Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats

By Shu Lih Oh, E. Ng, Ruyan Tan, U. Acharya. Comput. Biol. Medicine, 2018

643 citations recorded by the source corpus.

Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats. Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) signal. However, it can be challenging and time-consuming to visually assess the ECG signals due to the very low amplitudes. Implementing an automated system in the clinical setting can potentially help expedite diagnosis of arrhythmia, and improve the accuracies. In this paper, we propose an automated system using a combination of convolutional neural network (CNN) and long short-term memory (LSTM) for diagnosis of normal sinus rhythm, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature beats (APB) and premature ventricular contraction (PVC) on ECG signals. The novelty of this work is that we used ECG segments of variable length from the MIT-BIT arrhythmia physio bank database. The proposed system demonstrated high classification performance in the handling of variable-length data, achieving an accuracy of 98.10%, sensitivity of 97.50% and specificity of 98.70% using ten-fold cross validation strategy. Our proposed model can aid clinicians to detect common arrhythmias accurately on routine screening ECG.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/965cbac4919ab3eda46cf3eae182a78d3ec892d6

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In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

A review on deep learning methods for ECG arrhythmia classification (2020, background): “If it arises from the ventricles, it is called PVC. Fig.”
Detection of Coronavirus Disease (COVID-19) Based on Deep Features (2020, methodology): “The deep learning is one of the recent techniques applicable in the field of medicine for diagnosis purpose [16-28].”
Predicting residential energy consumption using CNN-LSTM neural networks (2019, methodology): “used a model combining CNN and LSTM to accurately detect arrhythmias in the ECG [21].”
Machine learning and deep learning approach for medical image analysis: diagnosis to detection (2022, methodology): “In addition, apart from AI techniques, ANN and CNN were also useful for predicting the disease by analyzing the image pattern and classification of the disease can be carried out with the help of classifiers [62, 63].”
Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets (2021, methodology): “data, but also can selectively remember or forget information checking the current memory state – and that made them popular for ECG signal classification [18,21].”