Classification Techniques for Arrhythmia Patterns Using Convolutional Neural Networks and Internet of Things (IoT) Devices-Reference-Cited by-同舟云学术

Classification Techniques for Arrhythmia Patterns Using Convolutional Neural Networks and Internet of Things (IoT) Devices

Author:

Opoku Agyeman Michael¹^ORCID,Guerrero Andres Felipe¹,Vien Quoc-Tuan²^ORCID

Affiliation:

1. Centre for Advanced and Smart Technologies (CAST), Faculty of Arts Sciences and Technologies (FAST), University of Northampton, Northampton, U.K.

2. Faculty of Science, and Technology, Middlesex University, London, U.K.

Funder

Faculty of Arts, Science and Technology (FAST) of University of Northampton

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference76 articles.

1. A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients;zheng;Data Science Journal,2020

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