scholarly journals Sensors used to Record Electrocardiogram

2020 ◽  
Vol 9 (2) ◽  
pp. 1175-1178
Keyword(s):  
Heart Rate ◽  
Cardiovascular Diseases ◽  
Early Detection ◽  
Heart Muscle ◽  
Monitoring Systems ◽  
Heart Rate Monitoring ◽  
Non Invasive ◽  
Different Types ◽  
Heart Disorders ◽  
Electrocardiogram Ecg

Diagnosing the heart disorders is major challenge because of their short–lasting and intermittent character. The convincing technologies with non–invasive heart rate monitoring systems acquire Electrocardiogram (ECG) have limitations with reference to sensitivity. There are different types of wearable, flexible electrocardiogram sensors that can yield important information about underlying physiological parameters of human for applications related to real time monitoring of health, fitness, and wellness. Sensors with leads are all derived using three electrodes which are used to pick the electrical activity from a different position on the heart muscle. Lead-less sensors are now widely used for acquisition of ECG and related signals for heart rate monitoring which has more advantages compared to other sensors. Henceforth, various sensors are studied to understand their relation with heart rate monitoring. It is inferred that MAX30100 sensor can improve the accuracy of ECG recordings for early detection of cardiovascular diseases.

scholarly journals Survey: smartphone-based assessment of cardiovascular diseases using ECG and PPG analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Muhammad Shabaan ◽  
Kaleem Arshid ◽  
Muhammad Yaqub ◽  
Feng Jinchao ◽  
M. Sultan Zia ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Early Detection ◽  
Healthcare Systems ◽  
Non Invasive ◽  
Cardiac Abnormalities ◽  
Causes Of Deaths ◽  
Electrocardiogram Ecg

Abstract A number of resources, every year, being spent to tackle early detection of cardiac abnormalities which is one of the leading causes of deaths all over the Globe. The challenges for healthcare systems includes early detection, portability and mobility of patients. This paper presents a categorical review of smartphone-based systems that can detect cardiac abnormalities by the analysis of Electrocardiogram (ECG) and Photoplethysmography (PPG) and the limitation and challenges of these system. The ECG based systems can monitor, record and forward signals for analysis and an alarm can be triggered in case of abnormality, however the limitation of smart phone’s processing capabilities, lack of storage and speed of network are major challenges. The systems based on PPG signals are non-invasive and provides mobility and portability. This study aims to critically review the existing systems, their limitation, challenges and possible improvements to serve as a reference for researchers and developers.

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.

Wearable Triboelectric Nanogenerators for Heart Rate Monitoring

2021 ◽  
Author(s):  
Sophia Shen ◽  
Xiao Xiao ◽  
Xiao Xiao ◽  
Jun Chen
Keyword(s):  
Heart Rate ◽  
Cardiovascular Diseases ◽  
Causes Of Death ◽  
Heart Rate Monitoring ◽  
Triboelectric Nanogenerators ◽  
Physiological Indicator

Cardiovascular diseases are currently the leading causes of death globally and are projected to remain the leading cause in 2040, making heart rate an important physiological indicator to regularly monitor....

Design of a Programmable ECG Generator Using a Dynamical Model

2013 ◽  
Vol 311 ◽  
pp. 485-490 ◽  
Author(s):  
Ying Chieh Wei ◽  
Ying Yu Wei ◽  
Shaang Tzuu Wey ◽  
Ling Sheng Jang
Keyword(s):  
Differential Equation ◽  
Heart Rate ◽  
Flash Memory ◽  
Qrs Complex ◽  
Universal Serial Bus ◽  
Bundle Branch Block ◽  
T Wave ◽  
Efficiency Of Algorithms ◽  
Different Types ◽  
Electrocardiogram Ecg

This article is to design and develop a programming electrocardiogram (ECG) generator. It can be used to test the efficiency of algorithms and to calibration and maintenance of electrocardiograph equipment. We simplified and modified the three coupled ordinary differential equations of McSharry’s model to single differential equation to generate the synthetic ECG waveforms. This generator can provide the adjusted amplitude, heart rate, QRS-complex slopes, and P- and T-wave position parameters. The system can set the rage of the average gear rate from 20 to 120 beat per minute (BPM) with an adjustable variation of 1 BPM. The parameters of the adjusted synthetic ECG model can be stored in Flash memory of the system through Universal Serial Bus (USB) 2.0 interface. The results were generated four different ECG waveforms for test which are Lead I, Lead II, hyperkalaemia and left bundle branch block. According to the experimental results, the system can not only generate the ECG waveforms of the setting heart rate but also can adjust the different types of ECG waveforms. ECG generator will generate the synthetic electrocardiograms for testing the electrocardiogram analytic algorithms. ECG generator will generate the synthetic electrocardiograms for testing the electrocardiogram analytic algorithms.

scholarly journals Analysis of Non-invasive Video Based Heart Rate Monitoring System obtained from Various Distances and Different Facial Spot

2018 ◽  
Vol 1049 ◽  
pp. 012003 ◽  
Author(s):  
Norwahidah Ibrahim ◽  
Razali Tomari ◽  
Wan Nurshazwani Wan Zakaria ◽  
Nurmiza Othman
Keyword(s):  
Heart Rate ◽  
Monitoring System ◽  
Heart Rate Monitoring ◽  
Non Invasive

scholarly journals Real-Time Cardiac Beat Detection and Heart Rate Monitoring from Combined Seismocardiography and Gyrocardiography

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3472 ◽  
Author(s):  
D’Mello ◽  
Skoric ◽  
Xu ◽  
Roche ◽  
Lortie ◽  
...  
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Time Algorithm ◽  
Cardiac Monitoring ◽  
Computational Time ◽  
Heart Rate Monitoring ◽  
Heart Rates ◽  
Non Invasive ◽  
Beat Detection ◽  
Speed Accuracy

Cardiography is an indispensable element of health care. However, the accessibility of at-home cardiac monitoring is limited by device complexity, accuracy, and cost. We have developed a real-time algorithm for heart rate monitoring and beat detection implemented in a custom-built, affordable system. These measurements were processed from seismocardiography (SCG) and gyrocardiography (GCG) signals recorded at the sternum, with concurrent electrocardiography (ECG) used as a reference. Our system demonstrated the feasibility of non-invasive electro-mechanical cardiac monitoring on supine, stationary subjects at a cost of $100, and with the SCG–GCG and ECG algorithms decoupled as standalone measurements. Testing was performed on 25 subjects in the supine position when relaxed, and when recovering from physical exercise, to record 23,984 cardiac cycles at heart rates in the range of 36–140 bpm. The correlation between the two measurements had r2 coefficients of 0.9783 and 0.9982 for normal (averaged) and instantaneous (beat identification) heart rates, respectively. At a sampling frequency of 250 Hz, the average computational time required was 0.088 s per measurement cycle, indicating the maximum refresh rate. A combined SCG and GCG measurement was found to improve accuracy due to fundamentally different noise rejection criteria in the mutually orthogonal signals. The speed, accuracy, and simplicity of our system validated its potential as a real-time, non-invasive, and affordable solution for outpatient cardiac monitoring in situations with negligible motion artifact.

A Simple Non-Invasive Automated Heart Rate Monitoring System Using Facial Images

2016 ◽  
pp. 100-122 ◽  
Author(s):  
Humaira Nisar ◽  
Zhen Yao Lim ◽  
Kim Ho Yeap
Keyword(s):  
Heart Rate ◽  
Power Spectrum ◽  
Color Model ◽  
Heart Rate Monitoring ◽  
Monitoring Method ◽  
Non Invasive ◽  
Average Accuracy ◽  
Contact Devices ◽  
The Face ◽  
Cie Lab

In this chapter we will discuss a simple non invasive automated heart rate monitoring method. Commonly heart rate is measured by using heart rate monitor devices. Many patients do not feel comfortable when they use contact devices for diagnostic purposes. Our algorithm gives a non-invasive way of heart rate measurement. The first step is to record a video. After 5 frames of the video are captured, the face is detected. A total of 300 frames will be used for further processing. At this stage, ROI (part of forehead) will be cropped out automatically. All image frames are in RGB color model, so these will be separated into 3 channels. For analysis, graph normalization is applied, which uses mean and standard deviation. Fast Fourier transform is used to plot the power spectrum of the traces. This power spectrum will have a peak if the heart rate is detected. We used RGB, HSI, YCbCr, YIQ, and CIE LAB color models for analysis. The best result is achieved with RGB color model followed by CIELab. The average accuracy is 95.32%.

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