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8/27/2015 Signal processing methods in Sleep Research G. Garcia-Molina 1Philips 2 Research NA – Acute Care Solutions Department – Clinical site UW Madison University of Wisconsin-Madison Tutorial lecture - EUSIPCO 2015 1 EUSIPCO 2015, August 31, 2015 Why do we sleep? After all from an evolutionary point of view sleep seems like a bad idea, yet • Sleep is universal across species. • Sleep affects the vast majority of body functions including: immune function, hormonal regulation, metabolism, and thermoregulation. • Sleep’s core function appears to be for the brain (and by the brain). In particular for plasticity and memory. • During sleep, despite the functional disconnection from the environment, most neurons remain spontaneously active at levels similar to wakefulness Cirelli, C., & Tononi, G. (2008). Is sleep essential? PLoS Biology, 6(8), e216. 2 EUSIPCO 2015, August 31, 2015 1 8/27/2015 Sleep occupies more than 35% of our time other activities (19%) work and work-related (16%) Sleep 36% sport (1%) television (11%) phone/e-mail (1%) household activities (8%) eat/drink (5%) socializing (3%) American Time Use Survey, Bureau of Labor Statistics, 2007 annual averages. Calculations based on U.S. life expectancy of 77.8 years (which includes ~ 243,362 hours of sleep) 3 EUSIPCO 2015, August 31, 2015 In humans sleep need changes across life • • • • Watson, N., et al. . (2015). Recommended Amount of Sleep for a Healthy Adult : A Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society. SLEEP, 38(6), 843–844. 4 Adults should sleep 7 or more hours per night on a regular basis. Sleeping less than 7 hours per night on a regular basis is associated with adverse health outcomes, including weight gain and obesity, diabetes, hypertension, heart disease and stroke, depression, and increased risk of death. Sleeping less than 7 hours per night is also associated with impaired immune function, increased pain, impaired performance, increased errors, and greater risk of accidents. Sleeping more than 9 hours per night on a regular basis may be appropriate for young adults, individuals recovering from sleep debt, and individuals with illnesses. EUSIPCO 2015, August 31, 2015 2 8/27/2015 Lack of sleep significantly affects cognitive function Decreased attention Impaired working memory Impaired divergent, innovative thinking Impaired verbal fluency Impaired inhibitory control Increased risk-taking behavior Impaired humor appreciation Killgore W, Effects of sleep deprivation on cognition. Prog Brain Res 185: 105-129, 2010 5 EUSIPCO 2015, August 31, 2015 Theories on the function of sleep 6 EUSIPCO 2015, August 31, 2015 3 8/27/2015 The synaptic homeostasis hypothesis (SHY) attempts to explain why we sleep: Ø Plastic processes occurring during wakefulness result in a net increase in synaptic strength in many brain circuits. Ø The role of sleep is to downscale synaptic strength to a baseline level that is energetically sustainable, makes efficient use of space, and is beneficial for learning and memory. Tononi, G., & Cirelli, C. (2014). Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron, 81(1), 12–34. Synaptic potentiating High synaptic strength • Energy costs • Space costs • Supply costs Learning Synapses (base strength) • Energy savings • Space savings • Supply savings Baseline Wakefulness 7 Slow wave increase Synaptic Renormalization Slow wave decrease Sleep EUSIPCO 2015, August 31, 2015 According to the sequential hypothesis for memory processing during sleep, • Memories acquired during wakefulness are processed during sleep in two serial steps that occur respectively during slow-wave sleep (SWS) and rapid eye movement (REM) sleep. • During SWS memories to be retained are distinguished from irrelevant or competing traces that undergo downgrading or elimination • Processed memories are stored again during REM sleep which integrates them with preexisting memories Giuditta, A. (2014). Sleep memory processing: the sequential hypothesis. Frontiers in Systems Neuroscience, 8(December), 1–8. 8 EUSIPCO 2015, August 31, 2015 4 8/27/2015 Recent highlighted research from the University of Rochester has found that the brain flushes out toxins during sleep The cleanup system in the brain, responsible for flushing out toxic waste products that cells produce with daily use, goes into overdrive in mice that are asleep. The cells even shrink in size to make for easier cleaning of the spaces around them. • The difference of cerebrospinal fluid influx is seen (through fluorescent dye ) in the brain of an awake and a sleeping mouse. Image created by Katherine Streeter for NPR The green represents conversely restricted flow in the awake animal. The red represents the greater flow in a sleeping animal. 9 Xie, L., Kang, H., Xu, Q., Chen, M. J., Liao, Y., Thiyagarajan, M., Nedergaard, M. (2013). Sleep drives metabolite clearance from the adult brain. Science, 342(6156), 373–377. EUSIPCO 2015, August 31, 2015 Recent highlighted research from the University of Rochester has found that the brain flushes out toxins during sleep The cleanup system in the brain, responsible for flushing out toxic waste products that cells produce with daily use, goes into overdrive in mice that are asleep. The cells even shrink in size to make for easier cleaning of the spaces around them. • The difference of cerebrospinal fluid influx is seen (through fluorescent dye ) in the brain of an awake and a sleeping mouse. Image created by Katherine Streeter for NPR The green represents conversely restricted flow in the awake animal. The red represents the greater flow in a sleeping animal. 10 Xie, L., Kang, H., Xu, Q., Chen, M. J., Liao, Y., Thiyagarajan, M., Nedergaard, M. (2013). Sleep drives metabolite clearance from the adult brain. Science, 342(6156), 373–377. EUSIPCO 2015, August 31, 2015 5 8/27/2015 Types of sleep: rapid eye movement (REM) and non rapid eye movement (NREM) sleep 11 EUSIPCO 2015, August 31, 2015 Sleep is a cyclic process where rapid eye movement (REM) and non-REM sleep stages alternate Sleep stage Hypnogram Deep sleep (N3 sleep) • NREM (non-REM) sleep is associated with a synchronized brain activity pattern as opposed to wakefulness. • REM is distinguished from wakefulness mainly by reduced responsiveness and muscle atonia. 12 EUSIPCO 2015, August 31, 2015 6 8/27/2015 Brain centers active during each type of sleep Brain activity increase Pontine tegmentum (PT) Thalamus (Th) Basal forebrain (BF) Amygdala (AMY) Hippocampus HIPPO Anterior cingulate cortex (ACC) Occipital area (OA) Chouchou, F., & Desseilles, M. (2014). Heart rate variability: a tool to explore the sleeping brain? Autonomic Neuroscience, 8(December), 1–9. Brain activity decrease Thalamus (Th) Basal ganglia (BG) Basal forebrain (BF) Prefrontal cortex (PFC) Anterior cingulate cortex (ACC) Precuneus (PC) Brain activity decrease Dorsolateral prefrontal (PFC) Posterior cingulate cortex (PCC) Interior parietal cortex (IPC) Brain activity increase Brainstem centers (BS) REM sleep NREM sleep 13 EUSIPCO 2015, August 31, 2015 Heart rate variability to explore the sleeping brain Reflex loops - Respiration - Baroreflex - Chemoreflex HR: heart rate SNS: Sympathetic activity PNS: parasympathetic activity Chouchou, F., & Desseilles, M. (2014). Heart rate variability: a tool to explore the sleeping brain? Autonomic Neuroscience, 8(December), 1–9. 14 EUSIPCO 2015, August 31, 2015 7 8/27/2015 Slow wave sleep is primarily present in the first part of the sleep episode Image modified from Rasch, B., & Born, J. (2013). About Sleep’s role in Memory. Physiol Rev, 93, 681–766. • • • • • • • • • 15 Sleep is entered through NREM sleep. NREM and REM sleep alternate with a period of about 90 minutes. Slow wave sleep predominates in the first third of the night and is linked to the initiation of sleep. REM sleep predominates in the last 3rd of the night. Wakefulness in sleep usually accounts for less than 5% of the night. N1 generally constitutes 2 to 5% of sleep. N2 generally constitutes 45 to 55% of sleep. Carskadon, M. A., & Dement, W. C. (2011). Normal human sleep: an overview. In M. H. Kryger, T. Roth, & W. C. Dement (Eds.), Principles N3 generally constitutes 13 to 23% of sleep. and practice of sleep medicine (5th ed., pp. 16–26). Elsevier.` NREM is therefore 75 to 80% of sleep. EUSIPCO 2015, August 31, 2015 The sleep architecture changes with age Time [minutes] WASO: Wake after sleep onset SWS (N3): Slow wave sleep From Ohayon MM, Carskadon MA, Guilleminault C, Vitiello MV: Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan, Sleep 27:1255-1273, 2004 16 EUSIPCO 2015, August 31, 2015 8 8/27/2015 Markov chains have been applied to model the sleep stage transitions Transition graph Hypnogram Gray level edges represent the sleep stage transition • Kim, J., Lee, J.-S., Robinson, P., & Jeong, D.-U. (2009). Markov Analysis of Sleep Dynamics. Physical Review Letters, 102(17), 1–4. • Jaaskinen, V. (2009). Modelling Sleep Stages With Markov Chains. Helsinki. 17 EUSIPCO 2015, August 31, 2015 Polysomnography 18 EUSIPCO 2015, August 31, 2015 9 8/27/2015 Polysomnography (PSG) provides a rich data source for understanding sleep and to obtain the hypnogram Visual inspection of these neurophysiological signals forms the basis for standard sleep staging 19 EUSIPCO 2015, August 31, 2015 Polysomnography (PSG) provides a rich data source for understanding sleep Wearables have recently emerged to monitor sleep Sleep cycle app Visual inspection of these neurophysiological signals forms the basis for standard sleep staging 20 EUSIPCO 2015, August 31, 2015 10 8/27/2015 PSG signals exhibit specific patterns characterizing sleep stages Low frequency, high amplitude pattern High frequency, low amplitude pattern Electroencephalogram Muscle atonia (REM) Electromyogram Electrooculogram Eye blinks 21 Rapid eye movements EUSIPCO 2015, August 31, 2015 Actigraphy signals (being based on movement monitoring) can roughly reveal periods of sleep and wakefulness (~57% accuracy) Actigraphy signal Activity decrease (indicative of the time of going to bed) 22 Sustained activity increase (indicative of the waking up time) EUSIPCO 2015, August 31, 2015 11 8/27/2015 Sleep is by the brain and for the brain: the EEG is the reference signal to study sleep The sleeping brain as revealed by the electroencephalogram (EEG) High frequency, low amplitude pattern Wake REM 200 µV 30 seconds N1 Sleep slow-waves are present during deep sleep 200 µV 30 seconds N2 NREM sleep N3 (deep sleep) Low frequency, high amplitude pattern 23 EUSIPCO 2015, August 31, 2015 From visualizing a whole night EEG recording, it would be possible to visually identify NREM sleep Large amplitude, low frequency regions 200μV EEG Wake Hypnogram REM N3 N2 N1 EOG-Left EOG-Right 1 hour 24 EUSIPCO 2015, August 31, 2015 12 8/27/2015 Sleep stages 25 EUSIPCO 2015, August 31, 2015 Wake (eyes closed) “Alpha” waves (8-12 Hz) 200μV EEG Wake Hypnogram EOG-Left EOG-Right 10 seconds 26 EUSIPCO 2015, August 31, 2015 13 8/27/2015 Wake (eyes closed) 200μV “Alpha” waves (8-12 Hz) EEG Wake 1 second Power spectrum density 27 EUSIPCO 2015, August 31, 2015 Alpha activity Transition from Wake to Sleep “Alpha” activity (8-12 Hz) “Theta” activity (4-7 Hz) 200μV K-complex EEG Wake Hypnogram N1 (transient stage): 2 to 5% of sleep “Delta” activity (0.5-4 Hz) “Spindle” (11-16 Hz) N2 EOG-Left EOG-Right 10 seconds Nine EEG stages characterize sleep onset (next slide) 28 EUSIPCO 2015, August 31, 2015 14 8/27/2015 Sleep onset can be characterized by nine EEG stages Hori, T., Hayashi, M., & Morikawa, T. (1993). Topographical EEG changes and hypnagogic experience. In R. D. Ogilvie & J. R. Harsh (Eds.), Sleep Onset: Normal and Abnormal Processes (pp. 237–253). 29 • The power in the highfrequency (>8 Hz) range gradually decreases. • The power in the lowfrequency (<6 Hz) range gradually increases. EUSIPCO 2015, August 31, 2015 Sleep onset can also characterized by Cardiac activity changes Sleep onset was divided into 4 phases in Burgess et al. 1999 Wakefulness Alpha activity Mixed α and θ Theta activity N2 sleep with arousals Central electrode EEG Stable N2 sleep Changes in cardiac variables were analyzed on a beat-by-beat basis • • TWA: T-wave amplitude (µV) RR duration (b/min) Occipital electrode EEG EMG Each line represents a participant EOG ECG Images modified from: Burgess, H. J., Kleiman, J., & Trinder, J. (1999). Cardiac activity during sleep onset. Psychophysiology, 36(3), 298–306. 30 EUSIPCO 2015, August 31, 2015 15 8/27/2015 K-complexes N2 sleep 200μV EEG N2: 45 to 55% of sleep Hypnogram EOG Left EOG Right 10 seconds • N2: one or more K-complexes unassociated with arousals • One or more trains of sleep spindles Iber, C.. (2007). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. American Academy of Sleep Medicine (First). American Academy of Sleep Medicine. 31 1 second EUSIPCO 2015, August 31, 2015 N3 sleep Slow waves 200μV EEG Hypnogram N3 EOG Left EOG Right • N3: 20% or more of an epoch consists of slow wave activity. Iber, C.. (2007). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. American Academy of Sleep Medicine (First). American Academy of Sleep Medicine. 32 EUSIPCO 2015, August 31, 2015 16 8/27/2015 EEG analysis across a whole sleep session Garcia-Molina, G., Bellesi, M., Pastoor, S., Pfundtner, S., Riedner, B. A., & Tononi, G. (2013). Online Single EEG Channel Based Automatic Sleep Staging. In D. Harris (Ed.), Engineering Psychology and Cognitive Ergonomics. Applications and Services (pp. 333–342). Springer Berlin Heidelberg. 33 EUSIPCO 2015, August 31, 2015 EEG analysis across a whole sleep session Garcia-Molina, G., Bellesi, M., Pastoor, S., Pfundtner, S., Riedner, B. A., & Tononi, G. (2013). Online Single EEG Channel Based Automatic Sleep Staging. In D. Harris (Ed.), Engineering Psychology and Cognitive Ergonomics. Applications and Services (pp. 333–342). Springer Berlin Heidelberg. 34 EUSIPCO 2015, August 31, 2015 17 8/27/2015 EEG analysis across a whole sleep session • Amplitude Normalization • NREM time normalization Garcia-Molina, G., Bellesi, M., Pastoor, S., Pfundtner, S., Riedner, B. A., & Tononi, G. (2013). Online Single EEG Channel Based Automatic Sleep Staging. In D. Harris (Ed.), Engineering Psychology and Cognitive Ergonomics. Applications and Services (pp. 333–342). Springer Berlin Heidelberg. 35 EUSIPCO 2015, August 31, 2015 Simultaneous power time-courses for the beta, sigma and clocklike delta frequency bands in the first NREM episode: in terms of neural activity The number of neurons (N) in each mode beta, sigma and delta, is expressed as a percentage of the number of neurons in beta mode (N0) at the start of the NREM episode. Merica, H., & Fortune, R. D. (2004). State transitions between wake and sleep, and within the ultradian cycle, with focus on the link to neuronal activity. Sleep Medicine Reviews, 8(6), 473–85. 36 EUSIPCO 2015, August 31, 2015 18 8/27/2015 Whole-night averages for NREM delta EEG power are significantly correlated with HF-HRV (high frequency heart-rate variability) EEG ECG Delta power estimation R-R detection + spectral analysis 0 25 50 75 N1 (norm. time %) 100 25 50 75 N2 (norm. time %) 100 25 50 75 N3 (norm. time %) 100 Image modified from: Rothenberger, S. D.,et al. (2014). Time-varying correlations between delta EEG power and heart rate variability in midlife women: The SWAN Sleep Study. Psychophysiology, 52(2015), 572–584. 37 EUSIPCO 2015, August 31, 2015 Automatic sleep staging Macro analysis of sleep 38 EUSIPCO 2015, August 31, 2015 19 8/27/2015 Automatic sleep staging PSG signals Preprocessing • Artifact rejection • Artifact correction 39 Sleep stage Feature extraction • Linear features • Nonlinear • Time / Frequency Feature classification • Linear • Nonlinear classifier EUSIPCO 2015, August 31, 2015 Artifacts: Artifacts are unwanted patterns not caused by the underlying physiological events of interest Non biological artifacts Biological artifacts • Noise from the power line (50Hz EU/60 Hz US) • Ocular artifacts, e.g. eye blinks, eye movements • Electrical equipment • Muscular activity (mainly from head and facial muscles) • Movement • Electrode pops 40 • Sweat • EKG/pulse spikes in EEG EUSIPCO 2015, August 31, 2015 20 8/27/2015 Movement artifact Power line artifact High power line noise ! EEG EOG Left EOG Right 30 seconds 41 EUSIPCO 2015, August 31, 2015 Power line artifact • Any fault in the recording circuitry can be seen as a 60 Hz artifact From the SLEEP 2015 tutorial: “Catch the wave” 42 EUSIPCO 2015, August 31, 2015 21 8/27/2015 Sweat artifact (electrodermal artifact) EEG Hypnogram N3 EOG Left EOG Right 43 EUSIPCO 2015, August 31, 2015 Sweat artifact (electrodermal artifact): HP filtered signal 44 EUSIPCO 2015, August 31, 2015 22 8/27/2015 The electrodermal artifact occurs more frequently during NREM sleep (N2, N3) Sano, A., & Picard, R. W. (1968). Quantitative Analysis of Electrodermal Activity during Sleep. International Journal of Psychophysiology, 94(3), 1968. 45 EUSIPCO 2015, August 31, 2015 EKG artifact in the EEG 1 second 46 EUSIPCO 2015, August 31, 2015 23 8/27/2015 Pulse artifact Movement artifact Pulse artifact N2 10 seconds 47 EUSIPCO 2015, August 31, 2015 Frequency-analysis based features δ: 0.5 – 4.5 Hz θ: 4.0 – 7.0 Hz α: 8.0 – 12 Hz σ: 11 – 15 Hz β: 15 – 30 Hz δ θ alpha σ beta Power spectrum density for Wake, NREM and REM stages. Radha, M., Garcia-Molina, G., Poel, M., & Tononi, G. (2014). Comparison of Feature and Classifier Algorithms for Online Automatic Sleep Staging Based on a Single EEG Signal. In 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1876–1880). 48 EUSIPCO 2015, August 31, 2015 24 8/27/2015 Time-analysis based features Radha, M., Garcia-Molina, G., Poel, M., & Tononi, G. (2014). Comparison of Feature and Classifier Algorithms for Online Automatic Sleep Staging Based on a Single EEG Signal. In 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1876–1880). 49 EUSIPCO 2015, August 31, 2015 Nonlinear analysis based features Nx • Time and frequency domain entropy H mi = - å p( xi ) log( p( xi )) i =1 50 • Higuchi’s fractal dimension (HD) • Lempel-Ziv complexity (LC) EUSIPCO 2015, August 31, 2015 25 8/27/2015 Machine learning methods and evaluation Signal 51 EUSIPCO 2015, August 31, 2015 Illustration: the meaning of kappa in the context of automatic sleep staging Reference (manually scored) Automatically scored (kappa = 0.4) Reference (manually scored) Automatically scored (kappa = 0.6) 52 EUSIPCO 2015, August 31, 2015 26 8/27/2015 Micro analysis of sleep 53 EUSIPCO 2015, August 31, 2015 Analysis of sleep microevents • Spindles • Slow-waves Zygierewicz, J., Blinowska, K. J., Durka, P. J., Szelenberger, W., Niemcewicz, S., & Androsiuk, W. (1999). High resolution study of sleep spindles. Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology, 110(12), 2136–47. 54 EUSIPCO 2015, August 31, 2015 27 8/27/2015 Sleep microevents play a role in memory consolidation Rasch, B., & Born, J. (2013). About Sleep’s role in Memory. Physiol Rev, 93, 681–766. 55 EUSIPCO 2015, August 31, 2015 Slow waves: detection Signal from frontal electrode 56 Detecting EEG events that have three elements: 1) a negative-going zerocrossing, 2) 2) a negative peak with absolute amplitude of at least 30 microvolts, and 3) a positive going zerocrossing which occurs between 100 and 900 milliseconds after the detected negativegoing zero-crossing. EUSIPCO 2015, August 31, 2015 28 8/27/2015 Slow waves: analysis over multiple EEG channels and wave propagation properties Slow waves Detected across several channels + sorting Massimini, M., Huber, R., Ferrarelli, F., Hill, S., & Tononi, G. (2004). The sleep slow oscillation as a traveling wave. The Journal of Neuroscience, 24(31), 6862–6870. Riedner et al., 2012 57 EUSIPCO 2015, August 31, 2015 Slow waves: analysis over multiple EEG channels and wave propagation properties EEG Traces Topography Riedner, B. A., Hulse, B. K., Murphy, M. J., Ferrarelli, F., & Tononi, G. (2011). Temporal dynamics of cortical sources underlying spontaneous and peripherally evoked slow waves. In Progress in Brain Research (Vol. 193, pp. 201–218). Trajectories Speed Riedner et al., 2012 58 EUSIPCO 2015, August 31, 2015 29 8/27/2015 Slow wave activity (EEG power in the 0.5 to 4 Hz) is a marker of sleep need 680 µV2 SWA 120 µV2 10000 100 slow-wave activity (mV2) 0 Fz Cz P4 8000 800 Fz Cz P4 6000 600 4000 40 0 2000 20 0 0 0 2 4 6 Time [h] 59 8 Huber, unpublished EUSIPCO 2015, August 31, 2015 Sleep spindle detection Andrillon, T., Nir, Y., Staba, R. J., Ferrarelli, F., Cirelli, C., Tononi, G., & Fried, I. (2011). Sleep spindles in humans: insights from intracranial EEG and unit recordings. The Journal of Neuroscience, 31(49), 17821–17834. 60 EUSIPCO 2015, August 31, 2015 30 8/27/2015 The rate of spindles appears to predict sleep stability in the presence of noise Dang-Vu, T. T., McKinney, S. M., Buxton, O. M., Solet, J. M., & Ellenbogen, J. M. (2010). Spontaneous brain rhythms predict sleep stability in the face of noise. Current Biology : CB, 20(15), R626–7. But sleep spindles seem to also be related to: learning • Gais, S., Mölle, M., Helms, K., & Born, J. (2002). Learning-dependent increases in sleep spindle density. The Journal of Neuroscience, 22(15), 6830–6834. Reduced sleep spindle activity is present in schizophrenia • Ferrarelli, F., Huber, R., Peterson, M. J., Massimini, M., Murphy, M., Riedner, B. a, … Tononi, G. (2007). Reduced sleep spindle activity in schizophrenia patients. The American Journal of Psychiatry, 164(3), 483–492. 61 EUSIPCO 2015, August 31, 2015 Models of sleep regulation 62 EUSIPCO 2015, August 31, 2015 31 8/27/2015 Models of sleep regulation: two process (S-C) model The longer one is awake, the higher the need for sleep is. S can be modelled as saturating exponentials Sleep need depends on time of the day C can be roughly modeled as a sinusoidal process 63 Borbély, A. A. (1982). A two process model of sleep regulation. Human Neurobiology, 1(3), 195–204. EUSIPCO 2015, August 31, 2015 Consequences of total sleep deprivation Instead of sleeping Borbély, A. A. (1982). A two process model of sleep regulation. Human Neurobiology, 1(3), 195–204. 64 EUSIPCO 2015, August 31, 2015 32 8/27/2015 Modeling the “S” process with SWA Sleep need builds up during wakefulness and dissipates during sleep. According to the two process model, the instantaneous rate of decrease in sleep need is related to the SWA during NREM sleep by a proportionality constant γ dS (t ) = -g × SWA(t ) dt 65 (1) EUSIPCO 2015, August 31, 2015 Modeling the “S” process with SWA 66 EUSIPCO 2015, August 31, 2015 33 8/27/2015 Modeling the “S” process with SWA • 67 The parameters γ and S0 are experimentally estimated using the information from the manually scored sleep hypnogram and the discretization (see Equation 3) of Equation (2). EUSIPCO 2015, August 31, 2015 Example of the application of the model to several nights from the same participant SWA and sleep-need dissipation 2 SWA [mV ] 1000 500 0 0 100 200 300 SWA + sleep-need estimation 2 SWA [mV ] 1000 500 100 200 300 400 2 SWA [mV ] 1000 68 500 0 0 100 200 300 Time [min] 400 500 1000 SWA [mV2] 0 0 500 0 0 100 200 Time [min] 300 EUSIPCO 2015, August 31, 2015 34 8/27/2015 Conclusive remarks 69 EUSIPCO 2015, August 31, 2015 Conclusive remarks • Sleep can only be understood from a holistic perspective involving: – Sleep architecture – Sleep microevents – Sleep need dissipation – Circadian rhythm • In all these steps, signal processing plays a key role. – Sleep architecture can be (should be) automatically estimated from PSG signals. – Sleep microevents (slow-waves and spindles) provide critical information on sleep restoration and memory processing. These events can only be detected automatically through signal processing algorithms. – Modelling of the two processes helps in understanding the effects of sleep deprivation and recovery. 70 EUSIPCO 2015, August 31, 2015 35 8/27/2015 Trends where signal processing is key • Real-time sleep staging • Unobtrusive monitoring of sleep. – Actigraphy, – Wrist PPG – Wearable EEG/EOG – Radar/video (touchless)- monitoring • High density EEG analysis 71 EUSIPCO 2015, August 31, 2015 36