QRS detection combining entropic criterion and wavelet transform

Author(s):  
Sawsan Rekik ◽  
Noureddine Ellouze
2016 ◽  
Vol 14 (4) ◽  
pp. 1662-1668 ◽  
Author(s):  
Ernano Arrais Junior ◽  
Ricardo Alexandro de Medeiros Valentim ◽  
Glaucio Bezerra Brandao

2014 ◽  
Vol 13 (15) ◽  
pp. 2385-2394 ◽  
Author(s):  
Pu Zhang ◽  
Qinyu Zhang ◽  
Shinsuke Konaka ◽  
Masatake Akutagawa ◽  
Yousuke Kinouchi

2020 ◽  
Vol 39 (7) ◽  
pp. 3610-3625
Author(s):  
Soham Talukder ◽  
Rajan Singh ◽  
Satyajit Bora ◽  
Roy Paily

2019 ◽  
Vol 8 (4) ◽  
pp. 2762-2768

During labor ECG Monitoring is one of the most used method to determine the condition of the fetus. The type of monitoring varies from patients to patients. Few require continuous monitoring because of medication while others require only intermittent monitoring. The fetal ECG is the only information source in early stage diagnosis of fetal health and status. This paper describes the implementation of a system based on FPGA which denoises the abdominal ECG and separates the Fetal ECG from the abdominal signal. For preprocessing a VLSI hardware in FPGA for wavelet transform method is designed and implemented. The embedded architecture on FPGA is based on quadrature spline wavelet transform. FPGA implementation of quadrature spline wavelet transform filter was done with different multipliers. The extraction of fetal electrocardiogram signal was done using slope threshold and two stage template search method where the fetal ECG is extracted from the abdominal ECG. The logical elements, power and delay for the proposed architecture is reported in this paper. For implementation Cyclone II kit and Quartus software was used. In future the classification method will be implemented.


Author(s):  
ANUJA DAS ◽  
SHREETAM BEHERA ◽  
TUSAR KANTA PANDA

In the recent years Cardiac disorder is a very common problem faced by the people. The ECG is the most important test for the interpretation of cardiac abnormalities. The ECG gives the electrical activity of the human heart and by analyzing the deviation in these electrical activities, conclusion can be drawn. The study is divided into two parts. In the first part it deals with the detection of real time ECG waveform from the MIT-BIT Arrhythmia database and then these signals is further diagnosed by applying Wavelet Transform for R-peak detection. The second part of the study deals with the calculation of heart rate with the help of R-peaks detected and accordingly the cardiac arrhythmia can be analyzed. The study has been inspired by the need to find an efficient method for ECG Signal Analysis which is simple and has good accuracy and takes less computation time.


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