Hardware implementation of 1D-CNN architecture for ECG arrhythmia classification

Prajapati, Priyank H.; Darji, Anand D.

doi:10.1016/j.bspc.2023.104865

Cited by 30 publications

(6 citation statements)

References 23 publications

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“…The CNN, as a variant derived from conventional NNs, offers a diverse set of functional NNs [24]. As the name suggests, 1D-CNNs are commonly employed for processing curve data [25,26]. They operate in a single direction, as illustrated in Figure 2.…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

Deep Learning-Enhanced Inverse Modeling of Terahertz Metasurface Based on a Convolutional Neural Network Technique

Gao,

Jiang,

Zhu

et al. 2024

Photonics

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The traditional design method for terahertz metasurface biosensors is cumbersome and time-consuming, requires expertise, and often leads to significant discrepancies between expected and actual values. This paper presents a novel approach for the fast, efficient, and convenient inverse design of THz metasurface sensors, leveraging convolutional neural network techniques based on deep learning. During the model training process, the magnitude data of the scattering parameters collected from the numerical simulation of the THz metasurface served as features, paired with corresponding surface structure matrices as labels to form the training dataset. During the validation process, the thoroughly trained model precisely predicted the expected surface structure matrix of a THz metasurface. The results demonstrate that the proposed algorithm realizes time-saving, high-efficiency, and high-precision inversion methods without complicated data preprocessing and additional optimization algorithms. Therefore, deep learning algorithms offer a novel approach for swiftly designing and optimizing THz metasurface sensors in biomedical detection, bypassing the complex and specialized design process of electromagnetic devices, and promising extensive prospects for their application in the biomedical field.

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Section: Deep Learning Algorithmsmentioning

confidence: 99%

Deep Learning-Enhanced Inverse Modeling of Terahertz Metasurface Based on a Convolutional Neural Network Technique

Gao,

Jiang,

Zhu

et al. 2024

Photonics

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“…The first transforms the ECG signal into a time-frequency representation [14,16,25,48]. The second employs a 1D-CNN [49] that directly interprets the one-dimensional ECG data.…”

Section: Deep Learning-based Atrial Fibrillation Detection Algorithmsmentioning

confidence: 99%

“…For instance, the Ventricular Activity methods proposed by Dash et al [40], Tateno et al [39], and Huang et al [42] all achieve over 95% on Sp, demonstrating robust negative class prediction capabilities. The method by Jiang et al [49], which integrates both atrial and ventricular activities, exhibits Se and Sp nearing 98%, suggesting that combining multiple cardiac activities could yield superior results. Some of the latest techniques in deep learning, like the 2D CNN by Xia et al [25], 1D-ResNet by Hannun et al [27], and Bi-directional LSTM by Xie et al [26], have both Se and Sp exceeding 98%, showcasing their potential in detecting complex arrhythmic patterns.…”

Section: Plos Onementioning

confidence: 99%

Multiscale dilated convolutional neural network for Atrial Fibrillation detection

Xia,

He,

Huang

et al. 2024

PLoS ONE

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Atrial Fibrillation (AF), a type of heart arrhythmia, becomes more common with aging and is associated with an increased risk of stroke and mortality. In light of the urgent need for effective automated AF monitoring, existing methods often fall short in balancing accuracy and computational efficiency. To address this issue, we introduce a framework based on Multi-Scale Dilated Convolution (AF-MSDC), aimed at achieving precise predictions with low cost and high efficiency. By integrating Multi-Scale Dilated Convolution (MSDC) modules, our model is capable of extracting features from electrocardiogram (ECG) datasets across various scales, thus achieving an optimal balance between precision and computational savings. We have developed three MSDC modules to construct the AF-MSDC framework and assessed its performance on renowned datasets, including the MIT-BIH Atrial Fibrillation Database and Physionet Challenge 2017. Empirical results unequivocally demonstrate that our technique surpasses existing state-of-the-art (SOTA) methods in the AF detection domain. Specifically, our model, with only a quarter of the parameters of a Residual Network (ResNet), achieved an impressive sensitivity of 99.45%, specificity of 99.64% (on the MIT-BIH AFDB dataset), and an F 1 a l l score of 85.63% (on the Physionet Challenge 2017 AFDB dataset). This high efficiency makes our model particularly suitable for integration into wearable ECG devices powered by edge computing frameworks. Moreover, this innovative approach offers new possibilities for the early diagnosis of AF in clinical applications, potentially improving patient quality of life and reducing healthcare costs.

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“…Cai et al [ 68 ] and Hill et al [ 69 ] used portable devices (KardiaMobile and a Mason linear ECG lead system, respectively) (AliveCor Inc., CA, USA), (CardioCloud Medical Technology, Beijing, China). Yang et al [ 67 ] used an integrated analog front-end for heart rate monitoring, while Rawal, Prajapati, and Darji [ 71 ] used the device ZYNQ Ultrascale ZCU106 FPGA (Advanced Micro Devices, Inc., Santa Clara, CA, USA).…”

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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Hardware implementation of 1D-CNN architecture for ECG arrhythmia classification

Cited by 30 publications

References 23 publications

Deep Learning-Enhanced Inverse Modeling of Terahertz Metasurface Based on a Convolutional Neural Network Technique

Deep Learning-Enhanced Inverse Modeling of Terahertz Metasurface Based on a Convolutional Neural Network Technique

Multiscale dilated convolutional neural network for Atrial Fibrillation detection

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

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