Inter-database validation of a deep learning approach for automatic sleep scoring

[Abstract] Study objectives Development of inter-database generalizable sleep staging algorithms represents a challenge due to increased data variability across different datasets. Sharing data between different centers is also a problem due to potential restrictions due to patient privacy protection. In this work, we describe a new deep learning approach for automatic sleep staging, and address its generalization capabilities on a wide range of public sleep staging databases. We also examine the suitability of a novel approach that uses an ensemble of individual local models and evaluate its impact on the resulting inter-database generalization performance. Methods A general deep learning network architecture for automatic sleep staging is presented. Different preprocessing and architectural variant options are tested. The resulting prediction capabilities are evaluated and compared on a heterogeneous collection of six public sleep staging datasets. Validation is carried out in the context of independent local and external dataset generalization scenarios. Results Best results were achieved using the CNN_LSTM_5 neural network variant. Average prediction capabilities on independent local testing sets achieved 0.80 kappa score. When individual local models predict data from external datasets, average kappa score decreases to 0.54. Using the proposed ensemble-based approach, average kappa performance on the external dataset prediction scenario increases to 0.62. To our knowledge this is the largest study by the number of datasets so far on validating the generalization capabilities of an automatic sleep staging algorithm using external databases. Conclusions Validation results show good general performance of our method, as compared with the expected levels of human agreement, as well as to state-of-the-art automatic sleep staging methods. The proposed ensemble-based approach enables flexible and scalable design, allowing dynamic integration of local models into the final ensemble, preserving data locality, and increasing generalization capabilities of the resulting system at the same time

Charakterisierung der Beinbewegungsaktivität während des Schlafes bei Erwachsenen mit Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung (ADHS)

Hintergrund: Die Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung (ADHS) ist die am häufigsten diagnostizierte Entwicklungsstörung der Kindheit. Oft bleibt diese aber auch bis ins Erwachsenenalter bestehen und wirkt sich negativ auf die Lebensqualität der Betroffenen aus. Dies liegt nicht nur an den Kardinalsymptomen der Erkrankung, sondern auch an den typischerweise damit verbundenen psychiatrischen Begleiterkrankungen und Schlafstörungen. Es wird berichtet, dass der Schlaf von Patienten mit ADHS in jedem Alter subjektiv gestört ist. Ergebnisse von Studien, die den Schlaf bei ADHS objektiv untersucht haben, sind bei Kindern jedoch inkonsistent, während bei Erwachsenen nur sehr wenige Studien vorliegen. Ziel: Periodische Beinbewegungen im Schlaf (PLMS) treten gehäuft beim Restless-Legs-Syndrom (RLS) auf und können in einer Schlaffragmentierung resultieren. Basierend auf dem klinisch beobachteten Zusammenhang zwischen RLS und ADHS, wurden die Schlafstruktur und insbesondere das Muster der motorischen Aktivität im Schlaf bei Patienten mit ADHS untersucht. Es wurde die Hypothese aufgestellt, dass sowohl RLS als auch die ADHS durch vermehrte PLMS gekennzeichnet sind, was die schlechte Schlafqualität von ADHS-Patienten erklären und sogar eine mögliche gemeinsame Pathophysiologie dieser beiden Erkrankungen nahelegen könnte. Um diese Hypothese zu testen, wurde erstmalig eine detaillierte Analyse der Beinbewegungen (LMs) im Schlaf bei Erwachsenen mit ADHS im Vergleich zu gesunden Kontrollpersonen durchgeführt. Methoden: Fünfzehn ADHS-Patienten und achtzehn alters- und geschlechtsausgeglichenen Kontrollpersonen nahmen an einer polysomnographischen Studie im Schlaflabor teil. Der periodische Charakter der LMs wurde mit neueren Verfahren zur Messung von "Periodizität" (d.h. des Periodizitätsindex, der Intermovement-Intervalle und der nächtlichen Zeitverteilung der LMs) zusätzlich zu Standardparametern wie dem PLMS-Index bewertet. Die subjektive Schlafqualität und das Vorliegen psychiatrischer Symptome wurden anhand von Screening-Fragebogen beurteilt. Ergebnisse: Ein Vergleich der objektiven Schlafparameter ließ lediglich für die Schlaflatenz einen signifikanten Unterschied erkennen. Diese war bei den ADHS-Probanden länger als bei den Kontrollen. Die Analyse der Beinbewegungen im Schlaf ergab eine längere Dauer von PLMS im REM-Schlaf bei ADHS-Probanden, sowie einen höheren PLMS-Index im REM-Schlaf. Daten aus den Schlaf- Fragebogen zeigten eine schlechtere subjektive Schlafqualität bei ADHS-Patienten. Schlussfolgerungen: Wie bereits bei Kindern gezeigt sind LMs bei ADHS-Erwachsenen nicht signifikant häufiger als bei gesunden Kontrollen und weisen keine erhöhte Periodizität auf. Diese Befunde scheinen ADHS von anderen pathophysiologisch verwandten Erkrankungen, wie RLS, zu unterscheiden. Die von ADHSPatienten subjektiv wahrgenommene schlechte Schlafqualität, steht im Gegensatz zu den normalen gemessenen polysomnographischen Parametern. Diese abweichende Einschätzung des Schlafes könnte auf subtilere, durch die traditionelle Schlafauswertung nicht erfassbaren Veränderungen im Schlaf hindeuten, welche sich auch bei detaillierterer Analyse von PLMS nicht erklären lassen.Background: Attention-deficit/hyperactivity disorder (ADHD) is the most commonly diagnosed neurodevelopmental disorder of the childhood, but also frequently persists into adulthood, impairing the quality of life of the affected patients. This is not only due to the cardinal symptoms of the disorder, but also to its typically associated psychiatric comorbidities and sleep disturbances. Sleep is reported to be subjectively disturbed by ADHD patients at any age, but the results of studies objectively assessing sleep in ADHD are not consistent in children and still lacking in adults. Aim: Periodic leg movements during sleep (PLMS) are common in Restless Legs Syndrome (RLS) and may result in sleep fragmentation. Based on the clinically observed association between RLS and ADHD, the sleep structure and the pattern of motor activity during sleep in patients with ADHD were investigated. It has been hypothesized that both RLS and ADHD are characterized by increased PLMS, which may explain the poor sleep quality experienced by ADHD patients and even suggest a possible common pathophysiology of these two disorders. To test this hypothesis, a detailed analysis of leg movements (LMs) during sleep was performed for the first time in adults with ADHD compared to healthy control subjects. Methods: Fifteen ADHD patients and eighteen gender- and age-matched control subjects underwent an in-lab polysomnographic study. The periodic character of LMs was evaluated by newer measures of “periodicity” (i.e. the periodicity index, intermovement intervals, and nocturnal time distribution of LMs), in addition to standard parameters, such as the PLMS-Index. Subjective sleep quality and psychiatric symptoms were assessed by screening questionnaire. Results A comparison of the objective sleep parameters revealed only a significant difference for the sleep latency. This was longer in ADHD subjects than in controls. The analysis of LMs showed a longer duration of PLMS in REM sleep in ADHD subjects, and a higher PLMS index in REM sleep. Data from the sleep questionnaire demonstrated a poorer subjective sleep quality in ADHD patients. Conclusions: As demonstrated in children, LMs are not significantly more common in ADHD adults than in healthy controls and show no increased periodicity. These findings seem to differentiate ADHD from other pathophysiologically related disorders, like RLS. The poor sleep quality perceived by ADHD patients contrasts with the normal polysomnographic parameters. This sleep-state misperception may point to more subtle sleep changes that can not be detected by the traditional sleep scoring, nor be explained by a more detailed analysis of PLMS

Big data analysis of cyclic alternating pattern during sleep using deep learning

Sleep scoring has been of great interest since the invention of the polysomnography method, which enabled the recording of physiological signals overnight. With the surge in wearable devices in recent years, the topic of what is high-quality sleep, how can it be determined and how can it be achieved attracted increasing interest. In the last two decades, cyclic alternating pattern (CAP) was introduced as a scoring alternative to traditional sleep staging. CAP is known as a synonym for sleep microstructure and describes sleep instability. Manual CAP scoring performed by sleep experts is a very exhausting and time-consuming task. Hence, an automatic method would facilitate the processing of sleep data and provide a valuable tool to enhance the understanding of the role of CAP. This thesis aims to expand the knowledge about CAP by developing a high-performance automated CAP scoring system that can reliably detect and classify CAP events in sleep recordings. The automated system is equipped with state-of-the-art signal processing methods and exploits the dynamic, temporal information in brain activity using deep learning. The automated scoring system is validated using large community-based cohort studies and comparing the output to verified values in the literature. Our findings present novel clinical results on the relationship between CAP and age, gender, subjective sleep quality, and sleep disorders demonstrating that automated CAP analysis of large population based studies can lead to new findings on CAP and its subcomponents. Next, we study the relationship between CAP and behavioural, cognitive, and quality-of-life measures and the effect of adenotonsillectomy on CAP in children with obstructive sleep apnoea as the link between CAP and cognitive functioning in children is largely unknown. Finally, we investigate cortical-cardiovascular interactions during CAP to gain novel insights into the causal relationships between cortical and cardiovascular activity that are underpinning the microstructure of sleep. In summary, the research outcomes in this thesis outline the importance of a fully automated end-to-end CAP scoring solution for future studies on sleep microstructure. Furthermore, we present novel critical information for a better understanding of CAP and obtain first evidence on physiological network dynamics between the central nervous system and the cardiovascular system during CAP.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 202

Sleep homeostasis in the European jackdaw (<i>Coloeus monedula</i>):Sleep deprivation increases NREM sleep time and EEG power while reducing hemispheric asymmetry

Introduction: Sleep is a wide-spread phenomenon that is thought to occur in all animals. Yet, the function of it remains an enigma. Conducting sleep experiments in different species may shed light on the evolution and functions of sleep. Therefore, we studied sleep architecture and sleep homeostatic responses to sleep deprivation in the European jackdaw (Coloeus monedula).Methods: A total of nine young adult birds were implanted with epidural electrodes and equipped with miniature data loggers for recording movement activity (accelerometery) and electroencephalogram (EEG). Individually-housed jackdaws were recorded under controlled conditions with a 12:12-h light-dark cycle.Results: During baseline, the birds spent on average 48.5% of the time asleep (39.8% non-rapid eye movement (NREM) sleep and 8.7% rapid eye movement (REM) sleep). Most of the sleep occurred during the dark phase (dark phase: 75.3% NREM sleep and 17.2% REM sleep; light phase 4.3% NREM sleep and 0.1% REM sleep). After sleep deprivation of 4 and 8 h starting at lights off, the birds showed a dose-dependent increase in NREM sleep time. Also, NREM sleep EEG power in the 1.5–3 Hz frequency range, which is considered to be a marker of sleep homeostasis in mammals, was significantly increased for 1-2 h after both 4SD and 8SD. While there was little true unihemispheric sleep in the Jackdaws, there was a certain degree of hemispheric asymmetry in NREM sleep EEG power during baseline, which reduced after sleep deprivation in a dose-dependent manner.Conclusion: In conclusion, jackdaws display homeostatic regulation of NREM sleep and sleep pressure promotes coherence in EEG power

Rhythmic Masticatory Muscle Activity during Sleep: Etiology and Clinical Perspectives

L’activité rythmique des muscles masticateurs (ARMM) pendant le sommeil se retrouve chez environ 60% de la population générale adulte. L'étiologie de ce mouvement n'est pas encore complètement élucidée. Il est cependant démontré que l’augmentation de la fréquence des ARMM peut avoir des conséquences négatives sur le système masticatoire. Dans ce cas, l'ARMM est considérée en tant que manifestation d'un trouble moteur du sommeil connue sous le nom de bruxisme. Selon la Classification Internationale des Troubles du Sommeil, le bruxisme est décrit comme le serrement et grincement des dents pendant le sommeil. La survenue des épisodes d’ARMM est associée à une augmentation du tonus du système nerveux sympathique, du rythme cardiaque, de la pression artérielle et elle est souvent en association avec une amplitude respiratoire accrue. Tous ces événements peuvent être décrits dans le contexte d’un micro-éveil du sommeil. Cette thèse comprend quatre articles de recherche visant à étudier i) l'étiologie de l’ARMM pendant le sommeil en relation aux micro-éveils, et à évaluer ii) les aspects cliniques du bruxisme du sommeil, du point de vue diagnostique et thérapeutique. Pour approfondir l'étiologie de l’ARMM et son association avec la fluctuation des micro-éveils, nous avons analysé le patron cyclique alternant (ou cyclic alternating pattern (CAP) en anglais), qui est une méthode d’analyse qui permet d’évaluer l'instabilité du sommeil et de décrire la puissance des micro-éveils. Le CAP a été étudié chez des sujets bruxeurs et des sujets contrôles qui ont participé à deux protocoles expérimentaux, dans lesquels la structure et la stabilité du sommeil ont été modifiées par l'administration d'un médicament (la clonidine), ou avec l'application de stimulations sensorielles (de type vibratoire/auditif) pendant le sommeil. Dans ces deux conditions expérimentales caractérisées par une instabilité accrue du sommeil, nous étions en mesure de démontrer que les micro-éveils ne sont pas la cause ou le déclencheur de l’ARMM, mais ils représentent plutôt la «fenêtre permissive» qui facilite l'apparition de ces mouvements rythmiques au cours du sommeil. Pour évaluer la pertinence clinique du bruxisme, la prévalence et les facteurs de risque, nous avons effectué une étude épidémiologique dans une population pédiatrique (7-17 ans) qui était vue en consultation en orthodontie. Nous avons constaté que le bruxisme est un trouble du sommeil très fréquent chez les enfants (avec une prévalence de 15%), et il est un facteur de risque pour l'usure des dents (risque relatif rapproché, RRR 8,8), la fatigue des muscles masticateurs (RRR 10,5), les maux de tête fréquents (RRR 4,3), la respiration bruyante pendant le sommeil (RRR 3,1), et divers symptômes liés au sommeil, tels que la somnolence diurne (RRR 7,4). Ces résultats nous ont amenés à développer une étude expérimentale pour évaluer l'efficacité d'un appareil d'avancement mandibulaire (AAM) chez un groupe d'adolescents qui présentaient à la fois du bruxisme, du ronflement et des maux de tête fréquents. L'hypothèse est que dans la pathogenèse de ces comorbidités, il y a un mécanisme commun, probablement lié à la respiration pendant le sommeil, et que l'utilisation d'un AAM peut donc agir sur plusieurs aspects liés. À court terme, le traitement avec un AAM semble diminuer l'ARMM (jusqu'à 60% de diminution), et améliorer le ronflement et les maux de tête chez les adolescents. Cependant, le mécanisme d'action exact des AAM demeure incertain; leur efficacité peut être liée à l'amélioration de la respiration pendant le sommeil, mais aussi à l'influence que ces appareils pourraient avoir sur le système masticatoire. Les interactions entre le bruxisme du sommeil, la respiration et les maux de tête, ainsi que l'efficacité et la sécurité à long terme des AAM chez les adolescents, nécessitent des études plus approfondies.Approximately 60% of the general adult population experiences rhythmic masticatory muscle activity (RMMA) during sleep. The etiology of this movement is not yet understood. However, it has been demonstrated that an increased frequency of RMMA may have detrimental consequences on the stomatognathic system. In this case, RMMA is considered the manifestation of a sleep-related motor disorder known as sleep bruxism (SB). According to the definition of the International Classification of Sleep Disorders, SB is the activity of tooth grinding and clenching during sleep. The occurrence of SB-related activity, i.e., RMMA, is associated with rises of sympathetic tone, heart rate, blood pressure, and it is frequently concomitant with larger respiratory breaths. All these events can be described within a sleep arousal. The present thesis includes four research articles aimed to study i) the etiology of RMMA during sleep in relation to sleep arousal; and ii) the clinical perspectives of SB assessment and management. To further investigate the etiology of RMMA and its association with sleep arousal fluctuations we analyzed the cyclic alternating pattern (CAP), a scoring method to assess sleep instability and describe sleep arousal pressure. CAP was scored in SB subjects and controls that participated in two experimental protocols in which sleep architecture and stability were altered by either a medication (i.e., clonidine), or sensory stimulations (i.e., vibratory/auditory). Under these experimental conditions known to increase sleep instability, we were able to show that sleep arousal is not the trigger or cause of RMMA, rather the “permissive window” that facilitates the occurrence of RMMA during sleep. To evaluate the clinical relevance of SB, we conducted a survey on a 7-17 year old orthodontic population to investigate the prevalence and risk factors associated with SB. It appeared that SB is a highly prevalent sleep disorders in children (15% of prevalence), and is a risk factor for tooth wear (odds ratio, OR 8.8), jaw muscle fatigue (OR 10.5), frequent headache (OR 4.3), loud breathing during sleep (OR 3.1), and several sleep complaints, such as daytime sleepiness (OR 7.4). These findings led us to design an experimental trial using a mandibular advancement appliance (MAA) in adolescents in order to investigate the possible relationship between SB, snoring, and headache. We hypothesized that a common underlying mechanism related to breathing during sleep may be responsible for all concomitant conditions. The short-term use of an MAA appeared to reduce SB (up to 60%), and improve snoring and headache complaints in adolescents. However, the precise mechanism of action of MAA remains under debate; its effectiveness can be either related to the improvement of breathing during sleep, or its influence on the masticatory system. The interactions between SB, breathing, and headache as well as the long-term effectiveness and safety of the MAA in adolescents need further investigations.L’attività ritmica dei muscoli masticatori (ARMM) durante il sonno si osserva in circa il 60% della popolazione generale adulta. L'eziologia di questo movimento non è stata ancora del tutto compresa. Tuttavia, è dimostrato che un’aumentata frequenza di ARMM può avere conseguenze negative sul sistema stomatognatico. In questo caso, l’ARMM è considerato la manifestazione di un disturbo motorio del sonno noto come bruxismo. Secondo la Classificazione Internazionale dei Disturbi del Sonno, il bruxismo è l'attività di digrignamento e serramento dei denti durante il sonno. La comparsa di episodi di ARMM durante il sonno è associata a un aumento del tono del sistema nervoso simpatico, della frequenza cardiaca, della pressione arteriosa, ed è spesso in concomitanza con un aumentato volume inspiratorio. Le variazioni di questi parametri fisiologici sono compresi nel contesto di un arousal (micro risveglio) del sonno. Questa tesi comprende quattro articoli di ricerca volti a studiare i) l'eziologia dell’ARMM durante il sonno in relazione all’arousal, ed a valutare ii) l’inquadramento clinico del bruxismo nel sonno. Per approfondire l'eziologia dell’ARMM e l’associazione con l’arousal nel sonno, abbiamo analizzato il cyclic alternating pattern (CAP), che permette di valutare l'instabilità del sonno e descrivere la potenza degli arousals. Il CAP è stato esaminato in soggetti con bruxismo e soggetti controllo che hanno partecipato in due protocolli sperimentali, in cui la struttura e la stabilità del sonno sono stati modificati con la somministrazione di un farmaco (la clonidina), o con l’applicazione di stimolazioni sensoriali (di tipo vibratorio/uditivo) durante il sonno. In queste condizioni sperimentali caratterizzate da un’aumentata instabilità del sonno, siamo stati in grado di dimostrare che l’arousal non è la causa o il generatore dell’ARMM, ma piuttosto la "finestra permissiva" che facilita il verificarsi di questi movimenti ritmici durante il sonno. Per valutare la rilevanza clinica del bruxismo, abbiamo condotto uno studio epidemiologico in una popolazione pediatrica afferente alla clinica di ortodonzia per studiare la prevalenza e i fattori di rischio associati al bruxismo. Questa ricerca ha evidenziato che il bruxismo è un comune disturbo del sonno nei bambini (con una prevalenza del 15%), ed è un fattore di rischio per usura dentale (odds ratio, OR 8.8), fatica dei muscoli masticatori (OR 10.5), mal di testa frequenti (OR 4.3), respirazione rumorosa durante il sonno (OR 3.1), e diversi sintomi legati al sonno, quali la sonnolenza diurna (OR 7.4). Questi risultati ci hanno portato a progettare uno studio sperimentale per valutare l’efficacia di un apparecchio di avanzamento mandibolare (AAM) in un gruppo di adolescenti che presentavano al contempo bruxismo, russamento e frequenti cefalee. L’ipotesi è che nella patogenesi di tali comorbidità, vi sia un meccanismo comune, probabilmente legato alla respirazione durante il sonno, e che l’utilizzo di un AAM possa quindi avere un beneficio multiplo. Il trattamento a breve termine con un AAM sembra diminuire l’ARMM (fino al 60%) e migliorare il russamento e i mal di testa negli adolescenti. Tuttavia, l'esatto meccanismo di azione degli AAM rimane incerto; la loro efficacia può essere correlata sia al miglioramento della respirazione durante il sonno, ma anche all’influenza che questi apparecchi svolgono sul sistema masticatorio. Le interazioni tra il bruxismo nel sonno, la respirazione, e le cefalee, così come l'efficacia e la sicurezza a lungo termine degli AAM negli adolescenti, necessitano di ulteriori studi clinici

Utilidad de las señales de oximetría y flujo aéreo en el diagnóstico simplificado de la apnea obstructiva del sueño. Diseño de un test automático domiciliario

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by recurrent episodes of total (apnea) or partial (hypopnea) absence of airflow during sleep. Untreated OSA produces a significant decrease in quality of life and is associated with the main causes of mortality in industrialized countries.However, OSA is considered an underdiagnosed chronic disease. Continuous positive airway pressure (CPAP) is the most common therapeutic option. Nocturnal polysomnography (PSG) in a specialized sleep unit is the reference diagnostic method, although it has low availability and accessibility. Consequently, in recent years there has been a significant demand for abbreviated methods, most of them at home, to reduce waiting lists. The fundamental hypothesis that the use of automatic processing techniques based on machine learning tools could allow maximizing the diagnostic accuracy of a reduced set of combined biomedical signals: overnight oximetry and airflow recorded at patient&#8217;s home. The main objective was to evaluate whether the joint analysis by means of machine learning algorithms of unsupervised SpO2 and AF signals acquired at patient's home leads to a significant increase in diagnostic performance compared to single-channel approaches. A prospective observational study was carried out in which a population referred consecutively to the Sleep Unit showing moderate-to-high clinical suspicion of having OSA was analyzed.All patients underwent an unsupervised PSG at home(gold standard) from which the SpO2 and AF signals were extracted, which were subsequently processed offline.The apnea-hypopnea index(AHI) derived from the PSG was used to confirm or rule out the presence of the disease.Three different approaches for screening patients with suspected OSA were assessed in terms of the source of information used: single-channel based on SpO2, single-channel based on AF, and two-channel combining information from both SpO2 and AF.The automatic processing of the SpO2 and AF signals was developed in 4 stages: preprocessing, feature extraction, feature selection, and pattern recognition. Unsupervised SpO2 and AF recordings were parameterized using the fast correlation-based filter(FCBF)algorithm.The following machine learning methods were used: linear regression(MLR), multilayer perceptron neural networks(MLP) and support vector machines(SVM). The population was divided into independent training and test groups. Agreement between the estimated and the actual AHIderived from at-home PSG was assessed, and typical OSA cutoff points(5, 15, and 30 events/h) were applied. A total of 299 unattended PSGs were performed at home, with a validity percentage of 85.6%. The highest agreement between the estimated AHI and the PSG AHI was reached by the SVMSpO2+AF model, with an CCI 0.93 and a 4-class kappa index 0.71, as well as with an overall accuracy for the 4 OSA severity categories equal to 81.25%, significantly higher than the individual analysis of the SpO2 signal and the airflow signal.The SVMSpO2+AF model achieved the highest diagnostic performance of all algorithms for the detection of severe OSA, with an accuracy of 95.83% and AUC ROC 0.98. In addition, the AUC ROC of the dual-channel models was significantly higher (p<0.01) than that achieved by all the single-channel approaches for the cutoff of 15events/h. The proposed methodology based on the joint automatic analysis of the SpO2 and AF signals acquired at home showed a high complementarity that led to a remarkable increase in diagnostic performance compared to single-channel approaches. The automatic models outperformed the conventional indices(desaturation and airflow-derived indexes) both in terms of correlation and concordance with the AHI from PSG, as well as in terms of overall diagnostic accuracy, providing a moderate increase in diagnostic performance, particularly in the detection of moderate-to-severe OSA.Our findings suggest that the joint analysis of oximetry and airflow signals by means of machine learning methods allows a simplified as well as accurate screening of OSA at patient's home.La Apnea Obstructiva del Sueño (AOS) es un trastorno respiratorio crónico infradiagnosticado caracterizado por la repetición recurrente de episodios de ausencia total (apnea) o parcial (hipopnea) del flujo aéreo (FA) durante el sueño, que disminuye la calidad de vida y aumenta la mortalidad. La CPAP es el tratamiento más habitual, no invasivo, eficaz y coste-efectivo, por lo que favorecer el proceso de diagnóstico es fundamental. La PSG nocturna es el método diagnóstico de referencia, presentando baja disponibilidad y accesibilidad, lo que ha contribuido a desbordar los recursos disponibles, retrasando el diagnóstico y el tratamiento. En contexto de la simplificación diagnóstica portátil, en auge, el uso de únicamente una (monocanal) o dos (bi-canal) señales, como las de SpO2 y FA ha sido ampliamente explorado, aunque la mayoría en entornos hospitalarios controlados. La hipótesis se fundamenta en que las técnicas de procesado automático basadas en machine learning podrían maximizar la precisión diagnóstica de un conjunto reducido de señales combinadas. El objetivo consistió en evaluar si el análisis conjunto mediante algoritmos de aprendizaje automático de las señales de SpO2 y FA no supervisadas adquiridas en el domicilio aumenta el rendimiento diagnóstico en comparación con los enfoques de un solo canal. Se llevó a cabo un estudio observacional prospectivo en pacientes con sospecha moderada-alta de AOS. Se realizó una PSG no supervisada en su domicilio (gold standard de referencia), de la que se extrajeron las señales de SpO2 y FA, procesadas offline posteriormente. El índice de apnea-hipopnea (IAH) derivado de la PSG se empleó para confirmar o descartar la presencia de la enfermedad. Se implementaron y compararon 3 metodologías de screening en función de la fuente de información empleada: (1) monocanal basado en SpO2, (2) monocanal basado en FA, (3) bi-canal combinando SpO2 y FA. El procesado automático de las señales de SpO2 y FA se desarrolló en 4 etapas: preprocesado, extracción de características, selección de características (mediante fast correlation-based filter, FCBF) y reconocimiento de patrones. Cada enfoque de screening se empleó para estimar automáticamente el IAH utilizando los siguientes métodos de machine learning: (1) regresión lineal múltiple (MLR), (2) redes neuronales perceptrón multicapa (MLP) y (3) máquinas vector soporte (SVM). La población se dividió en grupos independientes de entrenamiento (60%) y test (40%). Se realizaron un total de 299 PSGs domiciliarias. Los modelos de enfoque combinado bi-canal alcanzaron valores de concordancia entre el IAH estimado y el IAH de la PSG domiciliaria y de rendimiento diagnóstico para todos los puntos de corte típicos de AOS (5, 15 y 30 e/h) superiores al enfoque monocanal. La mayor concordancia fue alcanzada por el modelo SVMSpO2+FA (CCI 0.93, kappa4 clases 0.71, precisión global 81.25%), significativamente superior a los análisis individuales. El modelo SVMSpO2+FA alcanzó el mayor rendimiento diagnóstico de todos los algoritmos para la detección de AOS grave (precisión 95.83% y AUC ROC 0.98). Además, el AUC ROC de los modelos bi-canal fue superior (p <0.01) al de los enfoques monocanal para el punto de corte de 15 e/h. La metodología propuesta basada en el análisis automático conjunto de las señales de SpO2 y FA adquiridas en el domicilio mostró una alta complementariedad y un notable aumento del rendimiento diagnóstico en comparación con los enfoques monocanal. Los modelos automáticos superaron globalmente a los índices clásicos (de desaturación y de eventos de flujo aéreo), aportando un incremento moderado del rendimiento diagnóstico particularmente en la detección de AOS moderado-grave. Los resultados obtenidos indican que el análisis conjunto de las señales de oximetría y flujo mediante métodos de aprendizaje automático permite un screening simplificado a la vez que preciso de la AOS en el domicilio del paciente.Escuela de DoctoradoDoctorado en Investigación en Ciencias de la Salu