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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.

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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.

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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.

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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.

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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.

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Several medical examinations require that multiple modalities of exams are stored in a synchronized manner. For instance, an EEG exam needs that several physiological signals along with video of the exam are acquired synchronously to aid the neurophysiologists to perform their diagnostics. Furthermore support for telemedicine for such exams have become important in recent years. The existing EDF standard that is used for physiological signals makes it difficult to provide integrated support of adding video and compressed component data, however due to widespread use of EDF standard in the domain, cross compatibility with EDF standard for physiological data is also essential. We present in this work a novel idea to solve these issues. Our approach uses standard multimedia containers in which physiological data are embedded alongside video and audio. This paper provides our analyses of the state of the art of multimedia containers EDF, AVI, ASF, MPEG and MKV and their potentials for a telemedicine application and outlines how MKV stands out as an interesting option in this regard, allowing also capability of compression of physiological data if needed.

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A CMOS Buried Double Junction PN (BDJ) photodetector consists of two vertically-stacked photodiodes. It can be operated as a photodiode with improved performance and wavelength-sensitive response. This paper presents a review of this device and its applications. The CMOS implementation and operating principle are firstly described. This includes the description of several key aspects directly related to the device performances, such as surface reflection, photon absorption and electron-hole pair generation, photocurrent and dark current generation, etc. SPICE modelling of the detector is then presented. Next, design and process considerations are proposed in order to improve the BDJ performance. Finally, several BDJ-detector-based image sensors provide a survey of their applications.

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An abdominal aortic aneurysm (AAA) is a dilatation of the aorta at the abdominal level, whose rupture is a life threatening complication. Recent treatment procedures of AAA consists in endovascular treatment with covered stent grafts. Despite improving design of these devices, this treatment is still associated with close to 25% of failure, due to persisting pressure into the excluded aneurysmal sac. The follow-up becomes thus crucial and demands frequent examinations (CT-scan, IRM) which are not so liable given the complications. In order to evaluate the post-operative period of an AAA treatment, we designed a communicative stent, comprising of an integrated pressure sensor. This paper presents the conception of a communicative sensor, the elaboration of a numerical model, and the development of an experimental testbench reproducing the aortic flux across an AAA and allowing the optimization and validation of the measurement principle.

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