scholarly journals A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study

10.2196/18297 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e18297
Author(s):  
Ibrahim Sadek ◽  
Terry Tan Soon Heng ◽  
Edwin Seet ◽  
Bessam Abdulrazak
Keyword(s):  
Sleep Apnea ◽  
Event Detection ◽  
Vital Sign ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Fiber Optic Sensor ◽  
Sleep Study ◽  
Lack Of Physicians ◽  
Sign Detection ◽  
Apnea Detection

Background At present, there is an increased demand for accurate and personalized patient monitoring because of the various challenges facing health care systems. For instance, rising costs and lack of physicians are two serious problems affecting the patient’s care. Nonintrusive monitoring of vital signs is a potential solution to close current gaps in patient monitoring. As an example, bed-embedded ballistocardiogram (BCG) sensors can help physicians identify cardiac arrhythmia and obstructive sleep apnea (OSA) nonintrusively without interfering with the patient’s everyday activities. Detecting OSA using BCG sensors is gaining popularity among researchers because of its simple installation and accessibility, that is, their nonwearable nature. In the field of nonintrusive vital sign monitoring, a microbend fiber optic sensor (MFOS), among other sensors, has proven to be suitable. Nevertheless, few studies have examined apnea detection. Objective This study aims to assess the capabilities of an MFOS for nonintrusive vital signs and sleep apnea detection during an in-lab sleep study. Data were collected from patients with sleep apnea in the sleep laboratory at Khoo Teck Puat Hospital. Methods In total, 10 participants underwent full polysomnography (PSG), and the MFOS was placed under the patient’s mattress for BCG data collection. The apneic event detection algorithm was evaluated against the manually scored events obtained from the PSG study on a minute-by-minute basis. Furthermore, normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), and mean absolute percentage error (MAPE) were employed to evaluate the sensor capabilities for vital sign detection, comprising heart rate (HR) and respiratory rate (RR). Vital signs were evaluated based on a 30-second time window, with an overlap of 15 seconds. In this study, electrocardiogram and thoracic effort signals were used as references to estimate the performance of the proposed vital sign detection algorithms. Results For the 10 patients recruited for the study, the proposed system achieved reasonable results compared with PSG for sleep apnea detection, such as an accuracy of 49.96% (SD 6.39), a sensitivity of 57.07% (SD 12.63), and a specificity of 45.26% (SD 9.51). In addition, the system achieved close results for HR and RR estimation, such as an NMAE of 5.42% (SD 0.57), an NRMSE of 6.54% (SD 0.56), and an MAPE of 5.41% (SD 0.58) for HR, whereas an NMAE of 11.42% (SD 2.62), an NRMSE of 13.85% (SD 2.78), and an MAPE of 11.60% (SD 2.84) for RR. Conclusions Overall, the recommended system produced reasonably good results for apneic event detection, considering the fact that we are using a single-channel BCG sensor. Conversely, satisfactory results were obtained for vital sign detection when compared with the PSG outcomes. These results provide preliminary support for the potential use of the MFOS for sleep apnea detection.

scholarly journals A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study (Preprint)

2020 ◽  
Author(s):  
Ibrahim Sadek ◽  
Terry Tan Soon Heng ◽  
Edwin Seet ◽  
Bessam Abdulrazak
Keyword(s):  
Sleep Apnea ◽  
Event Detection ◽  
Vital Sign ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Fiber Optic Sensor ◽  
Sleep Study ◽  
Lack Of Physicians ◽  
Sign Detection ◽  
Apnea Detection

BACKGROUND At present, there is an increased demand for accurate and personalized patient monitoring because of the various challenges facing health care systems. For instance, rising costs and lack of physicians are two serious problems affecting the patient’s care. Nonintrusive monitoring of vital signs is a potential solution to close current gaps in patient monitoring. As an example, bed-embedded ballistocardiogram (BCG) sensors can help physicians identify cardiac arrhythmia and obstructive sleep apnea (OSA) nonintrusively without interfering with the patient’s everyday activities. Detecting OSA using BCG sensors is gaining popularity among researchers because of its simple installation and accessibility, that is, their nonwearable nature. In the field of nonintrusive vital sign monitoring, a microbend fiber optic sensor (MFOS), among other sensors, has proven to be suitable. Nevertheless, few studies have examined apnea detection. OBJECTIVE This study aims to assess the capabilities of an MFOS for nonintrusive vital signs and sleep apnea detection during an in-lab sleep study. Data were collected from patients with sleep apnea in the sleep laboratory at Khoo Teck Puat Hospital. METHODS In total, 10 participants underwent full polysomnography (PSG), and the MFOS was placed under the patient’s mattress for BCG data collection. The apneic event detection algorithm was evaluated against the manually scored events obtained from the PSG study on a minute-by-minute basis. Furthermore, normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), and mean absolute percentage error (MAPE) were employed to evaluate the sensor capabilities for vital sign detection, comprising heart rate (HR) and respiratory rate (RR). Vital signs were evaluated based on a 30-second time window, with an overlap of 15 seconds. In this study, electrocardiogram and thoracic effort signals were used as references to estimate the performance of the proposed vital sign detection algorithms. RESULTS For the 10 patients recruited for the study, the proposed system achieved reasonable results compared with PSG for sleep apnea detection, such as an accuracy of 49.96% (SD 6.39), a sensitivity of 57.07% (SD 12.63), and a specificity of 45.26% (SD 9.51). In addition, the system achieved close results for HR and RR estimation, such as an NMAE of 5.42% (SD 0.57), an NRMSE of 6.54% (SD 0.56), and an MAPE of 5.41% (SD 0.58) for HR, whereas an NMAE of 11.42% (SD 2.62), an NRMSE of 13.85% (SD 2.78), and an MAPE of 11.60% (SD 2.84) for RR. CONCLUSIONS Overall, the recommended system produced reasonably good results for apneic event detection, considering the fact that we are using a single-channel BCG sensor. Conversely, satisfactory results were obtained for vital sign detection when compared with the PSG outcomes. These results provide preliminary support for the potential use of the MFOS for sleep apnea detection. CLINICALTRIAL

scholarly journals Multiple Patient Monitoring in High Dependency Units: A Field Study

2019 ◽  
Vol 63 (1) ◽  
pp. 669-669
Author(s):  
Marie-Lys F. A. Deschamps ◽  
Penelope M. Sanderson
Keyword(s):  
Critical Care ◽  
Simulation Study ◽  
Vital Sign ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Well Being ◽  
Alarm Fatigue ◽  
Critical Care Units ◽  
High Dependency ◽  
Australian Hospital

Much of the focus related to alarm fatigue has been directed towards reducing the number of alarms associated with vital sign monitoring. However, recent fieldwork conducted in four high dependency and critical care units of an Australian hospital suggests that the most problematic alarms were often unassociated with vital signs, such as IV pumps and mattress alarms. Many nurses indicated that they like alarms, even when false, because they support awareness of their patients’ well-being. Results of the fieldwork are guiding the design of a simulation study investigating clinical monitoring displays.

scholarly journals Sleep apnea detection using deep learning

2019 ◽  
Vol 13 (4) ◽  
pp. 261-266 ◽  
Author(s):  
Hnin Thiri Chaw ◽  
Sinchai Kamolphiwong ◽  
Krongthong Wongsritrang
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Cost Effective ◽  
Deep Convolutional Neural Network ◽  
Sleep Study ◽  
Proposed Model ◽  
Apnea Detection ◽  
Sleep Recording

Sleep apnea is the cessation of airflow at least 10 seconds and it is the type of breathing disorder in which breathing stops at the time of sleeping. The proposed model uses type 4 sleep study which focuses more on portability and the reduction of the signals. The main limitations of type 1 full night polysomnography are time consuming and it requires much space for sleep recording such as sleep lab comparing to type 4 sleep studies. The detection of sleep apnea using deep convolutional neural network model based on SPO2 sensor is the valid alternative for efficient polysomnography and it is portable and cost effective. The total number of samples from SPO2 sensors of 50 patients that is used in this study is 190,000. The performance of the overall accuracy of sleep apnea detection is 91.3085% with the loss rate of 2.3 using cross entropy cost function using deep convolutional neural network.

scholarly journals The Mechanisms Responsible for Improved Information Transfer in Avatar-Based Patient Monitoring: Multicenter Comparative Eye-Tracking Study (Preprint)

2019 ◽  
Author(s):  
David Werner Tscholl ◽  
Julian Rössler ◽  
Lucas Handschin ◽  
Burkhardt Seifert ◽  
Donat R Spahn ◽  
...  
Keyword(s):  
Eye Tracking ◽  
Oxygen Saturation ◽  
Vital Sign ◽  
Skin Color ◽  
Information Transfer ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Virtual Patient ◽  
Anesthesia Providers ◽  
Phi Coefficient

BACKGROUND Patient monitoring is central to perioperative and intensive care patient safety. Current state-of-the-art monitors display vital signs as numbers and waveforms. Visual Patient technology creates an easy-to-interpret virtual patient avatar model that displays vital sign information as it would look in a real-life patient (eg, avatar changes skin color from healthy to cyanotic depending on oxygen saturation). In previous studies, anesthesia providers using Visual Patient perceived more vital signs during short glances than with conventional monitoring. OBJECTIVE We aimed to study the deeper mechanisms underlying information perception in conventional and avatar-based monitoring. METHODS In this prospective, multicenter study with a within-subject design, we showed 32 anesthesia providers four 3- and 10-second monitoring scenarios alternatingly as either routine conventional or avatar-based in random sequence. All participants observed the same scenarios with both technologies and reported the vital sign status after each scenario. Using eye-tracking, we evaluated which vital signs the participants had visually fixated (ie, could have potentially read and perceived) during a scenario. We compared the frequencies and durations of participants’ visual fixations of vital signs between the two technologies. RESULTS Participants visually fixated more vital signs per scenario in avatar-based monitoring (median 10, IQR 9-11 versus median 6, IQR 4-8, <i>P</i>&lt;.001; median of differences=3, 95% CI 3-4). In multivariable linear regression, monitoring technology (conventional versus avatar-based monitoring, difference=−3.3, <i>P</i>&lt;.001) was an independent predictor of the number of visually fixated vital signs. The difference was less prominent in the longer (10-second) scenarios (difference=−1.5, <i>P</i>=.04). Study center, profession, gender, and scenario order did not influence the differences between methods. In all four scenarios, the participants visually fixated 9 of 11 vital signs statistically significantly longer using the avatar (all <i>P</i>&lt;.001). Four critical vital signs (pulse rate, blood pressure, oxygen saturation, and respiratory rate) were visible almost the entire time of a scenario with the avatar; these were only visible for fractions of the observations with conventional monitoring. Visual fixation of a certain vital sign was associated with the correct perception of that vital sign in both technologies (avatar: phi coefficient=0.358; conventional monitoring: phi coefficient=0.515, both <i>P</i>&lt;.001). CONCLUSIONS This eye-tracking study uncovered that the way the avatar-based technology integrates the vital sign information into a virtual patient model enabled parallel perception of multiple vital signs and was responsible for the improved information transfer. For example, a single look at the avatar’s body can provide information about: pulse rate (pulsation frequency), blood pressure (pulsation intensity), oxygen saturation (skin color), neuromuscular relaxation (extremities limp or stiff), and body temperature (heatwaves or ice crystals). This study adds a new and higher level of empirical evidence about why avatar-based monitoring improves vital sign perception compared with conventional monitoring.

scholarly journals Optimal Central Frequency for Non-Contact Vital Sign Detection Using Monocycle UWB Radar

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2916 ◽  
Author(s):  
Artit Rittiplang ◽  
Pattarapong Phasukkit ◽  
Teerapong Orankitanun
Keyword(s):  
Vital Sign ◽  
Vital Signs ◽  
Ultra Wideband ◽  
Central Frequency ◽  
Additional Information ◽  
Advantages And Disadvantages ◽  
Uwb Radar ◽  
Sign Detection ◽  
Life Detection ◽  
New Formula

Ultra-wideband (UWB) radar has become a critical remote-sensing tool for non-contact vital sign detection such as emergency rescues, securities, and biomedicines. Theoretically, the magnitude of the received reflected signal is dependent on the central frequency of mono-pulse waveform used as the transmitted signal. The research is based on the hypothesis that the stronger the received reflected signals, the greater the detectability of life signals. In this paper, we derive a new formula to compute the optimal central frequency to obtain as maximum received reflect signal as possible over the frequency up to the lower range of Ka-band. The proposed formula can be applicable in the optimization of hardware for UWB life detection and non-contact monitoring of vital signs. Furthermore, the vital sign detection results obtained by the UWB radar over a range of central frequency have been compared to those of the former continuous (CW) radar to provide additional information regarding the advantages and disadvantages of each radar.

scholarly journals The Mechanisms Responsible for Improved Information Transfer in Avatar-Based Patient Monitoring: Multicenter Comparative Eye-Tracking Study

10.2196/15070 ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. e15070 ◽  
Author(s):  
David Werner Tscholl ◽  
Julian Rössler ◽  
Lucas Handschin ◽  
Burkhardt Seifert ◽  
Donat R Spahn ◽  
...  
Keyword(s):  
Eye Tracking ◽  
Oxygen Saturation ◽  
Vital Sign ◽  
Skin Color ◽  
Information Transfer ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Virtual Patient ◽  
Anesthesia Providers ◽  
Phi Coefficient

Background Patient monitoring is central to perioperative and intensive care patient safety. Current state-of-the-art monitors display vital signs as numbers and waveforms. Visual Patient technology creates an easy-to-interpret virtual patient avatar model that displays vital sign information as it would look in a real-life patient (eg, avatar changes skin color from healthy to cyanotic depending on oxygen saturation). In previous studies, anesthesia providers using Visual Patient perceived more vital signs during short glances than with conventional monitoring. Objective We aimed to study the deeper mechanisms underlying information perception in conventional and avatar-based monitoring. Methods In this prospective, multicenter study with a within-subject design, we showed 32 anesthesia providers four 3- and 10-second monitoring scenarios alternatingly as either routine conventional or avatar-based in random sequence. All participants observed the same scenarios with both technologies and reported the vital sign status after each scenario. Using eye-tracking, we evaluated which vital signs the participants had visually fixated (ie, could have potentially read and perceived) during a scenario. We compared the frequencies and durations of participants’ visual fixations of vital signs between the two technologies. Results Participants visually fixated more vital signs per scenario in avatar-based monitoring (median 10, IQR 9-11 versus median 6, IQR 4-8, P<.001; median of differences=3, 95% CI 3-4). In multivariable linear regression, monitoring technology (conventional versus avatar-based monitoring, difference=−3.3, P<.001) was an independent predictor of the number of visually fixated vital signs. The difference was less prominent in the longer (10-second) scenarios (difference=−1.5, P=.04). Study center, profession, gender, and scenario order did not influence the differences between methods. In all four scenarios, the participants visually fixated 9 of 11 vital signs statistically significantly longer using the avatar (all P<.001). Four critical vital signs (pulse rate, blood pressure, oxygen saturation, and respiratory rate) were visible almost the entire time of a scenario with the avatar; these were only visible for fractions of the observations with conventional monitoring. Visual fixation of a certain vital sign was associated with the correct perception of that vital sign in both technologies (avatar: phi coefficient=0.358; conventional monitoring: phi coefficient=0.515, both P<.001). Conclusions This eye-tracking study uncovered that the way the avatar-based technology integrates the vital sign information into a virtual patient model enabled parallel perception of multiple vital signs and was responsible for the improved information transfer. For example, a single look at the avatar’s body can provide information about: pulse rate (pulsation frequency), blood pressure (pulsation intensity), oxygen saturation (skin color), neuromuscular relaxation (extremities limp or stiff), and body temperature (heatwaves or ice crystals). This study adds a new and higher level of empirical evidence about why avatar-based monitoring improves vital sign perception compared with conventional monitoring.

Implementation of a Cyber-Physical System Using Wireless Sensor Networks for Monitoring Patients

2015 ◽  
Vol 1 ◽  
pp. 26 ◽  
Author(s):  
Vo Que Son ◽  
Do Tan A
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Physical System ◽  
Patient Monitoring ◽  
High Reliability ◽  
Vital Signs ◽  
Wireless Sensor ◽  
Cyber Physical System ◽  
Self Administration ◽  
Hospital Units

Sensing, distributed computation and wireless communication are the essential building components of a Cyber-Physical System (CPS). Having many advantages such as mobility, low power, multi-hop routing, low latency, self-administration, utonomous data acquisition, and fault tolerance, Wireless Sensor Networks (WSNs) have gone beyond the scope of monitoring the environment and can be a way to support CPS. This paper presents the design, deployment, and empirical study of an eHealth system, which can remotely monitor vital signs from patients such as body temperature, blood pressure, SPO2, and heart rate. The primary contribution of this paper is the measurements of the proposed eHealth device that assesses the feasibility of WSNs for patient monitoring in hospitals in two aspects of communication and clinical sensing. Moreover, both simulation and experiment are used to investigate the performance of the design in many aspects such as networking reliability, sensing reliability, or end-to-end delay. The results show that the network achieved high reliability - nearly 97% while the sensing reliability of the vital signs can be obtained at approximately 98%. This indicates the feasibility and promise of using WSNs for continuous patient monitoring and clinical worsening detection in general hospital units.

Patient Monitoring System using Computer Vision for Emotional Recognition and Vital Signs Detection

2020 ◽  
Author(s):  
Ahmad Anwar Zainuddin ◽  
Sakthyvell Superamaniam ◽  
Andrea Christella Andrew ◽  
Ramanand Muraleedharan ◽  
John Rakshys ◽  
...  
Keyword(s):  
Computer Vision ◽  
Monitoring System ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Emotional Recognition ◽  
Patient Monitoring System

scholarly journals Using Under-trained Deep Ensembles to Learn Under Extreme Label Noise: A Case Study for Sleep Apnea Detection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Konstantinos Nikolaidis ◽  
Thomas Plagemann ◽  
Stein Kristiansen ◽  
Vera Goebel ◽  
Mohan Kankanhalli
Keyword(s):  
Sleep Apnea ◽  
Label Noise ◽  
Apnea Detection

scholarly journals Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 776
Author(s):  
Xiaohui Tao ◽  
Thanveer Basha Shaik ◽  
Niall Higgins ◽  
Raj Gururajan ◽  
Xujuan Zhou
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Radio Frequency Identification ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Remote Patient Monitoring ◽  
Rfid Technology ◽  
Rpm System ◽  
Frequency Identification ◽  
Remote Patient

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward.

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