Nonlinear Characterization of ECG Signals for Automatic Arrhythmia Detection

2018 ◽  
Author(s):  
Jesus B. Alonso-Hernandez ◽  
Maria L. Barragan-Pulido ◽  
Carlos M. Travieso-Gonzalez ◽  
Miguel A. Ferrer-Ballester ◽  
Raquel Plata-Perez ◽  
...  
Keyword(s):  
Arrhythmia Detection ◽  
Ecg Signals ◽  
Nonlinear Characterization

scholarly journals Detection & Classification of Cardiac Arrhythmia

2017 ◽  
Vol 6 (1) ◽  
pp. 31
Author(s):  
Chetan M. Jadhav ◽  
V. K. Bairagi
Keyword(s):  
Cardiac Arrhythmia ◽  
Early Stage ◽  
Low Cost ◽  
Simple Method ◽  
Arrhythmia Detection ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Electrical Impulses ◽  
Very High

<p>The term Arrhythmia refers to any change from the normal sequence in the electrical impulses. It is also treated as abnormal heart rhythms or irregular heartbeats. The rate of growth of Cardiac Arrhythmia disease is very high &amp; its effects can be observed in any age group in society. Arrhythmia detection can be done in many ways but effective &amp; simple method for detection &amp; diagnosis of  Cardiac Arrhythmia is by doing analysis of Electrocardiogram signals from ECG sensors. ECG signal can give us the detail information of heart activities, so we can use ECG signals to detect the rhythm &amp; behaviour of heart beats resulting into detection &amp; diagnosis of Cardiac Arrhythmia. In this paper new &amp; improved methodology for early Detection &amp; Classification of Cardiac Arrhythmia has been proposed. In this paper ECG signals are captured using ECG sensors &amp; this ECG signals are used &amp; processed to get the required data regarding heart beats of the human being &amp; then proposed methodology applies for Detection &amp; Classification of Cardiac Arrhythmia. Detection of Cardiac Arrhythmia using ECG signals allows us for easy &amp; reliable way with low cost solution to diagnose Arrhythmia in its prior early stage.</p>

scholarly journals Experimental study of nonlinear characterization of hybrid SWCNTs/Ag-NPs fluids, using nonlinear diffraction technique

2018 ◽  
Vol 16 (36) ◽  
pp. 199-205
Author(s):  
Eman A A. Aboob
Keyword(s):  
Volume Fraction ◽  
Ccd Camera ◽  
Single Wall Carbon Nanotubes ◽  
Ag Nps ◽  
Charge Coupled Device ◽  
Nano Fluid ◽  
Nonlinear Characterization ◽  
Nonlinear Diffraction ◽  
Result Show

Based on nonlinear self- diffraction technique, the nonlinear optical properties of thin slice of matter can be obtained. Here, nonlinear characterization of nano-fluids consist of hybrid Single Wall Carbon Nanotubes and Silver Nanoparticles (SWCNTs/Ag-NPs) dispersed in acetone at volume fraction of 6x10-6, 9x10-6, 18x10-6 have been investigated experimentally. Therefore, CW DPSS laser at 473 nm focused into a quartz cuvette contains the previous nano-fluid was utilized. The number of diffraction ring patterns (N) has been counted using Charge - Coupled- Device (CCD) camera and Pc with a certain software, in order to find the maximum change of refractive index ( of fluids. Our result show that the fraction volume of 18x10-6 is more nonlinearity than others.

Nonlinear Characterization of Fiber Optic Submarine Cables

2017 ◽  
Author(s):  
Eduardo Mateo ◽  
Kohei Nakamura ◽  
Takanori Inoue ◽  
Yoshihisa Inada ◽  
Takaaki Ogata
Keyword(s):  
Fiber Optic ◽  
Nonlinear Characterization ◽  
Submarine Cables

Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals

2018 ◽  
Vol 161 ◽  
pp. 133-143 ◽  
Author(s):  
Muhammad Adam ◽  
Shu Lih Oh ◽  
Vidya K Sudarshan ◽  
Joel EW Koh ◽  
Yuki Hagiwara ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Ecg Signals ◽  
Nonlinear Features

Nonlinear characterization of the piecewise structural effects on whirl flutter of a rotor-nacelle system

2022 ◽  
Author(s):  
Anthony G. Quintana ◽  
Brian E. Saunders ◽  
Rui Vasconcellos ◽  
Abdessatar Abdelkefi
Keyword(s):  
Structural Effects ◽  
Nonlinear Characterization ◽  
Whirl Flutter

ECG Image Classification Using Deep Learning Approach

2021 ◽  
pp. 343-357
Author(s):  
Pratik Kanani ◽  
Mamta Chandraprakash Padole
Keyword(s):  
Deep Learning ◽  
Sampling Frequency ◽  
Heart Beat ◽  
Learning Approach ◽  
Recording Device ◽  
Noisy Signal ◽  
Arrhythmia Detection ◽  
Ecg Signals ◽  
Ecg Arrhythmia ◽  
Deep Learning Model

Cardiovascular diseases are a major cause of death worldwide. Cardiologists detect arrhythmias (i.e., abnormal heart beat) with the help of an ECG graph, which serves as an important tool to recognize and detect any erratic heart activity along with important insights like skipping a beat, a flutter in a wave, and a fast beat. The proposed methodology does ECG arrhythmias classification by CNN, trained on grayscale images of R-R interval of ECG signals. Outputs are strictly in the terms of a label that classify the beat as normal or abnormal with which abnormality. For training purpose, around one lakh ECG signals are plotted for different categories, and out of these signal images, noisy signal images are removed, then deep learning model is trained. An image-based classification is done which makes the ECG arrhythmia system independent of recording device types and sampling frequency. A novel idea is proposed that helps cardiologists worldwide, although a lot of improvements can be done which would foster a “wearable ECG Arrhythmia Detection device” and can be used by a common man.

Nonlinear characterization of ECGs in patients with Chagas’ disease

2019 ◽  
Vol 5 (2) ◽  
pp. 025042
Author(s):  
M Vizcardo ◽  
J Jiménez ◽  
E Alvarez ◽  
F Moleiro ◽  
A Rodríguez ◽  
...  
Keyword(s):  
Chagas Disease ◽  
Nonlinear Characterization
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