Detecting Slow Wave Sleep Using a Single EEG Signal Channel

Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods

Instability of parasympathetic nerve function evaluated by instantaneous time-frequency analysis in patients with obstructive sleep apnea

The purpose was to determine whether the instability of parasympathetic nerve (PN) function is associated with fragmentation of sleep and the instability can be improved by CPAP treatment in obstructive sleep apnea (OSA). Fifty-three OSA and 50 non-OSA subjects were examined by full-PSG and pulse rate variability (PRV) was recorded simultaneously using a photoplethysmograph and evaluated by instantaneous time-frequency analysis using the complex demodulation method. PN and sympathetic nerve (SN) activity were assessed by the mean high-frequency (HF) amplitude and ratio of low-frequency (LF) and HF amplitude (LF/ HF ratio), respectively. Furthermore, the shift in central frequency (CF) of the main HF peak over time was monitored continuously. The relative times over which the same main HF peak was sustained for at least 20 s and 5 min in total recording time (%HF20s and % HF5min) were considered as markers of PN stability. Twenty-two of 53 patients with OSA also examined under the treatment with continuous positive airway pressure (CPAP). A significant increase in mean LF/ HF ratio and decrease in HF amplitude were observed in severe OSA. Furthermore, both % HF20s and % HF5min were significantly decreased not only in mild-to-moderate OSA but also in severe OSA, and % HF20s was the strongest independent determinant for arousal index. Treatment with CPAP significantly decreased the LH/HF ratio and increased both % HF20s and % HF5min. These findings suggest that the stability of PN function is impaired by arousal due to repeated apnea and hypopnea in OSA, and that CPAP therapy improves SN activity and PN dysfunction.ArticleSLEEP AND BIOLOGICAL RHYTHMS.16(3):323-330(2018)journal articl

Single Channel ECG for Obstructive Sleep Apnea Severity Detection using a Deep Learning Approach

Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal breathing. The severity of OSA can lead to many symptoms such as sudden cardiac death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It records many signals from the patient's body for at least one whole night and calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or hypopnea incidences per hour. This value is then used to classify patients into OSA severity levels. However, it has many disadvantages and limitations. Consequently, we proposed a novel methodology of OSA severity classification using a Deep Learning approach. We focused on the classification between normal subjects (AHI $$ 30). The 15-second raw ECG records with apnea or hypopnea events were used with a series of deep learning models. The main advantages of our proposed method include easier data acquisition, instantaneous OSA severity detection, and effective feature extraction without domain knowledge from expertise. To evaluate our proposed method, 545 subjects of which 364 were normal and 181 were severe OSA patients obtained from the MrOS sleep study (Visit 1) database were used with the k-fold cross-validation technique. The accuracy of 79.45\% for OSA severity classification with sensitivity, specificity, and F-score was achieved. This is significantly higher than the results from the SVM classifier with RR Intervals and ECG derived respiration (EDR) signal feature extraction. The promising result shows that this proposed method is a good start for the detection of OSA severity from a single channel ECG which can be obtained from wearable devices at home and can also be applied to near real-time alerting systems such as before SCD occurs

Predicting Subjective Sleep Quality Using Objective Measurements in Older Adults

Humans spend almost a third of their lives asleep. Sleep has a pivotal effect on job performance, memory, fatigue recovery, and both mental and physical health. Sleep quality (SQ) is a subjective experience and reported via patients’ self-reports. Predicting subjective SQ based on objective measurements can enhance diagnosis and treatment of SQ defects, especially in older adults who are subject to poor SQ. In this dissertation, we assessed enhancement of subjective SQ prediction using an easy-to-use E4 wearable device, machine learning techniques and identifying disease-specific risk factors of abnormal SQ in older adults. First, we designed a clinical decision support system to estimate SQ and feeling refreshed after sleep using data extracted from an E4 wearable device. Specifically, we processed four raw physiological signals of heart rate variability (HRV), electrodermal activity, body movement, and skin temperature using distinct signal processing methodologies. Following this, we extracted signal-specific features and selected a subset of the features using recursive feature elimination cross validation strategy to maximize the accuracy of SQ classifiers in predicting the SQ of older caregivers. Second, we investigated discovering more effective features in SQ prediction using HRV features which are not only effortlessly measurable but also can reflect sleep stage transitions and some sleep disorders. Evaluation of two interpretable machine learning methodologies and a convolutional neural network (CNN) methodology demonstrated the CNN outperforms by an accuracy of 0.6 in predicting light, medium, and deep SQ. This outcome verified the capability of using HRV features measurable by easy-to-use wearable devices, in predicting SQ. Finally, we scrutinized daytime sleepiness risk factors as a sign of abnormal SQ from four perspectives: sleep fragmented, sleep propensity, sleep resilience, and non-restorative sleep. The analysis demonstrates distinguishability of the main risk factors of excessive daytime sleepiness (EDS) between patients suffering from fragmented sleep (e.g. apnea) and sleep propensity. We identified the average area under oxygen desaturation curve corresponds to apnea/hypopnea event as a disease-specific risk factor of abnormal SQ. Our further daytime sleepiness prediction demonstrated the significant role of the founded disease-specific risk factor as well

Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincent’s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature

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

Sleep Arousal and Cardiovascular Dynamics

Sleep arousal conventionally refers to any temporary intrusions of wakefulness into sleep. Arousals are usually considered as a part of normal sleep and rarely result in complete awakening. However, once their frequency increases, they may affect the sleep architecture and lead to sleep fragmentation, resulting in fatigue, poor executive functioning and excessive daytime sleepiness. In the electroencephalogram, arousals mostly appear as a shift of power in frequency to values greater than 16 Hz lasting 3-15 seconds. The general objective of this thesis was to investigate on the nature of sleep arousal and study arousal interaction and association with cardiovascular dynamics. At the first step of this research, an algorithm was developed and evaluated for automatic detection of sleep arousal. The polysomnographic (PSG) data of 9 subjects were analysed and 32 features were derived from a range of biosignals. The extracted features were used to develop kNN classifier model in to differentiate arousal from non-arousal events. The developed algorithm can detect arousal events with the average sensitivity and accuracy of 79% and 95.5%, respectively. The second aim was to investigate cardiovascular dynamics once an arousal occurs. Overnight continuous systolic and diastolic blood pressure (SBP and DSP), spectral components of heart rate variability (HRV) and the pulse transit time of 10 subjects (average arousal number of 51.5 +/- 21.1 per person) were analysed before and after arousal occurrence. Whether each cardiovascular variable increases or decreases was evaluated in different types of arousals through slpoe index (SI). The analysis indicated a post-arousal SBP and DBP elevation and PTT dropping. High frequency component of HRV (HF) dropped at arousal onset whilst low frequency (LF) component shifted. HRV spectral components extracted from ECG, lead I alongside with PTT were utilised for sleep staging in 22 healthy and insomnia subjects using linear and non-linear classifier models. Obtained result shows that developed model by DW-kNN classifier could detect sleep stages with mean accuracy of 73.4% +/- 6.4. An empirical curve fitting model for overnight continuous blood pressure estimation was developed and evaluated using the first and second derivatives of fingertip PPG (VPG, APG) along with ECG. The VPG-based model could estimate systolic and diastolic blood pressure with mean error of 3:96 mmHg with standard deviation of 1.41 mmHg and DBP with 6:88 mmHg with standard deviation of 3.03 mmHg. The QT and RR time intervals are two cardiac variables which represent beat to beat variability and ventricular repolarisation, respectively. PSG dataset of 2659 men aged older than 65 (MrOS Sleep Study) was analysed to compare on RR and QT interval variability pre- and post-arousal onset. The cardiac interval gradients were developed to monitor instantaneous changes pre-and post-onset. Analysis of gradients demonstrated that both RR and QT are likely to start shortening several second prior to onset by average probability of 73% and 64%. The QT/RR linear correlation was significantly rising after arousal inducing regardless of arousal type and associated pathological events (Rpost = 0.218 vs Rpre = 0.047). ANOVA test and Tukey’s honest post-hoc analysis indicated a significant difference between cardiac intervals variability between respiratory, movements and spontaneous arousals. In addition, respiratory disturbance index (RDI) as a measure of sleep apnoea severity was reversely correlated with both QT (RVarQT = -0.251, p 1:1 ms) and greater frequency of sleep arousal, less physical activity and medical history of several cardiovascular disease. Similarly participants in quartile DRR> - 8:8 were likelier to be obese with less physical activity, medical history of COPD and stroke and suffered from severer degree of sleep apnoea (RDI = 28:7 20:4 vs RDI = 25:5 +/- 17:6, p < 0:001). Kaplan-Meier analysis showed that the distribution DRR at arousal onset was significantly associated with cardiovascular (CV) mortality (p < 0:001). Cox proportional hazard regression models also indicated the effect of arousal duration in prediction of CV mortality, where longer arousals had more prognostic value for CV mortality than shorter arousals.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 202

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