On Improvement of Detection of Obstructive Sleep Apnea by Partial Least Square-based Extraction of Dynamic Features

This paper presents a methodology for Obstructive Sleep Apnea (OSA) detection based on the HRV analysis, where as a measure of relevance PLS is used. Besides, two different combining approaches for the selection of the best set of contours are studied. Attained results can be oriented in research focused on finding alternative methods minimizing the HRV-derived parameters used for OSA diagnosing, with a diagnostic accuracy comparable to a polysomnogram. For two classes (normal, apnea) the results for PLS are: specificity 90%, sensibility 91% and accuracy 93.56%

Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review

Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios.info:eu-repo/semantics/publishedVersio

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

Thermal imaging developments for respiratory airflow measurement to diagnose apnoea

Sleep-disordered breathing is a sleep disorder that manifests itself as intermittent pauses (apnoeas) in breathing during sleep. The condition disturbs the sleep and can results in a variety of health problems. Its diagnosis is complex and involves multiple sensors attached to the person to measure electroencephalogram (EEG), electrocardiogram (ECG), blood oxygen saturation (pulse oximetry, S

Review on biomedical sensors, technologies, and algorithms for diagnosis of sleep-disordered breathing: Comprehensive survey

This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient's physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB

Sleep-time predictors of cardiovascular complications in surgical peripheral arterial disease

ABSTRACT Patients with peripheral arterial disease (PAD) undergoing surgical revascularisation are in high risk of postoperative cardiovascular complications and death, due to advancing age and multiple comorbidities in the population. In addition, PAD needing surgery represents a severe form of systemic atherosclerosis but the exact underlying pathophysiology of acute myocardial infarction (AMI) in these patients is unclear and predicting outcome especially in the long-term is challenging. Obstructive sleep apnoea (OSA) is increasingly common in the general population and independently associated with various manifestations of cardiovascular disease or their risk factors; OSA is highly prevalent in patients with coronary artery disease (CAD), stroke, hypertension and diabetes. To expand this knowledge, we determined the prevalence and severity (in terms of the apnoeahypopnoea index, AHI) of OSA in surgical PAD as well as its impact on the incidence of major adverse cardiovascular and cerebrovascular events (MACCE) in this patient group. Heart rate variability (HRV) reflects fluctuations in sympathetic and parasympathetic activation responsible for neurocirculatory control in various physiological and pathophysiological situations. Depressed HRV is associated with increased cardiovascular morbidity and mortality following AMI and major surgery. In this study, the alterations of nocturnal HRV and their association with the severity of OSA and incidence of MACCE in patients with PAD was assessed, including the fractal correlation properties of HRV. HRV in a control group of 15 healthy subjects was also examined. Patients scheduled for sub-inguinal vascular surgery (n=84, age 67±9 years) underwent polysomnography and HRV analyses. OSA was detected in 86% of patients and in 56% it was moderate or severe. Age, male gender, depressed left ventricular function and decreasing high density lipoprotein/cholesterol ratio (HDL/Chol) predicted the presence and severity of OSA. The latter two remained significant after adjusting for age and gender. OSA with AHI ≥20/hour, used as a cut-off in the outcome analyses, predicted a higher risk of MACCE (p=0.001) along with pre-existing CAD (p=0.001), decreasing HDL/Chol (p=0.048) and <4 years history of PAD (p=0.018). HRV was altered in patients with PAD when compared to controls but the time domain measures were mostly unchanged. In the frequency domain, low frequency power was generally lower, high frequency power was mostly higher and fractal correlation was consistently lower. Very low frequency power was increased the most in patients with AHI 10-20/hour when compared to <10/hour while those with AHI ≥20/hour had lower fractal correlation in the morning. Patients suffering a MACCE had lower high frequency power during S3-4 and rapid eye movement sleep. In conclusion, OSA is associated with worsening atherosclerosis and predicts MACCE after vascular surgery. HRV alterations, although associated with PAD, have limited predictive value. Keywords: atherosclerosis, peripheral arterial disease, sleep apnoea, heart rate variabilityTIIVISTELMÄ Unenaikaiset sydänkomplikaatioiden ennustetekijät kirurgista hoitoa vaativassa perifeerisessä valtimotaudissa Perifeeristä valtimotautia sairastavilla potilailla on suuri leikkauksenjälkeisten sydänkomplikaatioiden riski johtuen yhä iäkkäämmästä väestöstä sekä lukuisista rinnakkaissairauksista. Lisäksi perifeerinen valtimotauti merkitsee vaikea-asteista yleistynyttä ateroskleroosia, mutta sydäninfarktin tarkka syntymekanismi näillä potilailla on epäselvä ja erityisesti pitkän aikavälin ennusteen arviointi on haastavaa. Obstruktiivinen uniapnea yleistyy väestössä ja sillä on itsenäinen yhteys useisiin sydän- ja verisuonisairauksiin ja niiden riskitekijöihin; uniapnea on erittäin yleinen sepelvaltimotauti-, aivohalvaus-, verenpainetauti- ja diabetespotilailla. Tämän tietopohjan laajentamiseksi tässä tutkimuksessa määritettiin uniapnean esiintyvyys ja vaikeusaste (määrittäjänä apnea-hypopneaindeksi, AHI) vaikea-asteista yleistynyttä ateroskleroosia sairastavilla potilailla sekä sen vaikutus vakavien sydän- ja aivotapahtumien ilmaantuvuuteen. Sydämen sykevaihtelu kuvastaa autonomisen hermoston toiminnan muutoksia, jotka puolestaan vastaavat verenkierron säätelystä erilaisissa fysiologisissa ja patofysiologisissa tilanteissa. Alentunut sykevaihtelu on yhteydessä lisääntyneeseen kardiovaskulaariseen sairastuvuuteen ja kuolleisuuteen sairastetun sydäninfarktin tai suuren leikkauksen jälkeen. Tässä tutkimuksessa arvioitiin yöllisen sydämen sykevaihtelun muutosten yhteyttä uniapnean vaikeusasteeseen sekä vakavien sydän- ja aivotapahtumien ilmaantuvuuteen, mukaan lukien sykevaihtelun fraktaalikorrelaatio-ominaisuudet. Tutkimuksessa analysoitiin sykevaihtelu myös 15 terveen henkilön vertailuryhmältä. Nivustason alapuoliseen verisuonileikkaukseen meneville potilaille (n=84, ikä 67±9 vuotta) tehtiin unipolygrafia ja sykevaihteluanalyysi. Uniapnea todettiin 86 %:lla potilaista ja 56 %:lla se oli kohtalainen tai vaikea. Ikä, miessukupuoli, heikentynyt vasemman kammion toiminta ja alentunut HDL-kolesterolin suhde kokonaiskolesteroliin ennustivat uniapneaa ja sen vaikeutumista; 2 viimeksi mainittua säilyivät merkitsevinä ikä- ja sukupuolivakioinnin jälkeen. AHI ≥20/tunti, joka valittiin kynnysarvoksi päätetapahtumaanalyyseihin, ennusti merkitsevästi vakavia sydän- ja aivotapahtumia (p=0.001). Muita merkitseviä tekijöitä olivat sepelvaltimotauti (p=0.001), alentunut HDL-suhde (p=0.048) ja lyhyt (alle 4 vuotta) perifeerisen valtimotaudin kesto ennen leikkaushoidon tarvetta (p=0.018). Sykevaihtelu oli muuttunut valtimotautipotilailla verrattuna kontrolleihin, mutta aikakenttäparametrit säilyivät lähes ennallaan. Pienitaajuuksinen sykevaihtelu oli yleisesti vähäisempää, suuritaajuuksinen enimmäkseen voimakkaampaa ja fraktaalikorrelaatio johdonmukaisesti heikompaa. Hyvin pienitaajuuksinen vaihtelu oli eniten lisääntynyt AHI 10-20/tunti -alaryhmässä verrattuna AHI <10/tunti -ryhmään, mutta AHI ≥20/tunti -potilailla aamun fraktaalikorrelaatio oli heikompaa. Potilaiden, jotka saivat vakavia sydän- ja aivotapahtumia, suuritaajuusvaihtelu oli heikompaa syvän unen ja vilkeunen aikana. Johtopäätöksinä todetaan, että uniapnea on yhteydessä vaikeutuvaan valtimotautiin sekä ennustaa vakavia sydän- ja aivotapahtumia verisuonileikkauksen jälkeen sykevaihtelun muutosten ennustearvon ollessa tässä aineistossa hyvin rajallinen. Avainsanat: ateroskleroosi, perifeerinen valtimotauti, uniapnea, sykevaihtel

수면 호흡음을 이용한 폐쇄성 수면 무호흡 중증도 분류

학위논문 (박사)-- 서울대학교 융합과학기술대학원 융합과학부, 2017. 8. 이교구.Obstructive sleep apnea (OSA) is a common sleep disorder. The symptom has a high prevalence and increases mortality as a risk factor for hypertension and stroke. Sleep disorders occur during sleep, making it difficult for patients to self-perceive themselves, and the actual diagnosis rate is low. Despite the existence of a standard sleep study called a polysomnography (PSG), it is difficult to diagnose the sleep disorders due to complicated test procedures and high medical cost burdens. Therefore, there is an increasing demand for an effective and rational screening test that can determine whether or not to undergo a PSG. In this thesis, we conducted three studies to classify the snoring sounds and OSA severity using only breathing sounds during sleep without additional biosensors. We first identified the classification possibility of snoring sounds related to sleep disorders using the features based on the cyclostationary analysis. Then, we classified the patients OSA severity with the features extracted using temporal and cyclostationary analysis from long-term sleep breathing sounds. Finally, the partial sleep sound extraction, and feature learning process using a convolutional neural network (CNN, or ConvNet) were applied to improve the efficiency and performance of previous snoring sound and OSA severity classification tasks. The sleep breathing sound analysis method using a CNN showed superior classification accuracy of more than 80% (average area under curve > 0.8) in multiclass snoring sounds and OSA severity classification tasks. The proposed analysis and classification method is expected to be used as a screening tool for improving the efficiency of PSG in the future customized healthcare service.Chapter 1. Introduction ................................ .......................1 1.1 Personal healthcare in sleep ................................ ..............1 1.2 Existing approaches and limitations ....................................... 9 1.3 Clinical information related to SRBD ................................ .. ..12 1.4 Study objectives ................................ .........................16 Chapter 2. Overview of Sleep Research using Sleep Breathing Sounds ........... 23 2.1 Previous goals of studies ................................ ................23 2.2 Recording environments and related configurations ........................ 24 2.3 Sleep breathing sound analysis ................................ ...........27 2.4 Sleep breathing sound classification ..................................... 35 2.5 Current limitations ................................ ......................36 Chapter 3. Multiple SRDB-related Snoring Sound Classification .................39 3.1 Introduction ................................ .............................39 3.2 System architecture ................................ ......................41 3.3 Evaluation ................................ ...............................52 3.4 Results ................................ ..................................55 3.5 Discussion ................................ ...............................59 3.6 Summary ................................ ..................................63 Chapter 4. Patients OSA Severity Classification .............................65 4.1 Introduction ................................ .............................65 4.2 Existing Approaches ................................ ......................69 4.3 System Architecture ................................ ......................70 4.4 Evaluation ................................ ...............................85 4.5 Results ................................ ..................................87 4.6 Discussion ................................ ...............................94 4.7 Summary ................................ ..................................97 Chapter 5. Patient OSA Severity Prediction using Deep Learning Techniques .....99 5.1 Introduction ................................ .............................99 5.2 Methods ................................ ..................................101 5.3 Results ................................ ..................................109 5.4 Discussion ................................ ...............................115 5.5 Summary ................................ ..................................118 Chapter 6. Conclusions and Future Work ........................................120 6.1 Conclusions ................................ ..............................120 6.2 Future work ................................ ..............................127Docto

Methods for Detecting and Monitoring of Sleep Disordered Breathing in Children using Overnight Polysomnography

Sleep is crucial for the health of every individual, especially children. One of the common causes of disturbed sleep in children is disordered breathing. Children who suffer from sleep disordered breathing are likely to have severe consequences for their physical growth, heart health and neuropsychological function. Sleep disordered breathing (SDB) comprises a spectrum of severity from a mild form of upper airway resistance syndrome (UARS) to severe form of obstructive sleep apnea syndrome (OSAS). While OSAS is considered clinically significant, UARS and its health consequences have been underestimated. The most common treatment for OSAS in children is adenotonsillectomy. However, breathing disturbances related to UARS may persist even after adenotonsillectomy. The current diagnostic marker for OSAS, the Apnea-Hypopnea Index (AHI) often overlooks the less severe conditions of breathing disturbances. Therefore, the research objective of this thesis is to investigate the new alternative markers for SDB in children using non-invasive physiological measurements, such as thoracoabdominal signals and the photoplethysmogram. As the body experiences an array of complex changes, specifically in respiratory and autonomic nervous system activation during breathing disturbances, advanced signal processing and analysis techniques were used to identify the physiological variables that could reflect changes in those systems in children with SDB. Thoraco-abdominal asynchrony (TAA), heart period (HP) and pulse wave amplitude (PWA) were the three physiological variables were investigated. A total of five studies were conducted on two high-quality clinical research datasets to test the potential of the proposed physiological variables to effectively identify children with SDB. In the thesis: 1) Hilbert transform was applied for TAA estimation on the childhood adenotonsillectomy trial (CHAT) dataset; 2) symbolic dynamic analysis on HP was used to assess the effect of adenotonsillectomy on autonomic activations in children with SDB; 3) the conventional method of estimating PWA was combined with joint symbolic analysis of PWA and HP to analyse the effect of SDB on autonomic activation compared to healthy controls; 4) to improve the performance of the previous PWA measurement technique, a more robust and simpler method was proposed to estimate PWA using a simple envelope method, and a more extensive dynamic analysis method was created to capture more complete information; and 5) adding TAA and HP information with AHI, unsupervised machine learning method K-means clustering and linear discriminant analysis were used to discover the pathophysiology nature difference of children with SDB in CHAT dataset. The main results from this thesis suggest that children with SDB have higher values in all three physiological variables, which indicates a high respiratory effort and elevated frequency of autonomic activation. Adenotonsillectomy showed to reverse the effects on these physiological variables, suggesting it assisted in the reduce of pathophysiological symptoms in those children. Interestingly, TAA was found inversely correlated with quality of life and unreported baseline difference in HP in children who had their AHI normalised spontaneously. These findings further indicate the limitation of AHI as the only marker for paediatric sleep disordered breathing. By combining the TAA and HP information with AHI, the alternative proposed diagnosing approach could help doctors predict who may benefit from adenotonsillectomy or not. In conclusion, this thesis provides new evidence that TAA, HP and PWA can provide additional information and may yield more effective markers for diagnosing paediatric sleep disordered breathing.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 201

Comparison of window shapes and lengths in short-time feature extraction for classification of heart sound signals

Heart sound signals, phonocardiography (PCG) signals, allow for the automatic diagnosis of potential cardiovascular pathology. Such classification task can be tackled using the bidirectional long short-term memory (biLSTM) network, trained on features extracted from labeled PCG signals. Regarding the non-stationarity of PCG signals, it is recommended to extract the features from multiple short-length segments of the signals using a sliding window of certain shape and length. However, some window contains unfavorable spectral side lobes, which distort the features. Accordingly, it is preferable to adapt the window shape and length in terms of classification performance. We propose an experimental evaluation for three window shapes, each with three window lengths. The biLSTM network is trained and tested on statistical features extracted, and the performance is reported in terms of the window shapes and lengths. Results show that the best performance is obtained when the Gaussian window is used for splitting the signals, and the triangular window competes with the Gaussian window for a length of 75 ms. Although the rectangular window is a commonly offered option, it is the worst choice for splitting the signals. Moreover, the classification performance obtained with a 75 ms Gaussian window outperforms that of a baseline method