LSTM-Based Auto-Encoder Model for ECG Arrhythmias Classification

Hou, Borui; Yang, Jianyong; Wang, Pu; Yan, Ruqiang

doi:10.1109/tim.2019.2910342

Cited by 221 publications

(92 citation statements)

References 42 publications

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“…Hou integrates a long short-term memory (LSTM)-based auto-encoder (AE) network with support vector machine (SVM) for ECG arrhythmias classification. In the model, the LSTM-based AE network extract ECG signal features, and the SVM classifier is applied for classifying different ECG arrhythmias signals [13].The result shows the proposed method has more than 99% accuracy. The author of [14] proposed a methods for the detection of pathological voice from healthy speech based on glottal source information.…”

Section: Related Workmentioning

confidence: 99%

A Classification Method for Judging the Depth of Chest Compression Based on CNN

Zhao¹,

Yu²,

Ye³

et al. 2020

Preprint

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Abstract The displacement can be calculated based on the integrated value of the acceleration signal waveform obtained by the acceleration sensor or gyroscope. However, this method is not effective in accurate measurement. Although some studies have improved the method of calculating accurate distance values by overcoming the effects of sensor noise or integration delay, the evaluation is still affected by sensor accuracy and environment. However, there are some special displacements, such as the chest compression. The displacement is a reciprocating motion and will return to the starting point again. Therefore, the acceleration waveform changes have obvious characteristics in the two stages from moving to the equilibrium position and returning to the starting point. Therefore, we propose an embedded classification method based on one-dimensional Convolutional Neural Network (CNN), which directly learns from the data of chest compressions and performs the signal formed by the Classification, distinguish the signal waveform under the standard pressing distance, so as to replace the calculation of distance measurement, and is not affected by factors such as pressure occlusion and electromagnetic wave interference, and has certain practical value on site. We tagged compressions and collected data from the simulator. The experiment evaluates the proposed CNN structure, and compares the classification results of the sample data with several CNN networks and SVMs with different structures in the literature. The results show that with sufficient training, the proposed 1D-CNN method can achieve an accuracy rate of more than 95%, and balances the accuracy rate and the hardware requirements.

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Section: Related Workmentioning

confidence: 99%

A Classification Method for Judging the Depth of Chest Compression Based on CNN

Zhao¹,

Yu²,

Ye³

et al. 2020

Preprint

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show abstract

“…LSTM is also used for the detection of ECG arrhythmias. [20–23]. Yildirim [20] proposed a new model for deep bidirectional LSTM network- (BLSTM-) based wavelet sequences (WS) to classified electrocardiogram (ECG) signals.…”

Section: Introductionmentioning

confidence: 99%

“…Oh et al [22] proposed a combined network model using CNN and LSTM for ECG arrhythmia diagnosis. Hou et al [23] introduced a new algorithm based on deep learning that combines LSTM with SVM for ECG arrhythmia classification.…”

Section: Introductionmentioning

confidence: 99%

An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset

Gao

Zhang

et al. 2019

Journal of Healthcare Engineering

122

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To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a challenging issue. Since ECG beat data exist in heavily imbalanced category, an effective long short-term memory (LSTM) recurrence network model with focal loss (FL) is proposed. For this purpose, the LSTM network can disentangle the timing features in complex ECG signals, while the FL is used to resolve the category imbalance by downweighting easily identified normal ECG examples. The advantages of the proposed network have been verified in the MIT-BIH arrhythmia database. Experimental results show that the LSTM network with FL achieved a reliable solution to the problem of imbalanced datasets in ECG beat classification and was not sensitive to quality of ECG signals. The proposed method can be deployed in telemedicine scenarios to assist cardiologists into more accurately and objectively diagnosing ECG signals.

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“…However, these methods require laborious rule adaptation when extended to cardiac morphologies not represented in the training dataset. More recently, Artificial Intelligence (AI) and Machine Learning techniques are being adapted to ECG processing, mainly for wave classification (Hou et al 2019;Hannun et al 2019), but also for ECG delineation (Jimenez-Perez et al 2020). The availability of annotated databases of ECG recordings corresponding to different cardiac patients such as the PhysioNet Resource (MIT Laboratory for Computational Physiology) (Goldberger et al 2000) has significantly contributed to the development of AIbased ECG processing algorithms.…”

Section: Introductionmentioning

confidence: 99%

Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset

Lampreave

Jiménez-Pérez

Sanz

et al. 2020

Computer Methods in Biomechanics and Biomedical Engineering: Im

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The interpretation of electrocardiograms (ECGs) is key for the diagnosis and monitoring of cardiovascular health. Despite the progressive digital transformation in healthcare, it is still common for clinicians to analyse ECG printed on paper. Although some systems provide signal processing-based ECG classification, clinicians often find it unreliable. Artificial Intelligence (AI) techniques are becoming state-of-the-art for ECG processing but the lack of digitised ECG has hampered the clinical translation of these techniques. Concurrently, we are living a rise in augmented reality (AR) technologies, with an increasing availability of devices. In this work, we present an automatic digitisation and assisted interpretation of ECG based on an AI-enabled Augmented Reality headset. The AR headset is used to acquire an image of the printed ECG, from which the digitised ECG signal is extracted. Afterwards, the digitised ECG is introduced into a Deep Learning (DL) algorithm pre-trained on a public database of 12-lead ECG recordings. The output of the DL algorithm classifies the ECG signal onto different cardiomyopathy categories, which is then visualized back in the AR headset. Preliminary classification results on simulated ECG images (96.5% of accuracy) confirm the potential of the developed approach to contribute on the digital transformation of ECG processing.

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LSTM-Based Auto-Encoder Model for ECG Arrhythmias Classification

Cited by 221 publications

References 42 publications

A Classification Method for Judging the Depth of Chest Compression Based on CNN

A Classification Method for Judging the Depth of Chest Compression Based on CNN

An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset

Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset

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