The Library
A convolutional neural network approach to detect congestive heart failure
Tools
Tools
Tools
Porumb, Mihaela, Iadanza, Ernesto, Massaro, Sebastiano and Pecchia, Leandro (2020) A convolutional neural network approach to detect congestive heart failure. Biomedical Signal Processing and Control, 55 . 101597. doi:10.1016/j.bspc.2019.101597 ISSN 1746-8094.
|
PDF
WRAP-convolutional-neural-network-detect-congestive-heart-Pecchia-2019.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1739Kb) | Preview |
Official URL: https://doi.org/10.1016/j.bspc.2019.101597
Abstract
Congestive Heart Failure (CHF) is a severe pathophysiological condition associated with high prevalence, high mortality rates, and sustained healthcare costs, therefore demanding efficient methods for its detection. Despite recent research has provided methods focused on advanced signal processing and machine learning, the potential of applying Convolutional Neural Network (CNN) approaches to the automatic detection of CHF has been largely overlooked thus far. This study addresses this important gap by presenting a CNN model that accurately identifies CHF on the basis of one raw electrocardiogram (ECG) heartbeat only, also juxtaposing existing methods typically grounded on Heart Rate Variability. We trained and tested the model on publicly available ECG datasets, comprising a total of 490,505 heartbeats, to achieve 100% CHF detection accuracy. Importantly, the model also identifies those heartbeat sequences and ECG’s morphological characteristics which are class-discriminative and thus prominent for CHF detection. Overall, our contribution substantially advances the current methodology for detecting CHF and caters to clinical practitioners’ needs by providing an accurate and fully transparent tool to support decisions concerning CHF detection.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RC Internal medicine |
||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
| SWORD Depositor: | Library Publications Router | ||||||||
| Library of Congress Subject Headings (LCSH): | Neural networks (Computer science), Congestive heart failure, Machine learning, Diagnostic imaging -- Data processing | ||||||||
| Journal or Publication Title: | Biomedical Signal Processing and Control | ||||||||
| Publisher: | Elsevier BV | ||||||||
| ISSN: | 1746-8094 | ||||||||
| Official Date: | 1 January 2020 | ||||||||
| Dates: |
|
||||||||
| Volume: | 55 | ||||||||
| Article Number: | 101597 | ||||||||
| DOI: | 10.1016/j.bspc.2019.101597 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | ** From Crossref via Jisc Publications Router | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Description: | Free access |
||||||||
| Date of first compliant deposit: | 8 October 2019 | ||||||||
| Date of first compliant Open Access: | 4 September 2020 |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
