DETECTION OF HEART FAILURE USING A CONVOLUTIONAL NEURAL NETWORK VIA ECG SIGNALS

SHAIK KHAJA UMAR; B MURALI

DETECTION OF HEART FAILURE USING A CONVOLUTIONAL NEURAL NETWORK VIA ECG SIGNALS

Authors: SHAIK KHAJA UMAR, B MURALI

Unique Paper ID: 168498
Volume: 11
Issue: 5
PageNo: 1024-1029

Keywords: Heart failure ECG Convolutional Neural Network chronic Heart condition.

Abstract:
“Heart failure (HF) is a chronic heart condition that increases mortality, morbidity, and healthcare costs. The electrocardiogram (ECG) is a noninvasive and straightforward diagnostic tool that can reveal detectable changes in HF. Because of their small amplitude and duration, these changes can be subtle and potentially misclassified during manual interpretation or when analyzed by clinicians. This paper reports a 7-layer deep convolutional neural network (CNN) model for HF automatic detection. The proposed CNN model requires only minimal preprocessing of ECG signals and does not require any engineered features.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 5
PageNo: 1024-1029

DETECTION OF HEART FAILURE USING A CONVOLUTIONAL NEURAL NETWORK VIA ECG SIGNALS

Available:https://ijirt.org/Article?manuscript=168498

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Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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