Automatic Atrial Fibrillation detection: A novel approach using discrete wavelet transform and heart rate variability

2017 ◽  
Author(s):  
Iben H. Bruun ◽  
Semira M. S. Hissabu ◽  
Erik S. Poulsen ◽  
Sadasivan Puthusserypady
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Novel Approach ◽  
Atrial Fibrillation Detection

Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method

2015 ◽  
Vol 81 ◽  
pp. 56-64 ◽  
Author(s):  
U. Rajendra Acharya ◽  
K. Sudarshan Vidya ◽  
Dhanjoo N. Ghista ◽  
Wei Jie Eugene Lim ◽  
Filippo Molinari ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Computer Aided Diagnosis ◽  
Discrete Wavelet ◽  
Transform Method ◽  
Computer Aided ◽  
Aided Diagnosis

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.  

Sign in / Sign up

Export Citation Format

Share Document