An Electrocardiogram Delineator via Deep Segmentation Network

Jia, Dongya; Zhao, Wei; Li, Zhenqi; Hu, Jing; Yan, Cong; Wang, Hongmei; You, Tianyuan

doi:10.1109/embc.2019.8856987

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…The 1D CNN is well-suited for real-time and low-cost applications in biomedical signal processing [12,[24][25][26]. The computational complexity of a 1D CNN is lower than that of a two-dimensional CNN.…”

Section: Convolutional Neural Network Architecturementioning

confidence: 99%

Empowering AI-Diagnosis: Deep Learning Abilities for Accurate Atrial Fibrillation Classification

Tutuko,

Darmawahyuni,

Nurmaini

et al. 2023

Int. J. Onl. Eng.

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Artificial intelligence (AI) is a powerful technology that can enhance clinical decision-making and the efficiency of global health systems. An AI-enabled electrocardiogram (ECG) is an essential tool for diagnosing heart abnormalities such as arrhythmias. The most prevalent arrhythmia globally is atrial fibrillation (AF), which is an irregular heart rhythm that originates in the atria and can lead to other heart-related complications. A trusted AI classification of AF is explored in this study. Deep learning (DL) has been used to analyze large amounts of publicly available ECG datasets in order to classify normal sinus rhythm (NSR), AF, and other types of arrhythmias. A convolutional neural network (CNN) has been proposed to extract ECG features and classify ECG signals. Based on a 10-fold cross-validation strategy, we conducted experiments involving three scenarios for AF classification: (i) a balanced set, an imbalanced set, and an extremely imbalanced set; (ii) a comparison of ECG denoising algorithms; and (iii) the classification of AF, NSR, and other arrhythmia types (15 classes). As a result, we have achieved 100% accuracy, sensitivity, specificity, precision, and F1-score for the AF, NSR, and non-AF classifications, both for balanced and imbalanced sets. In addition, for the classification of AF, NSR, and other types of arrhythmia (15 classes), the performance results achieved an accuracy of 99.77%, sensitivity of 96.48%, specificity of 99.87%, precision of 97.03%, and F1-score of 96.68%. The results can empower AI diagnosis and assist clinicians in classifying AF on routine screening ECGs.

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Section: Convolutional Neural Network Architecturementioning

confidence: 99%

Empowering AI-Diagnosis: Deep Learning Abilities for Accurate Atrial Fibrillation Classification

Tutuko,

Darmawahyuni,

Nurmaini

et al. 2023

Int. J. Onl. Eng.

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“…To remove the high-frequency noise and baseline wandering noise, each lead of ECG is processed by a bandpass Butterworth filter, with a range of 0.5Hz-48Hz. Then, each ECG recording is delineated by algorithm [9], so as to obtain the location of ECG fiducial points, including the onset and offset of the P wave, the onset and offset of the QRS complex and the offset of the T wave.…”

Section: Ecg Preprocessing and Delineationmentioning

confidence: 99%

“…In this paper, we propose a multimodal deep learning method to detect I-AVB from variable-length 12-lead ECG records. We use [9]' s algorithm to delineate ECG records, the delineation result is concatenated with 12-lead ECG waveform as the input of out neural network. The structure of our network is composed of CNN and Long Short-term Memory [10].…”

Section: Introductionmentioning

confidence: 99%

Detection of First-Degree Atrioventricular Block on Variable-Length Electrocardiogram via a Multimodal Deep Learning Method

Jia¹,

Zhao²,

Hu³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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Automatic detection of first-degree atrioventricular block (I-AVB) from electrocardiogram (ECG) is of great importance in prevention of more severe cardiac diseases. I-AVB is characterized by a prolonged PR interval. However, due to various artifacts and diversity of ECG morphology, existing ECG delineation algorithms is unable to provide robust measurement of the PR interval. Deep neural network is good at extracting high-level feature from ECG waveform, but merely using waveform as input of neural network may aggravate overfitting when lack of I-AVB records. In this paper, we propose a multimodal-input deep learning method to effectively detect I-AVB from 12lead ECG records. We utilize ECG waveform and delineation result as the multimodal input of neural network. Our neural network, mainly composed of convolutional neural network and Long Short-term Memory, is well designed to adapt to variable-length ECG. Our method is evaluated on dataset of CPSC2018, and outperforms the baseline methods in F1 score.

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Towards identifying most important leads for ECG classification. A Data driven approach employing Deep Learning

2020

Procedia Computer Science

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An Electrocardiogram Delineator via Deep Segmentation Network

Cited by 7 publications

References 11 publications

Empowering AI-Diagnosis: Deep Learning Abilities for Accurate Atrial Fibrillation Classification

Empowering AI-Diagnosis: Deep Learning Abilities for Accurate Atrial Fibrillation Classification

Detection of First-Degree Atrioventricular Block on Variable-Length Electrocardiogram via a Multimodal Deep Learning Method

Towards identifying most important leads for ECG classification. A Data driven approach employing Deep Learning

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