Exploration of interictal to ictal transition in epileptic seizures using a neural mass model.

An epileptic seizure can usually be divided into three stages: interictal, preictal, and ictal. However, the seizure underlying the transition from interictal to ictal activities in the brain involves complex interactions between inhibition and excitation in groups of neurons. To explore this mechanism at the level of a single population, this paper employed a neural mass model, named the complete physiology-based model (cPBM), to reconstruct electroencephalographic (EEG) signals and to infer the changes in excitatory/inhibitory connections related to excitation-inhibition (E-I) balance based on an open dataset recorded for ten epileptic patients. Since epileptic signals display spectral characteristics, spectral dynamic causal modelling (DCM) was applied to quantify these frequency characteristics by maximizing the free energy in the framework of power spectral density (PSD) and estimating the cPBM parameters. In addition, to address the local maximum problem that DCM may suffer from, a hybrid deterministic DCM (H-DCM) approach was proposed, with a deterministic annealing-based scheme applied in two directions. The H-DCM approach adjusts the temperature introduced in the objective function by gradually decreasing the temperature to obtain relatively good initialization and then gradually increasing the temperature to search for a better estimation after each maximization. The results showed that (i) reconstructed EEG signals belonging to the three stages together with their PSDs can be reproduced from the estimated parameters of the cPBM; (ii) compared to DCM, traditional D-DCM and anti D-DCM, the proposed H-DCM shows higher free energies and lower root mean square error (RMSE), and it provides the best performance for all stages (e.g., the RMSEs between the reconstructed PSD computed from the reconstructed EEG signal and the sample PSD obtained from the real EEG signal are 0.33 ± 0.08, 0.67 ± 0.37 and 0.78 ± 0.57 in the interictal, preictal and ictal stages, respectively); and (iii) the transition from interictal to ictal activity can be explained by an increase in the connections between pyramidal cells and excitatory interneurons and between pyramidal cells and fast inhibitory interneurons, as well as a decrease in the self-loop connection of the fast inhibitory interneurons in the cPBM. Moreover, the E-I balance, defined as the ratio between the excitatory connection from pyramidal cells to fast inhibitory interneurons and the inhibitory connection with the self-loop of fast inhibitory interneurons, is also significantly increased during the epileptic seizure transition. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-023-09976-6.

Data-driven systems to detect physical weakening from daily routine: A pilot study on elderly over 80 years old.

The use of telemonitoring solutions via wearable sensors is believed to play a major role in the prevention and therapy of physical weakening in older adults. Despite the various studies found in the literature, some elements are still not well addressed, such as the study cohort, the experimental protocol, the type of research design, as well as the relevant features in this context. To this end, the objective of this pilot study was to investigate the efficacy of data-driven systems to characterize older individuals over 80 years of age with impaired physical function, during their daily routine and under unsupervised conditions. We propose a fully automated process which extracts a set of heterogeneous time-domain features from 24-hour files of acceleration and barometric data. After being statistically tested, the most discriminant features fed a group of machine learning classifiers to distinguish frail from non-frail subjects, achieving an accuracy up to 93.51%. Our analysis, conducted over 570 days of recordings, shows that a longitudinal study is important while using the proposed features, in order to ensure a highly specific diagnosis. This work may serve as a basis for the paradigm of future monitoring systems.

Epileptic Seizure Prediction Using Deep Neural Networks Via Transfer Learning and Multi-Feature Fusion.

Epilepsy is one of the most common neurological diseases, which can seriously affect the patient's psychological well-being and quality of life. An accurate and reliable seizure prediction system can generate alarm before epileptic seizures to provide patients and their caregivers with sufficient time to take appropriate action. This study proposes an efficient seizure prediction system based on deep learning in order to anticipate the onset of the seizure as early as possible. Handcrafted features extracted based on the prior knowledge and hidden deep features are complementarily fused through the feature fusion module, and then the hybrid features are fed into the multiplicative long short-term memory (MLSTM) to explore the temporal dependency in EEG signals. A one-dimensional channel attention mechanism is implemented to emphasize the more representative information in the multi-channel output of the MLSTM. Finally, a transfer learning strategy is proposed to transfer the weights of the base model trained on the EEG data of all patients to the target patient model, and the latter is then continuously trained using the EEG data of the target patient. The proposed method achieves an average sensitivity of 95.56% and a false positive rate (FPR) of 0.27/h on the SWEC-ETHZ intracranial EEG data. For the more challenging CHB-MIT scalp EEG database, an average sensitivity of 89.47% and a FPR of 0.34/h are obtained. Experimental results demonstrate that the proposed method has good robustness and generalization ability in both intracranial and scalp EEG signals.

Investigation of two neural mass models for DCM-based effective connectivity inference in temporal epilepsy.

BACKGROUND AND OBJECTIVE: Recently, spectral Dynamic Causal Modelling (DCM) has been used increasingly to infer effective connectivity from epileptic intracranial electroencephalographic (iEEG) signals. In this context, the Physiology-Based Model (PBM), a neural mass model, is used as a generative model. However, previous studies have highlighted out the inability of PBM to properly describe iEEG signals with specific power spectral densities (PSDs). More precisely, PSDs that have multiple peaks around ß and γ rhythms (i.e. spectral characteristics at seizure onset) are concerned. METHODS: To cope with this limitation, an alternative neural mass model, called the complete PBM (cPBM), is investigated. The spectral DCM and two recent variants are used to evaluate the relevance of cPBM over PBM. RESULTS: The study is conducted on both simulated signals and real epileptic iEEG recordings. Our results confirm that, compared to PBM, cPBM shows (i) more ability to model the desired PSDs and (ii) lower numerical complexity whatever the method. CONCLUSIONS: Thanks to its intrinsic and extrinsic connectivity parameters as well as the input coming into the fast inhibitory subpopulation, the cPBM provides a more expressive model of PSDs, leading to a better understanding of epileptic patterns and DCM-based effective connectivity inference.

A Reliable Fall Detection System Based on Analyzing the Physical Activities of Older Adults Living in Long-Term Care Facilities.

Fall detection systems are designed in view to reduce the serious consequences of falls thanks to the early automatic detection that enables a timely medical intervention. The majority of the state-of-the-art fall detection systems are based on machine learning (ML). For training and performance evaluation, they use some datasets that are collected following predefined simulation protocols i.e. subjects are asked to perform different types of activities and to repeat them several times. Apart from the quality of simulating the activities, protocol-based data collection results in big differences between the distribution of the activities of daily living (ADLs) in these datasets in comparison with the actual distribution in real life. In this work, we first show the effects of this problem on the sensitivity of the ML algorithms and on the interpretability of the reported specificity. Then, we propose a reliable design of an ML-based fall detection system that aims at discriminating falls from the ambiguous ADLs. The latter are extracted from 400 days of recorded activities of older adults experiencing their daily life. The proposed system can be used in neck- and wrist-worn fall detectors. In addition, it is invariant to the rotation of the wearable device. The proposed system shows 100% of sensitivity while it generates an average of one false positive every 25 days for the neck-worn device and an average of one false positive every 3 days for the wrist-worn device.

D-SORM: A digital solution for remote monitoring based on the attitude of wearable devices.

BACKGROUND AND OBJECTIVE: E-health is a growing research topic, especially with the expansion of the Internet of Things (IoT). Miniaturized wearable sensors are auspicious tools for biomedicine and healthcare systems. In this paper, we present D-SORM, a sensor fusion-based digital solution intended to assist clinicians and improve their diagnosis by providing objective measurements and automatic recognition. The aim is to supply an interface for remote monitoring to the medical staff. METHODS: D-SORM platform estimates the wearable device attitude based on its acquired data, and visualizes it in real-time using a graphical user interface (GUI). It also integrates two modules which serve two different medical applications. The first one is arm tele-rehabilitation, where sessions are done online. The practitioner gives the instructions while wearing the device, and the patient has to reproduce the gestures. A processing unit is dedicated to compute statistical features and calculate the success rate. The second one is human motion tracking for elderly care. A novel machine learning architecture is proposed, based on feature fusion, to predict the activities of daily living. RESULTS: The rehabilitation mechanism was tested under supervised conditions, by performing a set of movements. D-SORM provides extra information and objective measurements, thus facilitates the diagnosis of clinicians. The human activity recognition is also validated using a public dataset. With D-SORM, an efficiency ranging from 97.7% to 99.65% is ensured under unsupervised conditions. CONCLUSIONS: The proposed design constitutes a digital clinical tool for medical teams allowing remote health monitoring. It overcomes geographical barriers while providing faster and highly accurate assessment.

Dynamic brain effective connectivity analysis based on low-rank canonical polyadic decomposition: application to epilepsy.

In this paper, a new method to track brain effective connectivity networks in the context of epilepsy is proposed. It relies on the combination of partial directed coherence with a constrained low-rank canonical polyadic tensor decomposition. With such combination being established, the most dominating directed graph structures underlying each time window of intracerebral electroencephalographic signals are optimally inferred. Obtained time and frequency signatures of inferred brain networks allow respectively to track the time evolution of these networks and to define frequency bands on which they are operating. Besides, the proposed method allows also to track brain connectivity networks through several epileptic seizures of the same patient. Understanding the most dominating directed graph structures over epileptic seizures and investigating their behavior over time and frequency plans are henceforth possible. Since only few but the the most important directed connections in the graph structure are of interest and also for a meaningful interpretation of obtained signatures to be guaranteed, the low-rank canonical polyadic tensor decomposition is prompted respectively by the sparsity and the non-negativity constraints on the tensor loading matrices. The main objective of this contribution is to propose a new way of tracking brain networks in the context of epileptic iEEG data by identifying the most dominant effective connectivity patterns underlying the observed iEEG signals at each time window. The performance of the proposed method is firstly evaluated on simulated data imitating brain activities and secondly on real intracerebral electroencephalographic signals obtained from an epileptic patient. The partial directed coherence-based tensor has been decomposed into space, time, and frequency signatures in accordance with the expected ground truth for each consecutive sequence of the simulated data. The method is also in accordance with the clinical expertise for iEEG epileptic signals, where the signatures were investigated through a simultaneous multi-seizure analysis.

Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods.

Electroencephalography (EEG) is a common tool used for the detection of epileptic seizures. However, the visual analysis of long-term EEG recordings is characterized by its subjectivity, time-consuming procedure and its erroneous detection. Various epileptic seizure detection algorithms have been proposed to deal with such issues. In this study, a novel automatic seizure-detection approach is proposed. Three different strategies are suggested to the user whereby he/she could choose the appropriate one for a given classification problem. Indeed, the feature extraction step, including both linear and nonlinear measures, is performed either directly from the EEG signals, or from the derived sub-bands of tunable-Q wavelet transform (TQWT), or even from the intrinsic mode functions (IMFs) of multivariate empirical mode decomposition (MEMD). The classification procedure is executed using a support vector machine (SVM). The performance of the proposed method is evaluated through a publicly available database from which six binary classification cases are formulated to discriminate between healthy, seizure and non-seizure EEG signals. Our results show high performance in terms of accuracy (ACC), sensitivity (SEN) and specificity (SPE) compared to the state-of-the-art approaches. Thus, the proposed approach for automatic seizure detection can be considered as a valuable alternative to existing methods, able to alleviate the overload of visual analysis and accelerate the seizure detection.

For a More Reliable Measure of Wrist Blood Pressure Using Smartwatch.

Background: If blood pressure (BP) measurement is important to monitor blood hypertension and other cardiac diseases, and can be taken using a wrist device, learned societies recommend to take it in specific conditions. In a telemedicine context, patients are likely to perform it without any help from a medical practitioner. Therefore, the device must guide individuals using it. Materials and Methods: A smartwatch application integrating an Attitude and Heading Reference System algorithm was developed. It was combined with a wrist BP monitor to help users position the BP monitor properly. Results: The system was tested on 30 individuals and a survey conducted to evaluate its usability. The experiment showed that individuals needed to be guided to measure correctly their BP and our application helped them in positioning the wrist BP monitor in a user-friendly way. Conclusions: In a telemedicine context, it is possible to guide easily individuals to position correctly any commercialized wrist BP monitor using a smartwatch. Manufacturers could also integrate affordable sensors into their BP monitors to provide this assistance without the need of external devices.

[Research on effective connectivity of intracerebral electroencephalogram based on Wiener-Granger Causality Index modified by generalized Akaike's Information Criterion].

The objective is to deal with brain effective connectivity among epilepsy electroencephalogram (EEG) signals recorded by use of depth electrodes in the cerebral cortex of patients suffering from refractory epilepsy during their epileptic seizures. The Wiener-Granger Causality Index (WGCI) is a well-known effective measure that can be useful to detect causal relations of interdependence in these kinds of EEG signals. It is based on the linear autoregressive model, and the issue of the estimation of the model parameters plays an important role in the calculation accuracy and robustness of WGCI to do research on brain effective connectivity. Focusing on this issue, a modified Akaike's information criterion algorithm is introduced in the computation of the WGCI to estimate the orders involved in the underlying models and in order to advance the performance of WGCI to detect brain effective connectivity. Experimental results support the interesting performance of the proposed algorithm to characterize the information flow both in a linear stochastic system and a physiology-based model.

A local adjustment strategy for the initialization of dynamic causal modelling to infer effective connectivity in brain epileptic structures.

This paper addresses the question of effective connectivity in the human cerebral cortex in the context of epilepsy. Among model based approaches to infer brain connectivity, spectral Dynamic Causal Modelling is a conventional technique for which we propose an alternative to estimate cross spectral density. The proposed strategy we investigated tackles the sub-estimation of the free energy using the well-known variational Expectation-Maximization algorithm highly sensitive to the initialization of the parameters vector by a permanent local adjustment of the initialization process. The performance of the proposed strategy in terms of effective connectivity identification is assessed using simulated data generated by a neuronal mass model (simulating unidirectional and bidirectional flows) and real epileptic intracerebral Electroencephalographic signals. Results show the efficiency of proposed approach compared to the conventional Dynamic Causal Modelling and the one wherein a deterministic annealing scheme is employed.

Relevance of health level 7 clinical document architecture and integrating the healthcare enterprise cross-enterprise document sharing profile for managing chronic wounds in a telemedicine context.

The number of patients with complications associated with chronic diseases increases with the ageing population. In particular, complex chronic wounds raise the re-admission rate in hospitals. In this context, the implementation of a telemedicine application in Basse-Normandie, France, contributes to reduce hospital stays and transport. This application requires a new collaboration among general practitioners, private duty nurses and the hospital staff. However, the main constraint mentioned by the users of this system is the lack of interoperability between the information system of this application and various partners' information systems. To improve medical data exchanges, the authors propose a new implementation based on the introduction of interoperable clinical documents and a digital document repository for managing the sharing of the documents between the telemedicine application users. They then show that this technical solution is suitable for any telemedicine application and any document sharing system in a healthcare facility or network.

Bias reduction in the estimation of mutual information.

This paper deals with the control of bias estimation when estimating mutual information from a nonparametric approach. We focus on continuously distributed random data and the estimators we developed are based on a nonparametric k-nearest-neighbor approach for arbitrary metrics. Using a multidimensional Taylor series expansion, a general relationship between the estimation error bias and the neighboring size for the plug-in entropy estimator is established without any assumption on the data for two different norms. The theoretical analysis based on the maximum norm developed coincides with the experimental results drawn from numerical tests made by Kraskov et al. [Phys. Rev. E 69, 066138 (2004)PLEEE81539-375510.1103/PhysRevE.69.066138]. To further validate the novel relation, a weighted linear combination of distinct mutual information estimators is proposed and, using simulated signals, the comparison of different strategies allows for corroborating the theoretical analysis.

A new strategy for model order identification and its application to transfer entropy for EEG signals analysis.

The background objective of this study is to analyze electrenocephalographic (EEG) signals recorded with depth electrodes during seizures in patients with drug-resistant epilepsy. Usually, different phases are observed during the seizure evolution, including a fast onset activity. We aim to ascertain how cerebral structures get involved during this phase, in particular whether some structures "drive" other ones. Regarding a recent theoretical information measure, namely the transfer entropy (TE), we propose two criteria, the first one is based on Akaike's information criterion, the second on the Bayesian information criterion, to derive models' orders that constitute crucial parameters in the TE estimation. A normalized index, named partial transfer entropy (PTE), allows for quantifying the contribution or the influence of a signal to the global information flow between a pair of signals. Experiments are first conducted on linear autoregressive models, then on a physiology-based model, and finally on real intracerebral EEG epileptic signals to detect and identify directions of causal interdependence. Results support the relevance of the new measures for characterizing the information flow propagation whatever unidirectional or bidirectional interactions.

Multivariate predictive model for dyslexia diagnosis.

Dyslexia is a specific disorder of language development that mainly affects reading. Etiological researches have led to multiple hypotheses which induced various diagnosis methods and rehabilitation treatments so that many different tests are used by practitioners to identify dyslexia symptoms. Our purpose is to determine a subset of the most efficient ones by integrating them into a multivariate predictive model. A set of screening tasks that are the most commonly used and representative of the different cognitive aspects of dyslexia was proposed to 78 children from elementary school (mean age = 9 years ± 7 months) exempt from identified reading difficulties and to 35 dyslexic children attending a specialized consultation for dyslexia. We proposed a multi-step procedure: within each category, we first selected the most representative tasks using principal component analysis and then we implemented logistic regression models on the preselected variables. Spelling and reading tasks were considered separately. The model with the best predictive performance includes eight variables from four categories of tasks and classifies correctly 94% of the children. The sensitivity (91%) and the specificity (95%) are both high. Forty minutes are necessary to complete the test.

A physiologically based model for temporal envelope encoding in human primary auditory cortex.

Communication sounds exhibit temporal envelope fluctuations in the low frequency range (<70 Hz) and human speech has prominent 2-16 Hz modulations with a maximum at 3-4 Hz. Here, we propose a new phenomenological model of the human auditory pathway (from cochlea to primary auditory cortex) to simulate responses to amplitude-modulated white noise. To validate the model, performance was estimated by quantifying temporal modulation transfer functions (TMTFs). Previous models considered either the lower stages of the auditory system (up to the inferior colliculus) or only the thalamocortical loop. The present model, divided in two stages, is based on anatomical and physiological findings and includes the entire auditory pathway. The first stage, from the outer ear to the colliculus, incorporates inhibitory interneurons in the cochlear nucleus to increase performance at high stimuli levels. The second stage takes into account the anatomical connections of the thalamocortical system and includes the fast and slow excitatory and inhibitory currents. After optimizing the parameters of the model to reproduce the diversity of TMTFs obtained from human subjects, a patient-specific model was derived and the parameters were optimized to effectively reproduce both spontaneous activity and the oscillatory part of the evoked response.

Temporal envelope processing in the human auditory cortex: response and interconnections of auditory cortical areas.

Temporal envelope processing in the human auditory cortex has an important role in language analysis. In this paper, depth recordings of local field potentials in response to amplitude modulated white noises were used to design maps of activation in primary, secondary and associative auditory areas and to study the propagation of the cortical activity between them. The comparison of activations between auditory areas was based on a signal-to-noise ratio associated with the response to amplitude modulation (AM). The functional connectivity between cortical areas was quantified by the directed coherence (DCOH) applied to auditory evoked potentials. This study shows the following reproducible results on twenty subjects: (1) the primary auditory cortex (PAC), the secondary cortices (secondary auditory cortex (SAC) and planum temporale (PT)), the insular gyrus, the Brodmann area (BA) 22 and the posterior part of T1 gyrus (T1Post) respond to AM in both hemispheres. (2) A stronger response to AM was observed in SAC and T1Post of the left hemisphere independent of the modulation frequency (MF), and in the left BA22 for MFs 8 and 16Hz, compared to those in the right. (3) The activation and propagation features emphasized at least four different types of temporal processing. (4) A sequential activation of PAC, SAC and BA22 areas was clearly visible at all MFs, while other auditory areas may be more involved in parallel processing upon a stream originating from primary auditory area, which thus acts as a distribution hub. These results suggest that different psychological information is carried by the temporal envelope of sounds relative to the rate of amplitude modulation.

Improving the dynamics of responses to amplitude modulated stimuli by modeling inhibitory interneurons in cochlear nucleus.

Amplitude modulation is an important feature of communication sounds. A phenomenological model of the auditory pathway that reproduces amplitude modulation coding from the outer ear to the inferior colliculus is presented. It is based on Hewitt and Meddis' work. To improve the temporal coding for high level stimuli, high spontaneous rate and low spontaneous rate auditory nerve fibers innervate chopper cells of the cochlear nucleus. Wideband inhibitory interneurons which limit high spontaneous rate fibers connected to chopper units are added in this nucleus. The realistic structure we propose gives results closer to physiological data in terms of synchronization.

Discriminatory validity of dyslexia screening tasks in French school age children.

Dyslexia is a specific disorder of language. Researches led on dyslexia origin have conducted to multiple hypotheses and various rehabilitation treatments. In this context, practitioners can be interested in using an automatic tool to help in diagnosing dyslexia. This tool should evaluate children's own deficit and advise adapted rehabilitation. This paper presents the conception of a preliminary test containing the most representative dyslexia evaluation tasks from literature and the first results concerning the discriminatory validity of this preliminary test in French school age children (8-10 years). Moreover a selection of significant tasks to optimize the detection of dyslexia is proposed. These tasks will build up the first step of the automatic tool.

Evidence of functional connectivity between auditory cortical areas revealed by amplitude modulation sound processing.

The human auditory cortex includes several interconnected areas. A better understanding of the mechanisms involved in auditory cortical functions requires a detailed knowledge of neuronal connectivity between functional cortical regions. In human, it is difficult to track in vivo neuronal connectivity. We investigated the interarea connection in vivo in the auditory cortex using a method of directed coherence (DCOH) applied to depth auditory evoked potentials (AEPs). This paper presents simultaneous AEPs recordings from insular gyrus (IG), primary and secondary cortices (Heschl's gyrus and planum temporale), and associative areas (Brodmann area [BA] 22) with multilead intracerebral electrodes in response to sinusoidal modulated white noises in 4 epileptic patients who underwent invasive monitoring with depth electrodes for epilepsy surgery. DCOH allowed estimation of the causality between 2 signals recorded from different cortical sites. The results showed 1) a predominant auditory stream within the primary auditory cortex from the most medial region to the most lateral one whatever the modulation frequency, 2) unidirectional functional connection from the primary to secondary auditory cortex, 3) a major auditory propagation from the posterior areas to the anterior ones, particularly at 8, 16, and 32 Hz, and 4) a particular role of Heschl's sulcus dispatching information to the different auditory areas. These findings suggest that cortical processing of auditory information is performed in serial and parallel streams. Our data showed that the auditory propagation could not be associated to a unidirectional traveling wave but to a constant interaction between these areas that could reflect the large adaptive and plastic capacities of auditory cortex. The role of the IG is discussed.