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A Review of Signals Used in Sleep Analysis Home Search Collections Journals About Contact us My IOPscience A review of signals used in sleep analysis This content has been downloaded from IOPscience. Please scroll down to see the full text. 2014 Physiol. Meas. 35 R1 (http://iopscience.iop.org/0967-3334/35/1/R1) View the table of contents for this issue, or go to the journal homepage for more Download details: IP Address: 132.239.142.130 This content was downloaded on 20/12/2013 at 00:11 Please note that terms and conditions apply. Institute of Physics and Engineering in Medicine Physiological Measurement Physiol. Meas. 35 (2014) R1–R57 doi:10.1088/0967-3334/35/1/R1 Topical Review ∗ A review of signals used in sleep analysis A Roebuck1, V Monasterio1,2,3, E Gederi1,MOsipov1, J Behar1, A Malhotra4, T Penzel5 and G D Clifford1,4 1 Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK 2 CIBER de Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain 3 Aragon Institute of Engineering Research, Universidad de Zaragoza, Zaragoza, Spain 4 Department of Medicine, University of California, San Diego, CA, USA 5 Sleep Medicine Center, ChariteUniversit´ atsmedizin¨ Berlin, Berlin, Germany E-mail: [email protected] Received 1 June 2012 Accepted for publication 10 September 2013 Published 17 December 2013 Abstract This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep. Keywords: actigraphy, audio, electrocardiogram, electroencephalogram, photoplethysmogram, respiration, signal processing, sleep (Some figures may appear in colour only in the online journal) Contents Glossary 2 1. Introduction 4 2. Physiological and clinical background 4 2.1. The phenomenology of sleep 4 2.2. Sleep disorders 6 2.3. Categorical surveys and demographics 8 2.4. Sleep apnoea 9 ∗ This review article is dedicated to the memory of Joe Mietus, who spent his life in the service of cardiorespiratory analysis, often with a focus in the field of sleep. His friendship, hard work, persistence and exceptional skills will be sadly missed. 0967-3334/14/010001+57$33.00 © 2014 Institute of Physics and Engineering in Medicine Printed in the UK R1 Physiol. Meas. 35 (2014) R1 Topical Review 3. Monitoring modalities 13 3.1. Non-cardiac electropotentials 13 3.2. Oximetry 13 3.3. Cardiovascular measures 14 3.4. Respiration 15 3.5. Audio 16 3.6. Body movement 17 3.7. Video 18 3.8. Temperature 19 4. Signal processing 20 4.1. EEG 20 4.2. ECG 24 4.3. The photoplethysmogram and oxygen saturation 30 4.4. Blood pressure and arterial tonometry 31 4.5. Respiration 32 4.6. Audio 32 4.7. Accelerometry 36 4.8. Video 40 4.9. mHealth and mobile phone-based systems 44 5. Discussion and conclusions 44 Acknowledgments 45 References 46 Glossary AARC-APT American Association of Respiratory Care–Association of Polysomnography Technologists AASM American Academy of Sleep Medicine AC Alternating current, pulsatile waveform ACC Accuracy AHI Apnoea hypopnoea index, the average number of apnoeas and hypopnoeas per hour of sleep AIS Athens insomnia scale APAP Autopositive airway pressure AR Autoregression ASPS Advanced sleep phase syndrome BiPAP Bilevel positive airway pressure BMI Body mass index, a proxy for measuring body fat based on an individual’s height and weight BP Blood pressure BQ Berlin questionnaire CAP Cyclic alternating pattern CBT Core body temperature CNS Central nervous system CPAP Continuous positive airway pressure CPC Cardiopulmonary coupling CRC Cardiorespiratory coupling CRDs Circadian rhythm disorders CSA Central sleep apnoea R2 Physiol. Meas. 35 (2014) R1 Topical Review DAP Decreases in the amplitude of the photoplethysmogram signal during polysomnography DC Direct current DIST Distal skin temperature DSPS Delayed sleep phase syndrome ECG Electrocardiogram. EDR Electrocardiogram derived respiration EDS Excessive daytime sleepiness EEG Electroencephalogram EMG Electromyogram EOG Electrooculogram ESS Epworth sleepiness scale HMM Hidden Markov model HR Heart rate HRV Heart rate variability HST Home sleep test IP Impedance pneumography ICSD International classification of sleep disorders MAD Mandibular advancement devices, tongue trusses NCAP Non-cyclic alternating pattern NPV Negative predictive value NREM Non-rapid eye movement OA Oral appliance ODI Oxygen desaturation index, the average number of oxygen desaturations per hour of sleep OHS Obesity hypoventilation syndrome OSA Obstructive sleep apnoea OSAS Obstructive sleep apnoea syndrome PAT Peripheral arterial tonometry PPG Photoplethysmogram PPV Positive predictive value PROX Proximal skin temperature PR Photoplethysmogram-derived heart rate PSG Polysomnogram, overnight sleep study PTT Pulse transit time RDI Respiratory disturbance index REM Rapid eye movement RIP Respiratory inductance plethysmography RMS Root mean square RSA Respiratory sinus arrhythmia SAS Sleep apnoea syndrome SDB Sleep disordered breathing SN Sensitivity SP Specificity SpO2 indirect measure of blood oxygen saturation from pulse oximetry STOP BANG A questionnaire used to identify the presence of obstructive sleep apnoea SWS Slow wave sleep SWSD Shift work sleep disorder TRDs Tongue retaining devices R3 Physiol. Meas. 35 (2014) R1 Topical Review TST Total sleep time UA Upper airway UARS Upper airway resistance syndrome US United States 1. Introduction The ICSD has identified over 80 different sleep disorders, all of which have associated treatments (Thorpy 1990, AASM 2005). The effects of sleep disorders are extensive, impacting sufferers physically, psychologically and financially. Up to 40% of the US adult population experience problems with falling asleep or daytime sleepiness, which are largely assumed to be due to disturbed sleep patterns (Hossain and Shapiro 2002). It is difficult to quantify the impact of poor sleep structure in a broad sense as it is often considered a symptom of other diseases, although it is intricately connected to many of the dominant burdens of disease (Ust¨ un¨ et al 1996). In fact, the health effects of sleep disorders span a wide range: from the apparently simple daytime sleepiness, which is a non-specific symptom common to other disorders (Pagel 2009), to the more severe effects of increased risk of cardiovascular disease and stroke (Young et al 2002). Daytime sleepiness is the cause of hundreds of road traffic accidents, and has even been linked to catastrophes such as Chernobyl (Hossain and Shapiro 2002). Moreover, poor sleep affects one’s mental status, leading to poor mental function, reduced compliance which compounds chronic disease treatment, and exacerbates mental conditions such as depression and schizophrenia (Cho et al 2008,Wulffet al 2012). Currently, the gold standard in terms of sleep disorder diagnosis is a sleep study, or an overnight PSG. However, PSGs are expensive and are limited by the number of beds available in the study centre and the number of specialists available to read the data. There are many home sleep recording systems on the market which aim to reduce the financial cost per patient and reach a larger population by reducing the number of parameters recorded. However, without the guidance of a specialist, the patient, who has no medical or technical training, has to place the sensors in the correct positions. If placed incorrectly, the results may be inconclusive or misleading. Even if done correctly there may not be a trained specialist readily available to analyse the data. There is therefore a need to increase the quality of automatic sleep analysis, particularly for low-cost systems. This work reviews the physiology and treatment of sleep disorders, focusing particularly on sleep apnoea, the monitoring modalities and most commonly used signal processing techniques applied to signals which are useful for sleep assessment. 2. Physiological and clinical background 2.1. The phenomenology of sleep Loomis et al (1936, 1937) provided the earliest detailed description of various stages of sleep, based on EEG, in the mid-1930s. In the early 1950s, Aserinsky and Kleitman (1953) identified REM sleep, which is related to dreaming. Sleep has been traditionally divided into two broad types: NREM and REM sleep. The sleep staging criteria were standardized in 1968 by Rechtschaffen and Kales (1968)(orR&K rules), based on EEG changes, dividing NREM sleep into a four further stages (stage I, stage II, stage III, stage IV). (It should be noted that some dreaming has been observed during NREM sleep.) In 2004, the AASM standards commissioned the AASM Visual Scoring Task Force to review the R&K scoring system. This document resulted in several minor changes, with R4 Physiol. Meas. 35 (2014) R1 Topical Review the most significant being the combining of stages III and IV into stage N3. Arousals and respiratory, cardiac, and movement events were also added to the scoring. The revised scoring was published in 2007 as The AASM Manual for the Scoring of Sleep and Associated Events (Iber et al 2007). NREM and REM sleep occur in alternating cycles, each lasting approximately 90–110 minutes (min) in adults, with approximately 4–6 cycles during the course of a normal 6–8 hour (h) sleep period. However, these timings change depending on the length of time asleep, age, medication, physical health and mental health. Furthermore, brief micro-arousals can occur, lasting (by definition) from 1.5–3 seconds (s) and short awakenings (defined to be longer 15 s) (Martin et al 1997). Generally, in a healthy young adult, NREM sleep accounts for 75–90% of TST 6. NREM sleep comprises approximately 3–5% in stage I, 50–60% stage II, and 10–20% stages III and IV. REM sleep accounts for 10–25% of sleep time.
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