MED-NET: a novel approach to ECG anomaly detection using LSTM auto-encoders-Reference-Cited by-同舟云学术

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MED-NET: a novel approach to ECG anomaly detection using LSTM auto-encoders

Published:2021 Issue:4 Volume:65 Page:343
ISSN:0952-8091
Container-title:International Journal of Computer Applications in Technology
language:en
Short-container-title:IJCAT

Author:

Dutta Koustav,Lenka Rasmita,Nayak Soumya Ranjan,Khandual Asimananda,Bhoi Akash Kumar

Publisher

Inderscience Publishers

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Computer Networks and Communications,Computer Science Applications,Information Systems,Software

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Adaptive Toeplitz Convolution- enhanced Classifier for Anomaly Detection in ECG Big Data;2024-07-30

2. ECG Anomaly Detection with LSTM-Autoencoder for Heartbeat Analysis;2024 IEEE International Conference on Consumer Electronics (ICCE);2024-01-06

3. Classification of Arrhythmia using Electrocardiogram based Image Features with Deep Learning;2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI);2023-10-19

4. Research on Anomaly Detection Algorithm of Chemical Storage Tanks Based on FCM-LSTM;2023 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC);2023-09-25

5. Techniques and Methods for Anomalies Detection in ECG as a Support for Medical Decision in Healthcare: A Review;2023 International Conference on Decision Aid Sciences and Applications (DASA);2023-09-16

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