Sleep, and its Relation to Non-Motor Deficits in Patients with Cerebellar Ataxia

The cerebellum is a highly connected structure, and its involvement in sleep – which is a dynamic process that is modulated by a complex set of neural systems – can come about through a number of neural pathways. We conducted two studies aimed at furthering our understanding of cerebellar involvement in sleep behavior and physiology, as well as measuring the impact of poor sleep on mood and cognition in patients with cerebellar degeneration. First, by means of an online battery including measures of sleep and neuropsychiatric function, we collected data from 176 patients with cerebellar ataxia. We found strong evidence of poor subjective sleep quality, symptoms of movement-related sleep disorders, and excessive daytime sleepiness in this sample. Importantly, poor subjective sleep was associated with both diminished perceptions of cognitive abilities and depression symptomatology. Second, in order to determine whether the benefit of sleep on declarative associative learning, previously observed in healthy controls, was affected by cerebellar degeneration, we compared overnight changes in performance on a word-pair association task between patients with pure cerebellar syndrome and matched-controls. By means of polysomnography recordings, we demonstrated significantly greater fragmentation of sleep and periodic limb movement indices in patients relative to controls. Although patients demonstrated impaired learning of the word-pair association task – which was significantly correlated with sleep fragmentation – there were no differences between patients and controls with respect to overnight change in accuracy on the word-pair task. Taken together, these findings suggest that inefficient sleep and the presence of sleep disorders in patients with cerebellar ataxia might exacerbate deficits in certain non-motor domains, while other processes – namely those associated with sleep-dependent declarative memory consolidation – remain intact

Combining wearables and nearables for patient state analysis

Recently, ambient patient monitoring using wearable and nearable sensors is becoming more prevalent, especially in the neurodegenerative (Rett syndrome) and sleep disorder (Obstructive sleep apnea) populations. While wearables capture localized physiological data such as pulse rate, wrist acceleration and brain signals, nearables record global passive data including body movements, ambient sound and environmental variables. Together, wearables and nearables provide a more comprehensive understanding of the patient state. The processing of data captured from wearables and nearables have multiple challenges including handling missing data, time synchronization between sensors and developing data fusion techniques for multimodal analysis. The research described in this thesis addresses these issues while working on data captured in the wild. First, we describe a Rett syndrome severity estimator using a wearable biosensor and uncover physio-motor biomarkers. Second, we present the applications of an edge computing and ambient data capture system for home and clinical environments. Finally, we describe a transfer learning and multimodal data fusion based sleep-wake detector for a mixed-disorder elderly population. We show that combining data from wearables and nearables improves the performance of sleep-wake detection in terms of the F1-score and the Cohen’s kappa compared to the unimodal models.Ph.D

Vauvojen unen luokittelu patja-sensorilla ja EKG:lla

Infants spend the majority of their time asleep. Although extensive studies have been carried out, the role of sleep for infant cognitive, psychomotor, temperament and developmental outcomes is not clear. The current contradictory results may be due to the limited precision when monitoring infant sleep for prolonged periods of time, from weeks to even months. Sleep-wake cycle can be assessed with sleep questionnaires and actigraphy, but they cannot separate sleep stages. The gold standard for sleep state annotation is polysomnography (PSG), which consist of several signal modalities such as electroencephalogram, electrooculogram, electrocardiogram (ECG), electromyogram, respiration sensor and pulse oximetry. A sleep clinician manually assigns sleep stages for 30 sec epochs based on the visual observation of these signals. Because method is obtrusive and laborious it is not suitable for monitoring long periods. There is, therefore, a need for an automatic and unobtrusive sleep staging approach. In this work, a set of classifiers for infant sleep staging was created and evaluated. The cardiorespiratory and gross body movement signals were used as an input. The different classifiers aim to distinguish between two or more different sleep states. The classifiers were built on a clinical sleep polysomnography data set of 48 infants with ages ranging from 1 week to 18 weeks old (a median of 5 weeks). Respiration and gross body movements were observed using an electromechanical film bed mattress sensor manufactured by Emfit Ltd. ECG of the PSG setup was used for extracting cardiac activity. Signals were preprocessed to remove artefacts and an extensive set of features (N=81) were extracted on which the classifiers were trained. The NREM3 vs other states classifier provided the most accurate results. The median accuracy was 0.822 (IQR: 0.724-0.914). This is comparable to previously published studies on other sleep classifiers, as well as to the level of clinical interrater agreement. Classification methods were confounded by the lack of muscle atonia and amount of gross body movements in REM sleep. The proposed method could be readily applied for home monitoring, as well as for monitoring in neonatal intensive care units.Vauvat nukkuvat suurimman osan vuorokaudesta. Vaikkakin laajasti on tutkittu unen vaikutusta lapsen kognitioon, psykomotoriikkaan, temperamenttiin ja kehitykseen, selkeää kuvaa ja yhtenäistä konsensusta tiedeyhteisössä ei ole saavutettu. Yksi syy tähän on että ei ole olemassa menetelmää, joka soveltuisi jatkuva-aikaiseen ja pitkäkestoiseen unitilan monitorointiin. Vauvojen uni-valve- sykliä voidaan selvittää vanhemmille suunnatuilla kyselyillä ja aktigrafialla, mutta näillä ei voi havaita unitilojen rakennetta. Kliinisenä standardina unitilojen seurannassa on polysomnografia, jossa samanaikaisesti mitataan mm. potilaan elektroenkelografiaa, elektro-okulografiaa, elektrokardiografiaa, electromyografiaa, hengitysinduktiivisesta pletysmografiaa, happisaturaatiota ja hengitysvirtauksia. Kliinikko suorittaa univaiheluokittelun signaaleista näkyvien, vaiheille tyypillisten, hahmojen perusteella. Työläyden ja häiritsevän mittausasetelman takia menetelmä ei sovellu pitkäaikaiseen seurantaan. On tarvetta kehittää tarkoitukseen sopivia automaattisia ja huomaamattomia unenseurantamenetelmiä. Tässä työssä kehitettiin ja testattiin sydämen syke-, hengitys ja liikeanalyysiin perustuvia koneluokittimia vauvojen unitilojen havainnointiin. Luokittimet opetettiin kliinisessa polysomnografiassa kerätyllä datalla 48 vauvasta, joiden ikä vaihteli 1. viikosta 18. viikkoon (mediaani 5 viikkoa). Vauvojen hengitystä ja liikkeitä seurattiin Emfit Oy:n valmistamalla elektromekaaniseen filmiin pohjatuvalla patja-sensorilla. Lisäksi ECG:lla seurattiin sydäntä ja opetuksessa käytettiin lääkärin suorittamaa PSG-pohjaista luokitusta. Esikäsittelyn jälkeen signaaleista laskettiin suuri joukko piirrevektoreita (N=81), joihin luokittelu perustuu. NREM3-univaiheen tunnistus onnistui parhaiten 0.822 mediaani-tarkkuudella ja [0.724,0.914] kvartaaleilla. Tulos on yhtenevä kirjallisuudessa esitettyjen arvojen kanssa ja vastaa kliinikkojen välistä toistettavuutta. Muilla luokittimilla univaiheet sekoituivat keskenään, mikä on oletattavasti selitettävissä aikuisista poikeavalla REM-unen aikaisella lihasjäykkyydellä ja kehon liikkeillä. Työ osoittaa, että menetelmällä voi seurata vauvojen uniluokkien oskillaatiota. Järjestelmää voisi käyttää kotiseurannassa tai vastasyntyneiden teholla unenvalvontaan

Investigating the role of sleep fragmentation in declarative memory and affect

Sleep plays an active role in the formation and storage of declarative memories. These processes are thought to depend both on the duration and continuity of sleep. This thesis investigated the proposition that sleep fragmentation uniquely contributes to the variance in encoding error and overnight forgetting whilst controlling for sleep duration. In Experiment 1, closely related word pairs had a general advantage over more distal word pairs at encoding but there were no group differences in overnight retention between the two conditions in a novel word learning task. In Experiment 2, results suggested that interference does not occur between spatial and verbal declarative memory tasks during sleep. Two large naturalistic pre-sleep/post-sleep online memory studies (Experiments 3 and 4) went on to use hierarchical multilevel modelling to control for the duration of sleep statistically. In Experiment 3, increased awakenings were associated with increased encoding error and increased overnight forgetting in a sample of new parents and healthy controls, but only when the level of encoding error was controlled for. In Experiment 4, having Restless Legs Syndrome, characterised by sleep fragmentation, was also associated with increased encoding error, and overnight forgetting, again only when the level of encoding error was controlled for. A series of mixed-effects mega-analyses were carried out in Chapter 5 to better understand the degree to which subjective and more objective sleep measures are related to one another (e.g., sunshine and happiness) and agree with one another (e.g., a sundial and a clock). Chapter 5 showed that subjective and objective measures are related to and in agreement with one another, albeit weakly, and even less so among those with sleep disorders. It was argued that continuity is important for the formation and storage of declarative memories independently of time slept, and implications arising out of these insights are discussed

Endocrine and neurophysiological examination of sleep disorders in Williams syndrome

Background: A high rate of sleep disturbances have been reported in individuals with Williams syndrome (WS), but the underlying aetiology has yet to be identified. Melatonin and cortisol levels are known to affect and regulate sleep/wake patterns. We investigated the changing levels of these hormones in order to explore any relationship with sleep disturbances in children with WS. Methods: Twenty seven children with WS and 27 typically developing (TD) children were recruited. Sleep was monitored using actigraphy and pulse oximetry. Parents completed Children’s Sleep Habit Questionnaire (CSHQ). Saliva and first void morning urine samples were collected from the children. Saliva was collected at three time points: 4-6pm, before bedtime and first thing after awakening. Levels of salivary melatonin and cortisol were analysed by enzyme linked immunoassays. For determination of melatonin, cortisol and their metabolites in urine samples, specific Ultra-High Performance Liquid Chromatography-Tandem Mass Spectrometry (UHPLC-MS/MS) method was developed. Results: CSHQ and actigraphy indicated that children with WS were significantly affected by several types of sleep disturbances, including: abnormally high sleep latency and excessive night waking. Children in WS group had shallower falls in salivary cortisol levels and less pronounced rises in salivary melatonin at bedtime compared to TD controls (p < 0.01 and p = 0.04 respectively). Furthermore, it was found that children with WS also had significantly higher levels of bedtime cortisol compared to TD controls (p = 0.03). Using UHPLC-MS/MS analysis it was shown that children with WS secrete less melatonin during the night compared to healthy controls (p < 0.01). Also, levels of cortisone, a metabolite of cortisol were significantly higher in the WS group (p = 0.05). Conclusions: We found that children with WS had significant sleep disturbances which may be associated with their increased bedtime cortisol and lower evening melatonin. Both hormones play a significant role in the circadian rhythm and sleep/wake cycle, therefore it was necessary to look closely at these endocrine markers in individuals suffering from sleep disorders. Sleep problems in children with WS may adversely affect daytime activity and the quality of life, as well as social, emotional, health and economic functioning of the entire family. Hence, finding their cause is of great importance for affected children and their families

Ultra low power wearable sleep diagnostic systems

Sleep disorders are studied using sleep study systems called Polysomnography that records several biophysical parameters during sleep. However, these are bulky and are typically located in a medical facility where patient monitoring is costly and quite inefficient. Home-based portable systems solve these problems to an extent but they record only a minimal number of channels due to limited battery life. To surmount this, wearable sleep system are desired which need to be unobtrusive and have long battery life. In this thesis, a novel sleep system architecture is presented that enables the design of an ultra low power sleep diagnostic system. This architecture is capable of extending the recording time to 120 hours in a wearable system which is an order of magnitude improvement over commercial wearable systems that record for about 12 hours. This architecture has in effect reduced the average power consumption of 5-6 mW per channel to less than 500 uW per channel. This has been achieved by eliminating sampled data architecture, reducing the wireless transmission rate and by moving the sleep scoring to the sensors. Further, ultra low power instrumentation amplifiers have been designed to operate in weak inversion region to support this architecture. A 40 dB chopper-stabilised low power instrumentation amplifiers to process EEG were designed and tested to operate from 1.0 V consuming just 3.1 uW for peak mode operation with DC servo loop. A 50 dB non-EEG amplifier continuous-time bandpass amplifier with a consumption of 400 nW was also fabricated and tested. Both the amplifiers achieved a high CMRR and impedance that are critical for wearable systems. Combining these amplifiers with the novel architecture enables the design of an ultra low power sleep recording system. This reduces the size of the battery required and hence enables a truly wearable system.Open Acces

Adaptive wake and sleep detection for wearable systems

Sleep problems and disorders have a serious impact on human health and wellbeing. The rising costs for treating sleep-related chronic diseases in industrialized countries demands efficient prevention. Low-cost, wearable sleep / wake detection systems which give feedback on the wearer's "sleep performance" are a promising approach to reduce the risk of developing serious sleep disorders and fatigue. Not all bio-medical signals that are useful for sleep / wake discrimination can be easily recorded with wearable systems. Sensors often need to be placed in an obtrusive location on the body or cannot be efficiently embedded into a wearable frame. Furthermore, wearable systems have limited computational and energetic resources, which restrict the choice of sensors and algorithms for online processing and classification. Since wearable systems are used outside the laboratory, the recorded signals tend to be corrupted with additional noise that influences the precision of classification algorithms. In this thesis we present the research on a wearable sleep / wake classifier system that relies on cardiorespiratory (ECG and respiratory effort) and activity recordings and that works autonomously with minimal user interaction. This research included the selection of optimal signals and sensors, the development of a custom-tailored hardware demonstrator with embedded classification algorithms, and the realization of experiments in real-world environments for the customization and validation of the system. The processing and classification of the signals were based on Fourier transformations and artificial neural networks that are efficiently implementable into digital signal controllers. Literature analysis and empiric measurements revealed that cardiorespiratory signals are more promising for a wearable sleep / wake classification than clinically used signals such as brain potentials. The experiments conducted during this thesis showed that inter-subject differences within the recorded physiological signals make it difficult to design a sleep / wake classification model that can generalize to a group of subjects. This problem was addressed in two ways: First by adding features from another signal to the classifier, that is, measuring the behavioral quiescence during sleep using accelerometers. Conducted research on different feature extraction methods from accelerometer data showed that this data generalizes well for distinct subjects in the study group. In addition, research on user-adaptation methods was conducted. Behavioral sleep and wake measures, notably the measurement of reactivity and activity, were developed to build up a priori knowledge that was used to adapt the classification algorithm automatically to new situations. This thesis demonstrates the design and development of a low-cost, wearable hardware and embedded software for on-line sleep / wake discrimination. The proposed automatic user-adaptive classifier is advantageous compared to previously suggested classification methods that generalize over multiple subjects, because it can take changes in the wearer's physiology and sleep / wake behavior into account without adjustment from a human expert. The results of this thesis contribute to the development of smart, wearable, bio-physiological monitoring systems which require a high degree of autonomy and have only low computational resources available. We believe that the proposed sleep / wake classification system is a first promising step toward a context-aware system for sleep management, sleep disorder prevention, and reduction of fatigue