Identification of sleep apnea events using discrete wavelet transform of respiration, ECG and accelerometer signals

Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recurrent pauses in breathing or by instances of abnormally low breathing. Current gold standard tests for the detection of apnea events are costly and have the addition of long waiting times. This paper investigates the use of cheap and easy to use sensors for the identification of sleep apnea events. Combinations of respiration, electrocardiography (ECG) and acceleration signals were analysed. Results show that using features, formed using the discrete wavelet transform (DWT), from the ECG and acceleration signals provided the highest classification accuracy, with an F1 score of 0.914. However, the novel employment of just the accelerometer signal during classification provided a comparable F1 score of 0.879. By employing one or a combination of the analysed sensors a preliminary test for sleep apnea, prior to the requirement for gold standard testing, can be performed

Characterization of the microvascular cerebral blood flow response to obstructive apneic events during night sleep

Peer ReviewedPostprint (author's final draft

Protocol of the SOMNIA project : an observational study to create a neurophysiological database for advanced clinical sleep monitoring

Introduction Polysomnography (PSG) is the primary tool for sleep monitoring and the diagnosis of sleep disorders. Recent advances in signal analysis make it possible to reveal more information from this rich data source. Furthermore, many innovative sleep monitoring techniques are being developed that are less obtrusive, easier to use over long time periods and in the home situation. Here, we describe the methods of the Sleep and Obstructive Sleep Apnoea Monitoring with Non-Invasive Applications (SOMNIA) project, yielding a database combining clinical PSG with advanced unobtrusive sleep monitoring modalities in a large cohort of patients with various sleep disorders. The SOMNIA database will facilitate the validation and assessment of the diagnostic value of the new techniques, as well as the development of additional indices and biomarkers derived from new and/or traditional sleep monitoring methods. Methods and analysis We aim to include at least 2100 subjects (both adults and children) with a variety of sleep disorders who undergo a PSG as part of standard clinical care in a dedicated sleep centre. Full-video PSG will be performed according to the standards of the American Academy of Sleep Medicine. Each recording will be supplemented with one or more new monitoring systems, including wrist-worn photoplethysmography and actigraphy, pressure sensing mattresses, multimicrophone recording of respiratory sounds including snoring, suprasternal pressure monitoring and multielectrode electromyography of the diaphragm

Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography

The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave)

The effect of respiratory event type and duration on heart rate variability in suspected obstructive sleep apnea patients

Abstract. Obstructive sleep apnea (OSA) patients have often reduced long-term heart rate variability (HRV) which is a known risk factor for several cardiovascular diseases such as hypertension and stroke. Albeit OSA being actively studied, it has remained uncharacterized how the duration and type of respiratory events affect the heart rate (HR), i.e. RR intervals, and ultra-short-term HRV during and immediately after the individual respiratory events. This study aimed to investigate whether the changes in ultra-short-term HRV and HR are modulated by the duration and type of the individual respiratory events and whether these changes are sex-specific. It was hypothesized that longer respiratory events cause higher ultra-short-term HRV and greater differences between RR intervals during and after the respiratory event. Moreover, it was hypothesized that the higher HRV and greater differences in HR are associated with apneas and men stronger than hypopneas and women. Electrocardiograms (ECG) of 862 suspected OSA patients were collected during clinical polysomnography (PSG) at the Princess Alexandra Hospital (Brisbane, Australia) and they were analyzed retrospectively. Ultra-short-term HRV was studied with time-domain parameters determined from the ECG segments measured during (in-event) and 15 seconds after (post-event) the respiratory event. The respiratory events of all subjects were divided into groups based on the sex, the type of the respiratory events (apneas and hypopneas), and the duration of the respiratory events (10–20 s, 20–30 s, over 30 s). A clear bradycardia-tachycardia rhythm associated with respiratory events was observed. The ultra-short-term HRV and the difference between in- and post-event RR intervals increased with increasing respiratory event duration. However, the difference between in- and post-event HRV parameter values decreased with increasing duration of the respiratory events. Furthermore, higher ultra-short-term HRV and a greater decrease in RR interval were observed in apneas and men. Based on the results, the duration and type of the respiratory events modulate the HR and ultra-short-term HRV during and after the respiratory events, and these phenomena appear to be sex-specific. Therefore, considering the characteristics of respiratory events and ultra-short-term HRV could be useful in OSA diagnostics when estimating the OSA-related cardiac consequences. A scientific article based on the results of this thesis, Hietakoste et al. Longer apneas and hypopneas are associated with greater ultra-short-term HRV in OSA, has been submitted to a peer-reviewed scientific journal.Tiivistelmä. Uniapneapotilailla havaitaan usein matalaa pitkän aikavälin sykevälivaihtelua, jonka tiedetään myös olevan riskitekijä useille sydän- ja verisuonisairauksille. Ei kuitenkaan tiedetä, miten uniapneaan liittyvät erimittaiset hengityskatkot tai niiden tyyppi vaikuttavat yksittäisten hengityskatkojen aikaiseen ja jälkeiseen ultralyhyeen sykevälivaihteluun ja sydämen lyöntien väliseen kestoon, ts. RR-intervalleihin. Tässä tutkimuksessa tavoitteena oli tutkia ultralyhyen sykevälivaihtelun ja RR-intervallien sukupuolisidonnaisia muutoksia eri mittaisten apneoiden ja hypopneoiden aikana ja jälkeen. Hypoteesina oli, että pidemmät hengityskatkot aiheuttavat suurempia muutoksia hengityskatkojen aikaisen ja jälkeisen keskimääräisen RR-intervallien kestojen välille ja siten korkeampaa ultralyhyttä sykevälivaihtelua. Oletettiin myös, että apneat aiheuttavat suurempia muutoksia kuin hypopneat ja havaitut muutokset ovat suurempia miehillä kuin naisilla. Potilasaineisto koostui 862 uniapneasta epäillyn potilaan sydänsähkökäyristä (EKG), jotka oli mitattu Prinsessa Alexandran sairaalassa (Brisbane, Australia) osana kliinistä unipolygrafiaa. Ultralyhyen sykevälivaihtelun määrittämiseen käytettiin keskimääräistä RR-intervallien kestoa ja aikatason sykevälivaihteluparametreja, jotka määritettiin hengityskatkojen aikaisista ja jälkeisistä (15 s hengityskatkon jälkeen) EKG-segmenteistä. Tutkittavat hengityskatkot jaettiin ryhmiin niiden tyypin (apneat ja hypopneat) ja keston (10–20 s, 20–30 s ja yli 30 s) perusteella. Lisäksi miesten ja naisten hengityskatkoja tutkittiin erikseen. Tutkimuksessa havaittiin, että hengityskatkojen aikaisten ja jälkeisten RR-intervallien ero sekä ultralyhyt sykevälivaihtelu kasvoivat hengityskatkojen keston kasvaessa riippumatta sukupuolesta tai hengityskatkojen tyypistä. Havaittiin myös, että ero hengityskatkojen aikaisten ja jälkeisten sykevälivaihteluparametrien arvojen välillä pieneni hengityskatkojen pidentyessä riippumatta sukupuolesta tai hengityskatkojen tyypistä. Apneat kuitenkin aiheuttivat suuremman muutoksen kuin hypopneat, ja muutokset olivat suurempia miehillä. Tulosten perusteella hengityskatkojen tyyppi ja kesto vaikuttavat ultralyhyeen sykevälivaihteluun ja RR-intervalleihin. Ultralyhyen sykevälivaihtelun ja hengityskatkojen ominaisuuksien huomioonottaminen uniapnean diagnostiikassa voisi olla hyödyllistä arvioitaessa taudin vakavuutta ja sydänterveyteen liittyviä riskejä. Tämän tutkimuksen tuloksista on kirjoitettu tieteellinen artikkeli Hietakoste ym. Longer apneas and hypopneas are associated with greater ultra-short-term HRV in OSA, joka on lähetetty vertaisarvioitavaksi alan kansainväliseen tieteelliseen julkaisusarjaan

Detecting Specific Health-Related Events Using an Integrated Sensor System for Vital Sign Monitoring

In this paper, a new method for the detection of apnea/hypopnea periods in physiological data is presented. The method is based on the intelligent combination of an integrated sensor system for long-time cardiorespiratory signal monitoring and dedicated signal-processing packages. Integrated sensors are a PVDF film and conductive fabric sheets. The signal processing package includes dedicated respiratory cycle (RC) and QRS complex detection algorithms and a new method using the respiratory cycle variability (RCV) for detecting apnea/hypopnea periods in physiological data. Results show that our method is suitable for online analysis of long time series data

The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), a heterogeneous and multifactorial sleep related breathing disorder with high prevalence, is a recognized risk factor for cardiovascular morbidity and mortality. Autonomic dysfunction leads to adverse cardiovascular outcomes in diverse pathways. Heart rate is a complex physiological process involving neurovisceral networks and relative regulatory mechanisms such as thermoregulation, renin-angiotensin-aldosterone mechanisms, and metabolic mechanisms. Heart rate variability (HRV) is considered as a reliable and non-invasive measure of autonomic modulation response and adaptation to endogenous and exogenous stimuli. HRV measures may add a new dimension to help understand the interplay between cardiac and nervous system involvement in OSA. The aim of this review is to introduce the various applications of HRV in different aspects of OSA to examine the impaired neuro-cardiac modulation. More specifically, the topics covered include: HRV time windows, sleep staging, arousal, sleepiness, hypoxia, mental illness, and mortality and morbidity. All of these aspects show pathways in the clinical implementation of HRV to screen, diagnose, classify, and predict patients as a reasonable and more convenient alternative to current measures.Peer Reviewe

Obstructive Sleep Apnea Screening by Joint Saturation Signal Analysis and PPG-derived Pulse Rate Oscillations

Obstructive sleep apnea (OSA) is a high-prevalence disease in the general population, often underdiagnosed. The gold standard in clinical practice for its diagnosis and severity assessment is the polysomnography, although in-home approaches have been proposed in recent years to overcome its limitations. Today's ubiquitously presence of wearables may become a powerful screening tool in the general population and pulse-oximetry-based techniques could be used for early OSA diagnosis. In this work, the peripheral oxygen saturation together with the pulse-to-pulse interval (PPI) series derived from photoplethysmography (PPG) are used as inputs for OSA diagnosis. Different models are trained to classify between normal and abnormal breathing segments (binary decision), and between normal, apneic and hypopneic segments (multiclass decision). The models obtained 86.27% and 73.07% accuracy for the binary and multiclass segment classification, respectively. A novel index, the cyclic variation of the heart rate index (CVHRI), derived from PPI's spectrum, is computed on the segments containing disturbed breathing, representing the frequency of the events. CVHRI showed strong Pearson's correlation (r) with the apnea-hypopnea index (AHI) both after binary (r=0.94, p < 0.001) and multiclass (r=0.91, p < 0.001) segment classification. In addition, CVHRI has been used to stratify subjects with AHI higher/lower than a threshold of 5 and 15, resulting in 77.27% and 79.55% accuracy, respectively. In conclusion, patient stratification based on the combination of oxygen saturation and PPI analysis, with the addition of CVHRI, is a suitable, wearable friendly and low-cost tool for OSA screening at home