scholarly journals 271 Sleep staging performance of a signal-agnostic cloud-based real-time sleep analytics platform

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A108-A109
Author(s):  
Zhao Siting ◽  
Kishan Kishan ◽  
Amiya Patanaik
Keyword(s):  
Real Time ◽  
Remote Monitoring ◽  
Sleep Medicine ◽  
Physiological Signals ◽  
Small Subset ◽  
Sleep Staging ◽  
Apnea Test ◽  
Instantaneous Heart Rate ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

Abstract Introduction The coronavirus pandemic has brought unprecedented changes to the health care system, including sleep medicine. Remote monitoring and telemedicine played a significant role in this shift. We anticipate these changes to continue in the future with internet-connected wearables (ICWs) playing an important role in measuring and managing sleep remotely. As these ICWs measures a small subset of signals traditionally measured during polysomnography (PSG), manual sleep staging becomes non-trivial and sometimes impossible. The ability to do accurate and reliable automatic sleep staging using different modalities of physiological signals remotely is becoming ever more important. Methods The current work seeks to quantify the sleep staging performance of Z3Score-Neo (https://z3score.com, Neurobit Technologies, Singapore), a signal agnostic, cloud-based real-time sleep analytics platform. We tested its staging performance on the CINC open dataset with N=994 subjects using various combinations of signals including Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG), and Instantaneous Heart Rate (IHR) derived from Electrocardiogram (ECG). The staging was compared against manual scoring based on PSG. For IHR based staging, N1 and N2 were combined. Results We achieved substantial agreement (all Cohen’s Kappa > 0.7) between automatic and manual staging using various combinations of EEG, EOG and EMG channels with accuracies varying between 81.76% (two central EEGs, one EOG, one EMG), 79.31% (EEG+EOG), 78.73% (EEG only) and 78.09% (one EOG). We achieved moderate agreement (accuracy: 72.8% κ=0.54) with IHR derived from ECG. Conclusion Our results demonstrated the accuracy of a cloud-based sleep analytics platform on an open dataset, using various combinations of ecologically valid physiological signals. EOG and EMG channels can be easily self-administered using sticker-based electrodes and can be added to existing home sleep apnea test (HSAT) kits significantly improving their utility. ICWs are already capable of accurately measuring EEG/EOG (Muse, InteraXon Inc., Toronto, Canada; Dreem band, Dreem, USA) and IHR derived from ECG (Movesense, Suunto, Finland) or photoplethysmogram (Oura Ring, Oura Health Oy, Finland) or through non-contact ballistocardiogram/radio-based measurements (Dozee, Turtle Shell Technologies, India; Sleepiz, Sleepiz AG, Switzerland). Therefore, a well-validated cloud-based staging platform solves a major technological hurdle towards the proliferation of remote monitoring and telehealth in sleep medicine. Support (if any):

scholarly journals Simultaneous Cardiac and Neurological Monitoring to Assess Chemical Exposures and Drug Toxicity in Xenopus Laevis

2021 ◽  
Author(s):  
Xing Xia ◽  
Jimmy Zhang ◽  
Manoj Vishwanath ◽  
Sadaf Sarafan ◽  
Ramses Seferino Trigo Torres ◽  
...  
Keyword(s):  
Xenopus Laevis ◽  
Real Time ◽  
Microelectrode Array ◽  
Drug Effects ◽  
Chemical Exposure ◽  
Toxicity Monitoring ◽  
Interictal Eeg ◽  
Signal Distortions ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

AbstractSimultaneous monitoring of electrocardiogram (ECG) and electroencephalogram (EEG) under chemical exposure requires innovative engineering techniques that can capture minute physiological changes in studied animal models. However, this is often administered with a bulky system that may cause signal distortions and discomfort for animals. We develop an integrated bioelectronic sensing system to provide simultaneous ECG and EEG assessment in real-time under chemical exposure for Xenopus laevis. The microelectrode array (MEA) membrane with integrated ECG and EEG sensing offers an opportunity to achieve multichannel noninvasive electrophysiological monitoring with favorable dimensions and spatial resolution. To validate the performance of our system, we assessed the ECG and EEG of Xenopus under exposure of Pentylenetetrazol (PTZ), an epilepsy-inducing drug. Effects of PTZ were detected with clear ECG and EEG alterations, including frequent ictal and interictal EEG events, 30 dB average EEG amplitude elevations, abnormal ECG morphology, and heart rate changes. Overall, our Xenopus-based real-time electrophysiology monitoring system holds high potential for many applications in drug screening and remote environmental toxicity monitoring.

scholarly journals A Review of Emotion Recognition Based on EEG using DEAP Dataset

2021 ◽  
pp. 298-303
Author(s):  
Rama Chaudhary ◽  
Ram Avtar Jaswal
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Physiological Signals ◽  
Emotional States ◽  
Modern Time ◽  
Human Machine Interaction ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg ◽  
Machine Interaction

In modern time, the human-machine interaction technology has been developed so much for recognizing human emotional states depending on physiological signals. The emotional states of human can be recognized by using facial expressions, but sometimes it doesn’t give accurate results. For example, if we detect the accuracy of facial expression of sad person, then it will not give fully satisfied result because sad expression also include frustration, irritation, anger, etc. therefore, it will not be possible to determine the particular expression. Therefore, emotion recognition using Electroencephalogram (EEG), Electrocardiogram (ECG) has gained so much attraction because these are based on brain and heart signals respectively. So, after analyzing all the factors, it is decided to recognize emotional states based on EEG using DEAP Dataset. So that, the better accuracy can be achieved.

scholarly journals Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4708 ◽  
Author(s):  
Javier Tejedor ◽  
Constantino A. García ◽  
David G. Márquez ◽  
Rafael Raya ◽  
Abraham Otero
Keyword(s):  
Blood Pressure ◽  
Markov Models ◽  
Simultaneous Detection ◽  
Physiological Signals ◽  
Signal Quality ◽  
Bayesian Techniques ◽  
Signal Normalization ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg ◽  
Delay Correction

This paper presents a review of the techniques found in the literature that aim to achieve a robust heartbeat detection from fusing multi-modal physiological signals (e.g., electrocardiogram (ECG), blood pressure (BP), artificial blood pressure (ABP), stroke volume (SV), photoplethysmogram (PPG), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG), among others). Techniques typically employ ECG, BP, and ABP, of which usage has been shown to obtain the best performance under challenging conditions. SV, PPG, EMG, EEG, and EOG signals can help increase performance when included within the fusion. Filtering, signal normalization, and resampling are common preprocessing steps. Delay correction between the heartbeats obtained over some of the physiological signals must also be considered, and signal-quality assessment to retain the best signal/s must be considered as well. Fusion is usually accomplished by exploiting regularities in the RR intervals; by selecting the most promising signal for the detection at every moment; by a voting process; or by performing simultaneous detection and fusion using Bayesian techniques, hidden Markov models, or neural networks. Based on the results of the review, guidelines to facilitate future comparison of the performance of the different proposals are given and promising future lines of research are pointed out.

scholarly journals Deep learning for automated sleep staging using instantaneous heart rate

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Niranjan Sridhar ◽  
Ali Shoeb ◽  
Philip Stephens ◽  
Alaa Kharbouch ◽  
David Ben Shimol ◽  
...  
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Massachusetts General Hospital ◽  
Sleep Stage ◽  
Health Study ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Instantaneous Heart Rate ◽  
And Gender ◽  
Electrocardiogram Ecg

Abstract Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide variety of both medical and consumer-grade devices. In this study, we applied deep learning methods to create an algorithm for automated sleep stage scoring using the instantaneous heart rate (IHR) time series extracted from the electrocardiogram (ECG). We trained and validated an algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA). The algorithm has an overall performance of 0.77 accuracy and 0.66 kappa against the reference stages on a held-out portion of the SHHS dataset for classifying every 30 s of sleep into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM). Moreover, we demonstrate that the algorithm generalizes well to an independent dataset of 993 subjects labeled by American Academy of Sleep Medicine (AASM) licensed clinical staff at Massachusetts General Hospital that was not used for training or validation. Finally, we demonstrate that the stages predicted by our algorithm can reproduce previous clinical studies correlating sleep stages with comorbidities such as sleep apnea and hypertension as well as demographics such as age and gender.

scholarly journals A Remote Monitoring System of Logistics Carrier Based on Wireless Sensor Network

2018 ◽  
Vol 14 (01) ◽  
pp. 4
Author(s):  
Wang Weidong
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Monitoring Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Remote Monitoring System ◽  
Monitoring Center

To improve the efficiency of the remote monitoring system for logistics transportation, we proposed a remote monitoring system based on wireless sensor network and GPRS communication. The system can collect information from the wireless sensor network and transmit the information to the ZigBee interpreter. The monitoring system mainly includes the following parts: Car terminal, GPRS transmission network and monitoring center. Car terminal mainly consists by the Zigbee microcontroller and peripherals, wireless sensor nodes, RFID reader, GPRS wireless communication module composed of a micro-wireless monitoring network. The information collected by the sensor communicates through the GPRS and the monitoring center on the network coordinator, sends the collected information to the monitoring center, and the monitoring center realizes the information of the logistics vehicle in real time. The system has high applicability, meets the design requirements in the real-time acquisition and information transmission of the information of the logistics transport vehicles and goods, and realizes the function of remote monitoring.

Joy in the Workplace: A Scoping Review of Positive Emotion and Electroencephalogram

2020 ◽  
Vol 64 (1) ◽  
pp. 1493-1497
Author(s):  
Yaqoub Yusuf ◽  
Jodi Boutte’ ◽  
Asante’ Lloyd ◽  
Emma Fortune ◽  
Renaldo C. Blocker
Keyword(s):  
Real Time ◽  
Scoping Review ◽  
Positive Emotion ◽  
Positive Emotions ◽  
Wearable Sensors ◽  
Recent Advances ◽  
Electrode Systems ◽  
Electroencephalogram Eeg ◽  
Occupational Settings

A workplace that is a conduit for positive emotions can be important to employees retention and can contribute optimal levels of productivity. Validated tools for examining emotions are primarily subjective and retrospective in nature. Recent advances in technology have led to more novel and passive ways of measuring emotions. Wearable sensors, such as electroencephalogram (EEG), are being explored to assess cognitive and physical burdens objectively and in real-time. Therefore, there exists a need to investigate and validate the use of EEG to examine emotions objectively and in real-time. In this paper, we conducted a scoping review of EEG to measure positive emotions and/or indicators of joy in the workplace. Our review results in 22 articles that employ EEG to study joy in occupational settings. Three major themes identified in the analysis include (1) EEG for symptoms detection and outcomes, (2) Populations studied using EEG, and (3) EEG electrode systems.

FPGA-based real-time remote monitoring system

2005 ◽  
Vol 49 (2) ◽  
pp. 272-285 ◽  
Author(s):  
Joshua Mendoza-Jasso ◽  
Gerardo Ornelas-Vargas ◽  
Rodrigo Castañeda-Miranda ◽  
Eusebio Ventura-Ramos ◽  
Alfredo Zepeda-Garrido ◽  
...  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Remote Monitoring System

Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation

2021 ◽  
pp. 1-11
Author(s):  
Najmeh Pakniyat ◽  
Mohammad Hossein Babini ◽  
Vladimir V. Kulish ◽  
Hamidreza Namazi
Keyword(s):  
Time Series ◽  
Biomedical Science ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Ecg Signals ◽  
Olfactory Stimuli ◽  
Electrocardiogram Ecg ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
The Brain

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart’s activity, a relationship should exist among their activities. OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18–22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION: We conclude that heart and brain activities are related.

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