ECG Hearbeat Classification Based on Multi-scale Convolutional Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F23%3A39920969" target="_blank" >RIV/00216275:25530/23:39920969 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-031-43078-7_29" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-43078-7_29</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-43078-7_29" target="_blank" >10.1007/978-3-031-43078-7_29</a>

Result language

angličtina

Original language name

ECG Hearbeat Classification Based on Multi-scale Convolutional Neural Networks

Original language description

Clinical applications require automating ECG signal processing and classification. This paper investigates the impact of multiscale input filtering techniques and feature map blocks on the performance of CNN models for ECG classification. We conducted an ablation study using the AbnormalHeartbeat dataset, with 606 instances of ECG time series divided into five classes. We compared five multiscale input filtering techniques and four multiscale feature map blocks against a base model and non-multiscale input. Results showed that the combination of mean filter for multiscale input and residual connections for multiscale block achieved the highest accuracy of 64.47%. Residual connections were consistently effective across different filtering techniques, highlighting their potential to enhance CNN model performance for ECG classification. These findings can guide the design of future CNN models for ECG classification tasks, with further experimentation needed for optimal combinations in specific applications.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Lecture Notes in Computer Science

ISBN

978-3-031-43077-0

ISSN

—

e-ISSN

—

Number of pages

12

Pages from-to

352-363

Publisher name

Springer Nature Switzerland AG

Place of publication

Cham

Event location

Ponta Delgada

Event date

Jun 19, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—