Automatic silence events detector from smartphone audio signals: a pilot mHealth system for sleep apnea monitoring at home

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Obstructive sleep apnea (OSA) is a prevalent disease, but most patients remain undiagnosed and untreated. Recently, mHealth tools are being proposed to screen OSA patients at home. In this work, we analyzed full-night audio signals recorded with a smartphone microphone. Our objective was to develop an automatic detector to identify silence events (apneas or hypopneas) and compare its performance to a commercial portable system for OSA diagnosis (ApneaLink™, ResMed). To do that, we acquired signals from three subjects with both systems simultaneously. A sleep specialist marked the events on smartphone and ApneaLink signals. The automatic detector we developed, based on the sample entropy, identified silence events similarly than manual annotation. Compared to ApneaLink, it was very sensitive to apneas (detecting 86.2%) and presented an 83.4% positive predictive value, but it missed about half the hypopnea episodes. This suggests that during some hypopneas the flow reduction is not reflected in sound. Nevertheless, our detector accurately recognizes silence events, which can provide valuable respiratory information related to the disease. These preliminary results show that mHealth devices and simple microphones are promising non-invasive tools for personalized sleep disorders management at homePostprint (published version

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

Entropy analysis of acoustic signals recorded with a smartphone for detecting apneas and hypopneas: A comparison with a commercial system for home sleep apnea diagnosis

Obstructive sleep apnea (OSA) is a prevalent disease, but most patients remain undiagnosed and untreated. Here we propose analyzing smartphone audio signals for screening OSA patients at home. Our objectives were to: (1) develop an algorithm for detecting silence events and classifying them into apneas or hypopneas; (2) evaluate the performance of this system; and (3) compare the information provided with a type 3 portable sleep monitor, based mainly on nasal airflow. Overnight signals were acquired simultaneously by both systems in 13 subjects (3 healthy subjects and 10 OSA patients). The sample entropy of audio signals was used to identify apnea/hypopnea events. The apnea-hypopnea indices predicted by the two systems presented a very high degree of concordance and the smartphone correctly detected and stratified all the OSA patients. An event-by-event comparison demonstrated good agreement between silence events and apnea/hypopnea events in the reference system (Sensitivity = 76%, Positive Predictive Value = 82%). Most apneas were detected (89%), but not so many hypopneas (61%). We observed that many hypopneas were accompanied by snoring, so there was no sound reduction. The apnea/hypopnea classification accuracy was 70%, but most discrepancies resulted from the inability of the nasal cannula of the reference device to record oral breathing. We provided a spectral characterization of oral and nasal breathing to correct this effect, and the classification accuracy increased to 82%. This novel knowledge from acoustic signals may be of great interest for clinical practice to develop new non-invasive techniques for screening and monitoring OSA patients at homePeer ReviewedPostprint (published version

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

Sleep apnea-hypopnea syndrome (SAHS) is a chronic and highly prevalent disease considered a major health problem in industrialized countries. The gold standard diagnostic methodology is in-laboratory nocturnal polysomnography (PSG), which is complex, costly, and time consuming. In order to overcome these limitations, novel and simplified diagnostic alternatives are demanded. Sleep scientists carried out an exhaustive research during the last decades focused on the design of automated expert systems derived from artificial intelligence able to help sleep specialists in their daily practice. Among automated pattern recognition techniques, artificial neural networks (ANNs) have demonstrated to be efficient and accurate algorithms in order to implement computer-aided diagnosis systems aimed at assisting physicians in the management of SAHS. In this regard, several applications of ANNs have been developed, such as classification of patients suspected of suffering from SAHS, apnea-hypopnea index (AHI) prediction, detection and quantification of respiratory events, apneic events classification, automated sleep staging and arousal detection, alertness monitoring systems, and airflow pressure optimization in positive airway pressure (PAP) devices to fit patients’ needs. In the present research, current applications of ANNs in the framework of SAHS management are thoroughly reviewed

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 review of automated sleep disorder detection

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand

Psychometric Properties of Obstructive Sleep Apnea Screening Measures in Patients Referred to a Sleep Clinic

Background: Obstructive Sleep Apnea (OSA) contributes to all-cause and cardiac mortality. There are no current guidelines for OSA screening in outpatient settings. An American Academy of Sleep Medicine task force is focusing on improving detection and categorization of OSA symptoms and severity to promote screening, assessment, and diagnosis of the disorder. The purpose of this study was to identify the psychometric properties of three self-report OSA screening measures (Berlin, Epworth Sleepiness Scale (ESS), STOP Bang) and an objective portable sleep monitor (PSM) compared to apnea-hypopnea index (AHI) levels (≥5, ≥ 15, and ≥ 30) from polysomnogram (PSG). Methods: A methodological design was used. Patients referred to a sleep specialist for an OSA consultation were recruited and enrolled at initial sleep evaluation. Participants completed the three OSA self-report screening measures and those participants who met inclusion criteria were sent home with a PSM for one night measurement. Automatic scoring was used. PSGs were ordered by the physician and AHI results were obtained from the medical record. Results: Participants (N=170) were enrolled (88 male, 82 female; age 54.5, SD 5.0 years). Almost all participants completed the self-report OSA screening measures, approximately half completed PSM measurement, and the majority completed laboratory PSG. The STOP Bang had the highest levels of sensitivity; the ESS had the lowest. The ESS had the highest specificity and reliability level. The PSM measure had the highest positive predictive value (PPV). The PSM measure had the strongest psychometric properties of the screening measures. Conclusions: The STOP Bang was the preferred self-report OSA screening measure because of high sensitivity levels. A positive STOP Bang warrants assessment for OSA. The ESS is the least desirable screening measure. If a patient qualifies, further screening with a PSM is indicated. PSM measurement consistently predicted the presence of OSA but at the expense of low sensitivity at AHI levels ≥ 30. PSM results can guide the referral process from primary or specialty clinicians to sleep specialist

The effect of therapy on arousal from sleep in patients with respiratory sleep disorders

Vibrotactile positional therapy (PT) is a relatively new treatment for positional obstructive sleep apnoea (POSA). It uses vibrotactile stimulus to encourage the sleeper to change position when supine. The overall aim of this thesis was to investigate the efficacy of vibrotactile PT as a clinical treatment for patients with POSA. To achieve this, different experimental approaches were used, including a systematic review and meta-analysis, a clinical trial, and a physiological study. The systematic review was carried out to evaluate the effect of vibrotactile PT on apnoea hypopnoea index (AHI), percentage of time spent in supine (%Tsupine), and patient-centred outcomes in patients with POSA compared to baseline. The results showed that vibrotactile PT was effective in reducing both AHI and %Tsupine. Although the Epworth Sleepiness Scale and the Functional Outcomes of Sleep Questionnaire minimally improved, these changes did not reach clinically important differences; however, limited data were found on quality of life (SF-36) vitality score. A prospective, three-month, multicentre, randomised, parallel, double-blind trial (The POSA Trial, ISRCTN51740863) was developed to investigate the effect of vibrotactile PT on AHI, quality of life and daytime functioning at follow-up, adjusted for the baseline, in patients with POSA compared to sham-vibrotactile PT. Baseline data (AHI, quality of life and daytime functioning) obtained from the participants recruited at the Royal Brompton Hospital are presented in the thesis. The mean baseline AHI for RBH participants was in the mild OSA category compared to the patients in the systematic review; however, a higher baseline %Tsupine was found. The baseline patient-centred outcomes were also comparable to those found in the systematic review. A physiological study in healthy participants (n=27) was carried out to investigate the effect of vibrotactile stimulus on arousability from sleep. A novel analysis method was developed to measure arousability. This included the duration from the vibrotactile stimulus to the position change using polysomnography. The results of this study showed heterogenous arousability responses to the vibrotactile stimulus with different phenotypes. Compared to males, healthy females took longer to respond to the vibrotactile stimulus and, therefore, were more resilient to arousability. In summary, the findings of this thesis have shown that vibrotactile PT devices are effective in treating patients with POSA. However, limited data on sensitive patient-centred outcomes exist. The POSA trial will provide data to address this evidence gap. Furthermore, the physiological findings in people without OSA showed that males are more arousable than females. This information may be of value when considering personalisation of clinical treatment. Future research of POSA will need to consider the arousability phenotype when planning treatment options.Open Acces

Methodological strategies in using home sleep apnea testing in research and practice

Purpose Home sleep apnea testing (HSAT) has increased due to improvements in technology, accessibility, and changes in third party reimbursement requirements. Research studies using HSAT have not consistently reported procedures and methodological challenges. This paper had two objectives: (1) summarize the literature on use of HSAT in research of adults and (2) identify methodological strategies to use in research and practice to standardize HSAT procedures and information. Methods Search strategy included studies of participants undergoing sleep testing for OSA using HSAT. MEDLINE via PubMed, CINAHL, and Embase with the following search terms: “polysomnography,” “home,” “level III,” “obstructive sleep apnea,” and “out of center testing.” Results Research articles that met inclusion criteria (n = 34) inconsistently reported methods and methodological challenges in terms of: (a) participant sampling; (b) instrumentation issues; (c) clinical variables; (d) data processing; and (e) patient acceptability. Ten methodological strategies were identified for adoption when using HSAT in research and practice. Conclusions Future studies need to address the methodological challenges summarized in this paper as well as identify and report consistent HSAT procedures and information

A Database For Exploratory Analysis of Human Sleep

This thesis focuses on the design, development, and exploratory analysis of a human sleep data repository. We have successfully collected comprehensive data for 1,046 sleep disorder patients and created a Terabyte-scale database system to handle it. The data for each patient was collected from the patient\u27s medical records, and from the patient\u27s allnight sleep study (for a total of about 0.6 Gigabytes per patient). Data collected from the patient\u27s medical record contain more than 70 attributes, including demographic data, smoking, drinking, and exercise habits, depression and daytime sleepiness questionnaires, and overall medical history. Data collected from the patient\u27s all-night sleep study consist of 50-55 time-series signals recorded during a period of 6-8 hours at the hospital\u27s sleep clinic. These signals include among others an electroencephalogram, electromyogram, electrooculogram, electrocardiogram, and signals tracking blood oxygen level, body position, limb movements, snoring and blood pressure. 350 additional attributes summarize sleep related events taking place during the night long study, including sleep stages, arousals, and respiratory disturbances. Particular attention during the development of our database system was paid to a database design that effectively handles the data size and complexity, that describes the structure of sleep data in clinically meaningful terms, and that will facilitates the discovery of patterns in sleep data using machine learning algorithms. We have interfaced our database with Weka, a well known data mining system. To the best of our knowledge, our database is one of the world\u27s largest and most comprehensive in the domain of human sleep disorders