Deep learning-based system to predict cardiac arrhythmia using hybrid features of transform techniques

Author:

Sahoo Santanu,Dash Pratyusa,Mishra B.S.P.,Sabut Sukanta KumarORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,Computer Vision and Pattern Recognition,Signal Processing,Computer Science (miscellaneous)

Reference53 articles.

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2. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network;Acharya;Information Sciences,2017

3. Wavelet transforms and the ECG: A review;Addison;Physiological Measurement,2005

4. Heartbeats classification using QRS and T-waves autoregressive features and RR interval features;Adnane;Expert Systems,2017

5. Arrhythmia classification of ECG signals using hybrid features;Anwar;Computational and Mathematical Methods,2018

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1. Revolutionizing Cardiac Care: A Comprehensive Review of ECG-Based Arrhythmia Prediction Techniques;Lecture Notes in Networks and Systems;2024

2. A Hybrid Approach Using SVM, kNN and Random Forest for ECG Classification;2023 11th International Conference on Intelligent Systems and Embedded Design (ISED);2023-12-15

3. Study of feature selection algorithms to improve arrhythmia detection performance on ECG signal;2023 3rd International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA);2023-12-13

4. Automatic Detection of Cardiac Arrhythmia Using Deep Transfer Learning based Approach on ECG;2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON);2023-05-01

5. A Machine Learning Algorithm-Based IoT-Based Message Alert System for Predicting Coronary Heart Disease;Advancements in Smart Computing and Information Security;2022

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