A Detector of Sleep Disorders for Using at Home

Current Respiratory Care Reports, 2012

Portable sleep apnea monitoring or home testing for sleep-disordered breathing focuses on recent developments of these powerful diagnostic tools. Evidence-based reviews and innovative single studies with specific systems are considered. Systems become less intrusive and self applicable. Electrocardiogram-derived respiration, photoplethysmogram analysis, midsagittal jaw movements, and respiratory sound analysis are reviewed. Categories of systems with 4 to 6 channels and 1 to 3 channels are introduced and presented. The importance of a high pretest probability is elucidated. Open research questions regarding these systems are mentioned. Technological issues are not most important in this debate. The health economic aspects in using portable sleep apnea monitoring have to be considered as well. Portable monitoring of sleep apnea is probably less expensive than cardiorespiratory polysomnography and can help to overcome the limited availability of sleep lab-based diagnostic places. But by increasing the quantity of investigations it may cause additional costs too.

Some diseases are still ambiguous for scientists and researchers, despite of studies and researches that have been done to study and diagnose them. The effects of these diseases may be known, but the unknown is their causes, times, and signs. These unobvious causes and signs make scientists unable to detect, nor diagnose these diseases. One of these diseases is the obstructive sleep apnea, which is defined as breathing difficulties and pauses in breathing during sleep. This research project aims to produce a diagnostic device for early prediction of sleep apnea through the analysis and classification of sleep levels. The idea is to establish an algorithm linking sleep apnea and snoring by the implementation of a device that measures the chest expansion during inspiration and expiration of subjects. Then, collected data from which snorers and apnea goers are pointed out so that we can provide the patient with quantitative measurements and statistics that end up with a classification of sleep disorders levels in terms of voltages interval of each level. Thus, it becomes easy to find a voltage interval just prior to obstructive sleep apnea occurrence. In such case, the machine is programmed to early predict and prevent the obstructive sleep apnea occurrence by providing a buzzing sound in order to wake the patient up right before OSA

Sleep, 1995

Obstructive sleep apnea is increasingly recognized as a common and debilitating disorder. As a result, a variety of diagnostic technologies have evolved to potentially decrease cost and improve access and ease of assessment. In this study we compared the Healthdyne NightWatch (NW) System (a home sleep diagnostic methodology) to standard polysomnography (PSG) in two sleep centers. Two separate studies were completed. NW was compared to a simultaneously obtained PSG in 30 patients (IN-LAB study). Seventy additional patients were studied in both the home with NW and in the laboratory with PSG (HOME-LAB study). The NW system records eye movement, leg movement, Sa0 2 , nasal-oral airflow, chest and abdominal wall motion, body position and heart rate on a solid state recorder, which permits sleep staging based on body and eye movement and standard respiratory assessment. For the PSG, standard paper recording techniques were used. The IN-LAB study revealed a correlation between NW and PSG for total sleep time of r = 0.72, with NW tending to score some awake time as nonrapid eye movement sleep. The correlation for apnea-hypopnea index (AHI) was r = 0.94 between systems, with a sensitivity of 100% and specificity of 63.6% at an AHI threshold of 10. The HOME-LAB study demonstrated understandably poor correlations between NW and PSG for most measures of sleep, which is likely a product of night-tonight variability in sleep, home versus laboratory effects and the differences in sleep staging methodology. However, the correlation for AHI was r = 0.92, with a sensitivity of 90.7% and a specificity of 70.4% at an AHI threshold of 10. Using a new methodology to assess agreement between diagnostic systems, we observed 78.6% diagnostic agreement between NW and PSG in the HOME-LAB study, with NW underestimating AHI 4.3% of the time and overestimating it in 17.1 % of cases. This may relate to night-tonight variability in AHI or greater NW computer sensitivity to subtle hypopneas. We conclude that NW provides an accurate determination of AHI in both the home and laboratory, using limited instrumentation. The analysis time for NW is also reduced compared to PSG, and patients generally prefer the NW evaluation.

Sleep

To assess the accuracy of a portable monitoring device based on peripheral arterial tonometry to diagnose obstructive sleep apnea (OSA). To propose a new standard for limited-channel device validation using synchronized polysomnography (PSG) home recordings and a population-based cohort. Design: Single-night, unattended PSG and Watch_PAT 100 (WP_100). Setting: Home environment. Participants: Ninety-eight subjects (55 men; age, 60 ± 7 year; body mass index, 28 ± 4 kg/m 2 ) consecutively recruited from the Skaraborg Hypertension and Diabetes Project. The WP_100 records peripheral arterial tone, heart rate, oxygen saturation and actigraphy for automatic analysis of respiratory disturbance index (RDI), apnea-hypopnea index (AHI), oxygen desaturation index (ODI), and sleep-wake state. The accuracy of WP_100 in RDI, AHI, ODI, and sleep-wake detection was assessed by comparison with data from simultaneous PSG recordings. The mean PSG-AHI in this population was 25.5 ± 22.9 events per hour. The WP_100 RDI, AHI, and ODI correlated closely (0.88, 0.90, and 0.92; p < .0001, respectively) with the corresponding indexes obtained by PSG. The areas under the curve for the receiver-operator characteristic curves for WP_100 AHI and RDI were 0.93 and 0.90 for the PSG-AHI and RDI thresholds 10 and 20 (p < .0001, respectively). The agreement of the sleep-wake assessment based on 30-second bins between the 2 systems was 82 ± 7%. Conclusions: The WP_100 was reasonably accurate for unattended home diagnosis of OSA in a population sample not preselected for OSA symptoms. The current design, including simultaneous home PSG recordings in population-based cohorts, is proposed as a reasonable validation standard for assessment of simplified recording tools for OSA diagnosis.

International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020

Obstructive sleep apnea (OSA) is one of the most common disorders which affects the people, mainly in the age of 30 to 70. In current population, the percentage of this disorder gets increased and death rate also increases. It is one of the under diagnosed and under treated disorder. In our project, we introduce a low-cost, easily usable and wireless prototype device to diagnose and to treat obstructive sleep apnea. The developed device used to track the level of respiration and snoring episodes. When a snoring episode is detected, we deliver a small electric current to pharyngeal muscles by use of pen like electrical stimulators that make them to regain their muscle tone, thus obstructive sleep apnea can be treated. And also, this device communicates with an IOT to keep a recording of respiration and apnea data to enable further studying of data by medical professionals and researchers.

2020

Low cost embedded devices with computational power have the potential to revolutionise detection and management of many diseases. This is especially true in the case of conditions like sleep apnea, which require continuous long term monitoring. In this paper, we give details of a portable, cost-effective and customisable Electrocardiograph(ECG) Signal analyser for real time sleep apnea detection. We have developed a data analysis pipeline using which we can identify sleep apnea using a single lead ECG signal. Our method combines steps including dataset extraction, segmentation, signal cleaning, filtration and finally apnea detection using Support Vector Machines (SVM). We analysed our proposed implementation through a complete run on the MIT-Physionet dataset. Due to the low computational complexity of our proposed method, we find that it is well suited for deployment on embedded devices such as the Raspberry Pi.

The present paper concerns acoustic measurements in connection with the sleep apnea syndrome. The latter induces pathophysiologic changes in the respiratory and circulatory systems and is diagnosed by polysomnography (PSG). PSG comprises the measurement of many parameters and thus tends to be expensive and time consuming. In contrast to available portable devices which use many distributed sensors, we propose a single acoustic sensor in the heart region from which multiple information is derived through excessive signal processing. Measurement is performed by means of a microphone complemented by a stethoscope head. Analysis of the detected signal by means of histogram, cross correlation and FFT shows that it includes information an all three the cardiac activity, the respiratory activity and the snoring sounds. In order to obtain automatic monitoring of obstructive apnea events, a series of signal characteristics in both the time and the spectral domain (e.g., power density, histogram width, PCA components) are subject of processing through adaptive algorithms which are also used for classification. In addition, the method yields breathing patterns and diagnostically relevant physiological data such as changes of the heart rate and the rate of respiration. For the detection of central apnea event, the acoustic sensor is complemented by a magnetostrictive one.

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008

Abstract²Sleep related breathing disorders are a highly prevalent disease associated with increased risk of cardiovascular complications like chronic arterial hypertension, myocardial infarction or stroke. Gold standard diagnostics (polysomnography) are complex and expensive; the need for simplified diagnostics is therefore obvious. As the ECG can be easily conducted during the night, the detection of sleep related breathing disorders by ECG analysis provides an easy and cheap approach. Using a combination of well known biosignals processing algorithms, we trained the algorithm on 35 prescored overnight recordings. We then applied the algorithm on 35 control recordings, achieving a diagnostic accuracy of 77%. We believe that with further improvements in ECG analysis this algorithm can be used for screening diagnostics of obstructive sleep apnea.

IEEE Access

Sleep Apnea is a breathing disorder that occurs while the patient is sleeping. Traditionally, Polysomnography is used to diagnose it. However, it is quite inconvenient and expensive. Because of the troublesome diagnosis, this ailment often remained undiagnosed. This paper aims at the development of such a method that provides an easy diagnostic solution to the doctors. Electrocardiogram (ECG) is one of the most common tests done at the hospitals. In this paper, we aim to develop a method which deploys ECG data to diagnose the sleep ailment, Apnea. A technique deploying wavelet packet transform on RR interval (RRI) of Electrocardiogram (ECG) has been presented. Probability density functions of data, both when Apnea is present and when it is not, are obtained by constructing histograms of decision variable for each signal segment. From the overlapping PDFs of the normal and abnormal cases, a threshold is then derived. This helped in segregating the Apnea cases from the normal cases. The stated method provided a 100% accuracy in diagnosing Sleep Apnea.

International journal of computer science and mobile computing, 2023

Sleep apnea (SA) is a common sleep-related breathing disorder that can occur hundreds of times per night and can lead to catastrophic cardiovascular and neurological disorders if it happens frequently enough. Polysomnography (PSG) is the standard diagnostic tool for sleep apnea. However, it requires suspected patients to spend one to two nights in the lab and capture roughly 16 signals under professional supervision. The broad use of PSG in public health applications is hampered by complex processes. Some researchers have recently advocated using a single-lead ECG signal to detect SA. These techniques are based on the assumption that SA only uses the current ECG signal segment. SA, on the other hand, is timedependent; that is, the SA of the preceding ECG segment has an impact on the current SA diagnosis. The authors present a time window artificial neural network that takes advantage of the time dependence between ECG signal segments while requiring no prior assumptions regarding training data distribution. The proposed method's performance has been greatly improved when compared to classic on-time window machine learning approaches as well as previous work after being verified on a genuine ECG signal dataset.