Post-Stroke identification of EEG signals using recurrent neural networks and long short-term memory

Stroke often causes disability, so patients need rehabilitation for recovery. Therefore, it is necessary to measure its effectiveness. An Electroencephalogram (EEG) can capture the improvement of activity in the brain in stroke rehabilitation. Therefore, the focus is on the identification of several post-rehabilitation conditions. This paper proposed identifying post-stroke EEG signals using Recurrent Neural Networks (RNN) to process sequential data. Memory control in the use of RNN adopted Long Short-Term Memory. Identification was provided out on two classes based on patient condition, particularly "No Stroke" and "Stroke". EEG signals are filtered using Wavelet to get the waves that characterize a stroke. The four waves and the average amplitude are features of the identification model. The experiment also varied the weight correction, i.e., Adaptive Moment Optimization (Adam) and Stochastic Gradient Descent (SGD). This research showed the highest accuracy using Wavelet without amplitude features of 94.80% for new data with Adam optimization model. Meanwhile, the feature configuration tested effect shows that the use of the amplitude feature slightly reduces the accuracy to 91.38%. The results also show that the effect of the optimization model, namely Adam has a higher accuracy of 94.8% compared to SGD, only 74.14%. The number of hidden layers showed that three hidden layers could slightly increase the accuracy from 93.10% to 94.8%. Therefore, wavelets as extraction are more significant than other configurations, which slightly differ in performance. Adam's model achieved convergence in earlier times, but the speed of each iteration is slower than the SGD model. Experiments also showed that the optimization model, number of epochs, configuration, and duration of the EEG signal provide the best accuracy settings

Analysis of Signal Decomposition and Stain Separation methods for biomedical applications

Nowadays, the biomedical signal processing and classification and medical image interpretation play an essential role in the detection and diagnosis of several human diseases. The problem of high variability and heterogeneity of information, which is extracted from digital data, can be addressed with signal decomposition and stain separation techniques which can be useful approaches to highlight hidden patterns or rhythms in biological signals and specific cellular structures in histological color images, respectively. This thesis work can be divided into two macro-sections. In the first part (Part I), a novel cascaded RNN model based on long short-term memory (LSTM) blocks is presented with the aim to classify sleep stages automatically. A general workflow based on single-channel EEG signals is developed to enhance the low performance in staging N1 sleep without reducing the performances in the other sleep stages (i.e. Wake, N2, N3 and REM). In the same context, several signal decomposition techniques and time-frequency representations are deployed for the analysis of EEG signals. All extracted features are analyzed by using a novel correlation-based timestep feature selection and finally the selected features are fed to a bidirectional RNN model. In the second part (Part II), a fully automated method named SCAN (Stain Color Adaptive Normalization) is proposed for the separation and normalization of staining in digital pathology. This normalization system allows to standardize digitally, automatically and in a few seconds, the color intensity of a tissue slide with respect to that of a target image, in order to improve the pathologist’s diagnosis and increase the accuracy of computer-assisted diagnosis (CAD) systems. Multiscale evaluation and multi-tissue comparison are performed for assessing the robustness of the proposed method. In addition, a stain normalization based on a novel mathematical technique, named ICD (Inverse Color Deconvolution) is developed for immunohistochemical (IHC) staining in histopathological images. In conclusion, the proposed techniques achieve satisfactory results compared to state-of-the-art methods in the same research field. The workflow proposed in this thesis work and the developed algorithms can be employed for the analysis and interpretation of other biomedical signals and for digital medical image analysis

ARTIFACT CHARACTERIZATION, DETECTION AND REMOVAL FROM NEURAL SIGNALS

Ph.DDOCTOR OF PHILOSOPH

Wearable electroencephalography for long-term monitoring and diagnostic purposes

Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting, miniature and wearable devices that can be easily worn by patients will result in more EEG data being collected for extended monitoring periods. This thesis presents three new fabricated systems, in the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of epilepsy and sleep disorders by detecting specific clinically important EEG events on the sensor node, while discarding background activity. The power consumption of the WEEG monitoring device incorporating these systems can be reduced since the transmitter, which is the dominating element in terms of power consumption, will only become active based on the output of these systems. Candidate interictal activity is identified by the developed analog-based interictal spike selection system-on-chip (SoC), using an approximation of the Continuous Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike selection SoC is fabricated in a 0.35 μm CMOS process and consumes 950 nW. Experimental results reveal that the SoC is able to identify 87% of interictal spikes correctly while only transmitting 45% of the data. Sections of EEG data containing likely ictal activity are detected by an analog seizure selection SoC using the low complexity line length feature. This SoC is fabricated in a 0.18 μm CMOS technology and consumes 1.14 μW. Based on experimental results, the fabricated SoC is able to correctly detect 83% of seizure episodes while transmitting 52% of the overall EEG data. A single-channel analog-based sleep spindle detection SoC is developed to aid the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic events of sleep. The system identifies spindle events by monitoring abrupt changes in the input EEG. An approximation of the median frequency calculation, incorporated as part of the system, allows for non-spindle activity incorrectly identified by the system as sleep spindles to be discarded. The sleep spindle detection SoC is fabricated in a 0.18 μm CMOS technology, consuming only 515 nW. The SoC achieves a sensitivity and specificity of 71.5% and 98% respectively.Open Acces

Patient-specific seizure onset detection

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 121-124).Approximately one percent of the world's population exhibits symptoms of epilepsy, a serious disorder of the central nervous system that predisposes those affected to experiencing recurrent seizures. The risk of injury associated with epileptic seizures might be mitigated by the use of a device that can reliably detect or predict the onset of seizure episodes and then warn caregivers of the event. In a hospital this device could also be used to initiate time-sensitive clinical procedures necessary for characterizing epileptic syndromes. This thesis discusses the design of a real-time, patient-specific method that can be used to detect the onset of epileptic seizures in non-invasive EEG, and then initiate time-sensitive clinical procedures like ictal SPECT. We adopt a patient-specific approach because of the clinically observed consistency of seizure and non-seizure EEG characteristics within patients, and their great heterogeneity across patients. We also treat patient-specific seizure onset detection as a binary classification problem. Our observation is a multi-channel EEG signal; its features include amplitude, fundamental frequency, morphology, and spatial localization on the scalp; and it is classified as an instance of non-seizure or seizure EEG based on the learned features of training examples from a single patient. We use a multi-level wavelet decomposition to extract features that capture the amplitude, fundamental frequency, and morphology of EEG waveforms. These features are then classified using a support vector machine or maximum-likelihood classifier trained on a patient's seizure and non-seizure EEG; non-seizure EEG includes normal and artifact contaminated EEG from various states of consciousness.(cont.) The outcome of the classification is examined in the context of automatically extracted spatial and temporal constraints before the onset of seizure activity is declared. During validation tests our method exhibited an average latency of 8.0[plus-minus]3.2 seconds while correctly identifying 131 of 139 seizure events from thirty-six, de-identified test subjects; and only 11 false-detections over 49 hours of randomly selected non-seizure EEG from these subjects. The validation tests also highlight the high learning rate of the detector; a property that allows it to exhibit excellent performance even when trained on as few as two seizure events from the test subject. We also demonstrate through a comparative study that our patient-specific detector outperforms a nonpatient-specific, or generic detector in terms of a lower average detection latency; a lower total number of false-detections; and a higher total number of true-detections. Our study also underscores the likely event of a generic detector performing very poorly when the seizure EEG of a subject in its training set matches the non-seizure EEG of the test subject.by Ali Hossam Shoeb.M.Eng

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Influence of beta and theta binaural beat stimulation on episodic memory: an EEG study

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas) Universidade de Lisboa, Faculdade de Ciências, 2021Binaural beats (BBs) are auditory illusions created by the brain when two coherent sounds with slightly different frequencies are presented to both ears dichotically. For example, if the subject is presented a 256 Hz tone to the right ear and a 250 Hz to the left ear, the beat in this phenomenon is referred to as a 6 Hz theta binaural beat. Conversely, a mix of two sinusoids presented to the same ear is called acoustic beat (AB), resulting in a periodic amplitude fluctuation. Although BBs were shown to have positive effects on cognition, there are no sufficient studies on BBs and episodic memory. Furthermore, there is no agreement to explain the brain mechanism underlying the perception of BBs. The primary goal of this study is to investigate the influence of BBs on episodic memory and the effects of BB stimulation on brain rhythms, more concretely to examine whether they can change the power of specific EEG frequencies, comparatively to ABs. The secondary objective focuses on an exploratory study to measure cortical auditory evoked potentials (CAEPs) applying Muse, a consumer-grade EEG device used in this study, in order to assess its potential and the corresponding data quality. To meet the goals, two separate experiments were designed: a classic CAEP paradigm, with a total of 5 participants (3 male, 2 female; aged 22-25 years old); and an experiment with 32 subjects (19 male, 13 female; aged 20-28 years old), divided into two groups (depending on type of stimulation performed: 20 Hz beta or 6 Hz theta beats), each one with two stimuli conditions (BB or AB), received 15 minutes before the episodic memory task, during memory encoding phase and during the free recall test, across 2 sessions with an interval of 1 week. To quantify the power of brain oscillations during AB and BB stimulation, time-frequency analysis was performed using Discrete Wavelet Transform (DWT) and Relative Wavelet Energy (RWE). Regarding CAEP paradigm, N1-P2 complex was detected in temporal regions with acceptable signal-to-noise ratio. Parametric and non-parametric paired t-tests showed several significant changes in RWE values within each group at different time points, frequency bands and channels during both sessions, between BB and AB conditions. Moreover, entrainment of brain activity with the frequency of the beat was detected within theta BB stimulation. Regarding the effects of BB stimulation on episodic memory performance, t-tests revealed significant differences in the memory scores between AB and BB conditions during the first session (t=−2.48, p=0.0133) and second session (t=−2.67, p=0.00914) in theta group, with higher scores observed after BB stimulation. In beta group, significant differences in the scores were observed between AB and BB conditions during first session (t=−2.40, p=0.0154), with higher scores registered in BB condition. Inter-group analysis demonstrated that beta group outperformed theta group in both AB (t=3.37, p=0.00244) and BB (t=3.58, p=0.00143) conditions during the second session. This study validates the use of Muse for neuroscientific research, demonstrating that is possible to rely on consumer-grade low-cost EEG systems. Furthermore, it demonstrates that 20 Hz beta and 6 Hz theta BBs have a positive influence on episodic memory performance. Based on findings of positive effects of BBs on cognition, these results were expected. Entrainment was observed during theta BB stimulation. In addition, it is suggested that BBs have a modulatory effect on brain frequencies, with involvement of dynamical processes.Batimentos binaurais (BBs, do inglês Binaural beats) são ilusões auditivas criadas pelo cérebro quando dois sons coerentes com frequências ligeiramente diferentes são apresentados dicoticamente, isto é, cada ouvido é estimulado por frequências diferentes. Existem diferentes tipos de BBs, dependendo das frequências a partir das quais são criados e da diferença entre elas, o que determina a frequência do batimento. Por exemplo, se o sujeito é apresentado com um tom de 256 Hz no ouvido direito e 250 Hz no ouvido esquerdo, cria-se um batimento binaural de 6 Hz, na frequência do ritmo teta. Por outro lado, a mistura de duas sinusoides apresentadas ao mesmo ouvido possui o nome de batimento acústico (AB, do inglês acoustic beat) e as suas interferências são refletidas em flutuações periódicas em amplitude. Estudos demonstram que os BBs têm um efeito positivo na memória de trabalho, memória de longo prazo, capacidade de atenção e nos níveis de ansiedade e relaxamento. No entanto, existem relatos do seu efeito negativo na atenção e na memória de curto prazo. Para além disso, não existe um consenso na comunidade científica para explicar o mecanismo cerebral subjacente à perceção dos BBs. Tudo isto sublinha a necessidade de mais unificação na pesquisa. Apesar do efeito benéfico dos BBs nos diferentes tipos de memória, não existe um leque de estudos suficientemente grande relativamente à sua influência na memória episódica, um tipo de memória associado à codificação de eventos autobiográficos. Destaques na pesquisa sugerem que as oscilações teta estão associadas a um melhor desempenho na memória episódica. Presumindo que a estimulação auditiva com BBs teta possa ter um efeito modulador das frequências cerebrais por meio de resposta pós-frequência, mais especificamente no ritmo teta, é razoável supor que os BBs podem influenciar a memória episódica. O sistema de EEG usado neste estudo é o Muse, desenvolvido para ajudar em técnicas de meditação. Como não se trata de um aparelho de grau médico, é necessário entender se o material é viável para o estudo. O método para alcançar esta validação foi medir os potenciais evocados auditivos corticais, uma resposta cerebral já bem conhecida. Posto isto, a primeira parte desta tese foca-se num estudo exploratório para medir os potenciais evocados auditivos corticais usando o Muse, com o objetivo de avaliar o potencial do dispositivo e a qualidade dos dados correspondentes. A segunda parte e a meta principal deste estudo é investigar a influência do BBs na memória episódica e estudar o seu efeito nas oscilações cerebrais, em comparação com ABs. Para concretizar a primeira experiência deste estudo, um paradigma clássico foi desenhado para medir os potenciais evocados. Um total de 5 voluntários participaram neste estudo, com idades compreendidas entre 22 e 25 anos. Os participantes receberam um total de 180 estímulos, que consistiam em tons puros de 1000 Hz, com 500 ms de plateau, 10 ms de subida e descida e apresentados a cada 2 segundos. A aquisição do EEG e os marcadores de evento foram concretizados através do Lab Streaming Layer, uma ferramenta que permite criar redes de conexões entre vários dispositivos e programas. O pré-processamento e o processamento dos dados foram executados no EEGLAB, uma extensão do MATLAB que oferece uma interface gráfica para realizar a análise do EEG. Os resultados obtidos foram satisfatórios: o complexo N1-P2 foi identificado em todos os sujeitos e também nas curvas de grande média, com uma melhor relação entre o sinal e o ruído comparativamente às curvas individuais. Relativamente à segunda parte desta tese, a experiência consiste em 2 grupos de sujeitos, 2 blocos de tarefas, cada um com 2 condições de estímulo (AB ou BB), concretizada durante 2 sessões, separadas por uma semana. Um total de 32 voluntários foram recrutados, com idades compreendidas entre 20 e 28 anos. Os sujeitos foram divididos em 2 grupos: grupo teta, que recebeu estimulação com BBs e ABs teta na frequência dos 6 Hz, criados a partir de tons puros de 247 Hz e 253 Hz; grupo beta, que foi estimulado com BBs e ABs beta na frequência dos 20 Hz, gerados a partir de tons puros de 240 Hz e 260 Hz. A primeira parte do primeiro bloco consistia numa tarefa passiva em que os sujeitos de cada grupo ouviam ABs durante 15 minutos, ao mesmo tempo em que aquisição do EEG era realizada. Seguiu-se uma tarefa de memória episódica, em que os participantes tinham que decorar uma sequência de 30 imagens de objetos, cada uma com a duração de 3 segundos. De seguida, uma tarefa de distração foi realizada consistindo numa contagem em voz alta de 20 até 0. Por fim, foi feito um teste de recordação livre em que os sujeitos apontavam num papel os objetos que se lembravam de ver, cujo número seria contabilizado como pontuações de memória. O segundo bloco de tarefas é idêntico ao primeiro, exceto que imagens de objetos diferentes foram usadas e a estimulação durante os 15 minutos iniciais foi feita com BBs. Na segunda sessão, os mesmos procedimentos foram repetidos, exceto o uso de imagens de objetos diferentes em cada bloco. Para quantificar a energia de cada banda de frequência do EEG, recorreu-se à Transformada de Wavelet Discreta, que decompõe o sinal em vários níveis, cada um correspondendo a uma banda de frequência de ritmos cerebrais, e à Energia de Wavelet Relativa (RWE, de Relative Wavelet Energy). Mudanças na RWE dum determinado nível de decomposição refletem mudanças na atividade cerebral na banda de frequências correspondente. Dois tipos de análise foram concretizados: um tendo conta a evolução temporal da RWE ao longo de 13 segmentos de 1 minuto; o segundo implicou calcular a RWE média ao longo de um único segmento de EEG, colapsando a dimensão temporal. Os testes t paramétricos e não paramétricos revelaram várias diferenças entre os valores de RWE durante a estimulação com ABs e a estimulação com BBs, ao longo de diferentes instantes de tempo, bandas de frequências, canais e sessões da experiência. Relativamente ao grupo teta, os testes revelaram que a RWE na banda de frequência alfa no canal AF8 durante a primeira sessão aumentou de AB para BB (t=2.2701, p=0.01919). Durante a segunda sessão, foi observado um aumento dos valores de RWE na banda de frequências teta no canal TP10 da condição AB para BB (t=2.4509, p=0.0135). Relativamente ao grupo beta, as seguintes observações correspondem à primeira sessão, da condição AB para BB: uma diminuição significativa de RWE na banda de frequências beta no canal TP10 (t=-2.3364, p=0.0181) e um aumento significativo de RWE na banda delta no canal TP10 (t=4.3193, p=0.0004164) e no canal TP9 (t=2.7144, p=0.00885). Quanto aos efeitos dos BBs na performance de memória episódica, os testes t revelaram diferenças significativas nas pontuações entre as condições AB e BB durante a primeira sessão (t=-2.48, p=0.0133) e segunda sessão (t=-2.67, p=0.00914) no grupo teta, com pontuações mais altas observadas após a estimulação com BB. No grupo beta, diferenças significativas nas pontuações foram observadas entre as condições AB e BB durante a primeira sessão (t=-2.40, p=0.0154), com pontuações mais elevadas registadoa na condição BB. A análise entre os grupos demonstrou que o grupo beta superou o grupo teta em ambas as condições AB (t=3.37, p=0.00244) e BB (t=3.58, p=0.00143) durante a segunda sessão. Uma análise fatorial ANOVA II demonstrou que o efeito principal da condição foi significativo, sendo que os participantes que foram submetidos à estimulação com batimentos binaurais tiveram resultados mais altos (F(1,115)=5.49, p=0.0208). O efeito principal da sessão também foi significativo, com pontuações mais altas obtidas durante a segunda sessão (F(1,115)=9.206, p=0.00298). Houve interação significativa entre grupo e sessão (F(1,115)=5.11, p=0.0256). Para além disso, regressões lineares demonstraram que o aumento das pontuações de memória está associado ao aumento de RWE na banda de frequências beta (F(5,114) = 5.876, p < 0.0001). Este estudo mostra que é possível quantificar os potenciais evocados auditivos corticais usando um dispositivo de EEG de grau de consumidor. Foi demonstrado que os batimentos binaurais teta de 6 Hz e beta de 20 Hz têm efeito positivo no desempenho da memória episódica, comparativamente aos respetivos acoustic beats. Os participantes que foram estimulados com BBs beta tiveram melhores resultados nos testes de memória comparativamente aos que receberam estimulação com BBs teta, o que pode ser explicado pelo facto da atividade teta, característica da memória episódica, ter sido despertada durante a estimulação BB beta. No entanto, foi demonstrado que o aumento nas pontuações de memória episódica é explicado pelo aumento da RWE no ritmo beta. A resposta pós-frequência foi observada durante a exposição aos BBs teta, porém o mesmo não se verifica relativamente aos BBs beta. Para concluir, este estudo prova que os batimentos binaurais são moduladores neuronais, com envolvimento de respostas dinâmicas. Este efeito modulador da atividade cerebral pode ser a razão por trás da influência destes batimentos na memória episódica

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Electronic devices and systems for monitoring of diabetes and cardiovascular diseases

Diabetes is a serious chronic disease which causes a high rate of morbidity and mortality all over the world. In 2007, more than 246 million people suffered from diabetes worldwide and unfortunately the incidence of diabetes is increasing at alarming rates. The number of people with diabetes is expected to double within the next 25 years due to a combination of population ageing, unhealthy diets, obesity and sedentary lifestyles. It can lead to blindness, heart disease, stroke, kidney failure, amputations and nerve damage. In women, diabetes can cause problems during pregnancy and make it more likely for the baby to be born with birth defects. Moreover, statistical analysis shows that 75% of diabetic patients die prematurely of cardiovascular disease (CVD). The absolute risk of cardiovascular disease in patients with type 1 (insulin-dependent) diabetes is lower than that in patients with type 2 (non-insulin-dependent) diabetes, in part because of their younger age and the lower prevalence of CVD risk factors, and in part because of the different pathophysiology of the two diseases. Unfortunately, about 9 out of 10 people with diabetes have type 2 diabetes. For these reasons, cardiopathes and diabetic patients need to be frequently monitored and in some cases they could easily perform at home the requested physiological measurements (i.e. glycemia, heart rate, blood pressure, body weight, and so on) sending the measured data to the care staff in the hospital. Several researches have been presented over the last years to address these issues by means of digital communication systems. The largest part of such works uses a PC or complex hardware/software systems for this purpose. Beyond the cost of such systems, it should be noted that they can be quite accessible by relatively young people but the same does not hold for elderly patients more accustomed to traditional equipments for personal entertainment such as TV sets. Wearable devices can permit continuous cardiovascular monitoring both in clinical settings and at home. Benefits may be realized in the diagnosis and treatment of a number of major 15 diseases. In conjunction with appropriate alarm algorithms, they can increase surveillance capabilities for CVD catastrophe for high-risk subjects. Moreover, they could play an important role in the wireless surveillance of people during hazardous operations (military, fire-fighting, etc.) or during sport activities. For patients with chronic cardiovascular disease, such as heart failure, home monitoring employing wearable device and tele-home care systems may detect exacerbations in very early stages or at dangerous levels that necessitate an emergency room visit and an immediate hospital admission. Taking into account mains principles for the design of good wearable devices and friendly tele-home care systems, such as safety, compactness, motion and other disturbance rejection, data storage and transmission, low power consumption, no direct doctor supervision, it is imperative that these systems are easy to use and comfortable to wear for long periods of time. The aim of this work is to develop an easy to use tele-home care system for diabetes and cardiovascular monitoring, well exploitable even by elderly people, which are the main target of a telemedicine system, and wearable devices for long term measuring of some parameters related to sleep apnoea, heart attack, atrial fibrillation and deep vein thrombosis. Since set-top boxes for Digital Video Broadcast Terrestrial (DVB-T) are in simple computers with their Operating System, a Java Virtual Machine, a modem for the uplink connection and a set of standard ports for the interfacing with external devices, elderly, diabetics and cardiopathes could easily send their self-made exam to the care staff placed elsewhere. The wearable devices developed are based on the well known photopletysmographic method which uses a led source/detector pair applied on the skin in order to obtain a biomedical signal related to the volume and percentage of oxygen in blood. Such devices investigate the possibility to obtain more information to those usually obtained by this technique (heart rate and percentage of oxygen saturation) in order to discover new algorithms for the continuous and remote or in ambulatory monitoring and screening of sleep apnoea, heart attack, atrial fibrillation and deep vein thrombosis

Snoring and arousals in full-night polysomnographic studies from sleep apnea-hypopnea syndrome patients

SAHS (Sleep Apnea-Hypopnea Syndrome) is recognized to be a serious disorder with high prevalence in the population. The main clinical triad for SAHS is made up of 3 symptoms: apneas and hypopneas, chronic snoring and excessive daytime sleepiness (EDS). The gold standard for diagnosing SAHS is an overnight polysomnographic study performed at the hospital, a laborious, expensive and time-consuming procedure in which multiple biosignals are recorded. In this thesis we offer improvements to the current approaches to diagnosis and assessment of patients with SAHS. We demonstrate that snoring and arousals, while recognized key markers of SAHS, should be fully appreciated as essential tools for SAHS diagnosis. With respect to snoring analysis (applied to a 34 subjects¿ database with a total of 74439 snores), as an alternative to acoustic analysis, we have used less complex approaches mostly based on time domain parameters. We concluded that key information on SAHS severity can be extracted from the analysis of the time interval between successive snores. For that, we built a new methodology which consists on applying an adaptive threshold to the whole night sequence of time intervals between successive snores. This threshold enables to identify regular and non-regular snores. Finally, we were able to correlate the variability of time interval between successive snores in short 15 minute segments and throughout the whole night with the subject¿s SAHS severity. Severe SAHS subjects show a shorter time interval between regular snores (p=0.0036, AHI cp(cut-point): 30h-1) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p=0.006, AHI cp: 30h-1) is seen for less severe SAHS subjects. Also, we have shown successful in classifying the subjects according to their SAHS severity using the features derived from the time interval between regular snores. Classification accuracy values of 88.2% (with 90% sensitivity, 75% specificity) and 94.1% (with 94.4% sensitivity, 93.8% specificity) for AHI cut-points of severity of 5 and 30h-1, respectively. In what concerns the arousal study, our work is focused on respiratory and spontaneous arousals (45 subjects with a total of 2018 respiratory and 2001 spontaneous arousals). Current beliefs suggest that the former are the main cause for sleep fragmentation. Accordingly, sleep clinicians assign an important role to respiratory arousals when providing a final diagnosis on SAHS. Provided that the two types of arousals are triggered by different mechanisms we hypothesized that there might exist differences between their EEG content. After characterizing our arousal database through spectral analysis, results showed that the content of respiratory arousals on a mild SAHS subject is similar to that of a severe one (p>>0.05). Similar results were obtained for spontaneous arousals. Our findings also revealed that no differences are observed between the features of these two kinds of arousals on a same subject (r=0.8, p<0.01 and concordance with Bland-Altman analysis). As a result, we verified that each subject has almost like a fingerprint or signature for his arousals¿ content and is similar for both types of arousals. In addition, this signature has no correlation with SAHS severity and this is confirmed for the three EEG tracings (C3A2, C4A1 and O1A2). Although the trigger mechanisms of the two arousals are known to be different, our results showed that the brain response is fairly the same for both of them. The impact that respiratory arousals have in the sleep of SAHS patients is unquestionable but our findings suggest that the impact of spontaneous arousals should not be underestimated