2019
DOI: 10.1109/access.2019.2931500
|View full text |Cite
|
Sign up to set email alerts
|

Automatic Cardiac Arrhythmia Classification Using Combination of Deep Residual Network and Bidirectional LSTM

Abstract: Cardiac arrhythmia is associated with abnormal electrical activities of the heart, which can be reflected by altered characteristics of electrocardiogram (ECG). Due to the simplicity and non-invasive nature, the ECG has been widely used for detecting arrhythmias and there is an urgent need for automatic ECG detection. Up to date, some algorithms have been proposed for automatic classification of cardiac arrhythmias based on the features of the ECG; however, their stratification rate is still poor due to unreli… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

1
72
1

Year Published

2020
2020
2024
2024

Publication Types

Select...
5
3
1

Relationship

0
9

Authors

Journals

citations
Cited by 157 publications
(74 citation statements)
references
References 53 publications
1
72
1
Order By: Relevance
“…This has been proved in many applications like Natural Language Processing (NLP), such as accurate context-sensitivity prediction [25] and unified tagging solution suggested by [26] using the BLSTM-RNN algorithm. Other applications that have been studied for heart disease detection such as sequence labeling for extraction of medical events from EHR notes was suggested using Bidirectional RNN [27], and an automatic classification of arrhythmia's based on Bidirectional LSTM [28]. Another work presented a new recurrent convolutional neural network (RCNN)-based disease risk assessment from hospital big data [29].…”
Section: Introductionmentioning
confidence: 99%
“…This has been proved in many applications like Natural Language Processing (NLP), such as accurate context-sensitivity prediction [25] and unified tagging solution suggested by [26] using the BLSTM-RNN algorithm. Other applications that have been studied for heart disease detection such as sequence labeling for extraction of medical events from EHR notes was suggested using Bidirectional RNN [27], and an automatic classification of arrhythmia's based on Bidirectional LSTM [28]. Another work presented a new recurrent convolutional neural network (RCNN)-based disease risk assessment from hospital big data [29].…”
Section: Introductionmentioning
confidence: 99%
“…The LSTM neural network is a deep learning method with wide range of application, where inputs and outputs can change with time. Because of its excellent performance on dealing with long-term dependency problem, LSTM neural network has been used in the fields of time series forecasting and attracted lots of attention [24], [25]. Specifically, the memory cell in network, which realizes the function of temporal state storage, is the foundation of the whole architecture.…”
Section: B Lstm Neural Networkmentioning
confidence: 99%
“…Many researches have demonstrated the effectiveness of RNN for time series prediction [22], [23]. As an improved version of RNN, long short term memory (LSTM) neural network has been applied in time series prediction, language progressing and other fields, and attracted more and more attentions [24], [25]. Due to the''gate'' structure of LSTM, the useless information can be strictly filtered and more valuable information can be extracted from historical dataset.…”
mentioning
confidence: 99%
“…For example, Wu proposed a novel approach based on deep belief networks (DBN) for features learning of ECG arrhythmias [ 10 ]. He proposed a new method for automatic classification of arrhythmias using the combination of deep residual network (DRN) and bidirectional long short-term memory (Bi-LSTM) network [ 11 ]. Fan proposed a multi-scaled deep convolutional neural network (CNN) fusion method to screen out AF recordings from single lead short ECG recordings, which employ the architecture of two-stream convolutional networks with different filter sizes to capture features of different scales [ 12 ].…”
Section: Introductionmentioning
confidence: 99%