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Optimizing Deep Learning Architectures with Convolutional Networks for Enhanced ECG Signal Classification: Addressing Class Imbalance and Hyperparameter Tuning Challenges

Publish Year: 1403
Type: Conference paper
Language: English
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ICPCONF10_084

Index date: 24 November 2024

Optimizing Deep Learning Architectures with Convolutional Networks for Enhanced ECG Signal Classification: Addressing Class Imbalance and Hyperparameter Tuning Challenges abstract

Artificial intelligence, through machine learning and deep learning (DL), is transforming fields like medical diagnosis, including ECG classification. Traditional methods, relying on manually crafted features, often fall short in complex tasks. Our study introduces a DL architecture using 1-D convolution and Fully Convolutional Network (FCN) layers, inspired by VGGNet and ResNet, to improve ECG classification. We tackle issues such as imbalanced datasets and multiclass classification by weighting classes and employing confusion matrices and F1-scores. Our approach demonstrates superior accuracy and efficiency, highlighting the transformative potential of DL in healthcare.

Optimizing Deep Learning Architectures with Convolutional Networks for Enhanced ECG Signal Classification: Addressing Class Imbalance and Hyperparameter Tuning Challenges Keywords:

Optimizing Deep Learning Architectures with Convolutional Networks for Enhanced ECG Signal Classification: Addressing Class Imbalance and Hyperparameter Tuning Challenges authors

Matineh Zavar

Department, of Computer Engineering, Ferdows Branch, Islamic Azad University, Ferdows, Iran

Hamid Reza Ghaffari

Department, of Computer Engineering, Ferdows Branch, Islamic Azad University, Ferdows, Iran

Hamid Tabatabaee

Department, of Computer Engineering, Ferdows Branch, Islamic Azad University, Ferdows, Iran