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

Detection and Assessment of Sleep-Disordered Breathing with Special Interest of Prolonged Partial Obstruction

Sleep-disordered breathing (SDB) has become more common and puts more strain on public health services than ever before. Obstructive sleep apnea (OSA) and its health consequences such as different cardiovascular diseases are nowadays well recognized. In addition to OSA, attention has recently been paid to another SDB; prolonged partial obstruction. However, it is often undiagnosed and easily left untreated because of the low number of respiratory events during polysomnography recording. This patient group has found to present with more atypical subjective symptoms than OSA patients.Polysomnography (PSG) is considered to be the gold standard in reference methods in SDB diagnostics. PSG is a demanding and laborious multichannel recording method and often requires subjects to spend one night in a sleep laboratory. There is long tradition in Finland to use mattress sensors in SDB diagnostics. Recently, smaller electromechanical film transducer (Emfit) mattresses have replaced the old Static Charge-Sensitive Bed (SCSB) mattresses. However, a proper clinical validation of Emfit mattresses in SDB diagnostics has not been carried out.In this work, the use of Emfit recording in the detection of sleep apneas, hypopneas, and prolonged partial obstruction with increased respiratory effort was evaluated. The general aim of the thesis is to develop and improve the diagnostic methods for sleep-related breathing disorders.Comparisons with both PSG with nasal pressure recording and transesophageal pressure were made. Special attention was paid to the existence of the spiking phenomenon in the Emfit mattress in relation to changes in negative intrathoracic pressure in estimating increased respiratory effort. This entails monitoring the esophageal pressure as a part of nocturnal polysomnography. The recording method is demanding and uncomfortable and is usually not used with ordinary sleep laboratory patients. Thus, reliable and easy indirect quantification methods for respiratory effort are needed in clinical work. According to the results presented in this work, the Emfit signal reveals increased respiratory effort as well as apneas/hypopneas.To find out the prevalence and consequences of prolonged partial obstruction among sleep laboratory patients was another aim of this thesis. This was done by retrospective analyses of sleep laboratory patients from one year. The prevalence of patients with prolonged partial obstruction was 11%. They were as sleepy as OSA patients, but their life quality was worse, as assessed by a survey. These results, along with the findings of the heart rate variation evaluation carried out in this thesis, suggest that prolonged partial obstruction and OSA should be considered as different entities of SDB.With the Emfit mattress sensor, the SDB types can be differentiated, which is expected to enhance the accuracy of diagnostics. However, there is increasing need for easy and cheap screening methods to evaluate nocturnal breathing. In this respect, the usability of compressed tracheal sound signal scoring in SDB screening was estimated. The method reveals apneas and hypopneas but, according to the present findings, it can also be used in the detection of prolonged partial obstruction. The findings encourage the use of compressed tracheal sound analysis in screening different SDB.The analysis of sleep recordings is still based on a doctor’s subjective and visual estimation. To date, no generally accepted and sufficiently reliable automatic analysis method exists. Robust, automatic quantification methods with easier techniques for non-invasive sleep recording would enable the analysis methods to be also used for screening purposes. In this technology-orientated world, people could take much more responsibility and take care of themselves better by following their own biosignals and by changing their health habits earlier. The need for good sleep as a necessity for good life and health is widely recognized

A Panoramic Study of Obstructive Sleep Apnea Detection Technologies

This study offers a literature research reference value for bioengineers and practitioner medical doctors. It could reduce research time and improve medical service efficiency regarding Obstructive Sleep Apnea (OSA) detection systems. Much of the past and the current apnea research, the vital signals features and parameters of the SA automatic detection are introduced.The applications for the earlier proposed systems and the related work on real-time and continuous monitoring of OSA and the analysis is given. The study concludes with an assessment of the current technologies highlighting their weaknesses and strengths which can set a roadmap for researchers and clinicians in this rapidly developing field of study

Snoring and arousals in full-night polysomnographic studies from sleep apnea-hypopnea syndrome patients

SAHS (Sleep Apnea-Hypopnea Syndrome) is recognized to be a serious disorder with high prevalence in the population. The main clinical triad for SAHS is made up of 3 symptoms: apneas and hypopneas, chronic snoring and excessive daytime sleepiness (EDS). The gold standard for diagnosing SAHS is an overnight polysomnographic study performed at the hospital, a laborious, expensive and time-consuming procedure in which multiple biosignals are recorded. In this thesis we offer improvements to the current approaches to diagnosis and assessment of patients with SAHS. We demonstrate that snoring and arousals, while recognized key markers of SAHS, should be fully appreciated as essential tools for SAHS diagnosis. With respect to snoring analysis (applied to a 34 subjects¿ database with a total of 74439 snores), as an alternative to acoustic analysis, we have used less complex approaches mostly based on time domain parameters. We concluded that key information on SAHS severity can be extracted from the analysis of the time interval between successive snores. For that, we built a new methodology which consists on applying an adaptive threshold to the whole night sequence of time intervals between successive snores. This threshold enables to identify regular and non-regular snores. Finally, we were able to correlate the variability of time interval between successive snores in short 15 minute segments and throughout the whole night with the subject¿s SAHS severity. Severe SAHS subjects show a shorter time interval between regular snores (p=0.0036, AHI cp(cut-point): 30h-1) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p=0.006, AHI cp: 30h-1) is seen for less severe SAHS subjects. Also, we have shown successful in classifying the subjects according to their SAHS severity using the features derived from the time interval between regular snores. Classification accuracy values of 88.2% (with 90% sensitivity, 75% specificity) and 94.1% (with 94.4% sensitivity, 93.8% specificity) for AHI cut-points of severity of 5 and 30h-1, respectively. In what concerns the arousal study, our work is focused on respiratory and spontaneous arousals (45 subjects with a total of 2018 respiratory and 2001 spontaneous arousals). Current beliefs suggest that the former are the main cause for sleep fragmentation. Accordingly, sleep clinicians assign an important role to respiratory arousals when providing a final diagnosis on SAHS. Provided that the two types of arousals are triggered by different mechanisms we hypothesized that there might exist differences between their EEG content. After characterizing our arousal database through spectral analysis, results showed that the content of respiratory arousals on a mild SAHS subject is similar to that of a severe one (p>>0.05). Similar results were obtained for spontaneous arousals. Our findings also revealed that no differences are observed between the features of these two kinds of arousals on a same subject (r=0.8, p<0.01 and concordance with Bland-Altman analysis). As a result, we verified that each subject has almost like a fingerprint or signature for his arousals¿ content and is similar for both types of arousals. In addition, this signature has no correlation with SAHS severity and this is confirmed for the three EEG tracings (C3A2, C4A1 and O1A2). Although the trigger mechanisms of the two arousals are known to be different, our results showed that the brain response is fairly the same for both of them. The impact that respiratory arousals have in the sleep of SAHS patients is unquestionable but our findings suggest that the impact of spontaneous arousals should not be underestimated

Automatic Heart Rate Detection during Sleep Using Tracheal Audio Recordings from Wireless Acoustic Sensor

Background: Heart rate is an essential diagnostic parameter indicating a patient’s condition. The assessment of heart rate is also a crucial parameter in the diagnostics of various sleep disorders, including sleep apnoea, as well as sleep/wake pattern analysis. It is usually measured using an electrocardiograph (ECG)—a device monitoring the electrical activity of the heart using several electrodes attached to a patient’s upper body—or photoplethysmography (PPG). Methods: The following paper investigates an alternative method for heart rate detection and monitoring that operates on tracheal audio recordings. Datasets for this research were obtained from six participants along with ECG Holter (for validation), as well as from fifty participants undergoing a full night polysomnography testing, during which both heart rate measurements and audio recordings were acquired. Results: The presented method implements a digital filtering and peak detection algorithm applied to audio recordings obtained with a wireless sensor using a contact microphone attached in the suprasternal notch. The system was validated using ECG Holter data, achieving over 92% accuracy. Furthermore, the proposed algorithm was evaluated against whole-night polysomnography-derived HR using Bland-Altman’s plots and Pearson’s Correlation Coefficient, reaching the average of 0.82 (0.93 maximum) with 0 BPM error tolerance and 0.89 (0.97 maximum) at ±3 BPM. Conclusions: The results prove that the proposed system serves the purpose of a precise heart rate monitoring tool that can conveniently assess HR during sleep as a part of a home-based sleep disorder diagnostics process

All night analysis of time interval between snores in subjects with sleep apnea hypopnea syndrome

Sleep apnea–hypopnea syndrome (SAHS) is a serious sleep disorder, and snoring is one of its earliest and most consistent symptoms. We propose a new methodology for identifying two distinct types of snores: the so-called non-regular and regular snores. Respiratory sound signals from 34 subjects with different ranges of Apnea-Hypopnea Index (AHI = 3.7–109.9 h−1) were acquired. A total number of 74,439 snores were examined. The time interval between regular snores in short segments of the all night recordings was analyzed. Severe SAHS subjects show a shorter time interval between regular snores (p = 0.0036, AHI cp: 30 h−1) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p = 0.006, AHI cp: 30 h−1) is seen for less severe SAHS subjects. Features derived from the analysis of time interval between regular snores achieved classification accuracies of 88.2 % (with 90 % sensitivity, 75 % specificity) and 94.1 % (with 94.4 % sensitivity, 93.8 % specificity) for AHI cut-points of severity of 5 and 30 h−1, respectively. The features proved to be reliable predictors of the subjects’ SAHS severity. Our proposed method, the analysis of time interval between snores, provides promising results and puts forward a valuable aid for the early screening of subjects suspected of having SAHS

Respiratory sound analysis as a diagnosis tool for breathing disorders

This paper provides an overview of respiratory sound analysis (RSA) and its functionality as a diagnostic tool for breathing disorders. A number of respiratory conditions and the techniques used to diagnose them, including sleep apnoea, lung sound analysis (LSA), wheeze detection and phase estimation are discussed. The technologies used, from multi-channel bespoke recording systems to using a smart phone application are explained. A new study that focusses on developing a non-invasive tool for the detection and characterisation of inducible laryngeal obstruction (ILO) is presented. ILO is a debilitating condition, caused by malfunctioning structures of the upper airway, commonly triggered by exertion, leaving children feeling out of breath and unable to exercise normally. In rare cases it can lead to critical laryngeal obstruction and admission to intensive care for endotracheal intubation. The current definitive method of diagnosis is by inserting a camera through the nose while the person is exercising. This approach is invasive, uncomfortable (in particular for young children) subjective and relies on the consultant's expertise. There are only a handful of consultants with the appropriate level of expertise in the UK to diagnose this condition

PVDF 필름 센서를 사용한 수면관련 호흡장애 및 수면 단계의 무구속적 모니터링

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 바이오엔지니어링전공, 2015. 8. 박광석.이 연구에서는 PVDF 필름 센서를 사용하여 무구속적으로 수면관련 호흡장애 및 수면 단계를 모니터링 할 수 있는 기법을 개발하였다. PVDF 센서는 4 x 1 배열로 구성되었으며, 센서 시스템의 총 두께는 약 1.1mm 였다. PVDF 센서는 침대보와 매트리스 사이에 위치시켜 참가자의 몸에 직접적인 접촉이 없도록 하였다. 수면무호흡증 검출 연구에는 26명의 수면무호흡증 환자와 6명의 정상인이 참가하였다. PVDF 신호의 표준편차에 근거하여 수면무호흡증 검출 방법을 개발하였고, 추정된 수면무호흡증 검출 결과를 수면 전문의의 판독 결과와 비교하였다. 추정된 결과와 판독 결과 간의 수면 무호흡-저호흡 지수 상관계수는 0.94 (p < 0.001) 이었다. 코골이 검출 연구에는 총 20명의 수면무호흡증 환자가 참가하였다. PVDF 신호를 단시간 푸리에 변환하여 얻은 파워 비율과 최대 주파수를 주파수 영역 특징으로 추출하였다. 추출된 특징들을 서포트 벡터 머신 분류기에 입력하였고, 분류기의 검출 결과에 따라 코골이 또는 코골이가 아닌 구간으로 나누었다. 제안된 방법에서 추정한 코골이 검출 결과를 정상 성인 3명의 청각 및 시각 기반 코골이 판독 결과와 비교하였다. 코골이 검출에 대한 평균 민감도 및 양성예측도는 각각 94.6% 및 97.5% 이었다. 수면 단계 검출 연구에는 11명의 정상인과 13명의 수면무호흡증 환자가 참가하였다. 렘 (REM) 수면 구간은 호흡의 주기와 그 변동률에 근거하여 추정하였다. 깸 구간은 움직임 신호에 근거하여 추정하였다. 깊은 수면 구간은 분당 호흡수의 변화폭에 기반하여 추정하였다. 각 수면 단계 검출 결과를 통합하여 최종 결과를 수면 전문의의 판독 결과와 비교하였다. 30초 단위로 수면 단계를 검출하였을 때, 평균 정확도는 71.3% 이었으며 평균 카파 값은 0.48 이었다. 연구에서 제안된 PVDF 센서와 알고리즘을 통하여 상용화된 수면 모니터링 장치에 비교할 수 있을 만한 수준의 성능을 확보하였다. 이 연구 결과가 가정환경 기반 수면모니터링 시스템의 활용도와 정확도를 높이는데 기여할 수 있을 것으로 기대한다.In this study, unconstrained sleep-related breathing disorders (SRBD) and sleep stages monitoring methods using a polyvinylidene fluoride (PVDF) film sensor were established and tested. Subjects physiological signals were measured in an unconstrained manner using the PVDF sensor during polysomnography (PSG). The sensor was comprised of a 4×1 array, and the total thickness of the system was approximately 1.1 mm. It was designed to be placed under the subjects back and installed between a bed cover and mattress. In the sleep apnea detection study, twenty six sleep apnea patients and six normal subjects participated. The sleep apnea detection method was based on the standard deviation of the PVDF signals, and the methods performance was assessed by comparing the results with a sleep physicians manual scoring. The correlation coefficient for the apnea-hypopnea index (AHI) values between the methods was 0.94 (p < 0.001). For minute-by-minute sleep apnea detection, the method classified sleep apnea with average sensitivity of 72.9%, specificity of 90.6%, accuracy of 85.5%, and kappa statistic of 0.60. In the snoring detection study, twenty patients with obstructive sleep apnea (OSA) participated. The power ratio and peak frequency from the short-time Fourier transform were used to extract spectral features from the PVDF data. A support vector machine (SVM) was applied to the spectral features to classify the data into either snore or non-snore class. The performance of the method was assessed using manual labelling by three human observers. For event-by-event snoring detection, PVDF data that contained snoring (SN), snoring with movement (SM), and normal breathing (NB) epochs were selected for each subject. The results showed that the overall sensitivity and the positive predictive values were 94.6% and 97.5%, respectively, and there was no significant difference between the SN and SM results. In the sleep stages detection study, eleven normal subjects and thirteen OSA patients participated. Rapid eye movement (REM) sleep was estimated based on the average rate and variability of the respiratory signal. Wakefulness was detected based on the body movement signal. Variability of the respiratory rate was chosen as an indicator for slow wave sleep (SWS) detection. The performance of the method was assessed by comparing the results with manual scoring by a sleep physician. In an epoch-by-epoch analysis, the method classified the sleep stages with average accuracy of 71.3% and kappa statistic of 0.48. The experimental results demonstrated that the performances of the proposed sleep stages and SRBD detection methods were comparable to those of ambulatory devices and the results of constrained sensor based studies. The developed system and methods could be applied to a sleep monitoring system in a residential or ambulatory environment.Chapter 1. Introduction 1 1.1. Background 1 1.2. Sleep Apnea 3 1.3. Snoring 5 1.4. Sleep Stages 8 1.5. Polyvinylidene Fluoride Film Sensor 11 1.6. Purpose 12 Chapter 2. Sleep Apnea Detection 13 2.1. Signal Acquisition System 13 2.2. Methods 16 2.2.1. Participants and PSG Data 16 2.2.2. Apneic Events Detection 18 2.2.3. Statistical Analysis 25 2.3. Results 26 2.3.1. AHI Estimation 26 2.3.2. Diagnosing Sleep Apnea 29 2.3.3. Minute-By-Minute Sleep Apnea Detection 30 2.4. Discussion 34 2.4.1. Agreement between Proposed Method and PSG 34 2.4.2. Comparisons with Previous Studies 34 2.4.3. Validation of PVDF Film Sensors 36 2.4.4. Validation of Sleep Apnea Detection Algorithm 37 Chapter 3. Snoring Detection 40 3.1. Methods 40 3.1.1. Participants and PSG Data 40 3.1.2. Feature Extraction for Snoring Event Detection 42 3.1.3. Data Selection and Reference Snoring Labelling 46 3.1.4. Snoring Event Classification Based on the SVM 48 3.2. Results 52 3.2.1. Event-By-Event Snoring Detection 52 3.2.2. Snoring Event Detection and Sleep Posture 56 3.2.3. Epoch-By-Epoch Snoring Detection 57 3.3. Discussion 59 3.3.1. Agreement between Proposed Method and Reference Snoring 59 3.3.2. Comparisons with Previous Studies 59 3.3.3. Validation of the Snoring Detection Algorithm 61 Chapter 4. Sleep Stages Detection 65 4.1. Methods 65 4.1.1. Participants and PSG Data 65 4.1.2. REM Sleep Detection 68 4.1.3. Wakefulness Detection 74 4.1.4. SWS Detection 80 4.2. Results 86 4.2.1. REM Sleep Detection 86 4.2.2. Wakefulness Detection 89 4.2.3. SWS Detection 92 4.2.4. Sleep Macro- and Microstructure Detection 94 4.3. Discussion 99 4.3.1. Agreement between Proposed Method and PSG 99 4.3.2. Comparisons with Previous Studies 99 4.3.3. Validation of Sleep Stages Detection Algorithm 102 Chapter 5. Conclusion 105 References 107 Abstract in Korean 118Docto