Cardioattentionnet: advancing ECG beat characterization with a high-accuracy and portable deep learning model.

ABSTRACT:

Introduction

The risk of mortality associated with cardiac arrhythmias is considerable, and their diagnosis presents significant challenges, often resulting in misdiagnosis. This situation highlights the necessity for an automated, efficient, and real-time detection method aimed at enhancing diagnostic accuracy and improving patient outcomes.

Methods

The present study is centered on the development of a portable deep learning model for the detection of arrhythmias via electrocardiogram (ECG) signals, referred to as CardioAttentionNet (CANet). CANet integrates Bi-directional Long Short-Term Memory (BiLSTM) networks, Multi-head Attention mechanisms, and Depthwise Separable Convolution, thereby facilitating its application in portable devices for early diagnosis. The architectu

SUBMITTER: He Y 

PROVIDER: S-EPMC11744002 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature