Unknown

Dataset Information

0

An Advanced Two-Step DNN-Based Framework for Arrhythmia Detection


ABSTRACT: Heart arrhythmia is a severe heart problem. Automated heartbeat classification provides a cost-effective screening for heart arrhythmia and allows at-risk patients to receive timely treatments, which is a highly demanded but challenging task. Recent works have brought visible improvements to this area, but to identify the problematic supraventricular ectopic (S-type) heartbeats is still a bottleneck in most existing studies. This paper presents a two-step DNN-based framework to identify arrhythmia-related heartbeats. In the first step, a deep dual-channel convolutional neural network (DDCNN) is proposed to classify all heartbeat classes, except for the normal and S-type heartbeats. In the second stage, a central-towards LSTM supportive model (CLSM) is specially designed to distinguish S-type heartbeats from the normal ones. By processing heart rhythms in central-towards directions, CLSM learns and abstracts hidden temporal information between a heartbeat and its neighbors to reveal the deep differences between the two heartbeat types. As an improvement, we also propose a rule-based data augmentation method to solve the training data imbalance problem. The proposed framework is evaluated over three real-world ECG databases. The results show that our method outperforms the baselines in most evaluation metrics.

SUBMITTER: Lauw H 

PROVIDER: S-EPMC7206245 | BioStudies | 2020-01-01

REPOSITORIES: biostudies

Similar Datasets

2018-01-01 | S-EPMC6235298 | BioStudies
2020-01-01 | S-EPMC7477611 | BioStudies
2014-01-01 | S-EPMC4270110 | BioStudies
2020-01-01 | S-EPMC7206251 | BioStudies
1000-01-01 | S-EPMC6121625 | BioStudies
2016-01-01 | S-EPMC4876374 | BioStudies
1000-01-01 | S-EPMC1871868 | BioStudies
2020-01-01 | S-EPMC7031229 | BioStudies
1000-01-01 | S-EPMC2553387 | BioStudies
2020-01-01 | S-EPMC7005164 | BioStudies