Classification of ECG anomaly with dynamically-biased LSTM for continuous cardiac monitoring

• Main Authors: Hu, Jinhai, Goh, Wang Ling, Gao, Yuan
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2024
• Subjects: Engineering; Long short-term memory; ECG classification; DB-LSTM
• Online Access: https://hdl.handle.net/10356/179112; https://ieeexplore.ieee.org/abstract/document/10181690
• Institution: Nanyang Technological University

This paper presents an electrocardiogram (ECG) signal classification model based on dynamically-biased Long Short-Term Memory (DB-LSTM) network. Compared to conventional LSTM networks, DB-LSTM introduces a set of parameters C which save the previous time-step cell gate states of the unit cell. Hence...