Automatic diagnosis of cardiovascular disorders by sub images of the ECG signal using multi-feature extraction methods and randomized neural network

Ertuğrul, Ömer Faruk; Acar, Emrullah; Aldemir, Erdoğan; Öztekin, Abdulkerim

doi:10.1016/j.bspc.2020.102260

Cited by 23 publications

(15 citation statements)

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“…With the continuous strengthening of the ability of data storage and the continuous improvement of computing power, deep learning algorithms have made breakthroughs in many fields. Especially in the field of medical imaging, the use of deep learning methods includes disease diagnosis of magnetic resonance imaging [36,37], diagnosis and reconstruction of computed tomography [38,39], and the rhythm and MI diagnosis of ECG [6,[40][41][42][43][44][45].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Zhang

Liu

2021

Micromachines

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Heart diseases such as myocardial ischemia (MI) are the main causes of human death. The prediction of MI and arrhythmia is an effective method for the early detection, diagnosis, and treatment of heart disease. For the rapid detection of arrhythmia and myocardial ischemia, the electrocardiogram (ECG) is widely used in clinical diagnosis, and its detection equipment and algorithm are constantly optimized. This paper introduces the current progress of portable ECG monitoring equipment, including the use of polymer material sensors and the use of deep learning algorithms. First, it introduces the latest portable ECG monitoring equipment and the polymer material sensor it uses and then focuses on reviewing the progress of detection algorithms. We mainly introduce the basic structure of existing deep learning methods and enumerate the internationally recognized ECG datasets. This paper outlines the deep learning algorithms used for ECG diagnosis, compares the prediction results of different classifiers, and summarizes two existing problems of ECG detection technology: imbalance of categories and high computational overhead. Finally, we put forward the development direction of using generative adversarial networks (GAN) to improve the quality of the ECG database and lightweight ECG diagnosis algorithm to adapt to portable ECG monitoring equipment.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Zhang

Liu

2021

Micromachines

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“…Convolutional Neural Networks (CNN) [27], which is used in many computer vision applications, is one of the deep learning approaches in which multiple layers are trained Automatic diagnosis of cardiovascular disorders [28] and Detection of unregistered electric distribution transformers in agricultural fields [29] are Works using CNN. CNN, which effectively reduces the number of Artificial Neural Network (ANN) parameters, has revolutionized many areas from image processing to voice recognition.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Effect on model performance of regularization methods

Budak¹,

Mençik²,

Asker³

2021

DÜMF Mühendislik Dergisi

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Artificial Neural Networks with numerous parameters are tremendously powerful machine learning systems. Nonetheless, overfitting is a crucial problem in such networks. Maximizing the model accuracy and minimizing the amount of loss is significant in reducing in-class differences and maintaining sensitivity to these differences. In this study, the effects of overfitting for different model architectures with the Wine dataset were investigated by Dropout, AlfaDropout, GausianDropout, Batch normalization, Layer normalization, Activity normalization, L1 and L2 regularization methods and the change in loss function the combination with these methods. Combinations that performed well were examined on different datasets using the same model. The binary cross-entropy loss function was used as a performance measurement metric. According to the results, the Layer and Activity regularization combination showed better training and testing performance compared to other combinations.

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“…Nowadays, diagnosis is done in an automatic manner where an automated ECG processing system usually consists of four successive stages [21] as follows: signal preprocessing, waves detection, features extraction, and finally, abnormalities detection and classification.…”

Section: Ecg Signal Denoising Overviewmentioning

confidence: 99%

Hardware Architecture for Adaptive Dual Threshold Filter and Discrete Wavelet Transform based ECG Signal Denoising

Mejhoudi¹,

JENKAL²,

Saddik³

et al. 2021

IJACSA

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The ECG signal, like all signals obtained when instrumenting a data acquisition system, is affected by noises of physiological and technical sources such as Electromyogram (EMG) and power line interferences, which can deteriorate its morphology. To overcome this issue, it's subjected to apply a preprocessing step to remove these noises. Filtring techniques are complex computations becoming more common in medical applications, which must be completed in real-time. As a result, these applications are geared at integrating high-performance embedded architectures. This paper presents an FPGA (Field Programmable Gate Array) embedded architecture designed for an ECG denoising hybrid technique based on the Discrete Wavelet transform (DWT) and the Adaptive Dual Threshold Filter (ADTF), dedicated to handle with noises affecting ECG signals.The architecture was designed following a hardware-software codesign using a high-level description language and synthetized to be implemented on different FPGAs due to the structural description flexibility. The global architecture was divided into a set of functional blocks to allow parallel processing of ECG data. The simulation results confirm the high performance of the system in noise reduction without affecting the morphology of the signal. The process takes 0.3 ms with an acquisition frequency of 360 Hz. The whole architecture requires a small area in different FPGAs in terms of resources utilization. It uses less than 1% of the total registers for all FPGA devices which represents a total of 292 registers for Cyclone III LS, Cyclone IV GX, Cyclone IV E, and Arria II GX; and a total of 329 registers for Cyclone V. The logic elements occupancy varies between 3% using Cyclone V and 60% using Cyclone IV GX freeing up space for other parallel processing tasks.

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Automatic diagnosis of cardiovascular disorders by sub images of the ECG signal using multi-feature extraction methods and randomized neural network

Cited by 23 publications

References 50 publications

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Effect on model performance of regularization methods

Hardware Architecture for Adaptive Dual Threshold Filter and Discrete Wavelet Transform based ECG Signal Denoising

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