Obstructive Sleep Apnea Screening by Joint Saturation Signal Analysis and PPG-derived Pulse Rate Oscillations

Obstructive sleep apnea (OSA) is a high-prevalence disease in the general population, often underdiagnosed. The gold standard in clinical practice for its diagnosis and severity assessment is the polysomnography, although in-home approaches have been proposed in recent years to overcome its limitations. Today's ubiquitously presence of wearables may become a powerful screening tool in the general population and pulse-oximetry-based techniques could be used for early OSA diagnosis. In this work, the peripheral oxygen saturation together with the pulse-to-pulse interval (PPI) series derived from photoplethysmography (PPG) are used as inputs for OSA diagnosis. Different models are trained to classify between normal and abnormal breathing segments (binary decision), and between normal, apneic and hypopneic segments (multiclass decision). The models obtained 86.27% and 73.07% accuracy for the binary and multiclass segment classification, respectively. A novel index, the cyclic variation of the heart rate index (CVHRI), derived from PPI's spectrum, is computed on the segments containing disturbed breathing, representing the frequency of the events. CVHRI showed strong Pearson's correlation (r) with the apnea-hypopnea index (AHI) both after binary (r=0.94, p < 0.001) and multiclass (r=0.91, p < 0.001) segment classification. In addition, CVHRI has been used to stratify subjects with AHI higher/lower than a threshold of 5 and 15, resulting in 77.27% and 79.55% accuracy, respectively. In conclusion, patient stratification based on the combination of oxygen saturation and PPI analysis, with the addition of CVHRI, is a suitable, wearable friendly and low-cost tool for OSA screening at home

SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal

Sleep apnea (SA) is the most commonly known sleeping disorder characterized by pauses of airflow to the lungs and often results in day and night time symptoms such as impaired concentration, depression, memory loss, snoring, nocturnal arousals, sweating and restless sleep. Obstructive Sleep Apnea (OSA), the most common SA, is a result of a collapsed upper respiratory airway, which is majorly undiagnosed due to the inconvenient Polysomnography (PSG) testing procedure at sleep labs. This paper introduces an automated approach towards identifying sleep apnea. The idea is based on efficient feature extraction of the electrocardiogram (ECG) signal by employing a hybrid of signal processing techniques and classification using a linear-kernel Support Vector Machine (SVM). The optimum set of RR-interval features of the ECG signal yields a high classification accuracy of 97.1% when tested on the Physionet Apnea-ECG recordings. The results provide motivating insights towards future developments of convenient and effective OSA screening setups.http://dx.doi.org/10.18201/ijisae.7907

Detecting Adverse Respiratory Effects of Anesthetics and Opiod Analgesics in the Postoperative Period

The underlying problem for two of the three most common patterns of unexpected hospital deaths (PUHD) is hypoventilation1. Current methods of post-operative respiratory monitoring give delayed signals and have a high false positive rate leading nurses to ignore alarms. We hypothesize there exists a combination of low cost sensors which are capable of providing real time feedback and alarms regarding obstructive sleep apnea and ventilatory depression. Such a monitor would be useful during space travel when monitoring personnel are limited following an injury or if astronauts were to be sedated during extended travel. Methods:Twenty-Six subjects will be recruited to participate in a study of the effects of Propofol and Remifentanil. Throughout the day, these patients will be exposed to varying levels of both drugs simultaneously via target controlled infusions. These patients will be attached to breathing and oxygen monitors including chest bands, pulse oximeters, nasal pressure sensors, CO2 capnography, breathing microphones, and thermistors. The patients are then observed for types of apnea or ventilatory depression. Results: The study is currently ongoing however preliminary analyses of the data indicate multiple low cost sensors are capable of detecting breathing as well as obstructive events and apnea. Conclusion: Using only a combination of low cost sensors, we can provide real time respiratory event data to nurses and practitioners

Doppler radar-based non-contact health monitoring for obstructive sleep apnea diagnosis: A comprehensive review

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage

Subjective Sleepiness Dynamics Dataset (SSDD) Presentation: the Study of Two Scales Consistency

While the first references to the system of sleepiness assessment are associated with medical re-search and the study of the effects of drugs on sleep, currently subjective sleepiness assessment is widely used across fundamental and practically oriented studies. The Stanford Sleepiness Scale (SSS) and the Karolinska Sleepiness Scale (KSS) are often used as ground truth in sleepiness re-search. Only a few studies applied both scales and practically none aimed at studying their con-sistency and specific features. The present study is devoted to analyzing the dynamics and con-sistency of subjective sleepiness as measured by the KSS and the SSS in the adult population. A particular task of the paper is to present the Subjective Sleepiness Dynamics Dataset (SSDD) with the evening and morning dynamics of situational subjective sleepiness. A total of 208 adults took part in the experiment. The results of the study revealed that sleepiness generally increased from evening till night and was maximal at early morning. The SSS score appeared to be more sensitive to some factors (e.g., the presence of sleep problems). The SSS and KSS scores were strongly consistent with each other. The KSS showed a generally more even distribution than the SSS. SSDD continues to be collected, we are going to equalize the sample by sex, we are actively adding older people. We plan to collect a sample of 1,000 people. Currently SSDD contains a lot of in-formation that can be used for scientific research.Comment: 18 pages, 6 figures, 3 table

Patterns of unexpected in-hospital deaths: a root cause analysis

<p>Abstract</p> <p>Background</p> <p>Respiratory alarm monitoring and rapid response team alerts on hospital general floors are based on detection of simple numeric threshold breaches. Although some uncontrolled observation trials in select patient populations have been encouraging, randomized controlled trials suggest that this simplistic approach may not reduce the unexpected death rate in this complex environment. The purpose of this review is to examine the history and scientific basis for threshold alarms and to compare thresholds with the actual pathophysiologic patterns of evolving death which must be timely detected.</p> <p>Methods</p> <p>The Pubmed database was searched for articles relating to methods for triggering rapid response teams and respiratory alarms and these were contrasted with the fundamental timed pathophysiologic patterns of death which evolve due to sepsis, congestive heart failure, pulmonary embolism, hypoventilation, narcotic overdose, and sleep apnea.</p> <p>Results</p> <p>In contrast to the simplicity of the numeric threshold breach method of generating alerts, the actual patterns of evolving death are complex and do not share common features until near death. On hospital general floors, unexpected clinical instability leading to death often progresses along three distinct patterns which can be designated as Types I, II and III. Type I is a pattern comprised of hyperventilation compensated respiratory failure typical of congestive heart failure and sepsis. Here, early hyperventilation and respiratory alkalosis can conceal the onset of instability. Type II is the pattern of classic CO2 narcosis. Type III occurs only during sleep and is a pattern of ventilation and SPO2 cycling caused by instability of ventilation and/or upper airway control followed by precipitous and fatal oxygen desaturation if arousal failure is induced by narcotics and/or sedation.</p> <p>Conclusion</p> <p>The traditional threshold breach method of detecting instability on hospital wards was not scientifically derived; explaining the failure of threshold based monitoring and rapid response team activation in randomized trials. Furthermore, the thresholds themselves are arbitrary and capricious. There are three common fundamental pathophysiologic patterns of unexpected hospital death. These patterns are too complex for early detection by any unifying numeric threshold. New methods and technologies which detect and identify the actual patterns of evolving death should be investigated.</p

Low Cost Plastic Optical Fiber Pressure Sensor Embedded in Mattress for Vital Signal Monitoring

[EN] The aim of this paper is to report the design of a low-cost plastic optical fiber (POF) pressure sensor, embedded in a mattress. We report the design of a multipoint sensor, a cheap alternative to the most common fiber sensors. The sensor is implemented using Arduino board, standard LEDs for optical communication in POF (¿ = 645 nm) and a silicon light sensor. The Super ESKA® plastic fibers were used to implement the fiber intensity sensor, arranged in a 4 × 4 matrix. During the breathing cycles, the force transmitted from the lungs to the thorax is in the order of tens of Newtons, and the respiration rate is of one breath every 2¿5 s (0.2¿0.5 Hz). The sensor has a resolution of force applied on a single point of 2.2¿4.5%/N on the normalized voltage output, and a bandwidth of 10 Hz, it is then suitable to monitor the respiration movements. Another issue to be addressed is the presence of hysteresis over load cycles. The sensor was loaded cyclically to estimate the drift of the system, and the hysteresis was found to be negligible.This research was supported by FINESSE project, funded by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Action grant agreement No. 722509 and PROMETEO 2017/103 Tecnologias y Aplicaciones Futura de la Fotonica de Microondas.Sartiano, D.; Sales Maicas, S. (2017). Low Cost Plastic Optical Fiber Pressure Sensor Embedded in Mattress for Vital Signal Monitoring. Sensors. 17 (12)(2900):1-11. https://doi.org/10.3390/s17122900S11117 (12)290

Analisi dei parametri di risposta cerebrale e vegetativa agli eventi respiratori nel sonno in pazienti affetti da sindrome delle apnee morfeiche

The arousal scoring in Obstructive Sleep Apnea Syndrome (OSAS) is important to clarify the impact of the disease on sleep but the currently applied American Academy of Sleep Medicine (AASM) definition may underestimate the subtle alterations of sleep. The aims of the present study were to evaluate the impact of respiratory events on cortical and autonomic arousal response and to quantify the additional value of cyclic alternating pattern (CAP) and pulse wave amplitude (PWA) for a more accurate detection of respiratory events and sleep alterations in OSAS patients. A retrospective revision of 19 polysomnographic recordings of OSAS patients was carried out. Analysis was focused on quantification of apneas (AP), hypopneas (H) and flow limitation (FL) events, and on investigation of cerebral and autonomic activity. Only 41.1% of FL events analyzed in non rapid eye movement met the AASM rules for the definition of respiratory event-related arousal (RERA), while 75.5% of FL events ended with a CAP A phase. The dual response (EEG-PWA) was the most frequent response for all subtypes of respiratory event with a progressive reduction from AP to H and FL. 87.7% of respiratory events with EEG activation showed also a PWA drop and 53,4% of the respiratory events without EEG activation presented a PWA drop. The relationship between the respiratory events and the arousal response is more complex than that suggested by the international classification. In the estimation of the response to respiratory events, the CAP scoring and PWA analysis can offer more extensive information compared to the AASM rules. Our data confirm also that the application of PWA scoring improves the detection of respiratory events and could reduce the underestimation of OSAS severity compared to AASM arousal