Abnormal Respiratory Event Detection in Sleep:A Prescreening System with Smart Wearables

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Sleeping is an important activity to monitor since it has a crucial role in the overall health and well-being of the people and society. In order to diagnose the problems in sleep, different monitoring systems are developed in the literature. The unobtrusiveness, reduced cost, objectiveness, protection of privacy and user-friendliness are the main design considerations and the proposed system design achieves those objectives by utilizing smart wearables, smart watch and smart phone. The accelerometer and heart rate monitor sensors on smart watch and the sound level sensor on the smart phone are activated. The experiments with this system are performed with 17 subjects in a sleep clinic. The data collected from these subjects is used to generate various combinations by employing varied feature extraction, feature selection and sampling approaches. Five different machine learning algorithms are implemented and the classification results are generated using the various combinations of data, training and scoring strategies. The system performance is measured in two ways, the accuracy rate of distinguishing abnormal respiratory events is 85.95% and the classification success of subjects according to the problems in their respiration is one misclassification among 17 subjects. With all the methodology utilized in this study, the proposed system is a novel prescreening tool which recognizes the severity of problems in respiration during sleep.

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... In [4], SVM, KNN, RF, and the hidden Markov model (HMM) were all used in the detection of sleep disorders. Categorization results were obtained by applying various permutations of data, training, and scoring procedures to five distinct machine learning algorithms. ...
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Hospitals must continually monitor their patients’ actions to lower the chance of accidents, such as patient falls and slides. Human behavior is difficult to track due to the complexity of human activities and the unpredictable nature of their conduct. As a result, creating a static link that is used to influence human behavior is challenging, since it is hard to forecast how individuals will think or act in response to a certain event. Mobility tracking depends on intelligent monitoring systems that apply artificial intelligence (AI) applications referred to as “categories”. Because motion sensors, such as gyroscopes and accelerometers, output unconnected data that lack labels, event detection is a vital task. The fall feature parameters of tridimensional accelerometers and gyroscope sensors are presented and used, and the classification technique is based on distinguishing characteristics. This study focuses on the age-old problem of tracking turbulence in motion to improve detection precision. We trained the model, considering that detection accuracy is limited by factors such as the subject’s mass, velocity, and gait style. This is performed by employing an experimental dataset. When we used the sophisticated technique of particle swarm optimization (PSO) in combination with a four-stage forward neural network (4SFNN) to forecast four different types of turbulent motion, we observed that the total prediction accuracy was 98.615% accurate.
... For a full characterization of sleep quality, there is a strong need to extend the analysis of respiratory signals beyond solely detecting the RR. More sophisticated methods have been introduced using alternative sensing methods compared to PSG routines [18][19][20][21][22][23], namely machine learning techniques to automatically identify apnea/hypopnea events. Van Steenkiste et al. presented a deep-learning approach with bio-impedance signals from the chest. ...
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Objective There is a great interest in observing breathing patterns during sleep, as sleep disturbances can be caused by respiratory irregularity and cessations. In this paper, we introduce the first steps to an accelerometer-based screening tool for respiratory rate estimation and a novel approach towards detecting breathing cessations such as apnea/hypopnea, by extending and combining established signal processing routines with machine learning. Methods From a single chest-worn accelerometer, we estimate the respiratory rate based on the inhalation/exhalation movements of the chest and carry out a full overnight validation. On this basis, we build a set of features customized to detect irregular respiratory activity, including a novel feature: the respiratory peak variance (RPV). From thirteen healthy subjects, a classification model was trained, validated, and tested with over 98 h of PSG-labeled accelerometer data. Results The algorithm estimated the respiratory rate with a mean difference of 1.8 breaths per minute compared to respiratory inductance plethysmography during overnight PSGs. The machine learning classifier detected respiratory cessations with a sensitivity and specificity of 76.05% and 70.05% respectively, with an overall accuracy of 70.95%. Conclusion We successfully demonstrated the potential of a novel respiratory feature set in a preliminary application with young healthy volunteers for respiratory rate estimation and in identifying apnea/hypopnea events during overnight sleep. Significance We present a simple and unobtrusive wearable system that can serve as a home screening tool for sleep-related breathing disorders.
... These days, vital signs, particularly heart rate and heart rate variability, can be measured in various ways. Wearable technologies, including smartwatches, smart bracelets, smart clothing (Breteler et al., 2020;Camcı et al., 2019;Liu et al., 2019), can be used efficiently to achieve such a task (Figure 1). Wearables can continuously monitor a broad range of patient vital signs; however, they bring about a few cons, e.g., battery life, skin irritation, and inadequate waterproofing. ...
Sleep is so important, particularly for the elderly. The lack of sleep may increase the risk of cognitive decline. Similarly, it may also increase the risk of Alzheimer’s disease. Nonetheless, many people underestimate the importance of getting enough rest and sleep. In-laboratory polysomnography is the gold-standard method for assessing the quality of sleep. This method is considered impractical in the clinical environment, seen as labour-intensive and expensive owing to its specialised equipment, leading to long waiting lists. Hence, user-friendly (remote and non-intrusive) devices are being developed to help patients monitor their sleep at home. In this paper, we first discuss commercially-available non-wearable devices that measure sleep, in which we highlight the features associated with each device, including sensor type, interface, outputs, dimensions, power supply, and connectivity. Second, we evaluate the feasibility of a non-wearable device in a free-living environment. The deployed device comprises a sensor mat with an integrated micro-bending multimode fibre. Raw sensor data were gathered from five senior participants living in a senior activity centre over a few to several weeks. We were able to analyse the participants’ sleep quality using various sleep parameters deduced from the sensor mat. These parameters include the wake-up time, bedtime, the time in bed, nap time. Vital signs, namely heart rate, respiratory rate, and body movements, were also reported to detect abnormal sleep patterns. We have employed pre-and post-surveys reporting each volunteer’s sleep hygiene to confirm the proposed system’s outcomes for detecting the various sleep parameters. The results of the system were strongly correlated with the surveys for reporting each sleep parameter. Furthermore, the system proved to be highly effective in detecting irregular patterns that occurred during sleep.
... (1) Home sleep apnea testing (HSAT) is unable to diagnose other sleep disorders, provides discomfort to the patient due to prolonged use, and improper evaluation using HSAT may result in inconclusive readings requiring repeat studies. (2) Sleep tracking through smart wearables is challenging due to battery limitations as well as the limited number of sensors available to procure information about sleep disorders [63]. (3) Biomotion sensors have showcased their relevance in OSA detection, but there are a few limitations to their application such as periodic limb movements in OSA patients might lead to inconclusive results in sleep/ wake patterns [64]. ...
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Introduction Obstructive sleep apnea (OSA) is a common sleep disorder with multiple co-morbidities including hypertension, diabetes and cardiovascular disorders. Detected based on an overnight sleep study called polysomnography (PSG), OSA still remains undiagnosed in majority of the population mainly attributed to lack of awareness. To overcome the limitations posed by PSG such as patient discomfort and overnight hospitalization, newer technologies are being explored. In addition, challenges associated with current management of OSA using continuous positive airway pressure (CPAP), etc. presents several pitfalls. Areas Covered Conventional and modern detection/management techniques including PSG, CPAP, smart wearable/pillows, bio-motion sensors etc. have both pros and cons. To fulfill the limitations in OSA diagnostics, there is an imperative need for new technology for screening of symptomatic and more importantly asymptomatic OSA patients to reduce the risk of several associated life-threatening comorbidities. In this line molecular marker-based diagnostics have shown great promises. Expert Opinion A detailed overview is presented on the OSA management and diagnostic approaches and recent advances in the molecular screening methods. The potentials of biomarker-based detection and its limitations are also portrayed and a comparison between the standard, current modern approaches and promising futuristic technologies for OSA diagnostics and management is set forth.
... For field studies, ACG is an appropriate measurement method based on the movement signals, i.e., ACC data. More recent studies also utilize BVP [6,14], SKT [46], and MIC [8,11]. Data from SpO2, ECG, RSP, and MIC allow us to diagnose the sleep apnea [15,28,33]. ...
... Respiratory sounds collected by wearable sensors have been used for wheeze and crackle analysis by applying a hybrid CNN-RNN framework on the Mel spectrogram [65]. Apnea events, another common abnormal respiratory event, can be detected using the sound-level sensor on a smartphone [151]. Compressive sensing is also involved in this issue to meet the real-time and low-consumption requirements of wearable devices [152]. ...
With the rapidly increasing number of patients with chronic disease, numerous recent studies have put great efforts into achieving long-term health monitoring and patient management. Specifically, chronic diseases including cardiovascular disease, chronic respiratory disease and brain disease can threaten patients’ health conditions over a long period of time, thus effecting their daily lives. Vital health parameters, such as heart rate, respiratory rate, SpO2 and blood pressure, are closely associated with patients’ conditions. Wearable devices and unobtrusive sensing technologies can detect such parameters in a convenient way and provide timely predictions on health condition deterioration by tracking these biomedical signals and health parameters. In this paper, we review current advancements in wearable devices and unobtrusive sensing technologies that can provides possible tools and technological supports for chronic disease management. Current challenges and future directions of related techniques are addressed accordingly.
... However, acquiring a PSG study can be expensive and uncomfortable for some individuals. Data on audio function and the equipped accelerometer on smartwatches and smartphones can be collected and combined to determine the basic sleep patterns, such as the number of hours of sleep, number of awakenings during the night, and occurrence of snoring [97]. ...
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The coronavirus disease 19 (COVID-19) pandemic that has been raging in 2020 does affect not only the physical state but also the mental health of the general population, particularly, that of the healthcare workers. Given the unprecedented large-scale impacts of the COVID-19 pandemic, digital technology has gained momentum as invaluable social interaction and health tracking tools in this time of great turmoil, in part due to the imposed statewide mobilization limitations to mitigate the risk of infection that might arise from in-person socialization or hospitalization. Over the last five years, there has been a notable increase in the demand and usage of mobile and wearable devices as well as their adoption in studies of mental fitness. The purposes of this scoping review are to summarize evidence on the sweeping impact of COVID-19 on mental health as well as to evaluate the merits of the devices for remote psychological support. We conclude that the COVID-19 pandemic has inflicted a significant toll on the mental health of the population, leading to an upsurge in reports of pathological stress, depression, anxiety, and insomnia. It is also clear that mobile and wearable devices (e.g., smartwatches and fitness trackers) are well placed for identifying and targeting individuals with these psychological burdens in need of intervention. However, we found that most of the previous studies used research-grade wearable devices that are difficult to afford for the normal consumer due to their high cost. Thus, the possibility of replacing the research-grade wearable devices with the current smartwatch is also discussed.
... The sleep studies conducted in the field commonly apply the actigraphy measurement method, which is based on the movement signals, i.e., ACC data [30]. More recent studies also utilize BVP [31,32], SKT [33], and MIC [34,35]. Data from SpO2, ECG, RSP, and MIC allow us to diagnose the sleep apnea [36][37][38]. ...
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Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals. In this study, we revised 55 off-the-shelf devices in recognition and analysis of emotion, stress, meditation, sleep, and physical activity, especially in field studies. Their usability directly comes from the types of sensors they possess as well as the quality and availability of raw signals. We found there is no versatile device suitable for all purposes. Empatica E4 and Microsoft Band 2 are good at emotion, stress, and together with Oura Ring at sleep research. Apple, Samsung, Garmin, and Fossil smart watches are proper in activity examination, while Muse and DREEM EEG headbands are suitable for meditation.
Sleep quality plays an essential role in human health and has become an index for assessing physical health. Self-powered, sensitive, noninvasive, comfortable, and low-cost sleep monitoring sensors for monitoring sleep behavior are still in high demand. Here, a pressure-sensitive, noninvasive, and comfortable smart pillow is developed based on a flexible and breathable triboelectric nanogenerator (FB-TENG) sensor array, which can monitor head movement in real time during sleep. The FB-TENG is based on flexible and breathable porous poly(dimethylsiloxane) (PDMS) with a fluorinated ethylene propylene (FEP) powder and exhibits pressure sensitivity and durability. The electrical output of the FB-TENG is further optimized by modifying the porous structure and the FEP powder. Combining the FB-TENG and the flexible printed circuit (FPC), a self-powered pressure sensor array is fabricated to realize touch sensing and motion track monitoring. The smart pillow is formed by laying the self-powered pressure sensor array on an ordinary pillow to realize real-time monitoring of the head position in a static state and head movement trajectory in a dynamic state during sleep. Additionally, the smart pillow also has an early warning function for falling out of bed. This work not only provides a viable sensing device for sleep monitoring but also could be extended to real-time monitoring of some diseases, such as brain diseases and cervical spondylosis, in the future. It is expected to introduce a practical strategy in the real-time mobile healthcare field for disease management.
Early detection of respiratory distress, marked by coughing associated with pandemics such as Covid, severe acute respiratory syndrome, and influenza, has become important for early public health preparedness. Recognizing respiratory distress from data pooled from accelerometers and other sensors common in phones/wearables can be a useful tool in tracking diseases in larger populations. However, detecting low-/medium-intensity coughs, which are a precursor to influenza/Covid, are harder to detect in the presence of human activity especially walking. In this letter, we study spectrum-spread features of triaxial accelerometer signals measured from the human torso during coughs. In particular, we analyze the vestigial sideband like spurs that cough-induced motion of the torso produces alongside walking signal between 0.2 and 2 Hz and propose the use of its spectral spread square metric in discerning coughs during walking action in test subjects of different sizes. Unlike prior works on time-domain measurements or spectral summation (units: g) in multiple bands, this work uses bandwidth, i.e., spectrum-spread features of acceleration signals (units: Hz <sup xmlns:mml="" xmlns:xlink="">2</sup> ) to detect low to medium intensity coughs from a single accelerometer worn on the chest or shirt pocket or stomach. Acceleration signals measured at these points in five test subjects of varying heights, age, and weight show its median square spectral spread increase prominently along Y (up-down) and Z axes (front-back) from between 0.016–0.0167 Hz <sup xmlns:mml="" xmlns:xlink="">2</sup> to between 0.023–0.026 Hz <sup xmlns:mml="" xmlns:xlink="">2</sup> with a cough-detection threshold observed at 0.02 Hz <sup xmlns:mml="" xmlns:xlink="">2</sup> for all axes. Using a machine learning (ML) classification model with these spectral spread features results in cough detection accuracy of 92.5, 92.2, and 91.5% with k-nearest neighbors (kNN), and 94.3, 96.1, and 93.6% using Support Vector Machine (SVM) ML models for all three torso points especially shirt pocket where phones are commonly worn.
This chapter is concerned with the use of wearable devices for disabled and extreme sports. These sporting disciplines offer unique challenges for sports scientists and engineers. Disabled athletes often rely on and utilize more specialist equipment than able-bodied athletes. Wearable devices could be particularly useful for monitoring athlete-equipment interactions in disability sport, with a view to improving comfort and performance, while increasing accessibility and reducing injury risks. Equipment also tends to be key for so called “extreme” sports, such as skiing, snowboarding, mountain biking, bicycle motocross, rock climbing, surfing, and white-water kayaking. These sports are often practiced outdoors in remote and challenging environments, with athletes placing heavy demands on themselves and their equipment. Extreme sports also encompass disability sports, like sit skiing and adaptive mountain biking, and the popularity and diversity of such activities is likely to increase with improvements in technology and training, as well as with the support of organizations like the High Fives Foundation ( and Disability Snowsport, United Kingdom ( Within this chapter in these two sporting contexts, wearable devices are broadly associated with those that can be used to monitor the kinetics and kinematics of an athlete and their equipment. This chapter will first consider image-based alternatives and then focus on wearable sensors, in three main sections covering, (1) sports wearables, (2) disability sport and the use of wearables, and (3) extreme sport and the use of wearables, as well as making recommendations for the future.
Wearable point-of-care testing (PoCTs) technologies offer great potential to advance healthcare by providing high-quality continuous biometric data to inform clinical care decisions. Driven by the success of consumer-grade devices and rapid technical advances, exciting new developments suggest the potential for chemical sensor data alongside mature movement and electrophysiology capabilities even when facing indirect or noisy measurements. Emerging trends suggest a promising role for wearable PoCT as an early detection and diagnosis tool through longitudinal monitoring of identified biomarkers facilitating proactive, preventative care. However, translating the promise of wearable PoCT to clinical practice remains slow. We argue that a lack of clinical validation research examining the role of wearable PoCT in clinical care pathways is a major bottleneck to adoption. To address these gaps, we recommend fostering a transdisciplinary approach by integrating technical, clinical, institutional, and patient roles in research and development teams.
Purpose The SARS-CoV-2 pandemic has caused a major impact on worldwide public health and economics. The lessons learned from the successful attempts to contain the pandemic escalation revealed that the wise usage of contact tracing and information systems can widely help the containment work of any contagious disease. In this context, this paper investigates other researches on this domain, as well as the main issues related to the practical implementation of such systems and specifies a technical solution. Design/methodology/approach The proposed solution is based on the automatic identification of relevant contacts between infected or suspected people with susceptible people; inference of contamination risk based on symptoms history, user navigation records and contact information; real-time georeferenced information of population density of infected or suspect people; and automatic individual social distancing recommendation calculated through the individual contamination risk and the worsening of clinical condition risk. Findings The solution was specified, prototyped and evaluated by potential users and health authorities. The proposed solution has the potential of becoming a reference on how to coordinate the efforts of health authorities and the population on epidemic control. Originality/value This paper proposed an original information system for epidemic control which was applied for the SARS-CoV-2 pandemic and could be easily extended to other epidemics.
Sleep is crucial for the proper functioning of bodily systems and for cognitive and emotional processing. Evidence indicates that sleep is vital for health, well-being, mood, and performance. Consumer sleep technologies (CSTs), such as multisensory wearable devices, have brought attention to sleep and there is growing interest in using CSTs in research and clinical applications. This article reviews how CSTs can process information about sleep, physiology, and environment. The growing number of sensors in wearable devices and the meaning of the data collected are reviewed. CSTs have the potential to provide opportunities to measure sleep and sleep-related physiology on a large scale.
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Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over- and under-sampling, and (iv) ensemble learning methods. The proposed toolbox only depends on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. The toolbox is publicly available in GitHub:
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Sleep-wake disturbances are common in hospitalized patients but few studies have assessed them systematically. The aim of the present study was to assess sleep quality in a group of medical inpatients, in relation to environmental factors, and the switch to daylight-saving time. Between March and April 2013, 118 consecutive inpatients were screened and 99 (76 ± 11 years; hospitalization: 8 ± 7 days) enrolled. They slept in double or quadruple rooms, facing South/South-East, and were qualified as sleeping near/far from the window. They underwent daily sleep assessment by standard questionnaires/diaries. Illuminance was measured by a luxmeter at each patient's eye-level, four times per day. Noise was measured at the same times by a phonometer. Information was recorded on room lighting, position of the rolling shutters and number/type of extra people in the room. Compliance with sleep-wake assessment was poor, with a range of completion of 2-59%, depending on the questionnaires. Reported sleep quality was sufficient and sleep timing dictated by hospital routine; 33% of the patients reported one/more sleepless nights. Illuminance was generally low, and rolling shutters half-way down for most of the 24 h. Patients who slept near the window were exposed to more light in the morning (i.e., 222 ± 72 vs. 174 ± 85 lux, p < 0.05 before the switch; 198 ± 72 vs. 141 ± 137 lux, p < 0.01 after the switch) and tended to sleep better (7.3 ± 1.8 vs. 5.8 ± 2.4 on a 1-10 scale, before the switch, p < 0.05; 7.7 ± 2.3 vs. 6.6 ± 1.8, n.s. after the switch). Noise levels were higher than recommended for care units but substantially comparable across times/room types. No significant differences were observed in sleep parameters before/after the switch. Medical wards appear to be noisy environments, in which limited attention is paid to light/dark hygiene. An association was observed between sleep quality and bed position/light exposure, which is worthy of further study.
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This work overviews some developments on the estimation of the Receiver Operating Characteristic (ROC) curve. Estimation methods in this area are constantly being developed, adjusted and extended, and it is thus impossible to cover all topics and areas of application in a single paper. Here, we focus on some frequentist and Bayesian methods which have been mostly employed in the medical setting. Although we emphasize the medical domain, we also describe links with other fields where related developments have been made, and where some modeling concepts are often known under other designations.
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Obstructive sleep apnea (OSA) is a serious sleep disorder which is characterized by frequent obstruction of the upper airway, often resulting in oxygen desaturation. The serious negative impact of OSA on human health makes monitoring and diagnosing it a necessity. Currently, polysomnography is considered the gold standard for diagnosing OSA, which requires an expensive attended overnight stay at a hospital with considerable wiring between the human body and the system. In this paper, we implement a reliable, comfortable, inexpensive, and easily available portable device that allows users to apply the OSA test at home without the need for attended overnight tests. The design takes advantage of a smatrphone's built-in sensors, pervasiveness, computational capabilities, and user-friendly interface to screen OSA. We use three main sensors to extract physiological signals from patients which are (1) an oximeter to measure the oxygen level, (2) a microphone to record the respiratory effort, and (3) an accelerometer to detect the body's movement. Finally, we examine our system's ability to screen the disease as compared to the gold standard by testing it on 15 samples. The results showed that 100% of patients were correctly identified as having the disease, and 85.7% of patients were correctly identified as not having the disease. These preliminary results demonstrate the effectiveness of the developed system when compared to the gold standard and emphasize the important role of smartphones in healthcare.
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Continuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnea (OSA). However, compliance is a significant problem and has been incompletely assessed in long-term studies. To assess the long-term compliance of OSA patients with CPAP therapy. Eighty patients who had had a diagnosis of OSA at least four years previously and received a written prescription for CPAP were evaluated. Subjects were identified by reviewing sleep laboratory records. Participants were contacted by telephone and were asked to quantitate their CPAP use (hours per night, nights per week) and to evaluate whether there had been improvement in symptoms. Those who commenced but subsequently abandoned therapy and those who never initiated treatment were questioned about their reasons for noncompliance. Patient demographics included mean (+/- SD) age (58+/-11 years), male sex (70 of 80 patients [88%]) and mean apnea-hypopnea index (70+/-44 events/h). At the time of the interview (64.0+/-3.7 months after diagnosis), 43 of 80 patients (54%) were still using CPAP and most reported an improvement in symptoms. Twelve of 80 patients (15%) had abandoned CPAP after using it for 10.1+/-15.5 months, and 25 of 80 patients (31%) had never commenced therapy after initial diagnosis and CPAP titration. Analysis of scores reflecting initial patient sleepiness revealed a significant association of this symptom with subsequent CPAP compliance. Although many patients with OSA derive subjective benefit from, and adhere to treatment with CPAP, a significant proportion of those so diagnosed either do not initiate or eventually abandon therapy. Initial experience with CPAP appears to be important, reinforcing the need for early education and support in these patients.
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The monitoring of breathing dynamics is an essential diagnostic tool in various clinical environments, such as sleep diagnostics, intensive care and neonatal monitoring. This paper introduces an innovative signal classification method that is capable of on-line detection of the presence or absence of normal breathing. Four different artificial neural networks are presented for the recognition of three different patterns in the respiration signals (normal breathing, hypopnea, and apnea). Two networks process the normalized respiration signals directly, while another two use sophisticatedly preprocessed signals. The development of the networks was based on training sets from the polysomnographic records of nine different patients. The detection performance of the networks was tested and compared by using up to 8000 untrained breathing patterns from 16 different patients. The networks which classified the preprocessed respiration signals produced an average detection performance of over 90%. In the light of the moderate computational power used, the presented method is not only viable in clinical polysomnographs and respiration monitors, but also in portable devices.
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This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms. The HMM framework has been visited because of its ability of beat detection, segmentation and classification, highly suitable to the electrocardiogram (ECG) problem. Our approach addresses a large panel of topics some of them never studied before in other HMM related works: waveforms modeling, multichannel beat segmentation and classification, and unsupervised adaptation to the patient's ECG. The performance was evaluated on the two-channel QT database in terms of waveform segmentation precision, beat detection and classification. Our waveform segmentation results compare favorably to other systems in the literature. We also obtained high beat detection performance with sensitivity of 99.79% and a positive predictivity of 99.96%, using a test set of 59 recordings. Moreover, premature ventricular contraction beats were detected using an original classification strategy. The results obtained validate our approach for real world application.
Respiratory rate and body position are two major physiological parameters in sleep study, and monitoring them during sleep can provide helpful information for health care. In this paper, we present SleepMonitor, a smartwatch based system which leverages the built-in accelerometer to monitor the respiratory rate and body position. To calculate respiratory rate, we design a filter to extract the weak respiratory signal from the noisy accelerometer data collected on the wrist, and use frequency analysis to estimate the respiratory rate from the data along each axis. Further, we design a multi-axis fusion approach which can adaptively adjust the estimates from the three axes and then significantly improve the estimation accuracy. To detect the body position, we apply machine learning techniques based on the features extracted from the accelerometer data. We have implemented our system on Android Wear based smartwatches and evaluated its performance in real experiments. The results show that our system can monitor respiratory rate and body position during sleep with high accuracy under various conditions.
Conference Paper
It is a common practice in supervised learning techniques to use human judgment to label training data. For this process, data reliability is fundamental. Research on sleep quality found that human sleep stage misperception may occur. In this paper we propose that human judgment be supported by software-driven evaluation based on physiological parameters, selecting as training data only data sets for which human judgment and software evaluation are aligned. A prototype system to provide a broad-spectrum perception of sleep quality data comparable with human judgment is presented. The system requires users to wear a smartwatch recording heartbeat rate and wrist acceleration. It estimates an overall percentage of the sleep stages, to achieve an effective approximation of conventional sleep measures, and to provide a three-class sleep quality evaluation. The training data are composed of the heartbeat rate, the wrist acceleration and the three-class sleep quality. As a proof of concept, we experimented the approach on three subjects, each one over 20 nights.
Conference Paper
This paper proposes SymDetector, a smartphone based application to unobtrusively detect the sound-related respiratory symptoms occurred in a user's daily life, including sneeze, cough, sniffle and throat clearing. SymDetector uses the built-in microphone on the smartphone to continuously monitor a user's acoustic data and uses multi-level processes to detect and classify the respiratory symptoms. Several practical issues are considered in developing SymDetector, such as users' privacy concerns about their acoustic data, resource constraints of the smartphone and different contexts of the smartphone. We have implemented SymDetector on Galaxy S3 and evaluated its performance in real experiments involving 16 users and 204 days. The experimental results show that SymDetector can detect these four types of respiratory symptoms with high accuracy under various conditions.
Sleep apnea is often diagnosed using an overnight sleep test called a polysomnography (PSG). Unfortunately, though it is the gold standard of sleep disorder diagnosis, a PSG is time consuming, inconvenient, and expensive. Many researchers have tried to ameliorate this problem by developing other reliable methods, such as using electrocardiography (ECG) as an observed signal source. Respiratory rate interval, ECG-derived respiration, and heart rate variability analysis have been studied recently as a means of detecting apnea events using ECG during normal sleep, but these methods have performance weaknesses. Thus, the aim of this study is to classify the subject into normal- or apnea-subject based on their single-channel ECG measurement in regular sleep. In this proposed study, ECG is decomposed into five levels using wavelet decomposition for the initial processing to determine the detail coefficients (D3-D5) of the signal. Approximately 15 features were extracted from every minute of ECG. Principal component analysis and a support vector machine are used for feature dimension reduction and classification, respectively. According to classification that been done from a data set consisting of thirty-five patients, the proposed minute-to-minute classifier specificity, sensitivity, and subject-based classification accuracy are 95.20%, 92.65%, and 94.3%, respectively. Furthermore, the proposed system can be used as a basis for future development of sleep apnea screening tools.
Conference Paper
In recent years, sleep apnea syndrome is considered an important research direction in sleep medicine. According to statistics, the disease prevalence of obstructive sleep apnea (OSA) is more than 3% of the total population and even up to 25% for 40-aged men. More and more clinical evidences showed that obstructive sleep apnea is highly associated with hypertension, diabetes, metabolic syndrome, cardiovascular disease, nocturnal enuresis, and even depression. If we can detect the potential OSA patients early, and offering them appropriate treatments, it is worth not only promoting the quality of patient's life, but also reducing the possible serious complications and medical costs. Mobile phones are now playing an ever more crucial role in people's daily lives. The latest generation of smart phones is increasingly viewed as handheld computers rather than as phones, due to their powerful on-board computing capability, capacious memories, large screens and open operating systems that encourage applications (Apps) development. In this study, an intelligent "OSA prediction App" on Android Smart phone has been developed based on medical decision rules from a clinical large dataset. The proposed application can provide an easy and efficient way to quickly pre-screen high-risk groups of OSA potential patients, aid medical works to achieve early diagnosis and treatment purposes, prevent the occurrence of complications, and thus reach the goal of preventive medicine.
Habitual snoring is a prevalent condition that is not only a marker of obstructive sleep apnea (OSA) but can also lead to vascular risk. However, it is not easy to check snoring status at home. We attempted to develop a snoring sound monitor consisting of a smartphone alone, which is aimed to quantify snoring and OSA severity. The subjects included 50 patients who underwent diagnostic polysomnography (PSG), of which the data of 10 patients were used for developing the program and that of 40 patients were used for validating the program. A smartphone was attached to the anterior chest wall over the sternum. It acquired ambient sound from the built-in microphone and analyzed it using a fast Fourier transform on a real-time basis. Snoring time measured by the smartphone highly correlated with snoring time measured by PSG (r = 0.93). The top 1 percentile value of sound pressure level (L1) determined by the smartphone correlated with the ambient sound L1 during sleep determined by PSG (r = 0.92). Moreover, the respiratory disturbance index estimated by the smartphone (smart-RDI) highly correlated with the apnea-hypopnea index (AHI) obtained by PSG (r = 0.94). The diagnostic sensitivity and specificity of the smart-RDI for diagnosing OSA (AHI ≥ 15) were 0.70 and 0.94, respectively. A smartphone can be used for effectively monitoring snoring and OSA in a controlled laboratory setting. Use of this technology in a noisy home environment remains unproven, and further investigation is needed. A commentary on this article appears in this issue on page 79. Nakano H; Hirayama K; Sadamitsu Y; Toshimitsu A; Fujita H; Shin S; Tanigawa T. Monitoring sound to quantify snoring and sleep apnea severity using a smartphone: proof of concept. J Clin Sleep Med 2014;10(1):73-78.
We sought to clarify the influence of the severity of obstructive sleep apnea (OSA) on heart rate (HR) in patients with OSA. We examined 136 patients who underwent overnight polysomnography together with 24-h Holter electrocardiography and who were diagnosed as having OSA [apnea-hypopnea index (AHI) >/=5]. The patients were divided into the following 3 groups: 30 with 5</= AHI <15 (group A); 33 with 15</= AHI <30 (group B); 73 with AHI >/=30 (group C). Mean HRs during 24h, wakefulness, and sleep were calculated. Mean HRs during 24h, wakefulness, and sleep were significantly higher in group C than in groups A and B. Mean HRs during 24h, wakefulness, and sleep correlated positively with AHI (Spearman's rho=0.36, p<0.001; Spearman's rho=0.32, p<0.001; Spearman's rho=0.38, p<0.001; respectively). Multiple regression analyses revealed that lnAHI was independently associated with mean HRs during 24h, wakefulness, and sleep. In 21 OSA patients who started nasal continuous positive airway pressure (nCPAP) therapy, mean HRs during 24h, wakefulness, and sleep were significantly reduced at 6 months after the initiation of nCPAP. The severity of OSA was independently associated with mean HRs during 24h, wakefulness, and sleep, and 6-month treatment with nCPAP reduced the values. The prognostic significance of elevated mean HRs during 24h, wakefulness, and sleep is necessary to be clarified in patients with OSA.
Split-night polysomnography (PSG) and unattended home sleep studies have come into use as less-expensive tests for obstructive sleep apnea syndrome, but their impact on cost-effectiveness of the overall evaluation and treatment is unknown. We compared the cost-effectiveness of evaluations that employ these 2 procedures with a conventional approach using full-night PSG. We used a decision-tree model that incorporated typical clinical algorithms for each of the 3 strategies to compare their cost-effectiveness from a third-party payer perspective over a 5-year period. Probabilities and test characteristics were derived from data from the published literature. Cost estimates were based on the 2004 Medicare Fee Schedule. Survival rates were taken from National Center for Health Statistics data and published studies. Effectiveness was measured as quality-adjusted life years. Trade-offs of overall costs versus effectiveness were identified. The home-studies strategy was less costly and less effective than split-night PSG and full-night PSG, as was split-night PSG compared with full-night PSG. Costs to attain additional quality-adjusted life years were below commonly accepted thresholds. A probabilistic analysis suggested that the home-studies approach was most cost-effective at the lowest amounts of third-party willingness to pay, whereas split-night PSG or full-night PSG was most cost-effective at higher amounts. Home studies and split-night PSG are cost-effective alternatives to full-night PSG. Willingness-to-pay is an important consideration in choosing the most cost-effective approach. This study points out the importance of considering the complexities within the entire process of obstructive sleep apnea syndrome evaluation when comparing costs among different procedures.
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