Study of ECG Signals based on Gender and Heart Abnormalities

Accurately differentiating between ventricular fibrillation (VF) and ventricular tachycardia (VT) episodes is crucial in preventing potentially fatal missed interpretations that could lead to needless shock to the patients, resulting in damaging the heart. Apart from accurately classifying between VT and VF, the predetermination of the onset of the ventricular arrhythmias is also important in order to allow for more efficient monitoring of patients and can potentially save one’s life. Thus, this research intends to focus on developing a system called Classification and Onset Determination System (CODS) that is able to classify, track and monitor ventricular arrhythmias by using a method called Second Order Dynamic Binary Decomposition (SOD-BD) technique. Two significant characteristics (the natural frequency and the input parameter) were extracted from Electrocardiogram (ECG) signals that are provided by Physiobank database and analyzed to find the significant differences for each ventricular arrhythmia types and classify the ECGs accordingly (N, VT and VF). The outcome from these ECG extractions was also used to locate the onset of ventricular arrhythmia that is useful to predict the occurrence of the heart abnormalities. All the ECGs analysis, parameters extraction, classification techniques, and the CODS are developed using LabVIEW software.

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Design of Portable Electrocardiograph Monitoring System

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.3.030403 ◽

2020 ◽

Author(s):

Jia Hua-Ping ◽

Zhao Jun-Long ◽

Liu Jun

Keyword(s):

Real Time ◽

Monitoring System ◽

Data Storage ◽

P Wave ◽

Ecg Monitoring ◽

The Public ◽

Ecg Signals ◽

Early Warning Mechanism ◽

Timely Treatment ◽

Ecg Data

Cardiovascular disease is one of the major diseases that threaten the human health. But the existing electrocardiograph (ECG) monitoring system has many limitations in practical application. In order to monitor ECG in real time, a portable ECG monitoring system based on the Android platform is developed to meet the needs of the public. The system uses BMD101 ECG chip to collect and process ECG signals in the Android system, where data storage and waveform display of ECG data can be realized. The Bluetooth HC-07 module is used for ECG data transmission. The abnormal ECG can be judged by P wave, QRS bandwidth, and RR interval. If abnormal ECG is found, an early warning mechanism will be activated to locate the user’s location in real time and send preset short messages, so that the user can get timely treatment, avoiding dangerous occurrence. The monitoring system is convenient and portable, which brings great convenie to the life of ordinary cardiovascular users.

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Noise Reduction in ECG Signals Using Notch Filter

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v5i8.147150 ◽

2017 ◽

Vol 5 (8) ◽

pp. 147-150

Author(s):

Chhavi Saxena ◽

Hemant Kumar Gupta ◽

P.D. Murarka

Keyword(s):

Noise Reduction ◽

Notch Filter ◽

Ecg Signals

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Feature Extraction And Classification For ECG Signals Processing Based On Stationary Multiwavelet Transform And Artificial Neural Network

10.36541/0231-000-029-008 ◽

2018 ◽

pp. 85

Author(s):

زهراء خضير طه

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Feature Extraction ◽

Ecg Signals ◽

Multiwavelet Transform ◽

Artificial Neural

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Performance Analysis for ECG Signals Using Data Warehouse Architecture

SSRN Electronic Journal ◽

10.2139/ssrn.3737160 ◽

2020 ◽

Author(s):

David R Henry

Keyword(s):

Performance Analysis ◽

Data Warehouse ◽

Ecg Signals ◽

Using Data

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Elimination of power line interference and its harmonics in ECG signals using adaptive Volterra filters

National Conference on Signal and Image Processing Applications ◽

10.1049/ic.2009.0176 ◽

2009 ◽

Author(s):

S. Bhattacharya

Keyword(s):

Power Line ◽

Volterra Filters ◽

Ecg Signals ◽

Power Line Interference ◽

Adaptive Volterra Filters ◽

Line Interference

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

npj Digital Medicine ◽

10.1038/s41746-021-00447-y ◽

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.

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