DenseNet-ABiLSTM: Revolutionizing Multiclass Arrhythmia Detection and Classification Using Hybrid Deep Learning Approach Leveraging PPG Signals

DenseNet-ABiLSTM: Revolutionizing Multiclass Arrhythmia Detection and Classification Using Hybrid Deep Learning Approach Leveraging PPG Signals

Abstract Arrhythmias (AM) are heart conditions that can lead to fatal cardiac arrest. Automated identification of arrhythmias is crucial for detecting cardiac diseases. Previous studies have used photoplethysmography (PPG) signals to identify arrhythmias, but there is limited research on their appli...

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Bibliographic Details
Main Authors: K. Saranya, U. Karthikeyan, A. Saran Kumar, Ayodeji Olalekan Salau, Ting Tin Tin
Format: Article
Language:English
Published: Springer 2025-02-01
Series:International Journal of Computational Intelligence Systems
Subjects:
DenseNet
BiLSTM
Photoplethysmography
Arrhythmias
Hybrid deep learning
Multiclass detection
Online Access:https://doi.org/10.1007/s44196-025-00765-z
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https://doi.org/10.1007/s44196-025-00765-z

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