Diagnosis

Obstructive sleep apnea (OSA) is often confused with the clinical symptoms of other adult/pediatric medical conditions and neurological disorders. Since OSA affects all systems in the body, it is important to establish a correct diagnosis. The first step in the evaluation of a patient with a sleep disorder is to identify the primary symptom. A detailed history of the sleep and wakefulness cycles constitutes the second step. This is followed by the medical history of the patient; a list of previously used medications; family history; detailed information about school, work, family, and social life; and a physical exam of bodily systems. Relevant laboratory tests are performed for differential diagnosis. Polysomnography (PSG) is a golden standard diagnostic method that records electrophysiological signals used for sleep physiology and diseases. PSG is an indispensable method in the diagnosis of OSA

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

Prediction of Obstructive Sleep Apnea Based on Respiratory Sounds Recorded Between Sleep Onset and Sleep Offset

Objectives To develop a simple algorithm for prescreening of obstructive sleep apnea (OSA) on the basis of respiratorysounds recorded during polysomnography during all sleep stages between sleep onset and offset. Methods Patients who underwent attended, in-laboratory, full-night polysomnography were included. For all patients, audiorecordings were performed with an air-conduction microphone during polysomnography. Analyses included allsleep stages (i.e., N1, N2, N3, rapid eye movement, and waking). After noise reduction preprocessing, data were segmentedinto 5-s windows and sound features were extracted. Prediction models were established and validated with10-fold cross-validation by using simple logistic regression. Binary classifications were separately conducted for threedifferent threshold criteria at apnea hypopnea index (AHI) of 5, 15, or 30. Prediction model characteristics, includingaccuracy, sensitivity, specificity, positive predictive value (precision), negative predictive value, and area under thecurve (AUC) of the receiver operating characteristic were computed. Results A total of 116 subjects were included; their mean age, body mass index, and AHI were 50.4 years, 25.5 kg/m2, and23.0/hr, respectively. A total of 508 sound features were extracted from respiratory sounds recorded throughoutsleep. Accuracies of binary classifiers at AHIs of 5, 15, and 30 were 82.7%, 84.4%, and 85.3%, respectively. Predictionperformances for the classifiers at AHIs of 5, 15, and 30 were AUC, 0.83, 0.901, and 0.91; sensitivity, 87.5%,81.6%, and 60%; and specificity, 67.8%, 87.5%, and 94.1%. Respective precision values of the classifiers were89.5%, 87.5%, and 78.2% for AHIs of 5, 15, and 30. Conclusion This study showed that our binary classifier predicted patients with AHI of ≥15 with sensitivity and specificityof >80% by using respiratory sounds during sleep. Since our prediction model included all sleep stage data, algorithmsbased on respiratory sounds may have a high value for prescreening OSA with mobile devices

Sleep Breath

PurposeDiagnosis of obstructive sleep apnea by the gold-standard of polysomnography (PSG), or by home sleep testing (HST), requires numerous physical connections to the patient which may restrict use of these tools for early screening. We hypothesized that normal and disturbed breathing may be detected by a consumer smartphone without physical connections to the patient using novel algorithms to analyze ambient sound.MethodsWe studied 91 patients undergoing clinically indicated PSG. Phase I: In a derivation cohort (n = 32), we placed an unmodified Samsung Galaxy S5 without external microphone near the bed to record ambient sounds. We analyzed 12,352 discrete breath/non-breath sounds (386/patient), from which we developed algorithms to remove noise, and detect breaths as envelopes of spectral peaks. Phase II: In a distinct validation cohort (n = 59), we tested the ability of acoustic algorithms to detect AHI 15 on PSG.ResultsSmartphone-recorded sound analyses detected the presence, absence, and types of breath sound. Phase I: In the derivation cohort, spectral analysis identified breaths and apneas with a c-statistic of 0.91, and loud obstruction sounds with c-statistic of 0.95 on receiver operating characteristic analyses, relative to adjudicated events. Phase II: In the validation cohort, automated acoustic analysis provided a c-statistic of 0.87 compared to whole-night PSG.ConclusionsAmbient sounds recorded from a smartphone during sleep can identify apnea and abnormal breathing verified on PSG. Future studies should determine if this approach may facilitate early screening of SDB to identify at-risk patients for definitive diagnosis and therapy.Clinical trialsNCT03288376; clinicaltrials.orgR43 DP006418/DP/NCCDPHP CDC HHS/United States2019-05-24T00:00:00Z30022325PMC65341346307vault:3223

Sincronización de sistemas de monitorización para el estudio de ronquidos en las distintas fases del sueño en pacientes con SAHS

El Síndrome de Apnea-Hipopnea del Sueño (SAHS) tiene una incidencia en sujetos de edad media, del 2-4% en mujeres y 4-6% en hombres, además de múltiples consecuencias asociadas. Sin embargo, a pesar de su prevalencia, menos de un 10% de la población con este síndrome es diagnosticada. Con el objetivo de identificar qué señales debería emplear un futuro método de diagnóstico para pacientes con sospecha de SAHS más eficaz que los actuales, se sugiere un estudio en detalle de los eventos respiratorios que tienen lugar durante la noche. Para ello se parte de los estudios de monitorización del sueño realizados a pacientes con síntomas de SAHS mediante dos plataformas comerciales distintas. En primer lugar, los registros procedentes de dichos estudios se combinan y sincronizan temporalmente de una forma precisa y robusta. Una vez llevada y sincronizada toda la información a una plataforma común, el presente estudio se centra en la relación del SAHS con una nueva información, el roncograma. El roncograma permite estudiar la evolución de los ronquidos según la fase de sueño. Aplicando esta medida sobre nuestra base de datos observamos como el tiempo en fase de vigilia, el tiempo en fase REM o la densidad de ronquidos en fases ligeras presentan diferencias estadísticamente significativas para pacientes con distinta severidad de SAHS.Postprint (published version

A Comparative study of Dynamic MRI with Drug induced Sleep Endoscopy in Obstructive Sleep Apnea Patients

BACKGROUND: OSAS is a disease of modern ages and it is found to predispose to hypertension, myocardial infarction and sudden death .It is imperative to identify the level of obstruction to decide the treatment protocol. Level of obstruction can be identified by Dynamic MRI and DISE. AIM OF THE STUDY: To identify and compare the level of obstruction in Dynamic MRI with DISE in OSAS patients. MATERIALS AND METHODS: Based on inclusion and exclusion criteria 35 patients were enrolled in our study. These patients were subjected to Sleep MRI to identify the level of obstruction. Then patients were subjected to Drug Induced Sleep Endoscopy to identify the level of obstruction. RESULTS; 82.8% correlation was seen between dynamic MRI and DISE in identifying the level of obstruction.3 patients had no obstruction in MRI but had obstruction at velum in DISE. ONE patient who had tongue base obstruction in Dynamic MRI had tongue base and epiglottis level obstruction in DISE .ONE patient had obstruction in velum and epiglottis in Dynamic MRI but had obstruction only in velum level in DISE. ONE patient had obstruction at oropharynx in Dynamic MRI was found to have obstruction in oropharynx and epiglottis in DISE. CONCLUSION: Both Dynamic MRI and DISE are equally good in identifying the level of obstruction. Dynamic MRI is non-invasive and gives accurate measurements of upper airway while in DISE patient is subjected to an anaesthetic procedure. In all cases planned for surgery after identifying level of obstruction with dynamic MRI, DISE can be done on table before the planned surgery to confirm the level of obstruction so that separate anesthesia for DISE can be avoided

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

Sleep estimates in children: parental versus actigraphic assessments

Background: In the context of increasing awareness about the need for assessment of sleep duration in community and clinical settings, the use of questionnaire-based tools may be fraught with reporter bias. Conversely, actigraphy provides objective assessments of sleep patterns. In this study, we aimed to determine the potential discrepancies between parentally-based sleep logs and concurrent actigraphic recordings in children over a one-week period. Methods: We studied 327 children aged 3–10 years, and included otherwise healthy, nonsnoring children from the community who were reported by their parents to be nonsnorers and had normal polysomnography, habitually-snoring children from the community who completed the same protocol, and children with primary insomnia referred to the sleep clinic for evaluation in the absence of any known psychiatric illness. Actigraphy and parental sleep log were concomitantly recorded during one week. Results: Sleep logs displayed an average error in sleep onset after bedtime of about 30 minutes (P , 0.01) and of a few minutes before risetime in all groups. Furthermore, subjective parental reports were associated with an overestimated misperception of increased sleep duration of roughly one hour per night independent of group (P , 0.001). Conclusion: The description of a child’s sleep by the parent appears appropriate as far as symptoms are concerned, but does not result in a correct estimate of sleep onset or duration. We advocate combined parental and actigraphic assessments in the evaluation of sleep complaints, particularly to rule out misperceptions and potentially to aid treatment. Actigraphy provides a more reliable tool than parental reports for assessing sleep in healthy children and in children with sleep problems