paper. It differs from the other approaches by its computational lightness and its algorithmic simplicity. The method is essentially based on a redefinition of the signal mean envelope, computed thanks to new characteristic points, which offers the possibility to decompose multivariate signals without any projection.

Fast Algorithms for the Computation of Sliding Sequency-Ordered Complex Hadamard Transform.

Fast algorithms for computing the forward and inverse sequency-ordered complex Hadamard transforms (SCHT) in a sliding window are presented. The first algorithm consists of decomposing a length-N inverse SCHT (ISCHT) into two length-N/2 ISCHTs. The second algorithm, calculating the values of window i+N/4 from those of window i and one length-N/4 ISCHT and one length-N/4 modified ISCHT (MISCHT), is implemented by two schemes to achieve a good compromise between the computation complexity and the implementation complexity.

Endocardial acceleration (sonR) vs. ultrasound-derived time intervals in recipients of cardiac resynchronization therapy systems.

Aims: Optimization of cardiac resynchronization therapy (CRT) requires the gathering of cardiac functional information. An accurate timing of the phases of the cardiac cycle is key in the optimization process.

Methods And Results: We compared Doppler echocardiography to an automated system, based on the recording of sonR (formerly endocardial acceleration), in the detection of mitral and aortic valves closures and measurements of the duration of systole and diastole.

On joint diagonalization of cumulant matrices for independent component analysis of MRS and EEG signals.

An extension of the original implementation of JADE, named eJADE((1)) hereafter, was proposed in 2001 to perform independent component analysis for any combination of statistical orders greater than or equal to three. More precisely, eJADE((1)) relies on the joint diagonalization of a set of several cumulant matrices corresponding to different matrix slices of one or several higher order cumulant tensors. An efficient way, without lose of statistical information, of reducing the number of third and fourth order cumulant matrices to be jointly diagonalized is proposed in this paper.

[Optical spectroscopy: a new approach to assess urological tumors].

Introduction: Optical spectroscopy refers to a group of novel technologies that uses interaction of light with tissues to analyze their structure and chemical composition. The objective of this article is to describe these technologies and detail their potential for assessing urological tumors.

Material And Methods: It has been shown that optical spectroscopy can accurately analyse multiple solid tumors.

Raman spectroscopy: a novel experimental approach to evaluating renal tumours.

Background: New optical techniques of spectroscopy have shown promising results in the evaluation of solid tumours.

Objective: To evaluate the potential of Raman spectroscopy (RS) to assess renal tumours at surgery.

Design, Setting, And Participants: Over a 5-mo period, Raman optical spectra were prospectively acquired on surgical renal specimens removed due to suspicion of cancer.