Finding the Inner Clock: A Chronobiology-based Calendar

Time and its lack of play a central role in our everyday lives. Despite increasing productivity, many people experience time stress, exhaustion and a longing for time affluence, and at the same time, a fear of not being busy enough. All this leads to a neglect of natural time, especially the patterns and rhythms created by physiological processes, subsumed under the heading of chronobiology. The present paper presents and evaluates a calendar application, which uses chronobiological knowledge to support people s planning activities. Participants found our calendar to be interesting and engaging. It especially made them think more about their bodies and appropriate times for particular activities. All in all, it supported participants in negotiating. external demands and personal health and wellbeing. This shows that technology does not necessarily has to be neutral or even further current (mal-)practices. Our calendar cares about changing perspectives and thus about enhancing users wellbeing.Comment: 7 pages to be published in the Extended Abstracts of the 2020 CHI ConferenceConference on Human Factors in Computing System

REGULATION PHYSIOLOGIQUE DE LA VIGILANCE (RELATIONS ENTRE LES ACTIVITES ELECTROENCEPHALOGRAPHIQUES ET CORTICOTROPE AU COURS DU SOMMEIL ET DE LA VEILLE)

LES MECANISMES PHYSIOLOGIQUES DE REGULATION DE LA VIGILANCE, CONCEPT OPERATIONNEL D'EFFICIENCE COMPORTEMENTALE ET DE PERFORMANCE, NE SONT PAS ENTIEREMENT ELUCIDES. SI CERTAINES INVESTIGATIONS ONT PU ETABLIR L'EXISTENCE DE RELATIONS ENTRE LE SYSTEME NEUROENDOCRINIEN ET LES DIFFERENTS ETATS DE VIGILANCE, LE ROLE DES RYTHMES HORMONAUX ET LEUR IMPLICATION DANS LA REGULATION DE LA VIGILANCE RESTENT OBSCURS. L'OBJECTIF DE CE TRAVAIL, CONDUIT CHEZ L'HOMME A L'AIDE DE NOUVELLES TECHNIQUES DE MESURE ET D'ANALYSE DE L'ELECTROENCEPHALOGRAMME (EEG) ET DES SECRETIONS HORMONALES, A ETE 1) D'IDENTIFIER DES INDICES D'ACTIVATION EEG QUI REFLETERAIENT LES FLUCTUATIONS ENDOGENES DE LA VIGILANCE ET 2) D'ETUDIER LES RELATIONS DE CES INDICES AVEC L'ACTIVITE DE L'AXE HYPOTHALAMO-HYPOPHYSO-CORTICOSURRENALIEN. LA DYNAMIQUE TEMPORELLE DU SPECTRE DE L'EEG DE VEILLE, MANIPULEE EXPERIMENTALEMENT PAR LA PRIVATION DE SOMMEIL ET PAR L'ADMINISTRATION DE STIMULANTS, MONTRE UNE RYTHMICITE CIRCADIENNE ET UNE REPONSE HOMEOSTASIQUE A LA PRIVATION DE SOMMEIL, QUI DEPENDENT DE LA FREQUENCE EEG, AINSI QU'UNE PERIODICITE ULTRADIENNE LENTE (3-4 HEURES) DE TOUT LE SPECTRE. LES RYTHMES CIRCADIENS DES ACTIVITES (13,5-20 HZ) ET (20-30 HZ) SEMBLENT REFLETER DEUX MODES DISTINCTS D'ACTIVATION CEREBRALE EN DEPHASAGE DE 6-7 HEURES ET POURRAIENT SERVIR DE BASE A L'ELABORATION D'UN NOUVEAU MODELE CAPABLE DE REPRODUIRE, A PARTIR DE MESURES DIRECTES DE L'EEG, L'ASPECT BIPHASIQUE DU PROFIL NYCTHEMERAL DE LA VIGILANCE. LES RYTHMES ULTRADIENS D'ACTIVATION CEREBRALE ET CORTICOTROPE MONTRENT UN FORT COUPLAGE TEMPOREL, LA LIBERATION DE CORTISOL ETANT ASSOCIEE EN OPPOSITION DE PHASE AVEC L'ACTIVITE (0,5-3,5 HZ) PENDANT LE SOMMEIL ET EN PHASE AVEC L'ACTIVITE (20-45 HZ) PENDANT LA PENDANT VEILLE. LORS DE LA PRIVATION DE SOMMEIL, L'ACTIVITE ET LA SECRETION DE CORTISOL SONT AUGMENTEES PROPORTIONNELLEMENT, LE COUPLAGE TEMPOREL DE LEURS FLUCTUATIONS ETANT AFFAIBLI. CES RESULTATS SUGGERENT L'EXISTENCE D'UN MECANISME COMMUN DE CONTROLE DE L'EEG ET DE LA SECRETION DE CORTISOL. LE COUPLAGE DES ACTIVITES EEG ET CORTICOTROPE AU COURS DU CYCLE VEILLE-SOMMEIL CONTRIBUE PROBABLEMENT A LA REGULATION DU METABOLISME ENERGETIQUE CEREBRAL. EN SITUATION PATHOLOGIQUE OU DE STRESS, LE DECOUPLAGE DE CES ACTIVITES POURRAIT ETRE A L'ORIGINE DE TROUBLES DE LA VIGILANCE ET DES RYTHMES BIOLOGIQUES.STRASBOURG-Sc. et Techniques (674822102) / SudocSudocFranceF

Automated sleep-wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules

International audienceThe classification of sleep-wake stages suffers from poor standardization in scoring criteria and heterogeneous conditioning of polysomnographic signals. To improve applicability of fully automated sleep staging, we have designed a formal classification framework to rigorously (1) select robust candidate features, (2) emulate artificial neural network classifiers, and (3) assign sleep-wake stages using flexible decision rules. An extensive database of 48 PSG records scored in 20s epochs by two independent clinicians was used. A small subset of 2 s elementary epochs representative of each stages with unequivocal expert scores was selected to form a limited set of learning exemplars. From 16 statistical, spectral and non-linear candidate features extracted in 2s epochs from EEG and EMG signals, a sequential forward search selected an optimal set of five features with a 22% error rate. Multiple layer perceptrons were trained from this optimal feature set while classification accuracy was assessed using the unequivocal instance subset. A simple majority vote among 10 consecutive classifier outputs ensured a final scoring resolution comparable to that of the experts. Poor classification performance was obtained for movement time, wakefulness, and intermediate sleep stages with a 36±15% error rate (Cohen's kappa 0.48±0.18). In contrast, deep and paradoxical sleep was classified with an 82% accuracy not far from inter-expert expert agreement (83±3%). Significant improvements should be expected using a larger learning set compensating for a high inter-individual variability, and decision rules incorporating more domain-knowledge

Self evaluated automatic classifier as a decision-support tool for sleep/wake staging

International audienceAn automatic sleep/wake stages classifier that deals with the presence of artifacts and that provides a confidence index with each decision is proposed. The decision system is composed of two stages: the first stage checks the 20 seconds epoch of polysomnographic signals (EEG, EOG and EMG) for the presence of artifacts and selects the artifact-free signals. The second stage classifies the epoch using one classifier selected out of four, using feature inputs extracted from the artifact-free signals only. A confidence index is associated with each decision made, depending on the classifier used and on the class assigned, so that the user's confidence in the automatic decision is increased. The two-stage system was tested on a large database of 46 night recordings. It reached 85.5% of overall accuracy with improved ability to discern NREM I stage from REM sleep. It was shown that only 7% of the database was classified with a low confidence index, and thus should be re-evaluated by a physiologist expert, which makes the system an efficient decision-support tool

Artificial Neural Network Scoring of Human Sleep-wake Stages Combining Short-epoch Feature Extraction and Post-processing Inference Rules

International audienc

A sleep/wake stages classifier dealing with artefacts corrupting the polysomnographic signals

International audienc

Feature selection for sleep/wake stages classification using data driven methods

This paper focuses on the problem of selecting relevant features extracted from human polysomnographic (PSG) signals to perform accurate sleep/wake stages classification. Extraction of various features from the electroencephalogram (EEG), the electro-oculogram (EOG) and the electromyogram (EMG) processed in the frequency and time domains was achieved using a database of 47 night sleep recordings obtained from healthy adults in laboratory settings. Multiple iterative feature selection and supervised classification methods were applied together with a systematic statistical assessment of the classification performances. Our results show that using a simple set of features such as relative EEG powers in five frequency bands yields an agreement of 71% with the whole database classification of two human experts. These performances are within the range of existing classification systems. The addition of features extracted from the EOG and EMG signals makes it possible to reach about 80% of agreement with the expert classification. The most significant improvement on classification accuracy is obtained on NREM sleep stage 1, a stage of transition between sleep and wakefulness.Web of Science2317917

A two-steps sleep/wake stages classifier taking into account artefacts in the polysomnographic signals

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Cortisol secretion is related to electroencephalographic alertness in human subjects during daytime wakefulness

ABSTRACT To determine whether human hypothalamo-pituitary-adrenal axis activity is related to the alertness level during wakefulness, 10 healthy young men were studied under resting conditions in the daytime (0900 -1800 h) after an 8-h nighttime sleep (2300 -0700 h). A serial 70-sec gaze fixation task was required every 10 min throughout the daytime experimental session. The corresponding waking electroencephalographic (EEG) segments were submitted to quantitative spectral analysis, from which EEG ␤ activity (absolute power density in the 13-35 Hz frequency band), an index of central alertness, was computed. Blood was collected continuously through an indwelling venous catheter and sampled at 10-min intervals. Plasma cortisol concentrations were measured by RIA, and the corresponding secretory rates were determined by a deconvolution procedure. Analysis of individual profiles demonstrated a declining tendency for EEG ␤ activity and cortisol secretory rate, with an overall temporal relationship indicated by positive and significant cross-correlation coefficients between the two variables in all subjects (average r ϭ 0.565, P Ͻ 0.001). Changes in cortisol secretion lagged behind fluctuations in EEG ␤ activity, with an average delay of 10 min for all the subjects. On the average, 4.6 Ϯ 0.4 cortisol secretory pulses and 4.9 Ϯ 0.5 peaks in EEG ␤ activity were identified by a detection algorithm. A significant, although not systematic, association between the episodes in the two variables was found: 44% of the peaks in EEG ␤ activity (relative amplitude, near 125%; P Ͻ 0.001) occurred during an ascending phase of cortisol secretion, cortisol secretory rates increasing by 40% (P Ͻ 0.01) 10-min after peaks in EEG ␤ activity. However, no significant change in EEG ␤ activity was observed during the period from 50 min before to 50 min after pulses in cortisol secretion. In conclusion, the present study describes a temporal coupling between cortisol release and central alertness, as reflected in the waking EEG ␤ activity. These findings suggest the existence of connections between the mechanisms involved in the control of hypothalamo-pituitary-adrenal activity and the activation processes of the brain, which undergoes varying degrees of alertness throughout daytime wakefulness. (J Clin Endocrinol Metab 83: [4263][4264][4265][4266][4267][4268] 1998) C ORTISOL is released in pulses by adrenocortical glands under pituitary ACTH control, with a periodicity of 80 -110 min in man (1). The 24-h pattern of cortisol levels shows an early-morning acrophase and an evening nadir (2). This is the consequence of an amplitude modulation of ACTH secretory bursts (3), probably driven by the suprachiasmatic nucleus of the hypothalamus, which controls CRH and arginine vasopressin cells of the paraventricular nucleus (4). Although cortisol secretion is known to be primarily under a circadian influence (5), independent of sleeping and waking, several studies in humans have suggested that sleep, especially slow-wave sleep, may exert an inhibitory influence on cortisol secretion (6, 7). Other authors argue that an underlying mechanism decreases cortisol secretion and facilitates sleep onset and slow-wave sleep installation (8 -10). Dynamic relationships have been described between human sleep electroencephalographic (EEG) activity, which reflects central nervous sleep processes, and cortisol secretory activity Although sleep EEG has been extensively studied (14), the time course of the waking EEG activity has been studied far less, because of artifacts contaminating EEG recordings. However, diurnal fluctuations of the human background EEG, a neurophysiological indicator of the brain's functional state (15), have been shown to occur spontaneously, with patterns depending on the EEG spectrum frequency ban