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京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第18166号 / 医博第3886号 / 新制||医||1003(附属図書館) / 31024 / 京都大学大学院医学研究科医学専攻 / (主査)教授 髙橋 良輔, 教授 三森 経世, 教授 佐藤 俊哉 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22578号 / 情博第715号 / 新制||情||123(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 黒田 知宏, 教授 守屋 和幸, 教授 吉川 正俊 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

No description available.

INTRODUCTION: The association of Sleep-Related Movement Disorders (SRMDs) such as Restless Legs Syndrome (RLS) and Periodic Limb Movements (PLMs) with cerebrovascular disease is underexplored. Emerging evidence links them to vascular disease, for which white matter hyperintensities (WMHs) are a well-recognized biomarker. METHODS: We conducted a cross-sectional hospital-based observational study in which high-risk TIA and minor stroke patients were assessed for vascular risk factors, WMHs and polysomnography-determined sleep variables. RESULTS: Ninety-seven patients were enrolled, of whom 44 completed polysomnography. Twenty-five percent had RLS, which was associated with lower quality of life. Independent of the effect of classical vascular risk factors, PLMs (but not RLS) were associated with WMHs on linear regression analyses (p=0.016). CONCLUSIONS: SRMDs are prevalent after minor stroke/TIA. RLS is associated with poor quality of life, while PLMs are associated with WMHs. Whether PLMs are implicated in the pathogenesis of WMHs or whether WMHs exacerbate PLMs remains uncertain.

Através dos dispositivos e técnicas desenvolvidas na área da Instrumentação Biomédica é possível oferecer um tratamento ou de forma geral soluções que permitam uma vivência mais plena em sociedade para pessoas que possuem algum tipo de deficiência ou doença. Com o aumento da capacidade computacional nos últimos anos foi possível desenvolver muitas técnicas e dispositivos apoiados em processamento digital de sinais e há um grande interesse pelo desenvolvimento de interfaces mais naturais, como sinais biológicos para o controle de próteses e dispositivos. Este trabalho tem por objetivo apresentar o desenvolvimento de um método de Inteligência Computacional baseado em Regressão Logística capaz de classificar 17 movimentos do segmento mão-braço realizados pelos voluntários do estudo através do processamento do sinal mioelétrico (SME) adquiridos dos sujeitos em questão. Adicionalmente é realizada uma avaliação da influência de alguns dos canais, características do sinal e movimentos executados na taxa de acerto global. Para a realização do sistema foi utilizado um aparato experimental capaz de adquirir os SME através de 12 canais utilizando eletrodos não invasivos e posteriormente digitalizá-los. Logo após efetua-se a extração das três características utilizadas no trabalho, que servem de entrada para o método de Regressão Logística. Para este estudo foram processados três bancos de dados que perfazem um total de 50 voluntários. A taxa média de acerto alcançada foi de 70,1%, considerando todas as variações de testes realizados enquanto a média para os melhores casos de cada variação de entrada realizada foi de 92,5%. / Through the devices and techniques developed in the field of Biomedical Instrumentation commonly is possible to offer treatment or solutions to provide a more pleasurable experience in society for people who have a disability or illness. With increasing computing capability in recent years, it has been possible to develop many techniques and devices supported by digital signal processing, and there is a great interest in the development of more natural interfaces, such as biological signals for the control of devices and prostheses. This work aims to present the development of a computational intelligence method based on Logistic Regression able to classify 17 movements of the hand-arm segment performed by the subjects of this study through the processing of the myoelectric signal (SME) acquired from the subject in question. Additionally, an assessment of the influence of some of the combination of the channels, signal characteristics and movements performed in the overall hit rate is additionally performed. To conduct the system has built an experimental apparatus able to acquire the SME through 12 channels using non-invasive electrodes and scan them. Thereafter there is a three features extraction from the signal which serves as input to the Logistic Regression method. For this study were processed three databases that compose 50 volunteers. The average hit rate achieved was 70.1%, considering all tests variations while the average for the best cases for each input variation performed was 92,5 %.

INTRODUCTION: The association of Sleep-Related Movement Disorders (SRMDs) such as Restless Legs Syndrome (RLS) and Periodic Limb Movements (PLMs) with cerebrovascular disease is underexplored. Emerging evidence links them to vascular disease, for which white matter hyperintensities (WMHs) are a well-recognized biomarker. METHODS: We conducted a cross-sectional hospital-based observational study in which high-risk TIA and minor stroke patients were assessed for vascular risk factors, WMHs and polysomnography-determined sleep variables. RESULTS: Ninety-seven patients were enrolled, of whom 44 completed polysomnography. Twenty-five percent had RLS, which was associated with lower quality of life. Independent of the effect of classical vascular risk factors, PLMs (but not RLS) were associated with WMHs on linear regression analyses (p=0.016). CONCLUSIONS: SRMDs are prevalent after minor stroke/TIA. RLS is associated with poor quality of life, while PLMs are associated with WMHs. Whether PLMs are implicated in the pathogenesis of WMHs or whether WMHs exacerbate PLMs remains uncertain.

Através dos dispositivos e técnicas desenvolvidas na área da Instrumentação Biomédica é possível oferecer um tratamento ou de forma geral soluções que permitam uma vivência mais plena em sociedade para pessoas que possuem algum tipo de deficiência ou doença. Com o aumento da capacidade computacional nos últimos anos foi possível desenvolver muitas técnicas e dispositivos apoiados em processamento digital de sinais e há um grande interesse pelo desenvolvimento de interfaces mais naturais, como sinais biológicos para o controle de próteses e dispositivos. Este trabalho tem por objetivo apresentar o desenvolvimento de um método de Inteligência Computacional baseado em Regressão Logística capaz de classificar 17 movimentos do segmento mão-braço realizados pelos voluntários do estudo através do processamento do sinal mioelétrico (SME) adquiridos dos sujeitos em questão. Adicionalmente é realizada uma avaliação da influência de alguns dos canais, características do sinal e movimentos executados na taxa de acerto global. Para a realização do sistema foi utilizado um aparato experimental capaz de adquirir os SME através de 12 canais utilizando eletrodos não invasivos e posteriormente digitalizá-los. Logo após efetua-se a extração das três características utilizadas no trabalho, que servem de entrada para o método de Regressão Logística. Para este estudo foram processados três bancos de dados que perfazem um total de 50 voluntários. A taxa média de acerto alcançada foi de 70,1%, considerando todas as variações de testes realizados enquanto a média para os melhores casos de cada variação de entrada realizada foi de 92,5%. / Through the devices and techniques developed in the field of Biomedical Instrumentation commonly is possible to offer treatment or solutions to provide a more pleasurable experience in society for people who have a disability or illness. With increasing computing capability in recent years, it has been possible to develop many techniques and devices supported by digital signal processing, and there is a great interest in the development of more natural interfaces, such as biological signals for the control of devices and prostheses. This work aims to present the development of a computational intelligence method based on Logistic Regression able to classify 17 movements of the hand-arm segment performed by the subjects of this study through the processing of the myoelectric signal (SME) acquired from the subject in question. Additionally, an assessment of the influence of some of the combination of the channels, signal characteristics and movements performed in the overall hit rate is additionally performed. To conduct the system has built an experimental apparatus able to acquire the SME through 12 channels using non-invasive electrodes and scan them. Thereafter there is a three features extraction from the signal which serves as input to the Logistic Regression method. For this study were processed three databases that compose 50 volunteers. The average hit rate achieved was 70.1%, considering all tests variations while the average for the best cases for each input variation performed was 92,5 %.

Através dos dispositivos e técnicas desenvolvidas na área da Instrumentação Biomédica é possível oferecer um tratamento ou de forma geral soluções que permitam uma vivência mais plena em sociedade para pessoas que possuem algum tipo de deficiência ou doença. Com o aumento da capacidade computacional nos últimos anos foi possível desenvolver muitas técnicas e dispositivos apoiados em processamento digital de sinais e há um grande interesse pelo desenvolvimento de interfaces mais naturais, como sinais biológicos para o controle de próteses e dispositivos. Este trabalho tem por objetivo apresentar o desenvolvimento de um método de Inteligência Computacional baseado em Regressão Logística capaz de classificar 17 movimentos do segmento mão-braço realizados pelos voluntários do estudo através do processamento do sinal mioelétrico (SME) adquiridos dos sujeitos em questão. Adicionalmente é realizada uma avaliação da influência de alguns dos canais, características do sinal e movimentos executados na taxa de acerto global. Para a realização do sistema foi utilizado um aparato experimental capaz de adquirir os SME através de 12 canais utilizando eletrodos não invasivos e posteriormente digitalizá-los. Logo após efetua-se a extração das três características utilizadas no trabalho, que servem de entrada para o método de Regressão Logística. Para este estudo foram processados três bancos de dados que perfazem um total de 50 voluntários. A taxa média de acerto alcançada foi de 70,1%, considerando todas as variações de testes realizados enquanto a média para os melhores casos de cada variação de entrada realizada foi de 92,5%. / Through the devices and techniques developed in the field of Biomedical Instrumentation commonly is possible to offer treatment or solutions to provide a more pleasurable experience in society for people who have a disability or illness. With increasing computing capability in recent years, it has been possible to develop many techniques and devices supported by digital signal processing, and there is a great interest in the development of more natural interfaces, such as biological signals for the control of devices and prostheses. This work aims to present the development of a computational intelligence method based on Logistic Regression able to classify 17 movements of the hand-arm segment performed by the subjects of this study through the processing of the myoelectric signal (SME) acquired from the subject in question. Additionally, an assessment of the influence of some of the combination of the channels, signal characteristics and movements performed in the overall hit rate is additionally performed. To conduct the system has built an experimental apparatus able to acquire the SME through 12 channels using non-invasive electrodes and scan them. Thereafter there is a three features extraction from the signal which serves as input to the Logistic Regression method. For this study were processed three databases that compose 50 volunteers. The average hit rate achieved was 70.1%, considering all tests variations while the average for the best cases for each input variation performed was 92,5 %.

Eine Reihe von Studien konnte zeigen, dass sich Schlaf förderlich auf den Prozess der Gedächtniskonsolidierung auswirkt. Dabei wurde die Konsolidierung unterschiedlicher Lerninhalte mit bestimmten Schlafstadien – z. B. perzeptiv-prozedurale Inhalte mit dem REM (von engl. rapid eye movement) Schlaf – in Verbindung gebracht. Da viele Antidepressiva den REM Schlaf teilweise oder sogar vollständig unterdrücken, stand die Frage im Raum, ob bzw. unter welchen Umständen deren Einnahme die Gedächtniskonsolidierung im Schlaf beeinträchtigen kann. In diesem Zusammenhang scheint zudem die Rolle von Schlafstörungen interessant, da der REM Schlaf im Falle einer Schlafstörung auch Bedeutung für die schlafabhängige Gedächtniskonsolidierung deklarativer Inhalte erlangen kann. Die Arbeit basiert auf einer klinischen Studie (EudraCT 2007-003546-14), in deren Rahmen 32 männliche Probanden im Alter von 18 bis 39 Jahren jeweils über eine Zeitspanne von 48 Stunden im Schlaflabor untersucht wurden. Sie umfasst drei Manuskripte. Im ersten Manuskript wird gezeigt, dass die Einnahme eines REM Schlaf-reduzierenden Antidepressivums (Amitriptylin) die REM Schlaf abhängige perzeptiv-prozedurale Gedächtniskonsolidierung im Schlaf beeinträchtigt, während sie auf die Konsolidierung REM Schlaf unabhängiger Inhalte keinen Effekt hat. Eine weitere unerwünschte Arzneimittelwirkung von Amitriptylin wird im Manuskript 2 beschrieben: Amitriptylin kann den Schlaf stören, indem es das Auftreten periodischer Gliedmaßenbewegungen im Schlaf verstärkt. Im dritten Manuskript wird dargestellt, dass eine neue, fremde Schlafumgebung den Schlaf beeinträchtigen und sich eine solche Beeinträchtigung ähnlich wie eine chronische Schlafstörung auf die schlafabhängige Gedächtniskonsolidierung auswirken kann. Die Ergebnisse werden in den Manuskripten ausführlich diskutiert und im Epilog zusammengefasst sowie in Zusammenhang gesetzt. / Numerous studies suggest that sleep benefits memory consolidation and that the consolidation of different types of memory is differentially influenced by certain sleep stages. For example, consolidation of a perceptual skill is linked with rapid eye movement (REM) sleep whereas declarative memory consolidation is linked with slow wave sleep. Antidepressants strongly suppress REM sleep. Therefore, it is important to determine whether their use can affect memory consolidation. In this context, sleep disturbances are also of interest because when these are experienced REM sleep rather than slow wave sleep seems to become important for sleep-dependent declarative memory consolidation. The work in this thesis is based on a clinical trial (EudraCT 2007-003546-14) in which 32 male subjects (aged 18 through 39 years) were studied in a sleep laboratory over a 48 hour period. Three manuscripts are included. In the first manuscript, it is demonstrated that the REM sleep-suppressing antidepressant amitriptyline specifically impairs REM sleep-dependent perceptual skill learning, but not REM sleep-independent motor skill or declarative learning. In the second manuscript, another adverse effect of amitriptyline is presented: for the first time it is shown that amitriptyline can disturb sleep by inducing or increasing the number of periodic limb movements during sleep. In the third manuscript, it is demonstrated how sleeping in an unfamiliar environment can disturb sleep and how this kind of sleep disturbance can affect memory consolidation during sleep. The results from the specific studies are discussed in detail in the respective manuscripts and are summarized in the epilogue.