This paper proposes an in-depth analysis of the tri-state inverter based digitally controlled oscillator. This oscillator topology has been reported in numerous publication, however its features remain poorly understood. In this study, we propose to focus on these lacks. We specifically addressed the oscillation period and the associated jitter as these quantities are the design key parameters. In this paper, we propose analytical expressions taking into consideration the design, the technology as well as the input code. These equations are suitable for hand calculations and have helped to establish a design methodology for rapid implementation. Two circuits have been designed in STMicroelectronics CMOS 65 nm process. The first one was evaluated through simulations. Then, the measurement results obtained with the second circuit manufactured within the same technology node are presented. Finally, the experimental data support the proposed theory.

To detect fast myoclonus jerks, doctors require a full-HD video capture of the patient at 100 frames per second. Real-time video compression becomes mandatory to archive/transmit the generated data. To achieve this goal, we used a certified medical imaging coder based on discrete wavelet transform (DWT). Thus, a major challenge was to design a 2D DWT architecture, achieving the throughput of 100 full-HD frames/s.The novel unified 2D DWT computation architecture performs both horizontal and vertical transform simultaneously and eliminates the problem of column-wise image pixel accesses to/from the off-chip DDR RAM. All of these factors have led to the reduction of the required off-chip DDR RAM bandwidth by more than 2X.The proposed concept uses four-port line buffers leading to pipelined parallel processing of direct memory access (DMA) read, horizontal 1D DWT, vertical 1D DWT, and DMA write. The proposed architecture has cycles per pixel of just 1/8, making it far exceeds 100 full-HD fps and well positioned for the 4K and 8K video processing. Finally, we highlighted that the developed architecture is highly scalable, outperforms state of the art and is deployed in a first video EEG medical prototype.

Automatic sleep staging is challenging since several issues need to be addressed. Traditional approaches from literature do not satisfy medical experts since they do not reflect the cognitive process they perform when scoring polysomnographic curves. We propose a new approach that is based on the implementation of medical knowledge by symbolic fusion. Medical knowledge coming from the international clinical practice guidelines for sleep medicine is formalized as a five-layer framework dedicated to data abstraction in order to deliver local and global propositions and support the interpretation of polysomnographic curves. Firstly, features are extracted from raw curves. Then these features are combined to recognize sleep events in accordance with guidelines. Sleep events are then fused into the criteria required to recognize the different sleep stages. Sleep is not homogeneous through the night. The physiological events observed during the night follow a dynamic that needs to be included into an automatic sleep staging system. In order to take this into account, decision rules are selected and applied to recognize a sleep stage according to the current context. Thereby, transitions are considered with interest. In this paper, we propose to use a Turing Machine-like decision process to handle transitions. To interpret the local observations and properly score a given state, the previous state which has been stored in a specific register is used as a context. One of the advantages of following the principles of symbolic fusion is to benefit from the full traceability of the decision. Hence, it makes possible to discuss each final - or intermediate - decision with an expert and check for relevance. The preliminary results are encouraging since agreement rates provided between decisions taken by our automatic approach and human experts are similar to those measured between human experts (average agreement rate = 54.60% / average Cohen's kappa = 0.40) on a dataset of 131 full polysomnographic recordings. (C) 2018 Elsevier Ltd. All rights reserved.

Epilepsy is known as the second reason to visit a neurophysiologist after migraine. In this paper, we propose a new approach to automatically detect crises of epilepsy in an Electroencephalogram (EEG). Our algorithm is based on image transformation, Wavelet Decomposition (DWT) and taking advantage of the correlation between wavelet coefficients in each sub-band. Therefore, an Expected Activity Measurement (EAM) is calculated for each coefficient as a feature extraction method. These features are fed into back propagation Neural Network (ANN) and the periods with epileptic seizures and non-seizures are classified. Our approach is validated using a public dataset and the results are very promising, reaching accuracy up to 99.44% for detection epileptic seizures.

This paper addresses the development of a new technique in the sleep analysis domain. Sleep is defined as a periodic physiological state during which vigilance is suspended and reactivity to external stimulations diminished. We sleep on average between six and nine hours per night and our sleep is composed of four to six cycles of about 90 min each. Each of these cycles is composed of a succession of several stages of sleep that vary in depth. Analysis of sleep is usually done via polysomnography. This examination consists of recording, among other things, electrical cerebral activity by electroencephalography (EEG), ocular movements by electrooculography (EOG), and chin muscle tone by electromyography (EMG). Recordings are made mostly in a hospital, more specifically in a service for monitoring the pathologies related to sleep. The readings are then interpreted manually by an expert to generate a hypnogram, a curve showing the succession of sleep stages during the night in 30s epochs. The proposed method is based on the follow-up of the thermal signature that makes it possible to classify the activity into three classes: “awakening,” “calm sleep,” and “restless sleep”. The contribution of this non-invasive method is part of the screening of sleep disorders, to be validated by a more complete analysis of the sleep. The measure provided by this new system, based on temperature monitoring (patient and ambient), aims to be integrated into the tele-medicine platform developed within the framework of the Smart-EEG project by the SYEL–SYstèmes ELectroniques team. Analysis of the data collected during the first surveys carried out with this method showed a correlation between thermal signature and activity during sleep. The advantage of this method lies in its simplicity and the possibility of carrying out measurements of activity during sleep and without direct contact with the patient at home or hospitals.

Développer une nouvelle technique d’analyse automatique des polysomnographies par approche symbolique avec intégration de préférences, pour une meilleure concordance avec la lecture manuelle. À partir de tracés de sujets normaux enregistrés plusieurs nuits consécutives, et de tracés de patients, deux experts ont codé les tracés selon les règles de l’AASM. Puis les données brutes anonymisées ont été exportées, pour en extraire les grapho-éléments individuels par fusion symbolique. Cent trente polysomnographies de10 témoins enregistrés sur 3 nuits consécutives, et 100 patients avec suspicion de syndrome d’apnées du sommeil ont été doublement scorées. Les grapho-éléments ont été extraits par fusion symbolique (concept sémantique et non numérique). Les règles d’inférence de l’AASM ont été traduites en langage mathématique avec intégration de préférences en fonction de ce que chaque expert interprète. Puis le programme informatique pour traduire la formalisation en langage JAVA a été élaboré. Dans un premier temps, nous nous sommes focalisés sur l’analyse des stades. Testée sur 8 patients, l’analyse donne les taux de concordance avec l’analyse manuelle suivants : N2 : 70,1 % ± 10,9 % N3 : 79,5 % ± 9,1 %. Ces premiers résultats montrent la nécessité de continuer à enrichir les règles formalisées en collaboration avec les experts. Cette nouvelle technique vise à reproduire la démarche analytique des experts, par opposition à celle reposant sur une analyse purement numérique du signal. Ceci devrait en améliorer ses performances.

Polysomnography is the gold standard test for sleep disorders among which the Sleep Apnea Syndrome (SAS) is considered a public health issue because of the increase of the cardio- and cerebro-vascular risk it is associated with. However, the reliability of this test is questioned since sleep scoring is a time-consuming task performed by medical experts with a high inter- and intra-scorers variability, and because data are collected from 15 sensors distributed over a patient's body surface area, using a wired connection which may be a source of artefacts for the patient's sleep. We have used symbolic fusion to support the automated diagnosis of SAS on the basis of the international guidelines of the AASM for the scoring of sleep events. On a sample of 70 patients, and for the Apnea-Hypopnea Index, symbolic fusion performed at the level of sleep experts (97.1% of agreement). The next step is to confirm these preliminary results and move forward to a smart wireless polysomnograph.

Diagnostic quality is an essential requirement in the medical images compression field to avoid misdiagnosis by radiologists. In this paper, a novel study on using the logarithm in medical images compression is presented. Two novel compression schemes are proposed to improve the image quality. The proposed compression schemes relies on discrete wavelet transform (DWT). The first approach is based on the logarithmic number system (LNS) arithmetic. The second approach (Log-DWT) is a hybrid of LNS and Linear arithmetic. Both schemes compromise between the computation speed and precision. Both approaches show a significant improvement in the image quality in addition to providing better compression rate compared to the classical approach which does not include any logarithmic operations. The structural similarity index (SSIM) was used to assess the two approaches in terms of the image quality. The performance has been evaluated for the proposed approaches and has been compared to the classical approach.

With the rapid extension of clinical data and knowledge, decision making becomes a complex task for manual sleep staging. In this process, there is a need for integrating and analyzing information from heterogeneous data sources with high accuracy. This paper proposes a novel decision support algorithm—Symbolic Fusion for sleep staging application. The proposed algorithm provides high accuracy by combining data from heterogeneous sources, like EEG, EOG and EMG. This algorithm is developed for implementation in portable embedded systems for automatic sleep staging at low complexity and cost. The proposed algorithm proved to be an efficient design support method and achieved up to 76% overall agreement rate on our database of 12 patients.

La reconnaissance des stades de sommeil est une etape indispensable au diagnostic des troubles du sommeil. L'approche habituelle est d'utiliser un classifieur avec des methodes d'apprentissage automatique. Nous proposons une approche innovante concue a partir de la connaissance et de l'observation de la pratique des experts. Notre approche permet de mieux prendre en compte les aspects dynamiques du sommeil, mais aussi les raisonnements a des niveaux d'abstraction differents, ainsi qu'a des echelles de temps differentes. Apres avoir extrait, a partir des signaux acquis, les grapho-elements necessaires a la decision en appliquant la fusion symbolique, un systeme expert incluant des regles d'inference, integrant si necessaire des preferences, est ap-plique. Mise en oeuvre sur trois epoques generalement mal scorees avec les classifieurs habituels, la methode s'est averee efficace.

This paper presents a novel study of logarithmic discrete wavelet transform (DWT) for medical image compression. It proposes a new technique to compute the DWT using the logarithmic number system (LNS) instead of floating point arithmetic. It investigates its impact on the image quality which is an essential factor for medical images to avoid any misdiagnose. The paper presents detailed experimental results for three medical images modalities: CT, MRI and X-Ray. The results show that the LNS approach gives a significant improvement in the image quality measured with the structural similarity index (SSIM).

Scoring sleep stages can be considered as a classification problem. Once the whole recording segmented into 30-seconds epochs, features, extracted from raw signals, are typically injected into machine learning algorithms in order to build a model able to assign a sleep stage, trying to mimic what experts have done on the training set. Such approaches ignore the advances in sleep medicine, in which guidelines have been published by the AASM, providing definitions and rules that should be followed to score sleep stages. In addition, these approaches are not able to solve conflict situations, in which criteria of different sleep stages are met. This work proposes a novel approach based on AASM guidelines. Rules are formalized integrating, for some of them, preferences allowing to support decision in conflict situations. Applied to a doubtful epoch, our approach has taken the appropriate decision.

In this paper, a novel methodology for high-level modeling of bus communication in embedded systems is introduced. It allows the dynamic evaluation of their signal integrity (SI) characteristics at the virtual prototyping step (i.e., before physical realization). The method is based on the association of functional and nonfunctional modules. Functional modules represent the ideal behavior of the system, while nonfunctional modules use neural networks to model SI effects. This approach was implemented in SystemC-AMS, using the timed data flow model of computation. The method is illustrated by a Universal Serial Bus (USB) 3.0 application, where modular and parameterizable models are introduced. The method achieved good accuracy (<;5%) while allowing significant simulation speedup (up to 2000 times), compared with SPICE-based reference models. This methodology can be used to perform an early SI analysis in the virtual prototyping of bus communication in the embedded systems.

More and more, exams require medical images as a tool to diagnose pathologies. Thus, the transfer and storage of the exam data becomes a critical issue. To address this issue, an image compression algorithm called Waaves has been developed and certified for medical imaging. Our work in this paper deals with a scenario of EEG exams where video of the patient is also recorded in order to correctly diagnose myoclonus pathologies. To achieve this goal, the video needs to be of high quality and at frame rate of at least 100 frames per second. This high data rate cannot be compressed on the fly by Waaves codec. In this paper, we present a novel codec based on the Waaves compression algorithm that fits the requirements of tele-video-EEG. We have used the characteristics of the input sequence and the analysis of the original codec, to improve the compression speed. The proposed video codec has shown a speed-up of around 3.4 times compared to the original algorithm. In addition, we have been able to improve the compression ratio while retaining necessary quality to identify myoclonus.

Sleep staging is a time-consuming work and inter-rater reliability variation exists in sleep-stages scorers. There is a need for automatic sleep staging which can facilitate this process and enhance the reliability. This paper presents a new method based on symbolic fusion to realize automatic sleep staging. By combining multiple signals of polysomnography (PSG) and considering temporal effects, symbolic fusion achieves improved accuracy and more specific inferences for sleep staging. This method was tested on 16 patientievPSG and the overall agreement of 65.58% was reached. Proposed symbolic fusion is developed for portable embedded system devices to assist sleep evaluation at home at low complexity and cost.

This paper presents a novel system for automatic sleep staging based on evolutionary technique and symbolic intelligence. Proposed system mimics decision making process of clinical sleep staging using Symbolic Fusion and considers personal singularity with an adaptive thresholds setting up system using Evolutionary Algorithm. It proved to be an effective and promising system in personalizing sleep staging. This system can also be integrated with other medical systems to realize remote sleep monitoring or home-care.

In this paper, we propose a new approach to compress Electroencephalogram (EEG) signals using the WAAVES compression algorithm and Independent Component Analyses (ICA). Firstly, ICA is applied to the 1D-EEG signals as a preprocessing stage to uncorrelate signals. Then, the output of the ICA is scaled and reformatted into a 2D-matrix to be compressed as an image using the WAAVES coder. This scheme gives a better compression ratio and a better Percentage Root-Mean-Squared Difference (PRD). Our work increases the compression efficiency (CR = 36.60) while reserving the EEG signal diagnostic quality (PRD=4.73).

Sleep staging is a fundamental step in diagnosis and treatment of sleep disorders. In current sleep staging systems, normally a set of thresholds should be set up to determine the boundaries in differentiating different linguistic or symbolic features. However, as far as we know, there are no fully satisfying automatic method to do this task. Thresholds are mostly set up manually. In this paper, an automatic thresholds setting-up method based on Cross Entropy is proposed. Person-dependent thresholds can be provided automatically by using Cross Entropy and used in personalized sleep staging analysis while considering individual variability. The feasibility of Cross Entropy has also been evaluated, computational results exhibit that the Cross Entropy-based method is an efficient, convenient and applicable stochastic method for automatically setting-up thresholds in sleep staging system. Compared with manual method, average F-Measures are improved more than 10% for all the stages and up-to 74% for stage N3 by using proposed method.