Screening of Obstructive Sleep Apnea with Empirical Mode Decomposition of Pulse Oximetry

Detection of desaturations on the pulse oximetry signal is of great importance for the diagnosis of sleep apneas. Using the counting of desaturations, an index can be built to help in the diagnosis of severe cases of obstructive sleep apnea-hypopnea syndrome. It is important to have automatic detection methods that allows the screening for this syndrome, reducing the need of the expensive polysomnography based studies. In this paper a novel recognition method based on the empirical mode decomposition of the pulse oximetry signal is proposed. The desaturations produce a very specific wave pattern that is extracted in the modes of the decomposition. Using this information, a detector based on properly selected thresholds and a set of simple rules is built. The oxygen desaturation index constructed from these detections produces a detector for obstructive sleep apnea-hypopnea syndrome with high sensitivity ($0.838$) and specificity ($0.855$) and yields better results than standard desaturation detection approaches.Comment: Accepted in Medical Engineering and Physic

Effects of the CPAP Treatment on the NON-REM Sleep Microstructures in Patients with Severe Apnea-Hypoapnea Syndrome

Sleep quality is affected in patients with sleep apnea- hypopnea syndrome (SAHS) with nocturnal and diurnal consequences. Most of these patients who are treated with positive airway pressure (CPAP) return to normal sleep patterns. We could consider good sleepers those patients who present more sleep spindles in stage II, and slower wave sleep as a good sign of better sleep quality. The objective in this research study was to compare the microstructure of stage II using the number of spindles and the increase of slow wave sleep before and after CPAP night titration. We developed a wavelet filter using a spline cubic function from a wavelet mother, which was appropriate to be used over electroencephalographic signal. By means of this filter in a multi-resolution mode, the spindles were detected from the increase of the IV band power; the sampling rate of the device determined the filter characteristics. The staging of polysomnographic studies was made by an expert according AASM (American Academy of Sleep Medicine) and then processed by the filter to get the index of sleep spindles before-and-after CPAP during stage II as well as the relationship between fast and slow powers from the EEG signal. An increase in the power of the slow waves vs. fast activity was observed in all the cases as a feature of better sleep. The neuroprotective effect described in previous research works regarding the density of the sleep spindles seems to be detected in patients improving their sleep quality after the correction of the apnea-hypopnea syndrome using CPAP.Fil: Smurra, Marcela. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos Dr. Enrique Tornú; ArgentinaFil: Blanco, Susana Alicia Ana. Universidad de Belgrano. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Eguiguren, Veronica. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos Dr. Enrique Tornú; ArgentinaFil: Di Risio, Cecilia Diana. Universidad de Belgrano. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Sleep apnea-hypopnea quantification by cardiovascular data analysis

Sleep apnea is the most common sleep disturbance and it is an important risk factor for cardiovascular disorders. Its detection relies on a polysomnography, a combination of diverse exams. In order to detect changes due to sleep disturbances such as sleep apnea occurrences, without the need of combined recordings, we mainly analyze systolic blood pressure signals (maximal blood pressure value of each beat to beat interval). Nonstationarities in the data are uncovered by a segmentation procedure, which provides local quantities that are correlated to apnea-hypopnea events. Those quantities are the average length and average variance of stationary patches. By comparing them to an apnea score previously obtained by polysomnographic exams, we propose an apnea quantifier based on blood pressure signal. This furnishes an alternative procedure for the detection of apnea based on a single time series, with an accuracy of 82%

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

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

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

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

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

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