Arrhythmia detection model using modified DenseNet for comprehensible Grad-CAM visualization

Arrhythmia Detection Using Gated Recurrent Unit Network with ECG Signals

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.2928 ◽

2020 ◽

Vol 10 (3) ◽

pp. 750-757

Author(s):

Gang Xu ◽

Guangxin Xing ◽

Juanjuan Jiang ◽

Jian Jiang ◽

Yongsheng Ke

Keyword(s):

Deep Learning ◽

Visual Analysis ◽

Classification Performance ◽

Ecg Signal ◽

Learning Networks ◽

Arrhythmia Detection ◽

Ecg Signals ◽

Detection Model ◽

High Degree ◽

Gated Recurrent Unit

Background: Arrhythmia is a kind of heart disorder characterized by irregular heartbeats which can be detected with Electrocardiographic (ECG) signals. Accurate and early detection along with differentiation of arrhythmias is of great importance in a clinical setting. However, visual analysis of ECG signal is a challenging and timeconsuming work. We have developed an automatic arrhythmia detection model with deep learning framework to expedite the diagnosis of arrhythmia with a high degree of accuracy. Methods: We proposed a novel automatic arrhythmia detection model utilizing a combination of 1D convolutional neural network (1D-CNN) and Gated Recurrent Unit (GRU) network for the diagnosis of five different arrhythmia on ECG signals taken from the MITBIT arrhythmia physio bank database. Results: The proposed system showed a high classification performance in handling variable length ECG signal data, achieving an accuracy rate of 99.45%, sensitivity of 98.35% and specificity of 99.21% and a F1-Score of 98.95% using a five-fold cross validation strategy. Conclusions: Combining 1D-CNN and GRU networks yielded a higher degree of accuracy compared with other deep learning networks. Our proposed arrhythmia detection method may be a powerful tool to aid clinicians in accurately detecting common arrhythmias on routine ECG screening.

Download Full-text

239 Arrhythmia detection from chronically implanted subcutaneous leads in presence of physical activity

EP Europace ◽

10.1016/s1099-5129(05)80148-5 ◽

2005 ◽

Vol 7 ◽

pp. 50-50

Keyword(s):

Physical Activity ◽

Arrhythmia Detection

Download Full-text

A Signal Detection Model of Moral Decision Making

PsycEXTRA Dataset ◽

10.1037/e527342012-637 ◽

2007 ◽

Author(s):

Jasper Jancik ◽

Dale J. Cohen

Keyword(s):

Decision Making ◽

Signal Detection ◽

Moral Decision ◽

Moral Decision Making ◽

Detection Model

Download Full-text

Frequency effects in recognition memory: Evidence for a multiprocess detection model?

PsycEXTRA Dataset ◽

10.1037/e537102012-320 ◽

2001 ◽

Author(s):

David G. Smith ◽

Marty W. Niewiadomski

Keyword(s):

Recognition Memory ◽

Frequency Effects ◽

Detection Model

Download Full-text

Recognition of classroom student state features based on deep learning algorithms and machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189232 ◽

2020 ◽

pp. 1-12

Author(s):

Hu Jingchao ◽

Haiying Zhang

Keyword(s):

Deep Learning ◽

Feature Recognition ◽

Subjective Evaluation ◽

Recognition Algorithm ◽

Video Data ◽

State Recognition ◽

Detection Model ◽

State Classification ◽

Intelligent Models ◽

Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Download Full-text