Loading...
Search for:
r-peak-detection
0.005 seconds
Fetal R Detection from Mixed Maternal and Fetal MCG Signals
, M.Sc. Thesis Sharif University of Technology ; Shamsollahi, Mohammad Bagher (Supervisor) ; Sameni, Reza (Supervisor)
Abstract
Analyzing cardiac function of the fetus during pregnancy is proved to be an important prenatal care procedure. Traditional methods like auscultation and ultrasonography could only lead to anatomical information about the fetal heart. So in the recent decades many researches on the abdominal electrical signals of the pregnant women have been done. Nowadays, it is possible to record the heart magnetic signals. With regard to the morphological similarity between the electrical and magnetical signals of the heart and the superiority of the magnetic ones, one could assume more diagnostic capacity for the fetal MCG. It should be mentioned that finding the location of the fetal R waves could help...
A hybrid deep model for automatic arrhythmia classification based on LSTM recurrent networks
, Article 15th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2020, 1 June 2020 through 3 June 2020 ; 2020 ; Amini, A ; Baghshah, M. S ; Khodajou Chokami, H ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
Electrocardiogram (ECG) recording of electrical heart activities has a vital diagnostic role in heart diseases. We propose to tackle the problem of arrhythmia detection from ECG signals totally by a deep model that does not need any hand-designed feature or heuristic segmentation (e.g., ad-hoc R-peak detection). In this work, we first segment ECG signals by detecting R-peaks automatically via a convolutional network, including dilated convolutions and residual connections. Next, all beats are aligned around their R-peaks as the most informative section of the heartbeat in detecting arrhythmia. After that, a deep learning model, including both dilated convolution layers and a Long-Short Term...