An automated ECG-based deep learning for the early-stage identification and classification of cardiovascular disease

Contributing an automated ECG-based DL system based on FFNN-CQNGT for early-stage cardiovascular disease identification and classification holds great potential for both patient care and public health.

Authors

M Sangeetha
Neetha Purushotham
Ajeet Singh (ORCID: https://orcid.org/0000-0002-4121-7834)
Prasanthi Boyapati (ORCID: https://orcid.org/0000-0002-1051-5695)
Abhay Chaturvedi (ORCID: https://orcid.org/0000-0002-3857-3918)
Anand Pandey (ORCID: https://orcid.org/0000-0002-9834-3428)

Institutions

SRM University (IN)
Sharda University (IN)
GLA University (IN)

Publication Details

Journal: Technology and Health Care
Published: 2024-09-20
DOI: https://doi.org/10.3233/thc-240543
Citations: 1
Primary Topic: ECG Monitoring and Analysis
Type: article
Field-Weighted Citation Impact: 0.40

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article

An automated ECG-based deep learning for the early-stage identification and classification of cardiovascular disease

M Sangeetha, Neetha Purushotham, Ajeet Singh, Prasanthi Boyapati et al.

Sep 20, 2024

1 citations

Technology and Health Care

ECG Monitoring and Analysis

0.40

article

An automated ECG-based deep learning for the early-stage identification and classification of cardiovascular disease

M Sangeetha, Neetha Purushotham, Ajeet Singh, Prasanthi Boyapati, Abhay Chaturvedi, Anand Pandey

September 20, 2024article en

1 citations

Abstract

Technology and Health CareVol. 32(6)

SRM University (IN), Sharda University (IN), GLA University (IN)

Good health and well-being

Openalex Percentile: Top 33%

ECG Monitoring and Analysis

0.40

DOI Source OpenAlex

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An automated ECG-based deep learning for the early-stage identification and classification of cardiovascular disease — M Sangeetha, Neetha Purushotham, et al. · Technology and Health Care (2024) | TGRS Research Map | TGRS