IoT-based ECG monitoring for arrhythmia classification using Coyote Grey Wolf optimization-based deep learning CNN classifier

Kumar, Abhishek; Kumar, SwarnAvinash; Dutt, Vishal; Dubey, Ashutosh Kumar; García‐Díaz, Vicente

doi:10.1016/j.bspc.2022.103638

Cited by 38 publications

(14 citation statements)

References 31 publications

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“…Zhao et al [ 49 ] used a Lenovo smart ECG chest (Lenovo Group Ltd., Beijing, China), while Farahani et al [ 48 ] used an unspecified set of electrodes and Yasin et al [ 47 ] used a custom-made VA processor/SoC. Finally, Kumar et al [ 58 ], Karthiga, Santhi, and Sountharrajan [ 63 ] and Shafi et al [ 65 ] used an unspecified set of sensors nodes.…”

Section: Research On Cvd Detection Using Iot/iomtmentioning

confidence: 99%

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Cuevas-Chávez,

Hernández,

Ortiz-Hernandez

et al. 2023

Healthcare

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According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.

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Section: Research On Cvd Detection Using Iot/iomtmentioning

confidence: 99%

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Cuevas-Chávez,

Hernández,

Ortiz-Hernandez

et al. 2023

Healthcare

View full text Add to dashboard Cite

show abstract

“…As an extension of machine learning (ML), deep learning (DL) has demonstrated its advantages over traditional ML algorithms in various classification and regression tasks [ 31 , 32 , 33 , 34 ]. In particular, image steganography based on deep learning has begun to emerge in recent years.…”

Section: Related Workmentioning

confidence: 99%

Deep Image Steganography Using Transformer and Recursive Permutation

Wang

Zhou

Liu

et al. 2022

Entropy

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Image steganography, which usually hides a small image (hidden image or secret image) in a large image (carrier) so that the crackers cannot feel the existence of the hidden image in the carrier, has become a hot topic in the community of image security. Recent deep-learning techniques have promoted image steganography to a new stage. To improve the performance of steganography, this paper proposes a novel scheme that uses the Transformer for feature extraction in steganography. In addition, an image encryption algorithm using recursive permutation is proposed to further enhance the security of secret images. We conduct extensive experiments to demonstrate the effectiveness of the proposed scheme. We reveal that the Transformer is superior to the compared state-of-the-art deep-learning models in feature extraction for steganography. In addition, the proposed image encryption algorithm has good attributes for image security, which further enhances the performance of the proposed scheme of steganography.

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“…A Butterworth low pass filter is a signal processing filter with a frequency response in the pass band that is as flat as feasible. [21] [22] In the frequency range of 0.5Hz to 100Hz, there is important information. All transmissions over 100Hz are considered noise.…”

Section: A Pre-processingmentioning

confidence: 99%

ECG Arrhythmia Classification Using Convolutional Neural Network

Thapliyal¹,

Verma²,

Kumar³

2022

IJETAE

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This study provides a thorough analysis of earlier DL techniques used to classify the ECG data. The large variability among individual patients and the high expense of labeling clinical ECG records are the main hurdles in automatically detecting arrhythmia by electrocardiogram (ECG). The classification of electrocardiogram (ECG) arrhythmias using a novel and more effective technique is presented in this research. A high-performance electrocardiogram (ECG)-based arrhythmic beats classification system is described in this research to develop a plan with an autonomous feature learning strategy and an effective optimization mechanism, based on the ECG heartbeat classification approach. We propose a method based on efficient 12-layer, the MIT-BIH Arrhythmia dataset's five micro-classes of heartbeat types and using the wavelet denoising technique. Compared to state-of-the-art approaches, the newly presented strategy enables considerable accuracy increase with quicker online retraining and less professional involvement.

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IoT-based ECG monitoring for arrhythmia classification using Coyote Grey Wolf optimization-based deep learning CNN classifier

Cited by 38 publications

References 31 publications

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Deep Image Steganography Using Transformer and Recursive Permutation

ECG Arrhythmia Classification Using Convolutional Neural Network

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