Using Neural Networks for ECG Classification

Ádám, Norbert; Valko, David; Havrilla, Martin

doi:10.1109/sami54271.2022.9780670

Cited by 5 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After each iteration, the most relevant and uncertain heartbeats in the test records are marked and used to update the DNN weight, which significantly improves the detection and classification accuracy. A model, based on a one-dimensional CNN, is proposed in [31], where the data of the MIT-BIH database is divided into normal and abnormal heart activity, and a grid search method is used to find the hyperparameters of the CNN.…”

Section: Related Workmentioning

confidence: 99%

G2-ResNeXt: A Novel Model for ECG Signal Classification

Hao

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Electrocardiograms (ECG) are the primary basis for the diagnosis of cardiovascular diseases. However, due to the large volume of patients' ECG data, manual diagnosis is time-consuming and laborious. Therefore, intelligent automatic ECG signal classification is an important technique for overcoming the shortage of medical resources. This paper proposes a novel model for inter-patient heartbeat classification, named G2-ResNeXt, which adds a two-fold grouping convolution (G2) to the original ResNeXt structure, as to achieve better automatic feature extraction and classification of ECG signals. Experiments, conducted on the MIT-BIH arrhythmia database, confirm that the proposed model outperforms all state-of-the-art models considered (except the GRNN model for one of the heartbeat classes), by achieving overall accuracy of 96.16%, and sensitivity and precision of 97.09% and 95.90%, respectively, for the ventricular ectopic heartbeats (VEB), and of 80.59% and 82.26%, respectively, for the supraventricular ectopic heartbeats (SVEB).INDEX TERMS Cardiovascular disease (CVD), ECG signal classification, ResNeXt, convolutional block attention module (CBAM), MIT-BIH.

show abstract

Section: Related Workmentioning

confidence: 99%

G2-ResNeXt: A Novel Model for ECG Signal Classification

Hao

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…It involves classifying an ECG or the heartbeats into different heart conditions based on the structure of the heartbeats from one or multiple leads. The most simple classification task performed on an ECG is to classify its output into binary classes, i.e., normal vs. abnormal [ 11 ]. However, there are multiple arrhythmia conditions and heart diseases that need to be individually identified for accurate diagnosis [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets

Sattar,

Mumtaz,

Qadir

et al. 2024

Sensors

View full text Add to dashboard Cite

ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs.

show abstract

Multi-Classifier Deep Learning based System for ECG Classification Using Fourier Transform

Eleyan,

Alboghbaish

2023

2023 5th International Conference on Bio-Engineering for Smart Technologies (BioSMART)

View full text Add to dashboard Cite

Using Neural Networks for ECG Classification

Cited by 5 publications

References 29 publications

G2-ResNeXt: A Novel Model for ECG Signal Classification

G2-ResNeXt: A Novel Model for ECG Signal Classification

Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets

Multi-Classifier Deep Learning based System for ECG Classification Using Fourier Transform

Contact Info

Product

Resources

About