Using EEG measures to quantify reduced daytime vigilance in patients diagnosed with obstructive sleep apnoea using a novel electroencephalogram analysis method

Introduction Vigilance in obstructive sleep apnoea (OSA) does not correlate well with disease severity/ symptoms: Hence the need for a simple objective test. One such method could be quantitative analysis of the awake electroencephalogram (qEEG). qEEG is conventionally analysed using Power Spectral Analysis (PSA) looking at different EEG frequencies of delta, theta, alpha and beta. A novel method of analyzing the qEEG: De-trended fluctuation analysis (DFA) provides a single value: the scaling exponent (SE), which measures the fluctuations in the EEG signal. Artefact removal from qEEG is mandatory with the gold standard being manual scoring. Another method of automated artefact removal is independent component analysis (ICA). Objective Investigate the role of PSA and DFA (SE) as an objective measure of testing vigilance and validate the use of ICA in patients diagnosed with OSA. Methodology Retrospective cross-sectional study of untreated OSA patients. Results ICA and manual artefact removal gave well-correlated results in the DFA (SE), but not PSA. EEG slowing measured by PSA and DFA did not correlate to impaired performance during a battery of 14 separate performance tests. Conclusion ICA and manual artefact removal can be interchangeably used in extracting DFA measurements with confidence. In PSA metrics the use of ICA may not be reliable

Using EEG measures to quantify reduced daytime vigilance in patients diagnosed with obstructive sleep apnoea using a novel electroencephalogram analysis method

Introduction Vigilance in obstructive sleep apnoea (OSA) does not correlate well with disease severity/ symptoms: Hence the need for a simple objective test. One such method could be quantitative analysis of the awake electroencephalogram (qEEG). qEEG is conventionally analysed using Power Spectral Analysis (PSA) looking at different EEG frequencies of delta, theta, alpha and beta. A novel method of analyzing the qEEG: De-trended fluctuation analysis (DFA) provides a single value: the scaling exponent (SE), which measures the fluctuations in the EEG signal. Artefact removal from qEEG is mandatory with the gold standard being manual scoring. Another method of automated artefact removal is independent component analysis (ICA). Objective Investigate the role of PSA and DFA (SE) as an objective measure of testing vigilance and validate the use of ICA in patients diagnosed with OSA. Methodology Retrospective cross-sectional study of untreated OSA patients. Results ICA and manual artefact removal gave well-correlated results in the DFA (SE), but not PSA. EEG slowing measured by PSA and DFA did not correlate to impaired performance during a battery of 14 separate performance tests. Conclusion ICA and manual artefact removal can be interchangeably used in extracting DFA measurements with confidence. In PSA metrics the use of ICA may not be reliable

Sleep duration and mood

It is widely believed that sleep and mood are interrelated and that prolongation of sleep may have beneficial effects on subsequent mood and general well-being. In the present investigation, it is hypothesised that excess sleep is in fact, detrimental to mood and is associated with a 'Wornout Syndrome', characterised by feelings of fatigue and lethargy, that can persist for up to 5 hours. The studies to be presented here compare the differential effects of Sleep Extension and Sleep Restriction on mood in healthy adults. The experimental design required subjects to undergo one night of Sleep Extension [+2h] and, following an interval of one-week, one night of Sleep Reduction [-2h]. The conditions were counterbalanced. Subjective assessments were conducted hourly on mood states and sleepiness using an adapted Profile of Mood States Questionnaire and the Stanford Sleepiness Scale. Actometers were worn throughout the experimental days and nights. In the first study of 10 subjects results indicated that four subjects were adversely affected by oversleep. Study 2 investigated the effects of sleep duration on mood in 20 healthy adults. Personality factors were assessed using Cattell's 16PF Questionnaire. Subjects maintaining regular sleep schedules reported negative effects of oversleep on subsequent mood. Results indicated that certain personality types were predisposed to the 'Wornout Syndrome' following Sleep Extension. In Study 3, thirty-four subjects were selected on the basis of personality type. It was hypothesised that Introverts, Morning types, Emotionally Tenderminded and Low Impulsives would report symptoms characteristic of the 'Wornout Syndrome' following one night of Sleep Extension. This was confirmed by reports of increased fatigue, diminished vigor, and increased confusion following Sleep Extension. Oversleeping produced greater detrimental effects on mood than a comparable reduction in sleep duration. There are many similarities in symptomatology between the 'Wornout Syndrome' and Chronic Fatigue Syndrome (CFS), specifically, intense fatigue and impaired concentration. Interestingly, chronically fatigued patients often complain of sleep disturbance, and spend much of their time resting in bed. It was hypothesised that the 'Wornout Syndrome' may be a confounding factor in the symptomatology of CFS. As a clinical dimension, twelve subjects were investigated polysomnographically [six were CFS patients]. Findings indicated that CFS patients acquired sleep of longer duration than controls. In addition to excess nocturnal sleep, CFS patients were taking daytime naps. EEG data indicated that these individuals obtained twice the normal amount of slow wave sleep. CFS sufferers may be better advised to regulate their sleep habits and reduce their total sleep time to avoid the confounding effects of the 'Wornout Syndrome'

EXPERIMENTAL-COMPUTATIONAL ANALYSIS OF VIGILANCE DYNAMICS FOR APPLICATIONS IN SLEEP AND EPILEPSY

Epilepsy is a neurological disorder characterized by recurrent seizures. Sleep problems can cooccur with epilepsy, and adversely affect seizure diagnosis and treatment. In fact, the relationship between sleep and seizures in individuals with epilepsy is a complex one. Seizures disturb sleep and sleep deprivation aggravates seizures. Antiepileptic drugs may also impair sleep quality at the cost of controlling seizures. In general, particular vigilance states may inhibit or facilitate seizure generation, and changes in vigilance state can affect the predictability of seizures. A clear understanding of sleep-seizure interactions will therefore benefit epilepsy care providers and improve quality of life in patients. Notable progress in neuroscience research—and particularly sleep and epilepsy—has been achieved through experimentation on animals. Experimental models of epilepsy provide us with the opportunity to explore or even manipulate the sleep-seizure relationship in order to decipher different aspects of their interactions. Important in this process is the development of techniques for modeling and tracking sleep dynamics using electrophysiological measurements. In this dissertation experimental and computational approaches are proposed for modeling vigilance dynamics and their utility demonstrated in nonepileptic control mice. The general framework of hidden Markov models is used to automatically model and track sleep state and dynamics from electrophysiological as well as novel motion measurements. In addition, a closed-loop sensory stimulation technique is proposed that, in conjunction with this model, provides the means to concurrently track and modulate 3 vigilance dynamics in animals. The feasibility of the proposed techniques for modeling and altering sleep are demonstrated for experimental applications related to epilepsy. Finally, preliminary data from a mouse model of temporal lobe epilepsy are employed to suggest applications of these techniques and directions for future research. The methodologies developed here have clear implications the design of intelligent neuromodulation strategies for clinical epilepsy therapy

Eating, sleeping and body maintenance

CHAPTER 1: A theory is put forward in which change in the level of cellular energy charge in response to the differing energy demands of the sleeping/waking rhythm is the fundamental reason why sleep is associated with restorative processes.Numerous reports from the literature are presented in which the time of sleep is associated with a higher rate of synthesis.CHAPTER 2: The literature is surveyed relating eating, sleeping and body maintenance.Hunger is associated with motor restlessness and feeding with sedation.Human studies indicate that a bedtime snack of milk and cereal promotes sleep.Losing weight leads to a reduction whereas gaining weight leads to an increased amount of sleep.CHAPTERS 3 and 4: A milk and cereal food (Horlicks) had no effect on sleep, whereas nitrazepam 5mg improved sleep. Withdrawal from the drug led to disrupted sleepA placebo pill had no effect on sleep.Nitrazepam had no significant effect on the plasma growth hormone, glucose, triglycerides or cholesterol, but prolonged Horlicks administration elevated triglyceride levels.CHAPTERS 5 to 7: The effects on sleep were compared among a placebo capsule, milk, Horlicks and a drink nutritionally equivalent to Horlicks but containing no milk or cereal. None of the three food drinks had any significant effect on sleep when compared with the inert capsule, but after Horlicks at bedtime sleep was less broken than after the other two food drinks.The dietary habits of subjects were found to have a considerable influence on how they slept after food at bedtime.CHAPTERS 8 and 9: Correlational analysis revealed that body weight, but not I.Q. was highly correlated with the mean amount of REM sleep.CHAPTER 10: It was also found that the mean sleep cycle length correlated with the degree of over or under-weight.CHAPTER 11: I attempt to answer questions raised by the findings described in earlier chapters. I tentatively propose a theory linking the maintenance of body weight, sleep cycle length and amounts of REM slee

Development of nonlinear techniques based on time-frequency representation and information theory for the analysis of EEG signals to assess different states of consciousness

Electroencephalogram (EEG) recordings provide insight into the changes in brain activity associated with various states of anesthesia, epilepsy, brain attentiveness, sleep disorders, brain disorders, etc. EEG's are complex signals whose statistical properties depend on both space and time. Their randomness and non-stationary characteristics make them impossible to be described in an accurate way with a simple technique, requiring analysis and characterization involves techniques that take into account their non-stationarity. For that, new advanced techniques in order to improve the efficiency of the EEG based methods used in the clinical practice have to be developed. The main objective of this thesis was to investigate and implement different methods based on nonlinear techniques in order to develop indexes able to characterize the frequency spectrum, the nonlinear dynamics and the complexity of the EEG signals recorded in different state of consciousness. Firstly, a new method for removing peak and spike in biological signal based on the signal envelope was successfully designed and applied to simulated and real EEG signals, obtaining performances significantly better than the traditional adaptive filters. Then, several studies were carried out in order to extract and evaluate EEG measures based on nonlinear techniques in different contexts such as the automatic detection of sleepiness and the characterization and prediction of the nociceptive stimuli and the assessment of the sedation level. Four novel indexes were defined by calculating entropy of the Choi-Williams distribution (CWD) with respect to time or frequency, by using the probability mass function at each time instant taken independently or by using the probability mass function of the entire CWD. The values of these indexes tend to decrease, with different proportion, when the behavior of the signals evolved from chaos or randomness to periodicity and present differences when comparing EEG recorded in eyes-open and eyes-closed states and in ictal and non-ictal states. Measures obtained with time-frequency representation, mutual information function and correntropy, were applied to EEG signals for the automatic sleepiness detection in patients suffering sleep disorders. The group of patients with excessive daytime sleepiness presented more power in ¿ band than the group without sleepiness, which presented higher spectral and cross-spectral entropy in the frontal zone in d band. More complexity in the occipital zone was found in the group of patients without sleepiness in ß band, while a stronger nonlinear coupling between the occipital and frontal regions was detected in patients with excessive daytime sleepiness, in ß band. Time-frequency representation and non-linear measures were also used in order to study how adaptation and fatigue affect the event-related brain potentials to stimuli of different modalities. Differences between the responses to infrequent and frequent stimulation in different recording periods were found in series of averaged EEG epochs recorded after thermal, electrical and auditory stimulation. Nonlinear measures calculated on EEG filtered in the traditional frequency bands and in higher frequency bands improved the assessment of the sedation level. These measures were obtained by applying all the developed techniques on signals recorded from patients sedated, in order to predict the responses to pain stimulation such as nail bad compression and endoscopy tube insertion. The proposed measures exhibit better performances than the bispectral index (BIS), a traditional indexes used for hypnosis assessment. In conclusion, nonlinear measures based on time-frequency representation, mutual information functions and correntropy provided additional information that helped to improve the automatic sleepiness detection, the characterization and prediction of the nociceptive responses and thus the assessment of the sedation level.El registro de la señal Electroencefalografíca (EEG) proporciona información sobre los cambios en la actividad cerebral asociados con varios estados de la anestesia, la epilepsia, la atención cerebral, los trastornos del sueño, los trastornos cerebrales, etc. Los EEG son señales complejas cuyas propiedades estadísticas dependen del espacio y del tiempo. Sus características aleatorias y no estacionarias hacen imposible que el EEG se describa de forma precisa con una técnica sencilla requiriendo un análisis y una caracterización que implica técnicas que tengan en cuenta su no estacionariedad. Todo esto aumenta la necesidad de desarrollar nuevas técnicas avanzadas con el fin de mejorar la eficiencia de los métodos utilizados en la práctica clínica que son basados en el análisis de EEG. En esta tesis se han investigado y aplicado diferentes métodos utilizando técnicas no lineales con el fin de desarrollar índices capaces de caracterizar el espectro de frecuencias, la dinámica no lineal y la complejidad de las señales EEG registradas en diferentes estados de conciencia. En primer lugar, se ha desarrollado un nuevo algoritmo basado en la envolvente de la señal para la eliminación de ruido de picos en las señales biológicas. Este algoritmo ha sido aplicado a señales simuladas y reales obteniendo resultados significativamente mejores comparados con los filtros adaptativos tradicionales. Seguidamente, se han llevado a cabo varios estudios con el fin de extraer y evaluar las medidas de EEG basadas en técnicas no lineales en diferentes contextos. Se han definido nuevos índices mediante el cálculo de la entropía de la distribución de Choi-Williams (DCW) con respecto al tiempo o la frecuencia. Se ha observado que los valores de estos índices tienden a disminuir, en diferentes proporciones, cuando el comportamiento de las señales evoluciona de caótico o aleatorio a periódico. Además, se han encontrado valores diferentes de estos índices aplicados a la señal EEG registrada en diferentes estados. Diferentes medidas basadas en la representación tiempo-frecuencia, la función de información mutua y la correntropia se han aplicado al EEG para la detección automática de la somnolencia en pacientes que sufren trastornos del sueño. Se ha observado en la zona frontal que la potencia en la banda θ es mayor en los pacientes con somnolencia diurna excesiva, mientras que la entropía espectral y la entropía espectral cruzada en la banda δ es mayor en los pacientes sin somnolencia. En el grupo sin somnolencia se ha encontrado más complejidad en la zona occipital, mientras que el acoplamiento no lineal entre las regiones occipital y frontal ha resultado más fuerte en pacientes con somnolencia diurna excesiva, en la banda β. La representación tiempo-frecuencia y las medidas no lineales se han utilizado para estudiar cómo la adaptación y la fatiga afectan a los potenciales cerebrales relacionados con estímulos térmicos, eléctricos y auditivos. Analizando el promedio de varias épocas de EEG grabadas después de la estimulación, se han encontrado diferencias entre las respuestas a la estimulación frecuente e infrecuente en diferentes períodos de registro. Todas las técnicas que se han desarrollado, se han aplicado a señales EEG registradas en pacientes sedados, con el fin de predecir las respuestas a la estimulación del dolor. Un conjunto de medidas calculadas en señales EEG filtradas en diferentes bandas de frecuencia ha permitido mejorar la evaluación del nivel de sedación. Las medidas propuestas han presentado un mejor rendimiento comparado con el índice bispectral, un indicador de hipnosis tradicional. En conclusión, las medidas no lineales basadas en la representación tiempofrecuencia, funciones de información mutua y correntropia han proporcionado informaciones adicionales que contribuyeron a mejorar la detección automática de la somnolencia, la caracterización y predicción de las respuestas nociceptivas y por lo tanto la evaluación del nivel de sedación

Frontal lobe epilepsy, sleep and parasomnias.

A close relationship exists between sleep and epilepsy. While many forms of epilepsy may be influenced by the sleep-wake cycle, this phenomenon is particularly evident in frontal lobe epilepsy where affected individuals may experience seizures exclusively during sleep (nocturnal frontal lobe epilepsy, NFLE). In this thesis, three aspects of the relationship between sleep and frontal lobe epilepsy are examined. Firstly, serotonergic neurotransmission across the human sleep-wake cycle was studied using the novel PET ligand l8F-MPPF, a serotonergic 5HT)A receptor radioligand sensitive to endogenous serotonin release. Fourteen individuals with narcolepsy underwent 18F-MPPF PET scans during sleep and wakefulness. The study demonstrated a 13% increase in 18F-MPPF binding potential (p<0.01) during sleep, indicating a reduction in serotoninergic neurotransmission, in line with existing animal data. Secondly, the characterisation of benign, non-epileptic parasomnias and their distinction from nocturnal frontal lobe seizures was addressed in two studies. The first comprised an analysis of the historical features of these conditions, and included the development and validation of a clinical scale for the diagnosis of nocturnal events. The second comprised a detailed semiological analysis of a series of parasomnias recorded on video-EEG monitoring, and a statistical comparison with seizures in NFLE. Although similarities between NFLE and parasomnias were observed, the results provide an evidence base for the confident distinction of these disorders. Finally, the familial form of NFLE (autosomal dominant nocturnal frontal lobe epilepsy, ADNFLE) is associated with mutations in genes for nicotinic acetylcholine receptor subunits, but recognised mutations account for only a minority of reported cases. The last study presented here is a clinical and genetic analysis of two large families with an unusually severe ADNFLE phenotype. Affected individuals had refractory epilepsy and increased rates of mental retardation and psychiatric disorders and, in one family, linkage studies suggest a previously unrecognised underlying mechanism

Characteristic time scales of electroencephalograms of narcoleptic patients and healthy controls

Sleep electroencephalograms (EEGs) typically showed correlated fluctuations that became random-like oscillations beyond a characteristic time scale. To investigate this behavior quantitatively, the detrended fluctuation analysis (DFA) was applied to EEGs of 10 narcoleptic patients (22.0 +/- 4.0 yrs; 6 males) and 8 healthy controls (24.0 +/- 2.0 yrs: 5 males). The characteristic time scales of the narcoleptics and controls were estimated as 1.8 +/- 0.7 and 4.4 +/- 1.2 s. respectively (significance level, p < 0.01). We further performed DFA of the EEGs segmented into 30s epochs and found that the DFA scaling exponents increased in deep sleep stages. These results were verified with power spectrum and auto-correlation analysis, and reproduced by a mathematical model. We thus concluded that characteristics of EEGs of narcoleptic patients could be differentiated from those of healthy subjects, suggesting a potential application of DFA in diagnosing narcolepsy. (C) 2010 Elsevier Ltd. All rights reserved.IBER C, 2007, AASM MANUAL SCORINGNishino S, 2005, SLEEP MED REV, V9, P269, DOI 10.1016/j.smrv.2005.03.004*AM AC SLEEP MED, 2005, ICSD 2 INT CLASS SLEKANTZ H, 2004, NONLINEAR TIME SERIE, P100Kantelhardt JW, 2002, PHYSICA A, V316, P87Hwa RC, 2002, PHYS REV E, V66, DOI 10.1103/PhysRevE.66.021901Ohayon MM, 2002, NEUROLOGY, V58, P1826Wing YK, 2002, ANN NEUROL, V51, P578, DOI 10.1002/ana.10162Lee JM, 2002, COMPUT BIOL MED, V32, P37Linkenkaer-Hansen K, 2001, J NEUROSCI, V21, P1370NIEDERMEYER E, 1999, ELECTROENCEPHALOGRAPNUNEZ PL, 1995, NEOCORTICAL DYNAMICSPENG CK, 1993, PHYS REV E, V47, P3730DIEKHOFF G, 1992, STAT SOCIAL BEHAV SCSTERIADE M, 1991, J NEUROSCI, V11, P3200RECHTSCHAFFEN A, 1968, MANUAL STANDARD TERMMANN HB, 1947, ANN MATH STAT, V18, P50

The study of some biological factors of wakefulness and their influence upon sleep

The thesis discusses the significance of some aspects of wakefulness, namely exercise and diet, upon human sleep using the electroencephalogram (EEG). Part One of this thesis examines the relationship between exercise and sleep. An initial literature review produced equivocal findings, although it appeared that several factors might be involved in this relationship such as the time of day of exercising, the fitness of the subject and the amount of stress associated with the exercise prescribed. [Continues.

Diagnosis of the sleep apnea-hypopnea syndrome : a comprehensive approach through an intelligent system to support medical decision

[Abstract] This doctoral thesis carries out the development of an intelligent system to support medical decision in the diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS). SAHS is the most common disorder within those affecting sleep. The estimates of the disease prevalence range from 3% to 7%. Diagnosis of SAHS requires of a polysomnographic test (PSG) to be done in the Sleep Unit of a medical center. Manual scoring of the resulting recording entails too much effort and time to the medical specialists and as a consequence it implies a high economic cost. In the developed system, automatic analysis of the PSG is accomplished which follows a comprehensive perspective. Firstly an analysis of the neurophysiological signals related to the sleep function is carried out in order to obtain the hypnogram. Then, an analysis is performed over the respiratory signals which have to be subsequently interpreted in the context of the remaining signals included in the PSG. In order to carry out such a task, the developed system is supported by the use of artificial intelligence techniques, specially focusing on the use of reasoning mechanisms capable of handling data imprecision. Ultimately, it is the aim of the proposed system to improve the diagnostic procedure and help physicians in the diagnosis of SAHS.[Resumen] Esta tesis aborda el desarrollo de un sistema inteligente de apoyo a la decisión clínica para el diagnóstico del Síndrome de Apneas-Hipopneas del Sueño (SAHS). El SAHS es el trastorno más común de aquellos que afectan al sueño. Afecta a un rango del 3% al 7% de la población con consecuencias severas sobre la salud. El diagnóstico requiere la realización de un análisis polisomnográfico (PSG) en una Unidad del Sueño de un centro hospitalario. El análisis manual de dicha prueba resulta muy costoso en tiempo y esfuerzo para el médico especialista, y como consecuencia en un elevado coste económico. El sistema desarrollado lleva a cabo el análisis automático del PSG desde una perspectiva integral. A tal efecto, primero se realiza un análisis de las señales neurofisiológicas vinculadas al sueño para obtener el hipnograma, y seguidamente, se lleva a cabo un análisis neumológico de las señales respiratorias interpretándolas en el contexto que marcan las demás señales del PSG. Para lleva a cabo dicha tarea el sistema se apoya en el uso de distintas técnicas de inteligencia artificial, con especial atención al uso mecanismos de razonamiento con soporte a la imprecisión. El principal objetivo del sistema propuesto es la mejora del procedimiento diagnóstico y ayudar a los médicos en diagnóstico del SAHS.[Resumo] Esta tese aborda o desenvolvemento dun sistema intelixente de apoio á decisión clínica para o diagnóstico do Síndrome de Apneas-Hipopneas do Sono (SAHS). O SAHS é o trastorno máis común daqueles que afectan ao sono. Afecta a un rango do 3% ao 7% da poboación con consecuencias severas sobre a saúde. O diagnóstico pasa pola realización dunha análise polisomnográfica (PSG) nunha Unidade do Sono dun centro hospitalario. A análise manual da devandita proba resulta moi custosa en tempo e esforzo para o médico especialista, e como consecuencia nun elevado custo económico. O sistema desenvolvido leva a cabo a análise automática do PSG dende unha perspectiva integral. A tal efecto, primeiro realizase unha análise dos sinais neurofisiolóxicos vinculados ao sono para obter o hipnograma, e seguidamente, lévase a cabo unha análise neumolóxica dos sinais respiratorios interpretándoos no contexto que marcan os demais sinais do PSG. Para leva a cabo esta tarefa o sistema apoiarase no uso de distintas técnicas de intelixencia artificial, con especial atención a mecanismos de razoamento con soporte para a imprecisión. O principal obxectivo do sistema proposto é a mellora do procedemento diagnóstico e axudar aos médicos no diagnóstico do SAHS