scholarly journals The RR Interval Spectrum, the ECG Signal, and Aliasing

2019 ◽  
Author(s):  
Alexander Gersten ◽  
Ori Gersten ◽  
Adi Ronen ◽  
Yair Cassuto
Keyword(s):  
Ecg Signal ◽  
Rr Interval

scholarly journals Development of Low-Cost ECG System for Comprehensive Monitoring Heart Function

2021 ◽  
Author(s):  
prihatin oktivasari ◽  
Ishartati Ishartati ◽  
Riandini Riandini ◽  
Amy Hamidah Salman ◽  
Freddy Haryanto ◽  
...  
Keyword(s):  
Heart Function ◽  
Low Cost ◽  
Integrated System ◽  
Ecg Signal ◽  
Analog To Digital ◽  
Rr Interval ◽  
Time Operation ◽  
The Status ◽  
Real Time Operation ◽  
Research And Education

Abstract A simple system, a low-cost, fully automated, and design for monitoring RR interval Electrocardiography (ECG) signal described in this paper. The platform, named Simple Low-Cost Electrocardiography System (SLES), is capable of monitoring RR interval and R peaks in 3 lead standards. The system is in .exe format, so it can be easily installed on a computer. The system's goal is to design a fully integrated system for measuring a characteristic of Heart Rate Variability (HRV) parameters for various applications in heart signal research and education. The ECG signal is analog filtered and amplified and processed from analog to digital. Eventually, the ECG signal will be shown on the monitor after digital filtering. The data obtained from the ECG will accurately reflect the status of human heart health. The system has the benefits of small volume, low power consumption, low cost, and real-time operation. All design and development reports, files, and system software will be given non-commercial use online on https://github.com/oktivasari.

scholarly journals Phonocardiographic Signal and Electrocardiographic Signal Analysis for the Detection of Cardiovascular Diseases

2018 ◽  
Vol 15 (1) ◽  
pp. 79-89 ◽  
Author(s):  
V. Kalaivani ◽  
R. Lakshmi Devi ◽  
V. Anusuyadevi
Keyword(s):  
Cardiovascular Diseases ◽  
Heart Sound ◽  
Physiological State ◽  
Heart Sounds ◽  
Sound Signal ◽  
Ecg Signal ◽  
Short Term ◽  
Rr Interval ◽  
Short Term Variability ◽  
Heart Sound Signal

The main objective is to develop a novel method for the heart sound analysis for the detection of cardiovascular diseases. It can be considered as one of the important phases in the automated analysis of PCG signals. Heart sounds carry information about mechanical activity of the cardiovascular system. This information includes specific physiological state of the subject and the short term variability related to the respiratory cycle. The interpretation of sounds and extraction of changes in the physiological state while maintaining the short term variability are still an open problem and is subject of this paper. The system deals with the process of de-noising of the heart sound signal(PCG) and the signal is decomposed into several sub-bands and the de-noised heart sound signal is segmented into the basic heart sounds S1 and S2, along with the systolic and diastolic interval.. Also, the ECG signal is de-noised. Meanwhile, the R-peaks are identified from the ECG signal and RR interval is obtained. Extraction of features are done from both the heart sound signal and the ECG signal. From the features, the R-peaks are identified from the ECG signal and RR interval is obtained. The attribute selection is to find the best attribute values that can be used for the classification process. Finally, using classification technique, cardiac diseases are detected. This work is implemented by using MATLAB software.

The Sensitivity of The Heart Rate Detection for Atrial Electrograms Signal

2021 ◽  
Vol 20 (2) ◽  
pp. 33-41
Author(s):  
Pang Seng Kong ◽  
Nasarudin Ahmad ◽  
Fazilah Hassan ◽  
Anita Ahmad
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Screening Tool ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Large Area ◽  
Rr Intervals ◽  
Rr Interval

Atrial Fibrillation (AF) is the most familiar example of arrhythmia that will occur health problems such as stroke, heart failure and other complications. Globally, the number of AF patients will more than triple by 2050 worldwide. Current methods involve performing large-area ablation without knowing the exact location of key parts. The reliability of the technology can be used as a target for atrial fibrillation’s catheter ablation. The factors that leading to the onset of atrial fibrillation include the triggering factors that induce arrhythmia and the substrate that maintains the arrhythmia. The project’s aim is to create a method for identifying AF that can be used as screening tool in medical practice. The primary goals for the detection method’s design are to develop a MATLAB software program that can compare the complexity of a normal ECG signal and an AF ECG signal. Currently, this can be achieved by the ECG Signal’s R peaks and RR Interval. For AF detection, there are more R peaks and RR Intervals and it is irregular. In this research, the detection of AF is based on the heart rate (RR Intervals). For the ECG preprocessing, Pan-Tompkins Algorithm and Discrete Wavelet Transform is used to detect the sensitivity on the R peaks and RR Intervals. As a result, Discrete Wavelet Transform algorithm gives 100% sensitivity for the dataset obtained from MIT-BIH Atrial Fibrillation and MIT-BIH Arrhythmia Database.  

scholarly journals Suppressing the Influence of Ectopic Beats by Applying a Physical Threshold-Based Sample Entropy

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 411 ◽  
Author(s):  
Lina Zhao ◽  
Jianqing Li ◽  
Jinle Xiong ◽  
Xueyu Liang ◽  
Chengyu Liu
Keyword(s):  
Time Series ◽  
Physical Meaning ◽  
Sample Entropy ◽  
Ectopic Beat ◽  
Ecg Signal ◽  
Interval Time ◽  
Rr Interval ◽  
Interval Time Series ◽  
Ectopic Beats ◽  
Hrv Analysis

Sample entropy (SampEn) is widely used for electrocardiogram (ECG) signal analysis to quantify the inherent complexity or regularity of RR interval time series (i.e., heart rate variability (HRV)), with the hypothesis that RR interval time series in pathological conditions output lower SampEn values. However, ectopic beats can significantly influence the entropy values, resulting in difficulty in distinguishing the pathological situation from normal situations. Although a theoretical operation is to exclude the ectopic intervals during HRV analysis, it is not easy to identify all of them in practice, especially for the dynamic ECG signal. Thus, it is important to suppress the influence of ectopic beats on entropy results, i.e., to improve the robustness and stability of entropy measurement for ectopic beats-inserted RR interval time series. In this study, we introduced a physical threshold-based SampEn method, and tested its ability to suppress the influence of ectopic beats for HRV analysis. An experiment on the PhysioNet/MIT RR Interval Databases showed that the SampEn use physical meaning threshold has better performance not only for different data types (normal sinus rhythm (NSR) or congestive heart failure (CHF) recordings), but also for different types of ectopic beat (atrial beats, ventricular beats or both), indicating that using a physical meaning threshold makes SampEn become more consistent and stable.

scholarly journals LABVIEW BASED ECG SIGNAL ACQUISITION AND ANALYSIS

2021 ◽  
Vol 5 (9) ◽  
Author(s):  
Shila Dhande
Keyword(s):  
Heart Rate ◽  
Research Work ◽  
Heart Rhythm ◽  
The Body ◽  
P Wave ◽  
Signal Acquisition ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Ecg Signals ◽  
Rr Interval

The system “LabVIEW based ECG signal acquisition and analysis” is developed to assist patients and doctors in health care. An arrhythmia is an abnormal heart rhythm. It may be so brief that it doesn’t change the overall heart rate, but it can cause the heart rate to be too slow or too fast. When arrhythmias are severe or last long enough, the heart may not be able to pump enough blood to the body. This can cause the patient to feel tired, lightheaded or may make him pass out. It can also cause death. Before treatment, it’s important for the doctor to know where an arrhythmia starts in the heart and whether it’s abnormal. An electrocardiogram (ECG) is often used to diagnose arrhythmias. “LabVIEW based ECG signal acquisition and analysis” is meant to acquire ECG signals from the patient and analyze it to detect and classify its anomalies and abnormalities. This is achieved by extracting amplitudes and durations of parameters of ECG waveform such as P wave, QRS complex, RR interval, and PR durations. These parameters are compared with the normal values to determine the type of abnormality- Tachycardia or Bradycardia. The database of the patient is maintained for further use by the doctor. The objective of LabVIEW based ECG signal acquisition and analysis aims at acquiring and analyzing temporal parameters of ECG signal such as P wave, QRS complex, RR interval, PR durations and amplitudes of the P wave, ST wave, identification of cardiac arrhythmia using LabVIEW. The research work has helped us to explore various features of LabVIEW like signal processing and automated database generation.

scholarly journals SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal

2016 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Laiali Almazaydeh ◽  
Khaled Elleithy ◽  
Miad Faezipour ◽  
Helen Ocbagabir
Keyword(s):  
Sleep Apnea ◽  
Ecg Signal ◽  
Rr Interval

A Fourier-Bessel Expansion-Based Method for Automated Detection of Atrial Fibrillation From Electrocardiogram Signals

2019 ◽  
pp. 248-277
Author(s):  
Ashish Sharma ◽  
Shivnarayan Patidar
Keyword(s):  
Atrial Fibrillation ◽  
Ecg Signal ◽  
Clinical Environment ◽  
Test Dataset ◽  
Ecg Signals ◽  
Rr Interval ◽  
Electrocardiogram Signals ◽  
Detection And Identification ◽  
Bagged Decision Trees ◽  
Electrocardiogram Ecg

This chapter presents a new methodology for detection and identification of cardiovascular diseases from a single-lead electrocardiogram (ECG) signal of short duration. More specifically, this method deals with the detection of the most common cardiac arrhythmia called atrial fibrillation (AF) in noisy and non-clinical environment. The method begins with appropriate pre-processing of ECG signals in order to get the RR-interval and heart rate (HR) signals from them. A set of indirect features are computed from the original and the transformed versions of RR-interval and HR signals along with a set of direct features that are obtained from ECG signals themselves. In all, 47 features are computed and subsequently they are fed to an ensemble system of bagged decision trees for classifying the ECG recordings into four different classes. The proposed method has been evaluated with 2017 PhysioNet/CinC challenge hidden test dataset (phase II subset) and the final F1 score of 0.81 is obtained.

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