Automated detection of cardiovascular disease by electrocardiogram signal analysis: a deep learning system

Zhang, Xin; Gu, Kai; Miao, Shumei; Zhang, Xiaoliang; Yin, Yuechuchu; Wan, Cheng; Yu, Yun; Hu, Jie; Wang, Zhongmin; Shan, Tao; Jing, Shenqi; Wang, Wenming; Ge, Yun; Yin, Changqing; Guo, Jianjun; Liu, Yun

doi:10.21037/cdt.2019.12.10

Cited by 44 publications

(27 citation statements)

References 21 publications

(23 reference statements)

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“…In phase 1 the methodology succeeded in detecting all disorders with specificity higher than 90% and sensitivity higher than 84%, except for STE and STD. These results are in line with those already present in literature [15], although as different databases are used, a comparison is not trivial. These results suggest that intra-patient and inter-patient models manage to capture electrophysiological disturbances of different nature and areas of the cardiac tissue.…”

Section: Discussionsupporting

confidence: 91%

“…The aim of this work was to propose an automatic algorithm capable of identifying different cardiovascular diseases using 6 different databases with 43,101 labeled recordings made available by the PhysioNet/Computing in Cardiology Challenge 2020 [4]. Several attempts have been already described in literature [3], [13], [14], [15]. Currently, the role of clinicians is still fundamental for the final diagnosis, but a support role from computers could provide a useful tool to aid them for early and correct diagnosis of cardiac abnormalities.…”

Section: Discussionmentioning

confidence: 99%

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ECG Morphological Decomposition for Automatic Rhythm Identification

García-Isla¹,

Laureanti²,

Corino³

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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Manual rhythm classification in 12-lead ECGs is timeconsuming and operator-biased. We present an automatic ECG classifier using CinC's 2020 challenge dataset. In the first phase of the Challenge, 9 categories were targeted with an ensemble of 4 classifiers. In the second phase, 7 classifiers were implemented to detect 24 cardiac electrophysiological disorders. Five classifiers identified abnormalities in different specific regions of the heart's conducting system. Two classifiers were dedicated to detect premature atrial and ventricular contractions. The methodology is based on the creation of rhythmspecific intra and inter-patient templates. Firstly, signals were divided into 6 regions of interests. Secondly, for each region, intra-patient models and inter-patient rhythmspecific models were computed. The distances from each intra-patient model to each rhythm-specific inter-patient model as well as heart rate variability features and Global Electric Heterogeneity features were introduced into the classifiers. After a 10-fold cross-validation, for the provided training data in the first phase an accuracy of 94.4%0.4, and a Challenge metric of 0.644±0.031 were obtained, whereas in the second phase an accuracy and Challenge metric of 15.0  1.0 % and 0.030  0.009 were obtained.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

ECG Morphological Decomposition for Automatic Rhythm Identification

García-Isla¹,

Laureanti²,

Corino³

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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“…Moreover, to demonstrate the superiority of our proposed method for ECG beat classification, we compared the performance of our method with recently published works. As shown in Table 5, the accuracy of our hybrid model is higher than the accuracy achieved from CNN model [14,64], the LSTM model [56], and the CNN and LSTM models used in this work. Also, the accuracy of our hybrid model is higher than that achieved from hybrid CNN-LSTM [52,54,72] and is comparable to that achieved by Oh et al [71].…”

Section: The Results From the Cnn Modelmentioning

confidence: 66%

“…Deep structures of a human brain have a huge number of hidden layers, this allows us to extract and abstract the deep features at different levels and from different aspects. Many deep learning algorithms have been proposed in recent years [1,6,12,14,16,17,64]. Convolutional Neural Network (CNN) [32,43] and Long Short-Term Memory (LSTM) [25,33,56] are the most widely used, powerful, and efficient deep learning methods.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

Obeidat¹,

Alqudah²

2021

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In this paper we have utilized a hybrid lightweight 1D deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods for accurate, fast, and automated beat-wise ECG classification. The CNN and LSTM models were designed separately to compare with the hybrid CNN-LSTM model in terms of accuracy, number of parameters, and the time required for classification. The hybrid CNN-LSTM system provides an automated deep feature extraction and classification for six ECG beats classes including Normal Sinus Rhythm (NSR), atrial fibrillation (AFIB), atrial flutter (AFL), atrial premature beat (APB), left bundle branch block (LBBB), and right bundle branch block (RBBB). The hybrid model uses the CNN blocks for deep feature extraction and selection from the ECG beat. While the LSTM layer will learn how to extract contextual time information. The results show that the proposed hybrid CNN-LSTM model achieves high accuracy and sensitivity of 98.22% and 98.23% respectively. This model is light and fast in classifying ECG beats and superior to other previously used models which makes it very suitable for embedded systems designs that can be used in clinical applications for monitoring heart diseases in faster and more efficient manner.

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“…They also designed a portable smart hardware device, along with an interactive mobile program, to demonstrate its practical use. Zhang et al [53] In the present study, we have proposed a one-dimensional CNNs for AF detection. We have experimented the testing (unseen data) with and without ECG recordings from Chapman University and Shaoxing People's Hospital.…”

Section: D-cnns Classifier Performancesmentioning

confidence: 89%

AFibNet: An Implementation of Atrial Fibrillation Detection With Convolutional Neural Network

Tutuko

Nurmaini

Tondas³

et al. 2021

Preprint

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Background: Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R-R intervals to determine the Heart Rate Variability (HRV). An accurate HRV is the gold standard for predicting the AF condition. Hence, we propose a DL approach to analyze massive amounts of ECG raw data in a broad range of devices to overcome a current challenge.Results: This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8,232 records based on three datasets and tested with 3,610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity. When tested with unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity in 0.02 seconds for one instance when processed in theDL-Cloud System.Conclusions: These findings demonstrate that the proposed model approach can used in a broad range of devices and validated to unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deployment.

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Automated detection of cardiovascular disease by electrocardiogram signal analysis: a deep learning system

Cited by 44 publications

References 21 publications

ECG Morphological Decomposition for Automatic Rhythm Identification

ECG Morphological Decomposition for Automatic Rhythm Identification

A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

AFibNet: An Implementation of Atrial Fibrillation Detection With Convolutional Neural Network

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