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

A review of ECG-based diagnosis support systems for obstructive sleep apnea

Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy

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

Portable detection of apnea and hypopnea events using bio-impedance of the chest and deep learning

Sleep apnea is one of the most common sleep-related breathing disorders. It is diagnosed through an overnight sleep study in a specialized sleep clinic. This setup is expensive and the number of beds and staff are limited, leading to a long waiting time. To enable more patients to be tested, and repeated monitoring for diagnosed patients, portable sleep monitoring devices are being developed. These devices automatically detect sleep apnea events in one or more respiration-related signals. There are multiple methods to measure respiration, with varying levels of signal quality and comfort for the patient. In this study, the potential of using the bio-impedance (bioZ) of the chest as a respiratory surrogate is analyzed. A novel portable device is presented, combined with a two-phase Long Short-Term Memory (LSTM) deep learning algorithm for automated event detection. The setup is benchmarked using simultaneous recordings of the device and the traditional polysomnography in 25 patients. The results demonstrate that using only the bioZ, an area under the precision-recall curve of 46.9% can be achieved, which is on par with automatic scoring using a polysomnography respiration channel. The sensitivity, specificity and accuracy are 58.4%, 76.2% and 72.8% respectively. This confirms the potential of using the bioZ device and deep learning algorithm for automatically detecting sleep respiration events during the night, in a portable and comfortable setup

Positive airway pressure and electrical stimulation methods for obstructive sleep apnea treatment: a patent review (2005-2014)

Producción CientíficaIntroduction. Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a major health problem with significant negative effects on the health and quality of life. Continuous positive airway pressure (CPAP) is currently the primary treatment option and it is considered the most effective therapy for OSAHS. Nevertheless, comfort issues due to improper fit to patient’s changing needs and breathing gas leakage limit the patient’s adherence to treatment. Areas covered. The present patent review describes recent innovations in the treatment of OSAHS related to optimization of the positive pressure delivered to the patient, methods and systems for continuous self-adjusting pressure during inspiration and expiration phases, and techniques for electrical stimulation of nerves and muscles responsible for the airway patency. Expert opinion. In the last years, CPAP-related inventions have mainly focused on obtaining an optimal self-adjusting pressure according to patient’s needs. Despite intensive research carried out, treatment compliance is still a major issue. Hypoglossal electrical nerve stimulation could be an effective secondary treatment option when CPAP primary therapy fails. Several patents have been granted focused on selective stimulation techniques and parameter optimization of the stimulating pulse waveform. Nevertheless, there remain important issues to address, like effectiveness and adverse events due to improper stimulation.Ministerio de Economía y Competitividad (TEC2011-22987)Junta de Castilla y León (VA059U13

A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry

Producción CientíficaThe gold standard approach to diagnose obstructive sleep apnea (OSA) in children is overnight in-lab polysomnography (PSG), which is labor-intensive for clinicians and onerous to healthcare systems and families. Simplification of PSG should enhance availability and comfort, and reduce complexity and waitlists. Airflow (AF) and oximetry (SpO2) signals summarize most of the information needed to detect apneas and hypopneas, but automatic analysis of these signals using deep-learning algorithms has not been extensively investigated in the pediatric context. The aim of this study was to evaluate a convolutional neural network (CNN) architecture based on these two signals to estimate the severity of pediatric OSA. PSG-derived AF and SpO2 signals from the Childhood Adenotonsillectomy Trial (CHAT) database (1638 recordings), as well as from a clinical database (974 recordings), were analyzed. A 2D CNN fed with AF and SpO2 signals was implemented to estimate the number of apneic events, and the total apnea-hypopnea index (AHI) was estimated. A training-validation-test strategy was used to train the CNN, adjust the hyperparameters, and assess the diagnostic ability of the algorithm, respectively. Classification into four OSA severity levels (no OSA, mild, moderate, or severe) reached 4-class accuracy and Cohen's Kappa of 72.55% and 0.6011 in the CHAT test set, and 61.79% and 0.4469 in the clinical dataset, respectively. Binary classification accuracy using AHI cutoffs 1, 5 and 10 events/h ranged between 84.64% and 94.44% in CHAT, and 84.10%–90.26% in the clinical database. The proposed CNN-based architecture achieved high diagnostic ability in two independent databases, outperforming previous approaches that employed SpO2 signals alone, or other classical feature-engineering approaches. Therefore, analysis of AF and SpO2 signals using deep learning can be useful to deploy reliable computer-aided diagnostic tools for childhood OSA.Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (project 10.13039/501100011033)Fondo Europeo de Desarrollo Regional - Unión Europea (projects PID2020-115468RB-I00 and PDC2021-120775-I00)Sociedad Española de Neumología y Cirugía Torácica (project 649/2018)Sociedad Española de Sueño (project Beca de Investigación SES 2019)Consorcio Centro de Investigación Biomédica en Red - Instituto de Salud Carlos III - Ministerio de Ciencia, Innovación y Universidades (project CB19/01/00012)National Institutes of Health (projects HL083075, HL083129, UL1-RR-024134 and UL1 RR024989)National Heart, Lung, and Blood Institute (projects R24 HL114473 and 75N92019R002)Ministerio de Educación, Cultura y Deporte (grant FPU16/02938)Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación - Fondo Social Europeo (grant RYC2019-028566-I)National Institutes of Health (grants HL130984, HL140548, and AG061824

The feasibility of the Emfit movement sensor as an automated screening tool for sleep apnea in the ischemic stroke patients

Stroke is a common cause of death and a major reason for disability. Stroke survivors can have very difficult symptoms and require very intensive and expensive rehabilitation. Sleep disordered breathing, sleep apnea, is common among stroke patients, it's a high risk factor for recurrent stroke and untreated sleep apnea has a negative influence on the stroke recovery. All stroke patients are recommended to be measured for sleep apnea, but the lack of resources don't allow it. Therefore there is a need for a screening tool to find the stroke patients who need the measurement most and who benefit the most of the treatment of the sleep apnea. We studied the possibility to use the Emfit movement sensor combined with a pulse oximeter as a screening tool. The Emfit movement sensor doesn't have connections to the patient, therefore it wouldn't require lots of resources to set up the measurement and there are no contacts that can cause interference during the measurement. The automatic scoring of the measurement would remove the need for an expert to manually score every measurement. The test subjects were measured at the same night using both the Emfit movement sensor and a conventional respiratory polygraphy device. The Emfit movement sensor and the standard respiratory polygraphy measurements were scored using Noxturnal's automatic analysis tool and the results were compared. The results were also compared to the manual scoring of the standard respiratory polygraphy. The Emfit movement sensor measurement slightly overestimates the apnea hypopnea index, as does the automatically scored standard respiratory polygraphy too. The automatic analysis ability to detect correctly the duration and timing of a respiratory event in the Emfit movement sensor measurement seems to depend on the amount of noise in the measurement. Our study indicates that the Emfit movement sensor has potential to be used as a screening tool for sleep apnea in the ischemic stroke patients, but the automatic analysis still needs improvements to provide more accurate results

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