Parallel classification model of arrhythmia based on DenseNet-BiLSTM

Gan, Yi; Shi, Jun-cheng; He, Wei‐Ming; Sun, Fujia

doi:10.1016/j.bbe.2021.09.001

Cited by 15 publications

(13 citation statements)

References 45 publications

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“…According to Table 20, compared to the accuracy from [15,68,[79][80][81] the proposed model achieved a better classification performance, which indicates that using RP, RR detection, and the ResNet architecture can improve the classification accuracy of ECG arrhythmias. In their respective studies, the models in [82,83] performed better than the model in this study. The reported accuracy was 1.27% and 1.23% higher than that of the proposed work.…”

Section: Discussionmentioning

confidence: 57%

“…Sensors 2022, 22, x FOR PEER REVIEW 21 of 26 arrhythmias. In their respective studies, the models in [82,83] performed better than the model in this study. The reported accuracy was 1.27% and 1.23% higher than that of the proposed work.…”

Section: Discussionmentioning

confidence: 57%

“…The reported accuracy was 1.27% and 1.23% higher than that of the proposed work. This study's F1-score and sensitivity were higher than those of [83]. The AAMI arrhythmia beat classification used in our study was based on the MITDB database.…”

Section: Discussionmentioning

confidence: 68%

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ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features

Mathunjwa

Lin²,

Lin³

et al. 2022

Sensors

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In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the MIT-BIH Malignant Ventricular Ectopy Database, and the Creighton University Ventricular Tachyarrhythmia Database. ECG time series were segmented and converted using an RP, and two-dimensional images were used as inputs to the CNN classifiers. In this study, two-stage classification is proposed to improve the accuracy. The ResNet-18 architecture was applied to detect ventricular fibrillation (VF) and noise during the first stage, whereas normal, atrial fibrillation, premature atrial contraction, and premature ventricular contractions were detected using ResNet-50 in the second stage. The method was evaluated using 5-fold cross-validation which improved the results when compared to previous studies, achieving first and second stage average accuracies of 97.21% and 98.36%, sensitivities of 96.49% and 97.92%, positive predictive values of 95.54% and 98.20%, and F1-scores of 95.96% and 98.05%, respectively. Furthermore, a 5-fold improvement in the memory requirement was achieved when compared with a previous study, making this classifier feasible for use in resource-constricted environments such as portable devices. Even though the method is successful, first stage training requires combining four different arrhythmia types into one label (other), which generates more data for the other category than for VF and noise, thus creating a data imbalance that affects the first stage performance.

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Section: Discussionmentioning

confidence: 57%

Section: Discussionmentioning

confidence: 57%

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ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features

Mathunjwa

Lin²,

Lin³

et al. 2022

Sensors

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“…DenseNet architecture has been vastly applied in various fields, including medical diagnosis, plant disease detection, and more. Most experimental tests in previous works demonstrated that the DenseNet provided good accuracy due to feature extraction and robust feature reuse abilities [27]. The architecture of DenseNet is different from other CNN architecture, which is densely connected to each network and can learn with fewer parameters.…”

Section: Densenetmentioning

confidence: 99%

Comparing machine learning and deep learning classifiers for enhancing agricultural productivity: case study in Larache Province, Northern Morocco

Belattar

Abdoun

Haimoudi

2023

IJECE

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<p><span lang="EN-US">The agriculture sector in the Tangier-Tetouan-Al-Hoceima-Region (Northern Morocco) contributes a significant percentage to the national revenue. The Larache Province is at the regional forefront in agriculture terms due to its large irrigated areas. Golden-Gogi is a biological farm located in the Larache Province, and its objective is to produce organic crops. Besides climate change, this farm suffers from biotic factors such as snails and insects. These problems cause diseases in plants, resulting in massive crop production losses. Early detection of disease and biotic factors in plants is a difficult task for farmers, but it is now possible thanks to artificial intelligence. For that reason, we aim to contribute to this Province by comparing the well-known models in machine learning (ML) and deep learning (DL) used in early plant disease detection to specify the best-classifier in terms of detecting mint plant diseases. Mint plant is a major crop on the Golden-Gogi farm, and its dataset was collected from there. As per findings, DL classifiers outperform ML classifiers in disease detection. The best-classifier is DenseNet201, with high accuracy of 94.12%. Hence, the system using DenseNet201 offers a solution for farmers of this Province in making urgent decisions to avoid mint yield losses.</span></p>

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“…The results revealed a specificity of 98.96% and a sensitivity of 86.04%, underscoring the algorithm's effectiveness in identifying AF across different datasets. Yi Gan et al [33]To enhance the classification performance of the model for various arrhythmias, a parallel classification model based on DenseNet-BiLSTM is proposed. This model incorporates a parallel structure that allows for the simultaneous capture of waveform features from both small-scale and large-scale heartbeats, achieved through wavelet denoising and heartbeat segmentation of ECG signals.…”

Section: Figure 3 Workflow Of Cnn-lstm Modelmentioning

confidence: 99%

Exploring ECG Signal Analysis Techniques for Arrhythmia Detection: A Review

Sandhya Samant

2023

IJRITCC

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The heart holds paramount importance in the human body as it serves the crucial function of supplying blood and nutrients to various organs. Thus, maintaining its health is imperative. Arrhythmia, a heart disorder, arises when the heart's rhythm becomes irregular. Electrocardiogram (ECG) signals are commonly utilized for analyzing arrhythmia due to their simplicity and cost-effectiveness. The peaks observed in ECG graphs, particularly the R peak, are indicative of heart conditions, facilitating arrhythmia diagnosis. Arrhythmia is broadly categorized into Tachycardia and Bradycardia for identification purposes. This paper explores diverse techniques such as Deep Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Support Vector Machines (SVM), Neural Network (NN) classifiers, as well as Wavelet and Time–Frequency Transform (TQWT), which have been employed over the past decade for arrhythmia detection using various datasets. The study delves into the analysis of arrhythmia classification on ECG datasets, highlighting the effectiveness of data preprocessing, feature extraction, and classification techniques in achieving superior performance in classifying ECG signals for arrhythmia detection.

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Parallel classification model of arrhythmia based on DenseNet-BiLSTM

Cited by 15 publications

References 45 publications

ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features

ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features

Comparing machine learning and deep learning classifiers for enhancing agricultural productivity: case study in Larache Province, Northern Morocco

Exploring ECG Signal Analysis Techniques for Arrhythmia Detection: A Review

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