Real-time premature ventricular contractions detection based on Redundant Discrete Wavelet Transform

Arrais, Ernano; Valentim, Ricardo Alexsandro de Medeiros; Brandão, Gláucio Bezerra

doi:10.1590/2446-4740.01618

Cited by 12 publications

(3 citation statements)

References 55 publications

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“…Khalaf et al [37] proposed an SVM-based method on MATLAB R2010a on Intel ® Core™ i5 3.2 GHz processor and 8 GB RAM, and it consumed 54.8 ms for each beat classification. Arrais Junior et al [38] reported an adaptive threshold and redundant discrete wavelet transform fusion method, which can process 30 min signals using only 61.2 s on the Matlab 2014a platform. These results showed that (1) the superposition of deep learning and time-frequency conversion processes will increase the complexity of the algorithm; The employed DL-based method (LSTM-AE module) was used to extract features from ECG heartbeats for K-means clustering, and the PVC identification was based on a combination of multiple rules, including template matching and rhythm characteristics.…”

Section: Discussionmentioning

confidence: 99%

Robust PVC Identification by Fusing Expert System and Deep Learning

et al. 2022

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Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. A long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the third China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.

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Section: Discussionmentioning

confidence: 99%

Robust PVC Identification by Fusing Expert System and Deep Learning

et al. 2022

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“…That is, the high-and low-frequency information of the signal or image is separated, and finally, it is decomposed into approximate signals and wavelet surfaces on different frequency channels. Moreover, the length of the approximation signal and the detail signal after the signal transformation are the same as the original signal length [39].…”

Section: Methods 221 Decomposition and Reconstruction Of Image Signal...mentioning

confidence: 99%

Tea Category Identification Using Wavelet Signal Reconstruction of Hyperspectral Imagery and Machine Learning

et al. 2022

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Accurately distinguishing the types of tea is of great significance to the pricing, production, and processing of tea. The similarity of the internal spectral characteristics and appearance characteristics of different types of tea greatly limits further research on tea identification. However, wavelet transform can simultaneously extract time domain and frequency domain features, which is a powerful tool in the field of image signal processing. To address this gap, a method for tea recognition based on a lightweight convolutional neural network and support vector machine (L-CNN-SVM) was proposed, aiming to realize tea recognition using wavelet feature figures generated by wavelet time-frequency signal decomposition and reconstruction. Firstly, the redundant discrete wavelet transform was used to decompose the wavelet components of the hyperspectral images of the three teas (black tea, green tea, and yellow tea), which were used to construct the datasets. Secondly, improve the lightweight CNN model to generate a tea recognition model. Finally, compare and evaluate the recognition results of different models. The results demonstrated that the results of tea recognition based on the L-CNN-SVM method outperformed MobileNet v2+RF, MobileNet v2+KNN, MobileNet v2+AdaBoost, AlexNet, and MobileNet v2. For the recognition results of the three teas using reconstruction of wavelet components LL + HL + LH, the overall accuracy rate reached 98.7%, which was 4.7%, 3.4%, 1.4%, and 2.0% higher than that of LH + HL + HH, LL + HH + HH, LL + LL + HH, and LL + LL + LL. This research can provide new inspiration and technical support for grade and quality assessment of cross-category tea.

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“…Junior et al developed a system based on threshold adaptive algorithm and wavelet transform for PVC detection. The result validated on the MIT-BIH arrhythmia database reported that Daubechies 2 wavelet mother is more indicated compared with Coiflets and Symlets [10]. Oliveira et al proposed a simplified set of features extracted from geometric figures constructed over QRS complexes and selected the most suitable classifiers based on the analytic hierarchy process (AHP).…”

Section: Introductionmentioning

confidence: 99%

Searching for Premature Ventricular Contraction from Electrocardiogram by Using One-Dimensional Convolutional Neural Network

Wang

Chen

et al. 2020

Electronics

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Premature ventricular contraction (PVC) is a common cardiac arrhythmia that can occur in ordinary healthy people and various heart disease patients. Clinically, cardiologists usually use a long-term electrocardiogram (ECG) as a medium to detect PVC. However, it is time-consuming and labor-intensive for cardiologists to analyze the long-term ECG accurately. To this end, this paper suggests a simple but effective approach to search for PVC from the long-term ECG. The recommended method first extracts each heartbeat from the long-term ECG by applying a fixed time window. Subsequently, the model based on the one-dimensional convolutional neural network (CNN) tags these heartbeats without any preprocessing, such as denoise. Unlike previous PVC detection methods that use hand-crafted features, the proposed plan rationally and automatically extracts features and identify PVC with supervised learning. The proposed PVC detection algorithm acquires 99.64% accuracy, 96.97% sensitivity, and 99.84% specificity for the MIT-BIH arrhythmia database. Besides, when the number of samples in the training set is 3.3 times that of the test set, the proposed method does not misjudge any heartbeat from the test set. The simulation results show that it is reliable to use one-dimensional CNN for PVC recognition. More importantly, the overall system does not rely on complex and cumbersome preprocessing.

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Real-time premature ventricular contractions detection based on Redundant Discrete Wavelet Transform

Cited by 12 publications

References 55 publications

Robust PVC Identification by Fusing Expert System and Deep Learning

Robust PVC Identification by Fusing Expert System and Deep Learning

Tea Category Identification Using Wavelet Signal Reconstruction of Hyperspectral Imagery and Machine Learning

Searching for Premature Ventricular Contraction from Electrocardiogram by Using One-Dimensional Convolutional Neural Network

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