Patrick Karasinski

Continuous Quantitative Monitoring of Spontaneous Opiate Withdrawal: Locomotor Activity and Sleep Disorders

The time course of drug abstinence is not readily amenable to examination using intermittent observations, because abstinence is known to interfere with circadian rhythms of general activity. Accordingly, we propose a model for continuous assessment of spontaneous withdrawal without any intervention by the investigator. This model is based on the automatic recording of locomotor activity. Experiments were performed in rectangular activity cages equipped with two infrared photoelectric cells. In a parallel experiment, to confirm the locomotor activity effects, continuous monitoring of EEG activities was achieved from two cortical and one reference electrodes. Morphine dependence was induced by intraperitoneal injections of increasing doses of morphine twice daily for 10 days (from 5 up to 90 mg/kg). Behavioral and EEG activities were recorded for 8 to 10 days following the last injection of morphine. Although control rats displayed a typical locomotor activity pattern characterized by nocturnal hyperactivity that was markedly reduced during the light phase, opiate abstinent rats developed a constant motor activity during the first 3 or 4 postinjection days and that was associated with a drastic reduction of overall rapid eye movement sleep (REM) and non-REM sleep and with an increase of waking (W). Although morphine-abstinent rats slowly resumed a normal circadian cycle after the fourth day in terms of horizontal activity, REMS, NREMS and W, long-term effects were revealed by the permanent motor instability recorded during both the light and the dark phases when the total amount of photocell counts was considered, and by the perturbation of the circadian rhythm of the ratio of REM sleep to total sleep time. Automatic continuous recording of total motor behavior appears to be a useful index with which to follow, over an extended period of time, the acute and long-term consequences of opiate abstinence. Therefore, long-term withdrawal-induced changes in activity could be a suitable model for the validation of antiabstinence therapies.

Adult rat vigilance states discrimination by artificial neural networks using a single EEG channel

Two multilayer neural networks were designed to discriminate vigilance states (waking, paradoxical sleep, and non-REM sleep) in the rat using a single parieto-occipital EEG derivation. After filtering (bandwidth 3.18-25 Hz) and digitization at 512 HZ, the EEG signal was segmented into eight second epochs. Five variables (three statistical, two temporal) were extracted from each epoch. The first network computed an epoch by epoch classification, while the second network also utilized contextual information from contiguous epochs. A specific postprocessing procedure was developed to enhance the vigilance state discrimination of the neural networks designed and especially paradoxical sleep state estimation. The classifications made by the networks (with or without the postprocessing procedure) for six rats were compared to these made by two human experts using EMG and EEG informations on 63,000 epochs. High rates of agreement (> 90%) between humans and neural networks classifications were obtained. In view of its development possibilities and its applicability to other signals, this method could prove of value in biomedical research.

Variation of the ultrasonic response of a dental implant embedded in tricalcium silicate-based cement under cyclic loading.

The use of tricalcium silicate-based cement (TSBC) as bone substitute material for implant stabilization is promising. However, its mechanical behavior under fatigue loading in presence of a dental implant was not reported so far because of the difficulty of measuring TSBC properties around a dental implant in a nondestructive manner. The aim of this study is to investigate the evolution of the 10 MHz ultrasonic response of a dental implant embedded in TSBC versus fatigue time. Seven implants were embedded in TSBC following the same experimental protocol used in clinical situations. One implant was left without any mechanical solicitation after its insertion in TSBC. The ultrasonic response of all implants was measured during 24 h using a dedicated device deriving from previous studies. An indicator I based on the temporal variation of the signal amplitude was derived and its variation as a function of fatigue time was determined. The results show no significant variation of I as a function of time without mechanical solicitation, while the indicator significantly increases (p<10(-5), F=199.1) at an average rate of 2.2 h(-1) as a function of fatigue time. The increase of the indicator may be due to the degradation of the Biodentine-implant interface, which induces an increase of the impedance gap at the implant surface. The results are promising because they show the potentiality of ultrasonic methods to (i) investigate the material properties around a dental implant and (ii) optimize the conception of bone substitute materials in the context of dental implant surgery.

Monitoring anesthesia using neural networks: a survey.

New methods of data processing combined with advances in computer technology have revolutionized monitoring of patients under anesthesia. The development of systems based on analysis of brain electrical activity (EEG or evoked potentials) by neural networks has provided impetus to many investigators. Though not claiming to be the end-all in patient monitoring, the potential and efficiency of the combination does indeed stand out. Various strategies are presented and discussed, as well as suggestions for further investigation.

Computer-assisted ABR Interpretation using the Automatic Construction of the Latency-Intensity Curve: Interpretatión asistida por computadora del ABR utilizando la Constructión Automática de la Curva Latencia-Intensidad

In this paper, we present a new computerised technique for the automatic construction of the latency-intensity curve (LI curve). We take a pattern recognition approach determined by a priori information. We use knowledge gained from the audiogram and from physiological considerations. Therefore, we consider all recordings at different intensities as well as results from the extraction of a single auditory brainstem response (ABR) at a given stimulus intensity. We tested our method successfully: it allows us to prevent misrecognition errors in response detection or in latency measurements. Automatic recognition of the waves and recognition by the ear, nose and throat (ENT) specialist coincided in at least 90 per cent of cases. For wave V, the average deviation between the response thresholds given by our automatic recognition algorithm and those given by the ENT specialist was 5 dB, and the average deviation of the latencies was 0.05 ms. En este trabajo, presentamos una nueva técnica computarizada para la constructión automática de la curva de latencia-intensidad (Curva LI). Tomamos un enfoque de reconocimiento de patrones, supervisado por informatión a priori, y hacemos referenda al conocimiento obtenido del audiograma y también a otras consideraciones fisiológicas. Por lo tanto, consideramos todos los registros a difcrentes intensidades, al igual que los resultados dc la extractión de una respuesta auditiva del tallo cerebral (ABR) única, a una intensidad dada del estímulo. Evaluamos con éxito nuestro método, lo que nos permite evitar errores de reconocimiento en la identificatión de respuestas o en las medidas de latencia. El reconocimiento automático de las ondas y el reconocimiento por parte del especialista en oídos, nariz y garganta (ORL) coincidieron en el 90 por ciento de los casos. Para la onda V, la desviación promedio entre el umbral de respuesta dado por nuestro algoritmo de reconocimiento automático y la brindada por el especialista en ORL fue dc 5 dB, y la desviacion promedio de las latencias fue de 0.05 ms.

ECG modelling using wavelet networks: application to biometrics

This paper deals with human identification using normal ECGs. Precisely, we would like to highlight how one can achieve human identification by considering only the most significant parameters extracted from a model. In this work, parameters are extracted by modelling the ECG using wavelet networks. The radial basis neural network method is then used to classify these parameters. Thus, a useful analysis is performed to evaluate the robustness of the identification. For each recording condition, the proposed technique has been evaluated on a set of ECG signals corresponding to normal subjects. Consequently, very encouraging results have been obtained.

Ultrasonic assessment of the in vitro biomechanical stability of a dental implant

Dental implants are widely used for oral rehabilitation. However, there remain risks of failure which are difficult to anticipate. The objective is to investigate the potentiality of a quantitative ultrasound method to assess the biomechanical stability of a dental implant in vitro. Two experimental configurations were considered using a 10 MHz contact transducer located at the implant extremity. For each ultrasound measurement, a quantitative indicator I is derived based on the time variation of the amplitude of the rf signal. Firstly, seven implants were embedded in tricalcium silicate-based cement. One implant was left without any mechanical solicitation and six implants were subjected to mechanical stresses during 24 hours. The ultrasonic response of each implant was measured during 24 hours. The results show no variation of I without mechanical solicitation, while I significantly increases as a function of fatigue time. Secondly, ten implants were unscrewed from bone tissue and their ultrasonic respo...

Biosignals: Acquisition and General Properties

The aim of this chapter is to provide the reader with some basic and general information related to the most common biosignals, in particular biopotentials, encountered in clinical routines. For this purpose, the chapter will be divided into two main sections. In Sect. 1.1, we will consider the basis of bipotential recording (i.e., electrodes, artifact rejection and safety). In the second section, some general properties of a set of biosignals, will be introduced briefly. This will concern essentially, ECG, EEG, EPs and EMG. This restriction is required to ensure an appropriate coherency over the subsequent chapters which will deal primarily with these signals.