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August 17, 2025

AI-Driven Early Diagnosis of Congestive Heart Failure from ECG Signals Using Convolutional Architectures

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Authors

RSRana Riyadh SaeedNorthern Technical University

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Implication

Enhanced convolutional neural networks improve accuracy in detecting congestive heart failure using ECG signals, suggesting robust clinical applications.

Key Points

The model achieves an accuracy of 99.31%, indicating high effectiveness in congestive heart failure detection.
Key metrics include 99.42% sensitivity and 99.19% specificity, demonstrating the model's reliability across different conditions.
An enhanced convolutional neural network framework was used to classify heart failure from ECG signals with robust evaluation protocols.
This approach emphasizes the importance of thorough evaluation for deploying diagnostic models in real-world healthcare settings.

Cite This Study

Rana Riyadh Saeed (2025) studied this question.

synapsesocial.com/papers/68a36a3f0a429f797332e915 https://doi.org/10.54660/ijfei.2025.2.4.170-176

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Implication

Enhanced convolutional neural networks improve accuracy in detecting congestive heart failure using ECG signals, suggesting robust clinical applications.

AI-Driven Early Diagnosis of Congestive Heart Failure from ECG Signals Using Convolutional Architectures

Discussion

Member takes

Implication

Key Points

Cite This Study

AI-Driven Early Diagnosis of Congestive Heart Failure from ECG Signals Using Convolutional Architectures

Discussion

Member takes

Implication

Key Points

Cite This Study

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