Fast unsupervised technique for extraction of endmembers spectra from hyperspectral data

One of the most challenging task underlying many hyperspectral imagery applications is the linear unmixing. The key to linear unmixing is to find the set of reference substances, also called endmembers, that are representative of a given scene. This paper presents the vertex component analysis (VCA) a new method to unmix linear mixtures of hyperspectral sources. The algorithm is unsupervised and exploits a simple geometric fact: endmembers are vertices of a simplex. The algorithm complexity, measured in floating points operations, is O (n), where n is the sample size. The effectiveness of the proposed scheme is illustrated using simulated data.

Parallel hyperspectral unmixing method via split augmented lagrangian on GPU

One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing and unmixing methods. This letter proposes an efficient implementation of the method called simplex identification via split augmented Lagrangian (SISAL) which exploits the graphics processing unit (GPU) architecture at low level using Compute Unified Device Architecture. SISAL aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore achieving high GPU occupancy. The experimental results obtained for the simulated and real hyperspectral data sets reveal speedups up to 49 times, which demonstrates that the GPU implementation can significantly accelerate the method's execution over big data sets while maintaining the methods accuracy.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Hyperspectral imagery framework for unmixing and dimensionality estimation

In hyperspectral imagery a pixel typically consists mixture of spectral signatures of reference substances, also called endmembers. Linear spectral mixture analysis, or linear unmixing, aims at estimating the number of endmembers, their spectral signatures, and their abundance fractions. This paper proposes a framework for hyperpsectral unmixing. A blind method (SISAL) is used for the estimation of the unknown endmember signature and their abundance fractions. This method solve a non-convex problem by a sequence of augmented Lagrangian optimizations, where the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. The proposed framework simultaneously estimates the number of endmembers present in the hyperspectral image by an algorithm based on the minimum description length (MDL) principle. Experimental results on both synthetic and real hyperspectral data demonstrate the effectiveness of the proposed algorithm.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Blind hyperspectral unmixing

Hyperspectral unmixing methods aim at the decomposition of a hyperspectral image into a collection endmember signatures, i.e., the radiance or reflectance of the materials present in the scene, and the correspondent abundance fractions at each pixel in the image. This paper introduces a new unmixing method termed dependent component analysis (DECA). This method is blind and fully automatic and it overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. DECA is based on the linear mixture model, i.e., each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abundances are modeled as mixtures of Dirichlet densities, thus enforcing the non-negativity and constant sum constraints, imposed by the acquisition process. The endmembers signatures are inferred by a generalized expectation-maximization (GEM) type algorithm. The paper illustrates the effectiveness of DECA on synthetic and real hyperspectral images.

Fast unsupervised extraction of endmembers spectra from hyperspectral data

Linear unmixing decomposes an hyperspectral image into a collection of re ectance spectra, called endmember signatures, and a set corresponding abundance fractions from the respective spatial coverage. This paper introduces vertex component analysis, an unsupervised algorithm to unmix linear mixtures of hyperpsectral data. VCA exploits the fact that endmembers occupy vertices of a simplex, and assumes the presence of pure pixels in data. VCA performance is illustrated using simulated and real data. VCA competes with state-of-the-art methods with much lower computational complexity.

Estimação de superfícies tridimensionais com modelos activos

Esta comunicação aborda a estimação da superfície de objectos a partir de um conjunto de pontos tridimensionais usando modelos activos. Propõe-se, uma extensão da Classe Unificada desenvolvida por Abrantes e Marques. A superfície é discretizada usando dois tipos de redes: redes de malha rectangular e redes Simplex. A Classe Unificada baseia-se no cálculo dos centróides dos dados na vizinhança de amostras pré-definidas da superfície deformável. Os pontos da superfície são atraídos na direcção dos centróides. O artigo revê os conceitos básicos de modelamento activo de superfícies, a Classe Unificada e as redes Simplex. Os modelos descritos são testados usando dados sintéticos e reais obtidos a partir de imagens ecográficas e de ressonância magnética.

As teses do defensor Pacis, II, XIII

pp. 205-227

Ciclosporina: Riscos em Idade Pediátrica

Introdução: A eficácia e segurança da ciclosporina têm sido demonstradas em patologias inflamatórias dermatológicas, nomeadamente psoríase e eczema, em adultos e crianças. Na idade pediátrica o seu uso é no entanto ainda limitado. Apresentamos três casos clínicos em que a ciclosporina, foi interrompida por aparecimento de complicações. Este trabalho visa alertar para potenciais efeitos secundários da ciclosporina, a fim de evitar utilizações abusivas. Casos clínicos: Foram submetidas a terapêutica com ciclosporina oral duas crianças de quatro e 13 anos de idade com eczema e uma criança de dois anos, com psoríase eritrodérmica. No primeiro caso interrompeu-se terapêutica pelo aparecimento de impétigo ao sexto dia de ciclosporina. Iniciou corticóides e inibidores tópicos da calcineurina com boa resposta. No segundo caso, a ciclosporina foi interrompida pelo aparecimento de herpes facial exuberante e toxicidade hepática e renal no quarto dia de tratamento. No último caso, de psoríase generalizada e impétigo, medicado com flucloxacilina e gentamicina,a terapêutica foi interrompida ao sexto dia por angioedema e urticária generalizados por quadro de angioedema e urticária generalizados, interpretado como reacção de hipersensibilidade a beta-lactâmicos, não sendo contudo possível excluir papel da ciclosporina. Discussão: Os dados sobre a utilização da ciclosporina em crianças são ainda escassos. A utilização deve ser limitada a casos com indicações precisas, após considerar riscos e benefícios.

Sarcoma de Kaposi da Conjuntiva. Apresentação Invulgar de SIDA

O sarcoma de Kaposi ocular isolado surge em 0,3% a 5% dos doentes com SIDA, mas, em doentes com tumor disseminado, esta incidência aumenta para 15% a 20%. Apresentamos um caso de sarcoma de Kaposi epidémico, mucocutâneo, cuja primeira manifestação foi ocular. O tratamento inicial consistiu na administração quinzenal de daunorrubicina lipossómica e de anti-retrovíricos. Sob terapêutica houve progressão da doença, tendo sido a sua regressão conseguida, apenas, com um esquema alternativo de quimioterapia associada a cidofovir. Aproveitamos para rever esta entidade, em particular as formas oculares, e discutir a utilização de cidofovir no tratamento do sarcoma de Kaposi associado ao vírus herpes humano tipo 8.

Soft tissue abscess and lymphadenitis due to Mycobacterium avium Complex as an expression of immune reconstitution inflammatory syndrome after a second scheme of highly active antiretroviral therapy

Immune reconstitution inflammatory syndrome (IRIS) is an atypical and unexpected reaction related to highly active antiretroviral therapy (HAART) in human immunodeficiency virus (HIV) infected patients. IRIS includes an atypical response to an opportunistic pathogen (generally Mycobacterium tuberculosis, Mycobacterium avium complex, cytomegalovirus and herpes varicella-zoster), in patients responding to HAART with a reduction of plasma viral load and evidence of immune restoration based on increase of CD4+ T-cell count. We reported a case of a patient with AIDS which, after a first failure of HAART, developed a subcutaneous abscess and supraclavicular lymphadenitis as an expression of IRIS due to Mycobacterium avium complex after starting a second scheme of HAART.

Gravidez na Transplantação Renal

Com a Transplantação Renal regista-se recuperação da fertilidade. A exposição a fármacos imunossupressores, como a prednisona, a ciclosporina, a azatioprina e o tacrolimus não está associada com um aumento da incidência de malformações congénitas. A Imunossupressão, particularmente com a ciclosporina, está relacionada com recém-nascidos com baixo peso ao nascer. Doentes transplantados têm um risco aumentado de complicações infecciosas, algumas com implicações importantes para o feto, como as infecções por citomegalovírus, herpes simples e toxoplasmose. Esta população tem uma maior frequência de prematuridade, variando a percentagem de nados vivos entre 70 e 100%. Impõe-se a manutenção de esforços continuados para identificar os factores de risco pré-gestacionais, optimizando as estratégias de abordagem neonatal para uma gravidez bem-sucedida.

Síndrome de Stevens-Johnson e Ibuprofeno

Introdução: A síndrome de Stevens-Johnson é uma doença rara com mortalidade de 1 a 5% e morbilidade significativa. Ocorre na sequência de uma reacção de hipersensibilidade imuno-mediada com susceptibilidade individual associada a factores genéticos. Pode ser desencadeada por agentes infecciosos, mas na maior parte dos casos o factor desencadeante é a exposição a fármacos. Caso clínico: Criança de 3 anos, previamente saudável, internada por febre alta, exantema papulovesicular generalizado com predomínio no tronco, dorso e face, enantema e hiperémia conjuntival. Posteriormente verificou-se coalescência das lesões cutâneas com evolução para necrose e descamação. Tinha adicionalmente erosões da mucosa oral, estomatite, edema e eritema dos lábios, períneo e balanite. Fotofobia, hiperémia conjuntival, edema palpebral, exsudado ocular sem sinéquias e córnea sem lesões. Duas semanas antes tinha sido medicado pela primeira vez com ibuprofeno e na admissão hospitalar realizou uma nova administração. Nega ingestão de outros fármacos. PCR para vírus do grupo herpes nas lesões, exames culturais negativos e serologias para Mycoplasma pneumoniae, Borrelia burgdoferi, vírus da hepatite B, Epstein-Barr e citomegalovírus negativos. TASO e anti-DNaseB sem alterações. IFI para vírus respiratórios negativa. Posteriormente identificou-se enterovírus por PCR nas fezes de que se aguarda cultura viral. Foi interrompida a administração de ibuprofeno e realizada terapêutica de suporte com fluidoterapia endovenosa, nutrição parentérica, analgesia sistémica e tópica. Manteve febre durante 10 dias, registando-se regressão progressiva da sintomatologia com melhoria das lesões ao fim de 3 semanas. Programou-se seguimento para rastreio de complicações cutâneo-mucosas e oftalmológicas e estudo de alergias medicamentosas. Comentários: O diagnóstico da síndrome de Stevens-Johnson é clínico e, em caso de dúvida, histológico, suportado por história de exposição a fármacos ou intercorrência infecciosa. A ingestão de ibuprofeno pela primeira vez com agravamento após a reexposição ao fármaco leva-nos a suspeitar ser esta a etiologia mais provável. Contudo, a identificação de enterovírus não permite excluir este agente como interveniente na doença.

Hipersonolência Diurna. Narcolepsia, Qual a Etiologia?

Introdução: A narcolepsia é uma doença do sono REM com desregulação do ciclo de sono-vigília, consequente sonolência diurna e eventual associação a alucinações hipnagógicas, paralisia do sono e cataplexia. A sua prevalência é de 0,05 a 0,02% no adulto mas desconhecida na idade pediátrica. Caso clínico: Criança de seis anos, previamente saudável com sonolência excessiva até 18 horas/dia e discinésia oromandibular, desequilíbrio na marcha e movimentos coreiformes dos membros superiores. Duas semanas antes realizara vacinação para a gripe pandémica. Registou-se ainda hiperfagia diurna e nocturna durante cinco dias com resolução espontânea, episódios de cataplexia perante riso e alterações emocionais e tremor da cabeça e dos membros superiores com melhoria clínica progressiva após oito dias. Realizou RMN-CE e EEG sem alterações. O exame líquido céfalo-raquidiano e PCR para painel de vírus herpes, Mycoplasma pneumoniae e enterovírus negativas. Nesta fase realizou polissonografia com teste de latências múltiplas do sono (TLMS) sem alterações. Exame cultural do exsudado faríngeo, TASO e anticorpo AntiDnase B negativos. Da exaustiva investigação que realizou apresentava serologias ELISA e WB compatíveis com infecção por Borrelia burdorferi, pelo que cumpriu ceftriaxone 14 dias. Serologias para influenza A mostraram IgM 39 UA/mL com IgG 194 UA/mL com segunda amostra com IgM 43 UA/mL e IgG 162 UA/mL (VR IgM<20;IgG<20). O estudo da autoimunidade revelou ANA 1/320, anticorpos anticardiolipina e antinucleares extraíveis negativos. Restantes autoanticorpos e doseamento de complemento normal. Anticorpos Anti-NMDA e VKCG negativos. Doseamento de hipocretina muito diminuído com HLA DR2 e DQB1*0602 presentes. A polissonografia com TLMS, sete meses após a primeira, confirmou sonolência excessiva com quatro inícios do sono REM sugestivos de narcolepsia. Faz terapêutica com metilfenidato, a sonolência diurna diminuiu e cumpre o seu horário escolar sem limitações. Comentários: O diagnóstico de narcolepsia foi sugerido pela clínica e confirmado pelo teste de latências múltiplas. O valor de hipocretina diminuído pode sugerir uma etiologia autoimune. Uma infecção como a borreliose ou a vacinação prévia para H1N1, responsabilizada por outros casos de narcolepsia podem ter sido desencadeantes de uma alteração imunitária responsável pela doença, nesta criança com a susceptibilidade HLA DR2 e DBQ1*0602.