Cardiac Arrhythmia Detection from 2D ECG Images by Using Deep Learning Technique

Izci, Elif; Özdemir, Mehmet Akif; Degirmenci, Murside; Akan, Aydın

doi:10.1109/tiptekno.2019.8895011

Cited by 81 publications

(37 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…After the experiment, authors then compared authors' result with related works that also used CNN 2D and used the MIT-BIH arrhythmia database but with different approaches as seen in Table IV. As seen in Table IV, authors' proposed approach shows that with smaller input over the state-of-the-art approach, the result showed the difference on accuracy with the highest accuracy being less than 1%. Authors' approach also has only 8 layers, which is less complex than the state-of-the-art approach that consists of 11 and 13 layers even though the complexity is higher than Izci et al [13]. This proves that authors' proposed approach has better accuracy.…”

Section: Comparison With Related Workmentioning

confidence: 68%

“…Izci et al [13] used 2D Convolutional Neural to classify 5 arrhythmia classes by transforming MIT-BIH Arrhythmia database to 128x128 size grayscale image and 5 layer CNN resulting in 97.42% accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Convolutional Neural Network (CNN) is often used by researcher to classify ECG signal patterns into arrhythmia classes by using both 1D CNN and 2D CNN showing accuracy up to 92 % for the former for up to 17 classes [3]- [10] and 99% for the latter up to 8 Classes using 128x128 pixel transformed ECG signal [11]- [13]. This shows that 2D CNN performed better in classifying arrhythmia with higher accuracy than 1D CNN [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Arrhythmia Classification using 2D Convolutional Neural Network

Rohmantri¹,

Surantha²

2020

IJACSA

View full text Add to dashboard Cite

Arrhythmia is an abnormal situation of heartbeat rate that may cause a critical condition to our body and this condition gets more dangerous as our cardiovascular system gets more vulnerable as we grow older. To diagnose this abnormality, the arrhythmia expert or cardiologist uses an electrocardiogram (ECG) by analyzing the pattern. ECG is a heartbeat signal that is produced by a tool called an electrocardiograph sensor that records the electrical impulses produced by the heart. Convolutional Neural Networks (CNN) is often used by researchers to classify ECG signals to Arryhtmia classes. The state-of-the-art research had applied CNN 2D (CNN 2D) with accuracy up to 99% with 128x128 image size obtained by transforming the ECG signal. In this paper, authors try to classify arrhythmia disorder with a different approach by creating simpler image classifier using CNN 2D with a smaller variety of input size that is smaller than state-the-art input and group the classes based on transformed ECG signal from MIT-BIH Arrhythmia database with the purpose to know what the most optimum input and the best accuracy to classify ECG signal image. The result of this research had produced an accuracy of up to 98.91% for 2 Classes, 98.10% for 7 Classes dan 98.45% for 8 Classes.

show abstract

Section: Comparison With Related Workmentioning

confidence: 68%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Arrhythmia Classification using 2D Convolutional Neural Network

Rohmantri¹,

Surantha²

2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…The table shows our model outperforms most state-of-the-art model in term of classification performance. The classification accuracy of our model is 99.02% better than the techniques mentioned in References [11,57,[62][63][64][65][66][67][68][69].…”

Section: -D Cnn Comparison With Other Algorithmsmentioning

confidence: 93%

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

Ullah

Rehman

et al. 2021

Sensors

View full text Add to dashboard Cite

Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness.

show abstract

“…These heart ailments can differ from insignificant to hazardous (Elola et al, 2019). To thoroughly see how an ECG uncovers essential data about the state of your heart requires a fundamental comprehension of the life systems and physiology of the heart (Cunningham et al, 2016;Ionasec et al, 2016;Izci et al, 2019). These different kinds of arrhythmias can further be categorized into two major categories.…”

Section: Introductionmentioning

confidence: 99%

From ECG signals to images: a transformation based approach for deep learning

Naz

Shah

Khan

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate electrical impulses in the ventricles of the heart. Different types of arrhythmias are associated with different patterns, which can be identified. An electrocardiogram (ECG) is the major analytical tool used to interpret and record ECG signals. ECG signals are nonlinear and difficult to interpret and analyze. We propose a new deep learning approach for the detection of VA. Initially, the ECG signals are transformed into images that have not been done before. Later, these images are normalized and utilized to train the AlexNet, VGG-16 and Inception-v3 deep learning models. Transfer learning is performed to train a model and extract the deep features from different output layers. After that, the features are fused by a concatenation approach, and the best features are selected using a heuristic entropy calculation approach. Finally, supervised learning classifiers are utilized for final feature classification. The results are evaluated on the MIT-BIH dataset and achieved an accuracy of 97.6% (using Cubic Support Vector Machine as a final stage classifier).

show abstract

Cardiac Arrhythmia Detection from 2D ECG Images by Using Deep Learning Technique

Cited by 81 publications

References 20 publications

Arrhythmia Classification using 2D Convolutional Neural Network

Arrhythmia Classification using 2D Convolutional Neural Network

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

From ECG signals to images: a transformation based approach for deep learning

Contact Info

Product

Resources

About