Automatic event detector from smartphone accelerometry: Pilot mHealth study for obstructive sleep apnea monitoring at home

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Obstructive sleep apnea (OSA) is a common disorder with a low diagnosis ratio, leaving many patients undiagnosed and untreated. In the last decades, accelerometry has been found to be a feasible solution to obtain respiratory activity and a potential tool to monitor OSA. On the other hand, many smartphone-based systems have already been developed to propose solutions for OSA monitoring and treatment. The objective of this work was to develop an automatic event detector based on smartphone accelerometry and pulse oximetry, and to assess its ability to detect thoracic movements. It was validated with a commercial OSA monitoring system at home. Results of this preliminary pilot study showed that the proposed event detector for accelerometry signals is a feasible tool to detect abnormal respiratory events, such as apneas and hypopneas, and has potential to be included in smartphone-based systems for OSA assessment.Postprint (published version

Medical Devices for Measuring Respiratory Rate in Children: a Review

Respiratory rate is an important vital sign used for diagnosing illnesses in children as well as prioritising patient care. All children presenting acutely to hospital should have a respiratory rate measured as part of their initial and ongoing assessment. However measuring the respiratory rate remains a subjective assessment and in children can be liable to measurement error especially if the child is uncooperative. Devices to measure respiratory rate exist but many provide only an estimate of respiratory rate due to the associated methodological complexities. Some devices are used within the intensive care, post-operative or more specialised investigatory settings none however have made their way into the everyday clinical setting. A non-contact device may be better tolerated in children and not cause undue stress distorting the measurement. Further validation and adaption to the acute clinical setting is needed before such devices can supersede current methods

Chest Expansion Measurement in 3-Dimension by Using Accelerometers

The chest expansion measurement is a part of physical therapy to track the progress of rehabilitation for checking the performance of lungs. The chest expansion mechanism moves like pump handle and bucket handle. These two movements cause the chest move many directions. For tracking the chest movement in each direction, the MEMS accelerometers are used to measure acceleration in each axis. Consequently, acceleration is converted to displacement by double integration. The acceleration from accelerometer in each axis is affected from the earth gravity force. Thus, rotation matrix is used for compensating the earth gravity force. It can track the acceleration vectors while rotating. The known movement from robot is simulated similar the chest expansion. 60 sets of accelerometer data were collected from robot demonstration and were analyzed for testing the accuracy of sensor and algorithm. For the highest expansion, the chest expansion measurement must be performed while doing deep breathe inhale and exhale. The deep breath signal is a low frequency and there is high frequency noise. Therefore, a low-pass filter was used for eliminating high frequency noise. The accelerometers and VICON’s markers were placed together on the body. The displacement results from accelerometers were compared with the displacement of VICON motion analysis system to find the accuracy of our purposed device. The average error of 20 sets of acceleration data from accelerometers which referred with VICON motion analysis is 7.195±4.361 mm. Accelerometer result trend follows VICON motion analysis and corresponds to each other

Estimation of respiration rate and sleeping position using a wearable accelerometer

The 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBS Virtual Academy, 20-24 July 2020Wearable inertial sensors offer the possibility to monitor sleeping position and respiration rate during sleep, enabling a comfortable and low-cost method to remotely monitor patients. Novel methods to estimate respiration rate and position during sleep using accelerometer data are presented, with algorithm performance examined for two sensor locations, and accelerometer-derived respiration rate compared across sleeping positions. Eleven participants (9 male; aged: 47.82±14.14 years; BMI 30.9±5.27 kg/m 2 ; AHI 5.77±4.18) undergoing a scheduled clinical polysomnography (PSG) wore a tri-axial accelerometer on their chest and upper abdomen. PSG cannula flow and position data were used as benchmark data for respiration rate (breaths per minute, bpm) and position. Sleeping position was classified using logistic regression, with features derived from filtered acceleration and orientation. Accelerometer-derived respiration rate was estimated for 30 s epochs using an adaptive peak detection algorithm which combined filtered acceleration and orientation data to identify individual breaths. Sensor-derived and PSG respiration rates were then compared. Mean absolute error (MAE) in respiration rate did not vary between sensor locations (abdomen: 1.67±0.37 bpm; chest: 1.89±0.53 bpm; p=0.52), while reduced MAE was observed when participants lay on their side (1.58±0.54 bpm) compared to supine (2.43±0.95 bpm), p<; 0.01. MAE was less than 2 bpm for 83.6% of all 30 s windows across all subjects. The position classifier distinguished supine and left/right with a ROC AUC of 0.87, and between left and right with a ROC AUC of 0.94. The proposed methods may enable a low-cost solution for in-home, long term sleeping posture and respiration monitoring.European Research CouncilScience Foundation IrelandInsight Research Centre2020-10-06 JG: PDF replaced with correct versio

Wearable technology: role in respiratory health and disease

In the future, diagnostic devices will be able to monitor a patient's physiological or biochemical parameters continuously, under natural physiological conditions and in any environment through wearable biomedical sensors. Together with apps that capture and interpret data, and integrated enterprise and cloud data repositories, the networks of wearable devices and body area networks will constitute the healthcare's Internet of Things. In this review, four main areas of interest for respiratory healthcare are described: pulse oximetry, pulmonary ventilation, activity tracking and air quality assessment. Although several issues still need to be solved, smart wearable technologies will provide unique opportunities for the future or personalised respiratory medicine

Виявлення шаблонів дихання людини за допомогою глибоких згорткових нейронних мереж

The method for real­time recognition of respiration types (patterns) of a patient to monitor his conditions and threats to his health, which is a special case of the problem of human activities recognition (HAR), was proposed. The method is based on application of deep machine learning using the convolution neural network (CNN) to classify the chest motion speed. It was shown that the decisions, taken in this case, are coordinated with mobile medicine technology (mHealth) of the use of body sensors and smartphones for signals processing, but CNN offer important additional opportunities at improving the quality of processing the accelerometer­sensor signals in the presence of interfering signals (noise) from other sources and instrumental errors of devices. We proposed the method of transformation of one­dimensional (1D) accelerometer signals into two­dimensional (2D) graphic images, processed using CNN with multiple processing layers, due to which the accuracy of determining the respiration pattern in various situations for different physical states of patients increases compared with the case when two­dimensional accelerometer signal conversion is not used. In this case, an increase in accuracy (or quality) of determining different types of respiration occurs while maintaining a sufficient speed of performing procedures of the planned method, which allows classification of respiration types in real time. This technique was tested as a component of the Body Sensor Network (BSN) and high accuracy (88 %) of determining the patient’s respiration state was established, which in combination with contextual data, obtained from other BSN nodes, makes it possible to determine the patient’s state and a signal of the aggravation of their respiratory diseases.Предложенметодраспознавания в реальном времени типов (шаблонов) дыхание пациента с целью мониторинга его состояния и угроз для его здоровья, что является частным случаем проблемы распознавания человеческих активностей (HAR). Метод основанный на применении глубинного машинного обучения с помощью сверточной нейронной сети (CNN) для классификации скорости движения его грудной клетки. Показано, что принятые при этом решения согласовываются с технологией мобильной медицины (mHealth) использования нательных датчиков и смартфонов для обработки их сигналов, но CNN открывают важные дополнительные возможности при повышении качества обработки сигналов датчика-акселерометра в условиях наличия мешающих сигналов (шумов) от других источников и инструментальных погрешностей устройств. Предложен способ преобразования одномерных сигналов (1d) акселерометра в двумерные (2d) графические изображения, обрабатываются с помощью CNN со несколькими обрабатывающими слоями, благодаря чему точность определения шаблона дыхания в различных ситуациях для различных физических состояний пациентов возрастает по сравнению со случаем, когда двумерные преобразования сигналов акселерометра не употребляются. При этом рост точности (или качества) определения различных типов дыхания происходит при сохранении достаточной скорости выполнения процедур запланированного метода, что позволяет проводить классификацию типов дыхания в реальном времени. Данную методику было испытано в качестве компонента Body Sensor Network (BSN) и установлено высокую точность (88 %) определения состояния дыхания пациента, что в сочетании с данными контекста, полученными из других узлов BSN, позволяет определять состояния пациентов и сигнализировать об обострения их респираторных болезнейЗапропоновано метод розпізнавання в реальному часі типів (шаблонів) дихання пацієнта з ціллю моніторингу його стану і загроз для здоров’я, що є частковим випадком проблеми розпізнавання людських активностей (HAR). Метод заснований на застосуванні глибинного машинного навчання з допомогою згорткової нейронної мережі (CNN) для класифікації швидкості руху його грудної клітки. Показано, що прийняті при цьому рішення узгоджуються з технологією мобільної медицини (mHealth) з використання натільних датчиків і смартфонів для оброблення їх сигналів в якості обчислювальних edge-вузлів, але CNN відкривають важливі додаткові можливості з підвищенні якості оброблення сигналів датчика-акселерометра в умовах наявності перешкоджаючих сигналів (шумів) від інших джерел та інструментальних похибок пристрою. Вхідні сигнали попередньо нормалізується щодо осі обертання, щоб зменшити вплив шуму на результати, оскільки акселерометр вимірює гравітаційне прискорення (g) і лінійне прискорення (a). Запропоновано спосіб перетворення одновимірних сигналів (1d) акселерометра в двовимірні (2d) графічні зображення, які оброблюються за допомогою CNN із декількома обробними шарами, завдяки чому точність визначення шаблону дихання в різних ситуаціях для різних фізичних станів пацієнтів зростає в порівнянні з випадком, коли двовимірні перетворення сигналів акселерометра не вживаються. При цьому зростання точності (або якості) визначення різних типів дихання відбувається при збереженні достатньої швидкості процедур запланованого методу, що дозволяє проводити класифікацію типів дихання в реальному часі. Дану методику було випробувано в якості компоненту Body Sensor Network (BSN) і встановлено високу точність (88%) визначення стану дихання пацієнта, що в поєднанні з даними контексту, отриманими з інших вузлів BSN, дозволяє визначати стани пацієнтів і передбачати загострення їх респіраторних хворo

Breathing Monitoring and Pattern Recognition with Wearable Sensors

This chapter introduces the anatomy and physiology of the respiratory system, and the reasons for measuring breathing events, particularly, using wearable sensors. Respiratory monitoring is vital including detection of sleep apnea and measurement of respiratory rate. The automatic detection of breathing patterns is equally important in other respiratory rehabilitation therapies, for example, magnetic resonance exams for respiratory triggered imaging, and synchronized functional electrical stimulation. In this context, the goal of many research groups is to create wearable devices able to monitor breathing activity continuously, under natural physiological conditions in different environments. Therefore, wearable sensors that have been used recently as well as the main signal processing methods for breathing analysis are discussed. The following sensor technologies are presented: acoustic, resistive, inductive, humidity, acceleration, pressure, electromyography, impedance, and infrared. New technologies open the door to future methods of noninvasive breathing analysis using wearable sensors associated with machine learning techniques for pattern detection

ESTIMATING THE VALUE OF THE VOLUME FROM ACCELERATION ON THE DIAPHRAGM MOVEMENTS DURING BREATHING

Information related to the movements of the diaphragm is very important and it is used in the detection of some respiratory diseases, which are common in all over the world, such as chronic obstructive pulmonary disease (COPD), asthma, and bronchitis. This article describes a practical method for estimating the value of the volume using the acceleration information on the diaphragm movements. The main goal of this paper is to develop a data collection system that measures acceleration values and to estimate the acceleration-volume relationship by examining the obtained data. Thus, two important parameters (TVC and FVC) in the diagnosis of COPD are measured in a more practical way. In the present case, these two parameters can be measured in a hospital environment by an expensive medical device called “spirometry”. For this purpose, our device is placed on the abdomen region of the patient, diaphragm movements are examined and values of the volume are estimated from acceleration data (total 416 accelerometric data). Measurements are performed simultaneously by the spirometry and the developed device. Pearson coefficient (p<0.01) is calculated to determine the correlation between the measured data by using devices. Results show us that there is a positive correlation between measured values of the two devices (accelerometric and spirometric). It can be concluded that there is an acceptable correlation (91.4%) between accelerometric and spirometric results and the estimate error margin is quite low (0.08). In this respect, this study is considered to be an alternative method to spirometry tests, which is used in diagnosing COPD

Respiration Rate Estimation Based on Independent Component Analysis of Accelerometer Data: Pilot Single-Arm Intervention Study

Background: As the mobile environment has developed recently, there have been studies on continuous respiration monitoring. However, it is not easy for general users to access the sensors typically used to measure respiration. There is also random noise caused by various environmental variables when respiration is measured using noncontact methods in a mobile environment. Objective: In this study, we aimed to estimate the respiration rate using an accelerometer sensor in a smartphone. Methods: First, data were acquired from an accelerometer sensor by a smartphone, which can easily be accessed by the general public. Second, an independent component was extracted to calibrate the three-axis accelerometer. Lastly, the respiration rate was estimated using quefrency selection reflecting the harmonic component because respiration has regular patterns. Results: From April 2018, we enrolled 30 male participants. When the independent component and quefrency selection were used to estimate the respiration rate, the correlation with respiration acquired from a chest belt was 0.7. The statistical results of the Wilcoxon signed-rank test were used to determine whether the differences in the respiration counts acquired from the chest belt and from the accelerometer sensor were significant. The P value of the difference in the respiration counts acquired from the two sensors was .27, which was not significant. This indicates that the number of respiration counts measured using the accelerometer sensor was not different from that measured using the chest belt. The Bland-Altman results indicated that the mean difference was 0.43, with less than one breath per minute, and that the respiration rate was at the 95% limits of agreement. Conclusions: There was no relevant difference in the respiration rate measured using a chest belt and that measured using an accelerometer sensor. The accelerometer sensor approach could solve the problems related to the inconvenience of chest belt attachment and the settings. It could be used to detect sleep apnea through constant respiration rate estimation in an internet-of-things environment.ope

A deep-learning approach to assess respiratory effort with a chest-worn accelerometer during sleep

Objective: The objective is to develop a new deep learning method for the estimation of respiratory effort from a chest-worn accelerometer during sleep. We evaluate performance, compare it against a state-of-the art method, and assess whether it can differentiate between sleep stages. Methods: In 146 participants undergoing overnight polysomnography data were collected from an accelerometer worn on the chest. The study data were partitioned into train, validation, and holdout (test) sets. We used the train and validation sets to generate and train a convolutional neural network and performed model selection respectively, while we used the holdout set (72 participants) to evaluate performance. Results: A convolutional neural network with 9 layers and 207,855 parameters was automatically generated and trained. The neural network significantly outperformed the best performing conventional method, based on Principal Component Analysis; it reduced the Mean Squared Error from 0.26 to 0.11 and it also performed better in the detection of breaths (Sensitivity 98.4 %, PPV 98.2 %). In addition, the neural network exposed significant differences in characteristics of respiratory effort between sleep stages (p &lt; 0.001). Conclusion: The deep learning method predicts respiratory effort with low error and is sensitive and precise in the detection of breaths. In addition, it reproduces differences between sleep stages, which may enable automatic sleep staging, using just a chest-worn accelerometer.</p