A Novel Personal Identity Verification Approach Using a Discrete Wavelet Transform of the ECG Signal

2008 ◽  
Author(s):  
Chuang-Chien Chiu ◽  
Chou-Min Chuang ◽  
Chih-Yu Hsu
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Personal Identity ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Identity Verification

DISCRETE WAVELET TRANSFORM APPLIED ON PERSONAL IDENTITY VERIFICATION WITH ECG SIGNAL

2009 ◽  
Vol 07 (03) ◽  
pp. 341-355 ◽  
Author(s):  
CHUANG-CHIEN CHIU ◽  
CHOU-MIN CHUANG ◽  
CHIH-YU HSU
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Personal Identity ◽  
Rejection Rate ◽  
Normal Subjects ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Identity Verification ◽  
Ecg Signals ◽  
Electrocardiogram Ecg

The main purpose of this study is to present a novel personal authentication approach with the electrocardiogram (ECG) signal. The electrocardiogram is a recording of the electrical activity of the heart and the recorded signals can be used for individual verification because ECG signals of one person are never the same as those of others. The discrete wavelet transform was applied for extracting features that are the wavelet coefficients derived from digitized signals sampled from one-lead ECG signal. By the proposed approach applied on 35 normal subjects and 10 arrhythmia patients, the verification rate was 100% for normal subjects and 81% for arrhythmia patients. Furthermore, the performance of the ECG verification system was evaluated by the false acceptance rate (FAR) and false rejection rate (FRR). The FAR was 0.83% and FRR was 0.86% for a database containing only 35 normal subjects. When 10 arrhythmia patients were added into the database, FAR was 12.50% and FRR was 5.11%. The experimental results demonstrated that the proposed approach worked well for normal subjects. For this reason, it can be concluded that ECG used as a biometric measure for personal identity verification is feasible.

False ventricular tachycardia alarm suppression in the ICU based on the discrete wavelet transform in the ECG signal

2014 ◽  
Vol 47 (6) ◽  
pp. 775-780 ◽  
Author(s):  
Rebeca Salas-Boni ◽  
Yong Bai ◽  
Patricia Rae Eileen Harris ◽  
Barbara J. Drew ◽  
Xiao Hu
Keyword(s):  
Ventricular Tachycardia ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Ecg Signal

scholarly journals ECG signal denoising using discrete wavelet transform: A comparative analysis of threshold values and functions

MASKANA ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 105-114
Author(s):  
Marco Gualsaquí ◽  
Iván Vizcaíno ◽  
Víctor Proaño ◽  
Marco Flores
Keyword(s):  
Wavelet Transform ◽  
Comparative Analysis ◽  
Discrete Wavelet Transform ◽  
Signal Denoising ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Threshold Values

An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform

2016 ◽  
Vol 36 (3) ◽  
pp. 499-508 ◽  
Author(s):  
Wissam Jenkal ◽  
Rachid Latif ◽  
Ahmed Toumanari ◽  
Azzedine Dliou ◽  
Oussama El B’charri ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Efficient Algorithm ◽  
Signal Denoising ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Threshold Filter

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.  

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