Thesis (doctoral)--Ludwig Maximilians-Universität zu München, 1979.

The purpose of this study was to test the reliability and validity of the Verran and Snyder-Halpern (VSH) Sleep Scale on patients hospitalized in a coronary care unit (CCU) and to investigate the sleep characteristics of patients hospitalized in the CCU setting. Eighteen subjects aged 43 to 78 completed 30 nights of study using the VSH Sleep Scale. Results were compared to means from groups of healthy subjects and subjects hospitalized on general medical-surgical units. The VSH Sleep Scale demonstrated reliability in this group of CCU subjects. Factor analysis showed loadings on four factors rather than the theoretical three factors probably due to small sample size. The mean scores for this sample of CCU patients indicated their nighttime sleep was disturbed and ineffective. Significantly different sleep characteristics were demonstrated between CCU and healthy subjects. Comparisons between CCU and general medical-surgical subjects evidenced no differences in sleep characteristics.

Pontomesencephalic tegmental cholinergic neurons were destroyed in cats by local injections of kainic acid in order to assess the role of these neurons in sleep-wakefulness states and in the defining variables of these states: EEG (electroencephalographic) and EMG (electromyographic) amplitude, PGO (ponto-geniculo-occipital) spike rate, REMs (rapid eye movements) and (OBS) olfactory bulb spindles. Loss of cholinergic innervation to forebrain and brainstem structures was also assessed by histochemistry. Histological and histochemical analysis of the brains after the lesion showed a major destruction of the pontomesencephalic cholinergic neurons and a major loss of innervation to thalamic nuclei and brainstem regions, including the reticular formation. Whereas the states of waking and slow wave sleep were relatively unaffected, paradoxical sleep (PS) was reduced or eliminated immediately following the lesions. Two to three weeks later, incipient PS-like episodes returned with a reduced PGO spike rate and REMs, and an elevated EMG amplitude, marking the loss of muscle atonia. Such results suggest pontomesencephalic cholinergic neurons and their projections to thalamic and brainstem regions are important for the expression of PS and its defining variables.

No description available.

<p> The study of the different sleep stages of a patient using his/her recorded EEG signals falls in the area of signal classification. In general, this involves extracting from the EEG signals, a signal feature on which the classification is performed. In this thesis, we apply the techniques of signal classification to the analysis of the sleep of a patient. The feature we use is the power spectral density (PSD) matrices of a multi-channel EEG signal. This not only allows us to examine the power spectrum contents of each signal which complies with what clinical experts use in their visual judgement of EEG signals, but also allows the correlation between the multi-channel signals to be studied. To establish a metric facilitating the classification, we analyze the structure as well as exploit the specific geometric properties of the space of PSD matrices. Specifically, we study this space from the viewpoint of Riemannian manifolds. We apply a Riemannian metric and, with the aid of fibre bundle theory, develop intrinsic (geodesic) distance measures for the PSD matrix manifold. To utilize such new distance measures effectively for EEG signal classification, we need to find a suitable weighting matrix for the PSD matrices so that the distances between similar features are minimized while those between dissimilar features are maximized. A closed form expression for this weighting matrix is obtained by solving an equivalent convex optimization problem. The effectiveness of using these novel weighted distance measures is verified by applying them to the sleep pattern classification of a collection of recorded EEG signals using the k-nearest neighbor decision algorithm with excellent results. </p> / Thesis / Doctor of Philosophy (PhD)

Diss. (sammanfattning) Stockholm : Karol. inst., 2002. / Härtill 5 uppsatser.

Researchers have struggled with the problem of obtaining an "accurate" operational definition of drowsiness. Drowsiness is difficult to define because it may involve many different indicators, such as different physiological measures. This thesis consists of two separate, but related, experiments to determine an optimal method of determining whether or not an individual is drowsy via physiological and observed measures. The first part of the experiment used behaviorally trained observers to rate different subjects on the level of drowsiness observed. The data collected showed that trained raters are relatively consistent when rating drowsiness. The second part of the experiment tried to determine if there is a good physiological model to predict performance impairment due to drowsiness by collecting data on sleep deprived subjects. The subjects were given two interleaved tasks, low level and high level cognitive tasks, to perform while twenty-one performance and behavioral measures were collected. The results show that a regression model can be developed using eyelid closure measures, simple EEG measures and simple heart-rate measures to predict performance impairment due to drowsiness. / Master of Science

The aging brain is characterized by alteration in synaptic contacts, which leads to decline of motor and cognitive functions. These changes are reflected in the age related shifts in power spectrum of electroencephalogram (EEG) signals in both wakefulness and sleep. Various non-linear measures have been used to obtain more insights from EEG analysis compared to the conventional spectral analysis. In our study we used Sample Entropy to quantify regularity of the EEG signal. Because elderly subjects arouse from sleep more often than younger subjects, we hypothesized that Entropy of EEG signals from elderly subjects would be higher than that from middle aged subjects, within a sleep stage. We also hypothesized that the entropy increases during and following an arousal and does not return to background levels immediately after an arousal. Our results show that Sample Entropy varies systematically with sleep state in healthy middle-aged and elderly female subjects, reflecting the changing regularity in the EEG. Sample Entropy is significantly higher in elderly in sleep Stage 2 and REM, suggesting that in these two sleep stages the cortical state is closer to wake than in middle-aged women. Sample Entropy is higher in post-arousal compared to the pre-arousal and stays high for a 30 sec period.

A DoenÃa Pulmonar Obstrutiva CrÃnica (DPOC) à uma condiÃÃo frequente no adulto, definida por obstruÃÃo crÃnica ao fluxo aÃreo, nÃo totalmente reversÃvel, e ocorre secundariamente a uma resposta inflamatÃria anormal dos pulmÃes à inalaÃÃo de partÃculas e gases tÃxicos, os quais sÃo oriundos primariamente do tabagismo. AlÃm dos sintomas pulmonares, a DPOC pode acompanhar-se de significativas manifestaÃÃes sistÃmicas, dentre as quais se destacam as alteraÃÃes do sono, aspecto importante, porÃm frequentemente negligenciado, tanto na prÃtica clÃnica quanto em estudos do impacto da doenÃa sobre a qualidade de vida desses pacientes. A SÃndrome da Apneia Obstrutiva do Sono (SAOS), caracteriza-se por pausas respiratÃrias repetitivas e à secundÃria ao colapso completo da via aÃrea superior durante o sono. Sua prevalÃncia tambÃm à elevada, de modo que as duas condiÃÃes clÃnicas, tanto a DPOC como a SAOS, podem acometer de forma simultÃnea e comprometer grande nÃmero de indivÃduos. A combinaÃÃo dos dois processos mÃrbidos, comumente denominada sÃndrome mista, apresenta importantes implicaÃÃes diagnÃsticas, terapÃuticas e prognÃsticas, que ainda nÃo foram suficientemente investigadas. Com o objetivo de avaliar, de forma comparativa, a qualidade do sono, sonolÃncia diurna e fadiga em pacientes com DPOC com e sem apneia obstrutiva do sono foram estudados consecutivamente 39 pacientes (27 homens; entre 53 e 81 anos com idade mÃdia+DP = 67,9Â7, 24 anos; IMC entre 18,83 e 41,41 igual a (26,3Â4,97 Kg/mÂ.) com diagnÃstico prÃvio de DPOC, clinicamente estÃveis, regularmente acompanhados em hospital terciÃrio da rede pÃblica de saÃde de Fortaleza. Todos os participantes realizaram estudo de sono tipo III (StardustÂ, Respironics Inc., USA), que à um exame multiparametrico realizado em domicÃlio composto por quatro canais: fluxo aÃreo oronasal, movimento respiratÃrio, registro de frequÃncia cardÃaca e saturaÃÃo da oxihemoglobina. Os indivÃduos que apresentaram Ãndice de apneia e hipopneia (IAH) > 15 foram classificados como portadores de apneia. A funÃÃo pulmonar foi avaliada por espirometria; a capacidade funcional respiratÃria mensurada pelo teste da caminhada de 6 minutos (TC6M); o grau de dispneia pela escala do Medical Research Council (MRC); a qualidade de vida, pelo Saint Georgeâs Respiratory Questionnaire (SGRQ); a qualidade de sono, pelo Ãndice de Qualidade de Sono de Pittsburgh (IQSP); o grau de sonolÃncia diurna, pela Escala de SonolÃncia de Epworth (ESE); a fadiga, pela Escala de Gravidade de Fadiga (EGF) e os sintomas depressivos pelo InventÃrio de DepressÃo de Beck (IDB). Mà qualidade do sono (IQSP>5) foi observada em 29 (74,4%); sonolÃncia excessiva diurna (ESE > 10) em 24(61,5%) e fadiga (EGF > 28) em 28 (71,8%). Sintomas depressivos foram observados em 20 (51,3%) pacientes; Dos 17 (43,6%) pacientes que apresentaram sÃndrome das pernas inquietas (SPI),11 (28,2%) foram moderados e 4 (10,3%) grave. O grupo com sÃndrome mista apresentou IMC, perimetria cervical e circunferÃncia abdominal mais elevados, pior qualidade do sono e mais sonolÃncia excessiva diurna e fadiga. Em conclusÃo nossos resultados indicam que a alta frequÃncia de sono de mà qualidade e sonolÃncia diurna nos pacientes com DPOC indicam a importÃncia da identificaÃÃo da SAOS para o tratamento da DPOC de forma a permitir uma abordagem mais adequada e eficiente dos portadores da sÃndrome mista.

Aim of this thesis is the classification of polysomnographic data. The first part of the thesis is a review of mentioned topic and also the statistical analysis of classification features calculated from real EEG, EOG and EMG for evaluating of the features suitability for sleep stages scoring. The second part is focused on the automatic classification of the data using artificial neural networks. All the results are presented and discussed.