A multiclass CNN cascade model for the clinical detection support of cardiac arrhythmia based on subject-exclusive ECG dataset.

Carmine Liotto, Alberto Petrillo, Stefania Santini, Gianluca Toscano, Vincenza Tufano

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Article in Biomedical engineering letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed

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2 citing papers in PubMed.

Robust QRS detection based on simulated degenerate optical parametric oscillator-assisted neural network.Heliyon · 2024
Article
Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE-Tomek method to detect cardiac arrhythmia based on 12Digital health
Article

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5 authors.

Carmine LiottoTeoresi Group S.p.a., Via Ferrante Imparato, 198, 80146 Naples, Italy.

Alberto PetrilloDepartment of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy.ORCID 0000-0003-4630-6673

Stefania SantiniDepartment of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy.

Gianluca ToscanoTeoresi Group S.p.a., Via Ferrante Imparato, 198, 80146 Naples, Italy.

Vincenza TufanoTeoresi Group S.p.a., Via Ferrante Imparato, 198, 80146 Naples, Italy.

Funding

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6 · The paper itself

Abstract

The accurate analysis of Electrocardiogram waveform plays a crucial role for supporting cardiologist in detecting and diagnosing the heartbeat disorders. To improve their detection accuracy, this work is devoted to the design of a novel classification algorithm which is composed of a cascade of two convolutional neural network (CNN), i.e a Binary CNN allowing the detection of the arrhythmic heartbeat and a Multiclass CNN able to recognize the specific disorder. Moreover, by combining the cascade architecture solution with a rule-based data splitting, which leverages the

Indexed as

Arrhythmia classificationCardiovascular disordersConvolutional neural network (CNN)ECGMulticlass CNN cascadeSubject-exclusive criteria

Identifiers

PMID36238367

DOI10.1007/s13534-022-00246-8

PMCPMC9551136

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Textmetadata

LicenceCC BY

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