Cardiac Arrhythmia Detection from ECG Combining Convolutional and Long Short-Term Memory Networks

Warrick, Philip; Homsi, Masun Nabhan

doi:10.22489/cinc.2017.161-460

Cited by 79 publications

(71 citation statements)

References 7 publications

(9 reference statements)

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“…In [11], Yildirim et al designed a deep LTSM network with wavelet-based layers for heartbeat classification. Some methods [12,13] combine LSTM with CNN.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous ECG Heartbeat Segmentation and Classification with Feature Fusion and Long Term Context Dependencies

Qiu

Shen

Zhang

2020

Advances in Knowledge Discovery and Data Mining

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Arrhythmia detection by classifying ECG heartbeats is an important research topic for healthcare. Recently, deep learning models have been increasingly applied to ECG classification. Among them, most methods work in three steps: preprocessing, heartbeat segmentation and beat-wise classification. However, this methodology has two drawbacks. First, explicit heartbeat segmentation can undermine model simplicity and compactness. Second, beat-wise classification risks losing inter-heartbeat context information that can be useful to achieving high classification performance. Addressing these drawbacks, we propose a novel deep learning model that can simultaneously conduct heartbeat segmentation and classification. Compared to existing methods, our model is more compact as it does not require explicit heartbeat segmentation. Moreover, our model is more context-aware, for it takes into account the relationship between heartbeats. To achieve simultaneous segmentation and classification, we present a Faster R-CNN based model that has been customized to handle ECG data. To characterize inter-heartbeat context information, we exploit inverted residual blocks and a novel feature fusion subroutine that combines average pooling with max-pooling. Extensive experiments on the well-known MIT-BIH database indicate that our method can achieve competitive results for ECG segmentation and classification.

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“…In [11], Yildirim et al designed a deep LTSM network with wavelet-based layers for heartbeat classification. Some methods [12,13] combine LSTM with CNN.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous ECG Heartbeat Segmentation and Classification with Feature Fusion and Long Term Context Dependencies

Qiu

Shen

Zhang

2020

Advances in Knowledge Discovery and Data Mining

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“…At the same time, many other participants of the Challenge also used neural networks [13,14,15,16] as feature detector in addition to their traditional feature extractors. One of them was Andreotti et al [17], who compared their featurebased classifiers to residual neural networks.…”

Section: Related Workmentioning

confidence: 99%

Strong deep learning baseline for single lead ECG processing

Csaba¹,

Hakkel²,

Goda³

et al. 2019

StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference

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Objective: Atrial fibrillation (AF) is one of the most common serious abnormal heart rhythm conditions, and the number of deaths related to atrial fibrillation has increased by an order of magnitude in the past decades. We aim to create a system, which can provide help for cardiologist, classifying and highlighting important segments in recordings. Approach: In this paper, we propose a novel approach for AF detection using only a deep neural architecture without any traditional feature extractor for real-time automated suggestions of possible cardiac failures that can detect class invariant anomalies in signals recorded by a single channel portable ECG device. Results: Detecting the four categories: Normal, AF, Other and Noisy in terms of the official, F1 metric of hidden dataset maintained by the organizers of PhysioNet Computing in Cardiology Challenge 2017, our proposed algorithm has scored 0.88, 0.80, 0.69, 0.64 points respectively, and 0.79 on average.

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“…[9,17,18]. The combination of CNNs and long short-term memory (LSTM) networks was used for cardiac arrhythmia detection in another study [19].…”

Section: Deep Learning For Automated Ecg Interpretationmentioning

confidence: 99%

Electrocardiogram Monitoring and Interpretation: From Traditional Machine Learning to Deep Learning, and Their Combination

Parvaneh¹,

Rubin²

2018

Computing in Cardiology Conference (CinC)

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Cardiac arrhythmia can lead to morbidity and mortality and is a substantial economic burden. Electrocardiogram (ECG) monitoring is widely used to detect arrhythmia. The number of ECG recordings is increasing due to aging populations and availability of easy-to-use wearable devices. Manual interpretation of the high volume of recorded ECGs might not be a feasible and scalable solution. Therefore, machine learning algorithms are widely used for automatic ECG interpretation. In this paper, the challenges and differences between machine learning techniques for ECG monitoring and interpretation are reviewed, from traditional machine learning classifiers to deep learning and their combination.

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Cardiac Arrhythmia Detection from ECG Combining Convolutional and Long Short-Term Memory Networks

Cited by 79 publications

References 7 publications

Simultaneous ECG Heartbeat Segmentation and Classification with Feature Fusion and Long Term Context Dependencies

Simultaneous ECG Heartbeat Segmentation and Classification with Feature Fusion and Long Term Context Dependencies

Strong deep learning baseline for single lead ECG processing

Electrocardiogram Monitoring and Interpretation: From Traditional Machine Learning to Deep Learning, and Their Combination

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