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Smartwatch based respiratory rate estimation during sleep using CNN/LSTM neural network
... Bidirectional-LSTM model has been applied to support the radiotherapy treatment for thoracic-abdominal tumors by analyzing respiratory motions (40). LSTM has been combined with CNN to determine respiratory actions during sleep (41). ...
... The key feature of CNN is convolution operation which provides a form of automated feature extraction (41). CNN's have been combined with LSTM networks (CNN-LSTM) to learn temporal features in tasks such as video image captioning (73), multi-view object recognition (74) and other fields such as medicine (41; 73; 75; 76; 77; 78). ...
... CNN is used for a multi-channel signal to extract deep features followed by LSTM for prediction. Havriushenko et al. (41) factorized photoplethysmogram signals into spatial and cross channels and applied depth-wise convolutions separately to each channel. CNN-LSTM has shown major improvement in classification over machine learning methods such as Dense-Net (132), extreme learning machine (78), and random forests (73). ...
In the past decade, deep learning models have been applied to bio-sensors used in a body sensor network for prediction. Given recent innovations in this field, the prediction accuracy of novel models needs to be evaluated for bio-signals. In this paper, we evaluate the performance of deep learning models for respiratory rate prediction. We consider three datasets from bio-sensors which include electrocardiogram (ECG), photoplethysmogram (PPG) data, and surface electromyogram (sEMG) data. The deep learning models include Long short-term memory (LSTM) networks, Bidirectional LSTM (Bi-LSTM), attention-based variants of LSTM, CNN-LSTM and Convolutional-LSTM networks. The deep learning models are evaluated for two separate windows which are 32 s and 64 s window. The models’ performance is evaluated using mean absolute error (MAE). The 64 s window has more accurate prediction compared to the 32 s window. Our results indicate Bi-LSTM with Bahdanu Attention has the best performance for the bio-signals. LSTM performs best with one of the datasets, yielding an MAE of 0.70 ± 0.02. Bi-LSTM with Bahdanau attention showed best results with two of the three datasets with MAE of 0.51 ± 0.03 for sEMG based data and MAE of 0.24 ± 0.03 with PPG and ECG based data.
... Останні моделі споживчих трекерів сну використовують кілька датчиків для збору фізіологічних даних, щоб розпізнавати періоди сну на основі руху. Наприклад, для вимірювання частоти серцевих скорочень і варіабельності серцевого ритму часто використовується фотоплетизмографія, яка поліпшує процес виявлення періодів сну/неспання та розпізнавання стадій сну [7][8][9][10]. Зазвичай, такі пристрої зв'язані з додатками для смартфонів і забезпечують зручне відображення і аналіз отриманої інформації про сон. ...
... Вони використовують вбудований одноканальний або багатоканальний ЕЕГ для вимірювання мозкових хвиль користувачів під час сну, які потім зіставляються з фазами сну. Тип пристроїв на основі актиграфів включає популярні трекери активності, розумні годинники, а також додатки для смартфонів [9]. Вони використовують вбудований акселерометр для вимірювання рухів кінцівок користувачів, а деякі носимі браслети вимірюють частоту серцевих скорочень. ...
... Хоча занепокоєння Fitbit визначається рухом зап'ястя, а не сигналами мозкових хвиль, воно досить добре узгоджується з мікрозбудженням ЕЕГ в медичних даних, як показує кількісне порівняння [18]. У дослідженнях, що проводилися компанією Samsung Electronics, взяли учать 165 учасників із середнім віком 39 років, індексом маси тіла -24 кг/м 2 та індексом апное-гіпопное -12 подій/год [8][9][10]. Всі учасники провели три ночі у медичному центрі Самсунг, де були записані дані ПСГ та одночасно на лівій або правій руці записані дані сенсорів розумного годинника Samsung Galaxy Watch (тривісний акселерометр та плетизмографія зеленого кольору). ...
Реферат – Сон - це складний психофізіологічний стан, який безпосередньо визначає психічну і біологічну активність людини. Важливість сну обумовлена в першу чергу його необхідністю для організму. Сон – унікальний механізм відновлення та адаптації до умов життя. Одна безсонна ніч знижує стійкість імунітету до інфекційних захворювань і швидкість реакції на зовнішні імпульси. У віддаленій перспективі постійний дефіцит і зниження якості сну підвищують ризик розвитку серцево-судинних і ендокринних захворювань. Золотий стандарт об'єктивної оцінки сну, полісомнографія, зазвичай виконується в лабораторії сну, і дані вручну оцінюються фахівцем зі сну, що робить цей процес незручним, дорогим і менш придатним для довготривалих досліджень. Тому існує потреба в перевіреному недорогому обладнанні для оцінки сну, яке було б зручним і точним. З клінічної та дослідницької точки зору можливість отримання даних про безсоння, може персоналізувати рішення про лікування та оптимізацію здоров'я, а також поліпшити фенотипування захворювання. Мета даної статті – дослідження сучасних методів аналізу якості сну, визначення їх переваг та недоліків, та пошук альтернативних засобів для моніторингу сну. В роботі наведені результати порівняльного аналізу технологій для відстеження сну, а також запропоновано альтернативу полісомнографії, яка може використовуватися для надійного та довгострокового моніторингу. Зокрема, проаналізовані роботи, що вивчали застосування біорадара як засобу аналізу фізіологічного стану людини. Біорадар - це новий вид радіолокатора, що поєднує технології біомедичної інженерії та радіолокації. Його ціль – безконтактне виявлення життєво важливих ознак через неметалеві перешкоди, такі як одяг та стіни, передаючи спеціальну електромагнітну хвилю. Дана технологія може стати кроком вперед в індустрії відстеження сну.Ключові слова: полісомнографія, моніторинг сну, актиграфія, дистанційний моніторинг, біорадари.
... In terms of mobile and wearable devices, extracted physiological data (for example, using the most popular HRV and activity information which can be simply measured by standard accelerometers, gyroscopes or plethysmography (PPG) sensors available almost in all wearable and mobile devices) can be effectively used to estimate a number of related vital signs and pathological states (such as blood oxygen saturation, sleep apnea and hypopnea, snore, blood pressure, etc.) by applying specialized algorithms [3,[7][8][9]. Recently, deep-learning based models have shown promising results in the field of biomedical engineering, in particular for the analysis of sensors data, recognition of specific medical patterns, identification of hidden models, and decision-making in the field of healthcare. ...
... As a basis of our current system the algorithms developed in our previous papers for sleep stages analysis, respiratory events classification and smart alarm, were used [3,[7][8][9]15]. ...
... We use the same dataset as in our recent studies [3,[7][8][9]15]. The full dataset consists of 263 logs from 176 different users and was prepared by Samsung Medical Center. ...
Recently, mobile and wearable devices have become an increasingly integral part of our lives. They provide a possibility of detailed health monitoring using noninvasive and user-friendly techniques. However, lack of continuous monitoring, the need of specific sensors, and the limitations in memory and power consumption are only some of the potential drawbacks of such devices. In the current paper a system based on a deep recurrent neural network is developed for an automatic continuous monitoring of sleep-related physiological parameters by means of a wearable biosignal monitoring systems. Smartwatches based algorithm for non-invasive monitoring of sleep stages, respiratory events (including sleep apnea and hypopnea), snore and blood oxygen saturation is developed. Our experimental results demonstrate that proposed model constitutes a noninvasive and inexpensive screening system for sleep-related physiological parameters and pathological states. The model has shown a 77 % accuracy in sleep stages prediction, more than 80 % accuracy in epoch-by-epoch respiratory events classification, above 60 % accuracy in snore events classification and above 70 % accuracy in blood oxygen saturation (SpO2) level classification (for a two class problem with a SpO2 threshold of 95 %).
... Havriushenko et al. [43] proposed a method for estimating a user's respiratory rate from pulse wave data by using neural networks. The respiratory rate is often measured with a thermal sensor placed in the nasal channels or an elastic chest belt, but these devices may interfere with sleep. ...
The extensive research on wearable devices has led to devices with various shapes and mounting locations.Wearable devices are often used to record the user’s biometric information, and methods have been proposed to detect physical abnormalities from the acquired data. Among various kinds of biometric data, pulse data has been used in methods such as heart rate monitoring and emotion recognition. The most common type of pulse sensor uses photoplethysmography (PPG), which irradiates a green LED on the skin and measures pulse data from changes in the light reflected from blood vessels. PPG sensors have been implemented in commercially available wearable devices such as smartwatches. However, a PPG sensor requires blood flow for data acquisition, and when a smartwatch is worn on an artificial body part such as a prosthetic hand or a robotic arm, data cannot be acquired because there is no blood flow. In this study, we propose a method that enables a PPG sensor to measure arbitrary pulse data by using a display. If this method is successful, it will enable pulse data measured at the junction of a living limb and an artificial limb to be input to the display; then, a smartwatch attached to the artificial limb will read the same pulse data. In this paper, we focus on the heart rate and report the results of an experiment in which a target heart rate was input and the display was controlled accordingly to determine whether the target heart rate could be obtained by a smartwatch. We implemented a display drawing program and conducted the evaluation with five kinds of smartwatches and four kinds of displays. The results showed that the error between the target heart rate and the heart rate acquired by the smartwatch was within 3 beats per minute (bpm) in many cases when the target heart rate was set to 60–100 bpm. When the target heart rate was set to 40–55 and 105–200 bpm, the heart rate could also be input to the smartwatch with a small error under certain conditions. Moreover, when generated PPG data was imported into heart rate variability (HRV) analysis software, it was recognized as a pulse wave in the same way as real PPG data obtained from a person.We compared the heart rate, RR interval, and SDNN calculated from the real and generated PPG data, and we confirmed that the proposed method could generate stable PPG data. On the other hand, when the waveforms were compared, the generated PPG waveform differed greatly from the real PPG waveform, which indicated that the software could calculate the heart rate, RR interval, SDNN, and LF/HF ratio regardless of the waveform. This result suggests that calculation of these parameters without verifying the waveform would be vulnerable to an attack with fake PPG data.
... Havriushenko et al. [12] proposed using neural networks to estimate a user's respiratory rate from pulse wave data. The respiratory rate is often measured with a thermal sensor placed in the nasal channels or an elastic chest belt, but these devices may interfere with sleep. ...
The electromyogram (EMG) is a waveform representation of the action potential generated by muscle cells using electrodes. EMG acquired using surface electrodes is called surface EMG (sEMG), and it is the acquisition of muscle action potentials transmitted by volume conduction from the skin. Surface electrodes require disposable conductive gel or adhesive tape to be attached to the skin, which is costly to run, and the tape is hard on the skin when it is removed. Muscle activity can be evaluated by acquiring muscle potentials and analyzing quantitative, temporal, and frequency factors. It is also possible to evaluate muscle fatigue because the frequency of the EMG becomes lower as the muscle becomes fatigued. Research on human activity recognition from EMG signals has been actively conducted and applied to systems that support arm and hand functions. This paper proposes a method for recognizing the muscle activity state of the arm using pulse wave data (PPG: Photoplethysmography) and a method for estimating EMG using pulse wave data. This paper assumes that the PPG sensor is worn on the user’s wrist to measure the heart rate. The user also attaches an elastic band to the upper arm, and when the user exerts a force on the arm, the muscles of the upper arm contract. The arteries are then constricted, and the pulse wave measured at the wrist becomes weak. From the change in the pulse wave, the muscle activity of the arm can be recognized and the number of action potentials of the muscle can be estimated. From the evaluation experiment with five subjects, three types of muscle activity were recognized with 80+%, and EMG was estimated with approximately 20% error rate.
Accurate and continuous breathing rate detection is crucial as it can help people to assess their physical health and provide early warning and diagnosis for potential human diseases. Traditional breathing detection approaches involving intrusive devices are uncomfortable for long-term continuous monitoring. While contactless detection approaches utilizing radio-frequency (RF) signals or acoustic signals mainly focus on sensing the changes of chest and abdomen displacements, which are not a good indicator recording breathing event due to existing false body movements. In this paper, we present Wi-Tracker, a contactless breathing detection system based on commercial off-the-shelf (COTS) smartphones, which detects breathing event through capturing the Doppler effect caused by human exhaled airflow on the reflected acoustic wave. Specifically, Wi-Tracker uses the speaker on smartphone to transmit ultrasound signals and its microphone to receive the reflected acoustic signals recording breathing event. Then, we adopt a cumulative power spectral density (CPSD) method to extract fine-grained breathing pattern from the received signals. Finally, we design algorithms to accurately capture the breathing event from the extracted breathing pattern. We evaluate Wi-Tracker with 6 volunteers for a period of 1 month. Experimental results show that Wi-Tracker is able to achieve contactless breathing detection with a mean estimation error (MEE) of 0.17 bpm, which is even better as compared to RFID-based or WiFi-based approaches.
Respiratory rate is of great value in health care, especially when it can be continuously monitored using wearable devices in daily life. Recent works employ Photoplethysmography (PPG) on smartwatch for continuous respiration monitoring, based on a certain medical discovery called Respiratory Sinus Arrhythmia (RSA), which describes the relationship between respiratory and heart rate. However, we find that these works fall short of robustness. In particular, the respiratory estimation accuracy drops significantly when people breathe faster (e.g., after sports). We further identify the root reason that the RSA gradually weakens as the respiratory rate increases. In this paper, we propose BreathAnalyzer, which can estimate respiratory rate accurately even at high respiratory rates. To achieve this, BreathAnalyzer boosts the weakened RSA and also handles the motion artifacts, by integrating features from multiple domains, i.e., frequency, time and non-linear Poincare domain, instead of using the single spectrum or raw signal in previous studies. Moreover, BreathAnalyzer custom-designs a tree-based learning model, which fits multi-domain features, while considering limitations of smartwatch. We implement BreathAnalyzer prototype on COTS smartwatch, and extensive evaluation demonstrates that BreathAnalyzer outperforms the state-of-the-art approaches, with accuracy improvement by 35.37% ∼ 80.42% across a variety of practical scenarios including high respiratory rates.
The COVID-19 pandemic has induced dramatic effects in all areas of society worldwide. It has led to drastic restrictions on academic exchanges and caused research projects to be put on hold. The need to maintain social distancing has severely affected the research communities that rely on in-person studies. In particular, Quality of Experience (QoE) research relies heavily on user studies and in-person subjective tests as means of obtaining ground truths for system designs. In this position paper, we focus on the impact of COVID-19 on conducting subjective tests for digital media quality assessment. An overview of the related discussions that take place in associated research communities is provided. A number of challenges are posed in terms of hygiene, ethics, standardization, and research quality issues. Opportunities for consolidating QoE research under COVID-19 and beyond are put forward, including alternative experimental designs and adaptation of research methodologies. Numerous enablers are suggested and discussed, revealing various avenues that can be followed to assist in conducting subjective tests under pandemic conditions. We believe that this position paper will be helpful for researchers and practitioners relying on in- person studies to keep abreast about the potential measures that support coming out of the COVID-19 pandemic strong and being prepared for future endeavors.
There is an ever-growing demand for measuring respiratory variables during a variety of applications, including monitoring in clinical and occupational settings, and during sporting activities and exercise. Special attention is devoted to the monitoring of respiratory rate because it is a vital sign, which responds to a variety of stressors. There are different methods for measuring respiratory rate, which can be classed as contact-based or contactless. The present paper provides an overview of the currently available contact-based methods for measuring respiratory rate. For these methods, the sensing element (or part of the instrument containing it) is attached to the subject’s body. Methods based upon the recording of respiratory airflow, sounds, air temperature, air humidity, air components, chest wall movements, and modulation of the cardiac activity are presented. Working principles, metrological characteristics, and applications in the respiratory monitoring field are presented to explore potential development and applicability for each method.
Depthwise separable convolution has shown great efficiency in network design, but requires time-consuming training procedure with full training-set available. This paper first analyzes the mathematical relationship between regular convolutions and depth-wise separable convolutions, and proves that the former one could be approximated with the latter one in closed form. We show depthwise separable convolutions are principal components of regular convolutions. And then we propose network decoupling (ND), a training-free method to accelerate convolutional neural networks (CNNs) by transferring pre-trained CNN models into the MobileNet-like depthwise separable convolu-tion structure, with a promising speedup yet negligible accuracy loss. We further verify through experiments that the proposed method is orthogonal to other training-free methods like channel decomposition, spatial decomposition, etc. Combining the proposed method with them will bring even larger CNN speedup. For instance, ND itself achieves about 2× speedup for the widely used VGG16, and combined with other methods, it reaches 3.7× speedup with graceful accuracy degradation. We demonstrate that ND is widely applicable to classification networks like ResNet, and object detection network like SSD300.
The human has a very complex perception system, including vision, auditory, olfactory, touch, and gustation. This paper will introduce the recent studies about providing a technical solution for image recognition, by applying a algorithm called Convolutional Neural Network (CNN) which is inspired by animal visual system. Convolution serves as a perfect realization of an optic nerve cell which merely responds to its receptive field and it performs well in image feature extraction. Being highly-hierarchical networks, CNN is structured with a series of different functional layers. The function blocks are separated and described clearly by each layer in this paper. Additionally, the recognition process and result of a pioneering CNN on MNIST database are presented.
In this study, we proposed a novel method for extracting the instantaneous respiratory rate (IRR) from the pulse oximeter photoplethysmogram (PPG). The method was performed in three main steps: (1) a time-frequency transform called synchrosqueezing transform (SST) was used to extract the respiratory-induced intensity, amplitude and frequency variation signals from PPG, (2) the second SST was applied to each extracted respiratory-induced variation signal to estimate the corresponding IRR, and (3) the proposed peak-conditioned fusion method then combined the IRR estimates to calculate the final IRR. We validated the implemented method with capnography and nasal/oral airflow as the reference RR using the limits of agreement (LOA) approach. Compared to simple fusion and single respiratory-induced variation estimations, peak-conditioned fusion shows better performance. It provided a bias of 0.28 bpm with the 95% LOAs ranging from −3.62 to 4.17, validated against capnography and a bias of 0.04 bpm with the 95% LOAs ranging from −5.74 to 5.82, validated against nasal/oral airflow. This algorithm would expand the functionality of a conventional pulse oximetry beyond the measurement of heart rate and oxygen saturation to measure the respiratory rate continuously and instantly.
Photoplethysmography (PPG) is one of the key technologies for unobtrusive physiological monitoring, with ongoing attempts to use it in several medical fields, ranging from night to night sleep analysis to continuous cardiac arrhythmia monitoring. However, the PPG signals are susceptible to be corrupted by noise and artifacts, caused, e.g., by limb or sensor movement. These artifacts affect the morphology of PPG waves and prevent the accurate detection and localization of beats and subsequent cardiovascular feature extraction. In this paper a new algorithm for beat detection and pulse quality assessment is described. The algorithm segments the PPG signal in pulses, localizes each beat and grades each segment with a quality index. The obtained index results from a comparison between each pulse and a template derived from the surrounding pulses, by mean of dynamic time warping barycenter averaging. The quality index is used to discard corrupted pulse beats. The algorithm is evaluated by comparing the detected beats with annotated PPG signals and the results are published over the same data. The described method achieves an improved sensitivity and a higher predictive value.
Sleep apnea is highly prevalent in patients with cardiovascular disease. These disordered breathing events are associated with a profile of perturbations that include intermittent hypoxia, oxidative stress, sympathetic activation, and endothelial dysfunction, all of which are critical mediators of cardiovascular disease. Evidence supports a causal association of sleep apnea with the incidence and morbidity of hypertension, coronary heart disease, arrhythmia, heart failure, and stroke. Several discoveries in the pathogenesis, along with developments in the treatment of sleep apnea, have accumulated in recent years. In this review, we discuss the mechanisms of sleep apnea, the evidence that addresses the links between sleep apnea and cardiovascular disease, and research that has addressed the effect of sleep apnea treatment on cardiovascular disease and clinical endpoints. Finally, we review the recent development in sleep apnea treatment options, with special consideration of treating patients with heart disease. Future directions for selective areas are suggested.
This article derives an estimation procedure to evaluate how many Monte Carlo realizations need to be done in order to achieve prescribed accuracies in the estimated mean value and also in the cumulative probabilities of achieving values greater than, or less than, a particular value as the chosen particular value is allowed to vary. In addition, by inverting the argument and asking what the accuracies are that result for a prescribed number of Monte Carlo realizations, one can assess the computer time that would be involved should one choose to carry out the Monte Carlo realizations. These two complementary procedures are of great benefit in attempting to control the worth of undertaking an unknown number of Monte Carlo realizations, and of continuing to carry out the unknown number until the results have reached a level of accuracy that one deems acceptable. Such a procedure is not only computer intensive; however, is also very open-ended, a less than desirable trait when running a complex computer program that might take many hours or days to run through even once. The procedure presented here allows one to assess, ahead of performing a large number of Monte Carlo realizations, roughly how many are actually needed. Several illustrative numerical examples provide indications how one uses this novel procedure in practical situations.
Taking Out the Trash
The purpose of sleep remains mysterious. Using state-of-the-art in vivo two-photon imaging to directly compare two arousal states in the same mouse, Xie et al. (p. 373 ; see the Perspective by Herculano-Houzel ) found that metabolic waste products of neural activity were cleared out of the sleeping brain at a faster rate than during the awake state. This finding suggests a mechanistic explanation for how sleep serves a restorative function, in addition to its well-described effects on memory consolidation.
A cardiorespiratory-based automatic sleep staging system for subjects with sleep-disordered breathing is described. A simplified three-state system is used: Wakefulness (W), rapid eye movement (REM) sleep (R), and non-REM sleep (S). The system scores the sleep stages in standard 30-s epochs. A number of features associated with the epoch RR-intervals, an inductance plethysmography estimate of rib cage respiratory effort, and an electrocardiogram-derived respiration (EDR) signal were investigated. A subject-specific quadratic discriminant classifier was trained, randomly choosing 20% of the subject's epochs (in appropriate proportions of W, S and R) as the training data. The remaining 80% of epochs were presented to the classifier for testing. An estimated classification accuracy of 79% (Cohen's kappa value of 0.56) was achieved. When a similar subject-independent classifier was trained, using epochs from all other subjects as the training data, a drop in classification accuracy to 67% (kappa = 0.32) was observed. The subjects were further broken in groups of low apnoea-hypopnea index (AHI) and high AHI and the experiments repeated. The subject-specific classifier performed better on subjects with low AHI than high AHI; the performance of the subject-independent classifier is not correlated with AHI. For comparison an electroencephalograms (EEGs)-based classifier was trained utilizing several standard EEG features. The subject-specific classifier yielded an accuracy of 87% (kappa = 0.75), and an accuracy of 84% (kappa = 0.68) was obtained for the subject-independent classifier, indicating that EEG features are quite robust across subjects. We conclude that the cardiorespiratory signals provide moderate sleep-staging accuracy, however, features exhibit significant subject dependence which presents potential limits to the use of these signals in a general subject-independent sleep staging system.
Sleep apnea is a sleep disorder with a high prevalence in the
adult male population. Sleep apnea is regarded as an independent risk
factor for cardiovascular sequelae such as ischemic heart attacks and
stroke. The diagnosis of sleep apnea requires polysomnographic studies
in sleep laboratories with expensive equipment and attending personnel.
Sleep apnea can be treated effectively using nasal ventilation therapy
(nCPAP). Early recognition and selection of patients with sleep related
breathing disorders is an important task. Although it has been suggested
that this can be done on the basis of the ECG, careful quantitative
studies of the accuracy of such techniques are needed. An annotated
database with 70 nighttime ECG recordings has been created to support
such studies. The annotations were based on visual scoring of disordered
breathing during sleep
Wireless detection of respiration rates is crucial for many applications. Most of the state-of-art solutions estimate breathing rates with the prior knowledge of crowd numbers as well as assuming the distinct breathing rates of different users, which is neither natural nor realistic. However, few of them can leverage the estimated breathing rates to recognize human subjects (a.k.a, identity matching). In this work, using the channel state information (CSI) of a single pair of commercial WiFi devices, a novel system is proposed to continuously track the breathing rates of multiple persons without such impractical assumptions. The proposed solution includes an adaptive subcarrier combination method that boosts the signal to noise ratio (SNR) of breathing signals, and an iterative dynamic programming and a trace concatenating algorithm that continuously track the breathing rates of multiple users. By leveraging both the spectrum and time diversity of the CSI, our system can correctly extract the breathing rate traces even if some of them merge together for a short time period. Furthermore, by utilizing the breathing traces obtained, our system can do people counting and recognition simultaneously. Extensive experiments are conducted in two environments (an on-campus lab and a car). The results show that 86% of average accuracy can be achieved for people counting up to 4 people for both cases. For 97.9% out of all the testing cases, the absolute error of crowd number estimates is within 1. The system achieves an average accuracy of 85.78% for people recognition in a smart home case.
This paper provides a novel best in the domain automatic classifier of sleep stages based on wrist photoplethysmography and 3D-accelerometer data obtained from smartwatches. The sleep is classified into rapid-eye-movement (REM), Light, Deep, or Wake stages. State of the art classifiers based on wearable sensors suffer from motion artifacts and apnea events. Proposed novel techniques of artifacts and apnea events elimination result in the high accuracy of sleep stages classification and robustness to apnea events. The model provides the Cohen's Kappa score of 0.65 and accuracy of 0.80 on 254 night-logs of 173 subjects with a broad distribution of apnea-hypopnea index until 90. The approach is applicable to unobtrusive sleep monitoring by wearables.
There is a growing interest in designing models that can deal with images from different visual domains. If there exists a universal structure in different visual domains that can be captured via a common parameterization, then we can use a single model for all domains rather than one model per domain. A model aware of the relationships between different domains can also be trained to work on new domains with less resources. However, to identify the reusable structure in a model is not easy. In this paper, we propose a multi-domain learning architecture based on depthwise separable convolution. The proposed approach is based on the assumption that images from different domains share cross-channel correlations but have domain-specific spatial correlations. The proposed model is compact and has minimal overhead when being applied to new domains. Additionally, we introduce a gating mechanism to promote soft sharing between different domains. We evaluate our approach on Visual Decathlon Challenge, a benchmark for testing the ability of multi-domain models. The experiments show that our approach can achieve the highest score while only requiring 50% of the parameters compared with the state-of-the-art approaches.
Sleep apnea is highly prevalent in patients with cardiovascular disease. These disordered breathing events are associated with a profile of perturbations that include intermittent hypoxia, oxidative stress, sympathetic activation, and endothelial dysfunction, all of which are critical mediators of cardiovascular disease. Evidence supports a causal association of sleep apnea with the incidence and morbidity of hypertension, coronary heart disease, arrhythmia, heart failure, and stroke. Several discoveries in the pathogenesis, along with developments in the treatment of sleep apnea, have accumulated in recent years. In this review, we discuss the mechanisms of sleep apnea, the evidence that addresses the links between sleep apnea and cardiovascular disease, and research that has addressed the effect of sleep apnea treatment on cardiovascular disease and clinical endpoints. Finally, we review the recent development in sleep apnea treatment options, with special consideration of treating patients with heart disease. Future directions for selective areas are suggested.
While the incidence of sleep disorders is continuously increasing in western societies, there is a clear demand for technologies to asses sleep-related parameters in ambulatory scenarios. The present study introduces a novel concept of accurate sensor to measure RR intervals via the analysis of photo-plethysmographic signals recorded at the wrist. In a cohort of 26 subjects undergoing full night polysomnography, the wrist device provided RR interval estimates in agreement with RR intervals as measured from standard electrocardiographic time series. The study showed an overall agreement between both approaches of 0.05 ± 18 ms. The novel wrist sensor opens the door towards a new generation of comfortable and easy-to-use sleep monitors.
To record sleep, actigraph devices are worn on the wrist and record movements that can be used to estimate sleep parameters with specialized algorithms in computer software programs. With the recent establishment of a Current Procedural Terminology code for wrist actigraphy, this technology is being used increasingly in clinical settings as actigraphy has the advantage of providing objective information on sleep habits in the patient's natural sleep environment. Actigraphy has been well validated for the estimation of nighttime sleep parameters across age groups, but the validity of the estimation of sleep-onset latency and daytime sleeping is limited. Clinical guidelines and research suggest that wrist actigraphy is particularly useful in the documentation of sleep patterns prior to a multiple sleep latency test, in the evaluation of circadian rhythm sleep disorders, to evaluate treatment outcomes, and as an adjunct to home monitoring of sleep-disordered breathing. Actigraphy has also been well studied in the evaluation of sleep in the context of depression and dementia. Although actigraphy should not be viewed as a substitute for clinical interviews, sleep diaries, or overnight polysomnography when indicated, it can provide useful information about sleep in the natural sleep environment and/or when extended monitoring is clinically indicated.
We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.
Design and development of real time respiratory rate monitor using non-invasive biosensor
- B G Rao
- Sudarshan
Rao and B.G. Sudarshan, "Design and development of real time
respiratory rate monitor using non-invasive biosensor," International
Journal of Research in Engineering and Technology, 2015, pp.437-442.
The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications
- R B Berry
R. B. Berry et al., "The AASM manual for the scoring of sleep and
associated events: rules, terminology and technical specifications,"
version 2.5, Darien. IL: American Academy of Sleep Medicine, 2018.
LSTM knowledge transfer for HRV-based sleep
- M Radha
M. Radha et al., "LSTM knowledge transfer for HRV-based sleep,"
2018, arXiv 1809.06221, unpublished.