IoMT-Based Seizure Detection System Leveraging Edge Machine Learning-Reference-Cited by-同舟云学术

IoMT-Based Seizure Detection System Leveraging Edge Machine Learning

Published:2023-09-15 Issue:18 Volume:23 Page:21474-21483
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Zhao Wenshan¹^ORCID,Wang Yiyuan¹,Sun Xuejiao¹,Zhang Siyu¹,Li Xia²

Affiliation:

1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China

2. School of Biomedical Engineering and the Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China

Funder

National Natural Science Foundation of China

Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10251409/10209422.pdf?arnumber=10209422

Reference28 articles.

1. Optimized adaptive neuro fuzzy inference system (OANFIS) based EEG signal analysis for seizure recognition on FPGA

2. A Fully Integrated 16-Channel Closed-Loop Neural-Prosthetic CMOS SoC With Wireless Power and Bidirectional Data Telemetry for Real-Time Efficient Human Epileptic Seizure Control

3. Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals

4. Low-Power Hardware Implementation of a Support Vector Machine Training and Classification for Neural Seizure Detection

5. ForeSeiz: An IoMT based headband for Real-time epileptic seizure forecasting

Cited by 3 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. Channel Selection for Seizure Detection Based on Explainable AI With Shapley Values;IEEE Sensors Journal;2024-08-15

2. FPGA implementation of automatic seizure detection in EEG signals using machine learning algorithm;Discover Applied Sciences;2024-07-17

3. Toward the Internet of Medical Things: Architecture, trends and challenges;Mathematical Biosciences and Engineering;2023