Integrated S-Transform-Based Learning System for Detection of Arrhythmic Fetus-Reference-Cited by-同舟云学术

Integrated S-Transform-Based Learning System for Detection of Arrhythmic Fetus

Published:2023 Issue: Volume:72 Page:1-8
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Gupta Kapil¹^ORCID,Bajaj Varun¹^ORCID,Ansari Irshad Ahmad¹^ORCID

Affiliation:

1. Department of Electronics and Communication Engineering, Indian Institute of Information Technology Design and Manufacturing Jabalpur, Jabalpur, Madhya Pradesh, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10113294.pdf?arnumber=10113294

Reference32 articles.

1. Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling

2. Treatment of Fetal Arrhythmias

3. A novel algorithm for early detection of fetal arrhythmia using ICA

4. Fetal Arrhythmia Detection Using Fetal ECG Signal

5. SqueezeNet: AlexNet-level accuracy with 50× fewer parameters and < 0.5 MB model size;iandola;arXiv 1602 07360,2016

Cited by 12 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. Multi-Model Machine Learning for Automated Identification of Rice Diseases Using Leaf Image Data;2024-07-13

2. Deep Siamese Domain Adaptation Convolutional Neural Network Optimized with Honey Badger Algorithm for Fetal Arrhythmia Detection and Classification from ECG Signals;International Journal of Pattern Recognition and Artificial Intelligence;2024-07

3. Multi‐model deep learning system for screening human monkeypox using skin images;Expert Systems;2024-06-09

4. A robust deep learning system for screening of obstructive sleep apnea using T-F spectrum of ECG signals;Computer Methods in Biomechanics and Biomedical Engineering;2024-06-03

5. Automated assessment of mental workload from PPG sensor data using cross-wavelet coherence and transfer learning;Biomedical Engineering Letters;2024-05-11