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 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):

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.

A novel microfluidic microelectrode chip for a significantly enhanced monitoring of NPY-receptor activation in live mode

Lab on a Chip ◽  
2017 ◽  
Vol 17 (24) ◽  
pp. 4294-4302 ◽  
Author(s):  
Franziska D. Zitzmann ◽  
Heinz-Georg Jahnke ◽  
Felix Nitschke ◽  
Annette G. Beck-Sickinger ◽  
Bernd Abel ◽  
...  
Keyword(s):  
Real Time ◽  
Microfluidic Chip ◽  
Microelectrode Array ◽  
Fem Simulation ◽  
Receptor Activation ◽  
Living Cells ◽  
Real Time Monitoring ◽  
Non Invasive ◽  
Npy Receptor ◽  
Simulation Based

We present a FEM simulation based step-by-step development of a microelectrode array integrated into a microfluidic chip for the non-invasive real-time monitoring of living cells.

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.

scholarly journals Fourier analysis: the simplification of a complex waveform into simple component sine waves of different amplitudes and frequencies

2020 ◽  
pp. S102-S103
Author(s):  
AI Mamoojee
Keyword(s):  
Fourier Analysis ◽  
Fundamental Frequency ◽  
Simple Wave ◽  
Sine Wave ◽  
Frequency Wave ◽  
Invasive Blood Pressure ◽  
Time Frequency ◽  
Simple Component ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

Fourier analysis is the simplification of a complex waveform into simple component sine waves of different amplitudes and frequencies. A discussion on Fourier analysis necessitates reiteration of the physics of waves. A wave is a series of repeating disturbances that propagate in space and time. Frequency: the number of oscillations, or cycles per second. It is measured in Hertz and denoted as 1/time or s-1. Fundamental frequency: the lowest frequency wave in a series. It is also known as the first harmonic. Every other wave in the series is an exact multiple of the fundamental frequency. Harmonic: whole number multiples of the fundamental frequency. Amplitude: the maximum disturbance or displacement from zero caused by the wave. This is the height of the wave. Period: time to complete one oscillation. Wavelength: physical length of one complete cycle. This can be between two crests or two troughs. The higher the frequency, the shorter the wavelength. Velocity: frequency x wavelength. Phase: displacement of one wave compared to another, described as 0°–360°. A sine wave is a simple wave. It can be depicted as the path of a point travelling round a circle at a constant speed, defined by the equation ‘y = sinx’. Combining sine waves of different frequency, amplitude and phase can yield any waveform, and, conversely, any wave can be simplified into its component sine waves. Fourier analysis is a mathematical method of analysing a complex periodic waveform to find its constituent frequencies (as sine waves). Complex waveforms can be analysed, with very simple results. Usually, few sine and cosine waves combine to create reasonably accurate representations of most waves. Fourier analysis finds its anaesthetic applications in invasive blood pressure, electrocardiogram (ECG) and electroencephalogram (EEG) signals, which are all periodic waveforms. It enables monitors to display accurate representations of these biological waveforms. Fourier analysis was developed by Joseph Fourier, a mathematician who analysed and altered periodic waveforms. It is done by computer programmes that plot the results of the analysis as a spectrum of frequencies with amplitude on the y-axis and frequency on the x-axis.

scholarly journals Microfabricated FSCV-compatible microelectrode array for real-time monitoring of heterogeneous dopamine release

The Analyst ◽  
2010 ◽  
Vol 135 (7) ◽  
pp. 1556 ◽  
Author(s):  
Matthew K. Zachek ◽  
Jinwoo Park ◽  
Pavel Takmakov ◽  
R. Mark Wightman ◽  
Gregory S. McCarty
Keyword(s):  
Real Time ◽  
Dopamine Release ◽  
Microelectrode Array ◽  
Real Time Monitoring

Understanding the nonlinear behavior of EEG with advanced machine learning in artifact elimination

2021 ◽  
Author(s):  
Md Samiul Haque Sunny ◽  
Shifat Hossain ◽  
Nashrah Afroze ◽  
Md. Kamrul Hasan ◽  
Eklas Hossain ◽  
...  
Keyword(s):  
Nonlinear Behavior ◽  
Cognitive Status ◽  
Brain Diseases ◽  
Eeg Signal ◽  
Hilbert Huang Transform ◽  
Neuro Fuzzy ◽  
Research Goal ◽  
Adaptive Noise ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

Abstract Steady-state Visually Evoked Potential (SSVEP) based Electroencephalogram (EEG) signal is utilized in brain-computer interface paradigms, diagnosis of brain diseases, and measurement of the cognitive status of the human brain. However, various artifacts such as the Electrocardiogram (ECG), Electrooculogram (EOG), and Electromyogram (EMG) are present in the raw EEG signal, which adversely affect the EEG-based appliances. In this research, Adaptive Neuro-fuzzy Interface Systems (ANFIS) and Hilbert-Huang Transform (HHT) are primarily employed to remove the artifacts from EEG signals. This work proposes Adaptive Noise Cancellation (ANC) and ANFIS based methods for canceling EEG artifacts. A mathematical model of EEG with the aforementioned artifacts is determined to accomplish the research goal, and then those artifacts are eliminated based on their mathematical characteristics. ANC, ANFIS, and HHT algorithms are simulated on the MATLAB platform, and their performances are also justified by various error estimation criteria using hardware implementation.

Epileptic Seizure-Induced Hypoxemia in Infants with Apparent Life-Threatening Events

PEDIATRICS ◽  
1994 ◽  
Vol 94 (2) ◽  
pp. 148-156
Author(s):  
John Hewertson ◽  
Martin P. Samuels ◽  
David P. Southall ◽  
Christian F. Poets ◽  
Stewart G. Boyd ◽  
...  
Keyword(s):  
Median Duration ◽  
Epileptic Seizure ◽  
Sinus Tachycardia ◽  
Partial Seizures ◽  
Severe Hypoxemia ◽  
Life Threatening ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg ◽  
Normal Standard ◽  
Median Interval

Objective. To describe the physiologic changes that occur during epileptic seizure (ES)-induced apparent life-threatening events (ALTE) and to provide an explanation for the mechanism whereby the hypoxemia characterizing these events occurred. Patients and design. Six infants were retrospectively selected from a group of 17 because they had ALTE documented on physiologic recordings where the first change in signals was in the electroencephalogram (EEG). The 17 infants had clinical features suggestive of partial seizures (but normal standard EEGs) and were from a sample of 172 infants with recurrent ALTE. All 17 infants underwent continuous recordings of breathing, electrocardiogram (ECG), oxygenation, and EEG, but only in 6 was an ES-induced ALTE recorded and the physiologic changes described. Results. Twenty-three ALTE were documented in six infants. Events commenced with an abnormality in the EEG, followed by a decrease in SaO2 after a median interval of 27 seconds (range 2 to 147). Despite resuscitation, the median duration of severe hypoxemia (SaO2 ≤60%) was 40 seconds (range 8 to 74). In 18 events (five infants) there was a median of four apneic pauses (range 1 to 9) preceding the decrease in SaO2 by a median duration of 24 seconds (range 3 to 48). The longest apneic pause per event lasted a median of 19 seconds (range 8 to 47). Breathing movements continued in five events (four infants), and expiratory airflow in one. Sinus tachycardia was found in 19 of the 23 events (six infants), but there were no cardiac arrhythmias. Conclusions. ES in infants can manifest as ALTE and be accompanied by potentially life-threatening episodes of severe hypoxemia and apnea, despite a normal EEG between events.

Nonlinear Approach to Brain Signal Modeling

2011 ◽  
pp. 2834-2839
Author(s):  
Tugce Balli ◽  
Ramaswamy Palaniappan
Keyword(s):  
Signal Processing ◽  
Electrical Activity ◽  
Nonlinear Modeling ◽  
Biological Mechanisms ◽  
Linear Modeling ◽  
Memory Impairments ◽  
Biological Signal ◽  
Biological Signals ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

Biological signal is a common term used for time series measurements that are obtained from biological mechanisms and basically represent some form of energy produced by the biological mechanisms. Examples of such signals are electroencephalogram (EEG), which is the electrical activity of brain recorded by electrodes placed on the scalp; electrocardiogram (ECG), which is electrical activity of heart recorded from chest, and electromyogram (EMG), which is recorded from skin as electrical activity generated by skeletal muscles (Akay, 2000). Nowadays, biological signals such as EEG and ECG are analysed extensively for diagnosing conditions like cardiac arrhythmias in the case of ECG and epilepsy, memory impairments, and sleep disorders in case of EEG. Apart from clinical diagnostic purposes, in recent years there have been many developments for utilising EEG for brain computer interface (BCI) designs (Vaughan & Wolpaw, 2006). The field of signal processing provides many methods for analysis of biological signals. One of the most important steps in biological signal processing is the extraction of features from the signals. The assessment of such information can give further insights to the functioning of the biological system. The selection of proper methods and algorithms for feature extraction (i.e., linear/nonlinear methods) are current challenges in the design and application of real time biological signal analysis systems. Traditionally, linear methods are used for the analysis of biological signals (mostly in analysis of EEG). Although the conventional linear analysis methods simplify the implementation, they can only give an approximation to the underlying properties of the signal when the signal is in fact nonlinear. Because of this, there has been an increasing interest for utilising nonlinear analysis techniques in order to obtain a better characterisation of the biological signals. This chapter will lay the backgrounds to linear and nonlinear modeling of EEG signals, and propose a novel nonlinear model based on exponential autoregressive (EAR) process, which proves to be superior to conventional linear modeling techniques.

Efficient and Robust Approach for Heartbeat Detection of ECG Signal

2019 ◽  
Vol 29 (08) ◽  
pp. 2050133
Author(s):  
Anas Fouad Ahmed ◽  
Mohammed Abdulmunem Ahmed ◽  
Hussain Mustafa Bierk
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Suggested Method ◽  
The Other ◽  
Ecg Signal ◽  
Excellent Performance ◽  
Robust Approach ◽  
Different Types ◽  
Three Stages ◽  
Electrocardiogram Ecg

This paper introduces an efficient and robust method for heartbeat detection based on the calculated angles between the successive samples of electrocardiogram (ECG) signal. The proposed approach involves three stages: filtering, computing the angles of the signal and thresholding. The suggested method is applied to two different types of ECG databases (QTDB and MIT-BIH). The results were compared with the other algorithms suggested in previous works. The proposed approach outperformed the other algorithms, in spite of its simplicity and their fast calculations. These features make it applicable in real-time ECG diagnostics systems. The suggested method was implemented in real-time using a low cost ECG acquisition system and it shows excellent performance.

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