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 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

Automated characterization of arrhythmias using nonlinear features from tachycardia ECG beats

2016 ◽  
Author(s):  
U Rajendra Acharya ◽  
Hamido Fujita ◽  
Muhammad Adam ◽  
Oh Shu Lih ◽  
Tan Jen Hong ◽  
...  
Keyword(s):  
Nonlinear Features

scholarly journals Study of cardiac arrhythmia classification based on Convolutional Neural Network

2020 ◽  
Vol 17 (2) ◽  
pp. 445-458
Author(s):  
Yonghui Dai ◽  
Bo Xu ◽  
Siyu Yan ◽  
Jing Xu
Keyword(s):  
Neural Network ◽  
Cardiovascular Diseases ◽  
Convolutional Neural Network ◽  
Classification Performance ◽  
Time Intervals ◽  
Ecg Signals ◽  
Layer Structures ◽  
Time Periods ◽  
The One

Cardiovascular disease is one of the diseases threatening the human health, and its diagnosis has always been a research hotspot in the medical field. In particular, the diagnosis technology based on ECG (electrocardiogram) signal as an effective method for studying cardiovascular diseases has attracted many scholars? attention. In this paper, Convolutional Neural Network (CNN) is used to study the feature classification of three kinds of ECG signals, which including sinus rhythm (SR), Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF). Specifically, different convolution layer structures and different time intervals are used for ECG signal classification, such as the division of 2-layer and 4-layer convolution layers, the setting of four time periods (1s, 2s, 3s, 10s), etc. by performing the above classification conditions, the best classification results are obtained. The contribution of this paper is mainly in two aspects. On the one hand, the convolution neural network is used to classify the arrhythmia data, and different classification effects are obtained by setting different convolution layers. On the other hand, according to the data characteristics of three kinds of ECG signals, different time periods are designed to optimize the classification performance. The research results provide a reference for the classification of ECG signals and contribute to the research of cardiovascular diseases.

scholarly journals Smartphone based ECG Acquisition and Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 539-543
Author(s):  
Renuka Vijay Kapse
Keyword(s):  
Cardiovascular Diseases ◽  
Cause Of Death ◽  
Health Monitoring ◽  
Early Warning ◽  
Heart Diseases ◽  
Ecg Signal ◽  
Android Application ◽  
Ecg Signals ◽  
Heart Damage ◽  
Electrocardiogram Ecg

Health monitoring and technologies related to health monitoring is an appealing area of research. The electrocardiogram (ECG) has constantly being mainstream estimation plan to evaluate and analyse cardiovascular diseases. Heart health is important for everyone. Heart needs to be monitored regularly and early warning can prevent the permanent heart damage. Also heart diseases are the leading cause of death worldwide. Hence the work presents a design of a mini wearable ECG system and it’s interfacing with the Android application. This framework is created to show and analyze the ECG signal got from the ECG wearable system. The ECG signals will be shipped off an android application via Bluetooth device. This system will automatically alert the user through SMS.

scholarly journals Characterization of Brazilian hospital admissions due to cardiovascular diseases: a longitudinal study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Luiza Gabriela de Araújo Fonseca ◽  
Illia Nadinne Dantas Florentino Lima ◽  
Lucien Peroni Gualdi
Keyword(s):  
Longitudinal Study ◽  
Cardiovascular Diseases ◽  
Hospital Admissions
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