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 A method of remote physiological monitoring of a fully mobile primate in a single animal cage

1989 ◽  
Vol 23 (2) ◽  
pp. 180-187 ◽  
Author(s):  
P. C. Pearce ◽  
M. J. Halsey ◽  
J. A. S. Ross ◽  
N. P. Luff ◽  
R. A. Bevilacqua ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Physiological Monitoring ◽  
Papio Anubis ◽  
Blood Samples ◽  
Arterial Blood ◽  
Intravenous Drugs ◽  
Single Animal ◽  
Hyperbaric Environment ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

A system was designed to allow the physiological monitoring of a fully mobile, unstressed baboon ( Papio anubis) in a single animal cage for the purpose of measuring the changes occurring in a hyperbaric environment. It was required to operate for at least three months, both inside a pressure chamber and outside, and to measure the following parameters: electroencephalogram (EEG, three channels), electrooculogram (EOG), electromyelogram (EMG, two channels), electrocardiogram (ECG), arterial blood pressure, respiration and body temperature. Also in the system were catheters through which blood samples could be taken and intravenous drugs given. The overall system consisted of a harness and jacket, an umbilical and back pack, a combined electrical and fluid transmission swivel and a monitoring implant and catheters.

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 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 Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

scholarly journals Screening for atrial fibrillation during automated blood pressure measurement among patients admitted to internal medicine ward

2021 ◽  
Author(s):  
Giacomo Pucci ◽  
Edoardo Santoni ◽  
Valeria Bisogni ◽  
Camilla Calandri ◽  
Alberto Cerasari ◽  
...  
Keyword(s):  
Internal Medicine ◽  
Atrial Fibrillation ◽  
Blood Pressure ◽  
Adult Population ◽  
Blood Pressure Measurement ◽  
University Hospital ◽  
Daily Routine ◽  
Internal Medicine Ward ◽  
Clinical Conditions ◽  
Electrocardiogram Ecg

AbstractAtrial fibrillation (AF), the commonest sustained cardiac arrhythmia affecting the adult population, is often casually discovered among hospitalized people. AF onset is indeed triggered by several clinical conditions such as acute inflammatory states, infections, and electrolyte disturbance, frequently occurring during the hospitalization. We aimed to evaluate whether systematic AF screening, performed through an automated oscillometric blood pressure (BP) device (Microlife WatchBP Office AFIB, Microlife AG, Switzerland), is effective for detecting AF episodes in subjects admitted to an Internal Medicine ward. 163 patients consecutively hospitalized at the Unit of Internal Medicine of the “Santa Maria” Terni University Hospital between November 2019 and January 2020 (mean age ± standard deviation: 77 ± 14 years, men proportion: 40%) were examined. Simultaneously with BP measurement and AF screening, a standard 12-lead electrocardiogram (ECG) was performed in all subjects. AF was diagnosed by ECG in 29 patients (18%). AF screening showed overall 86% sensitivity and 96% specificity. False negatives (n = 4) had RR-interval coefficient of variation lower than true positives (n = 25, p < 0.01), suggesting a regular ventricular rhythm during AF. The repeated evaluation substantially confirmed the same level of agreement. AF screening was positive in all patients with new-onset AF (n = 6, 100%). Systematic AF screening in patients admitted to Internal Medicine wards, performed using the Microlife WatchBP Office AFIB, is feasible and effective. The opportunity to implement such technology in daily routine clinical practice to prevent undiagnosed AF episodes in hospitalized patients should be the subject of further research.

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 Can Lipoic Acid Attenuate Cardiovascular Disturbances Induced by Ethanol and Disulfiram Administration Separately or Jointly in Rats?

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Anna Bilska-Wilkosz ◽  
Magdalena Kotańska ◽  
Magdalena Górny ◽  
Barbara Filipek ◽  
Małgorzata Iciek
Keyword(s):  
Blood Pressure ◽  
Rat Liver ◽  
Lipoic Acid ◽  
Aldh Activity ◽  
Dihydrolipoic Acid ◽  
Treatment Groups ◽  
Intracellular Compartments ◽  
Electrocardiogram Ecg ◽  
First Time ◽  
All Treatment

The exogenous lipoic acid (LA) is successfully used as a drug in the treatment of many diseases. It is assumed that after administration, LA is transported to the intracellular compartments and reduced to dihydrolipoic acid (DHLA) which is catalyzed by NAD(P)H-dependent enzymes. The purpose of this study was to investigate whether LA can attenuate cardiovascular disturbances induced by ethanol (EtOH) and disulfiram (DSF) administration separately or jointly in rats. For this purpose, we measured systolic and diastolic blood pressure, recorded electrocardiogram (ECG), and estimated mortality of rats. We also studied the activity of aldehyde dehydrogenase (ALDH) in the rat liver. It was shown for the first time that LA partially attenuated the cardiac arrhythmia (extrasystoles and atrioventricular blocks) induced by EtOH and reduced the EtOH-induced mortality of animals, which suggests that LA may have a potential for use in cardiac disturbance in conditions of acute EtOH intoxication. The administration of EtOH, LA, and DSF separately or jointly affected the ALDH activity in the rat liver since a significant decrease in the activity of the enzyme was observed in all treatment groups. The results indicating that LA is an inhibitor of ALDH activity are very surprising.

scholarly journals Classification of Hypertensive and Normotensive Subjects Using Bilateral Differential Biopotential Signals

2021 ◽  
Author(s):  
Arindam Sarkar ◽  
Bhaswati Goswami ◽  
Ratna Ghosh
Keyword(s):  
Blood Pressure ◽  
Random Forest ◽  
High Blood Pressure ◽  
Pivotal Role ◽  
Modern World ◽  
Hypertensive Subject ◽  
Normotensive Subjects ◽  
Electrocardiogram Ecg ◽  
Day By Day

Abstract Hypertension or high blood pressure is a severe health issue in the modern world, especially in this pandemic scenario, that can cause many heart related diseases or even death, and it is increasing day by day. For this reason, a reliable, automatic and easy to use system for hypertensive subject detection is an important focus for the researchers. Biopotential signals can play a pivotal role in this regard. Though, few strategies were proposed based on electrocardiogram (ECG) or electrodermal (EDA) signals, but those require special circuitry, as well as trained persons. In this article, a method is proposed to classify hypertensive and normotensive subjects using differential biopotential signals. Neither special circuitry, nor much expertise is required for handling this system. It was assumed that progression of rest is dependent upon blood pressure. To serve the purpose, signals were acquired from both hypertensive and normotensive subjects bilaterally for 10 continuous minutes. Result of the random forest (RF) classification establishes that from the analysis of the progression of the bilaterally acquired differential biopotential signals, hypertensive subjects can be distinguished from normotensive subjects.

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.

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