ECG feature extraction and classification of anteroseptal myocardial infarction and normal subjects using discrete wavelet transform

Banerjee, Swati; Mitra, Madhuchhanda

doi:10.1109/icsmb.2010.5735345

Cited by 23 publications

(7 citation statements)

References 22 publications

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“…The minimum series length in this MTS dataset is T = 32,000. This database has been broadly used for ECG classification [26,[64][65][66][67]. Before performing the classification task, we have decided to make the following preprocessing steps.…”

Section: Application To Ecg Datamentioning

confidence: 99%

F4: An All-Purpose Tool for Multivariate Time Series Classification

López-Oriona

Vilar

2021

Mathematics

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We propose Fast Forest of Flexible Features (F4), a novel approach for classifying multivariate time series, which is aimed to discriminate between underlying generating processes. This goal has barely been addressed in the literature. F4 consists of two steps. First, a set of features based on the quantile cross-spectral density and the maximum overlap discrete wavelet transform are extracted from each series. Second, a random forest is fed with the extracted features. An extensive simulation study shows that F4 outperforms some powerful classifiers in a wide variety of situations, including stationary and nonstationary series. The proposed method is also capable of successfully discriminating between electrocardiogram (ECG) signals of healthy subjects and those with myocardial infarction condition. Additionally, despite lacking shape-based information, F4 attains state-of-the-art results in some datasets of the University of East Anglia (UEA) multivariate time series classification archive.

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Section: Application To Ecg Datamentioning

confidence: 99%

F4: An All-Purpose Tool for Multivariate Time Series Classification

López-Oriona

Vilar

2021

Mathematics

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“…Ramakrishnan et al combined adaptive moving window difference thresholding and front back difference thresholding to detect the R wave [14]. Banerjee et al applied discrete wavelet transform to learn frequency features and then conducted classification of anteroseptal myocardial infarction [15]. Minami et al designed a Fourier transform neural network for real-time detection of ventricular tachyarrhythmia [16].…”

Section: Related Workmentioning

confidence: 99%

Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors

Zhong

Hai

Cheng

et al. 2021

Sensors

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Heart rate is one of the most important diagnostic bases for cardiovascular disease. This paper introduces a deep autoencoding strategy into feature extraction of electrocardiogram (ECG) signals, and proposes a beat-to-beat heart rate estimation method based on convolution autoencoding and Gaussian mixture clustering. The high-level heartbeat features were first extracted in an unsupervised manner by training the convolutional autoencoder network, and then the adaptive Gaussian mixture clustering was applied to detect the heartbeat locations from the extracted features, and calculated the beat-to-beat heart rate. Compared with the existing heartbeat classification/detection methods, the proposed unsupervised feature learning and heartbeat clustering method does not rely on accurate labeling of each heartbeat location, which could save a lot of time and effort in human annotations. Experimental results demonstrate that the proposed method maintains better accuracy and generalization ability compared with the existing ECG heart rate estimation methods and could be a robust long-time heart rate monitoring solution for wearable ECG devices.

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“…To improve the MI detection accuracy, effective features are extracted from multi-lead ECG information. Banerjee and Mitral [22] extract QRS_vector from the chest leads V1-V4 and use a simple classification rule method to detect anteroseptal MI in the PTB database. Dohare et al [23] use the principal component analysis (PCA) method to reduce the dimension of the extracted multidimensional features, and then implement the detection of MI with the SVM method based on radial basis function (RBF).…”

Section: Related Workmentioning

confidence: 99%

Localization of Myocardial Infarction With Multi-Lead Bidirectional Gated Recurrent Unit Neural Network

Zhang

Dai

et al. 2019

IEEE Access

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Myocardial infarction (MI) is an acute disease. Early detection and early treatment are of great significance for improving the health of people. In order to reduce the misdiagnosis rate of MI diseases, this paper proposes a multi-lead bidirectional gated recurrent unit neural network (ML-BiGRU) learning algorithm based on current research status in the field of intelligent medical diagnosis, combined with the timing and multi-lead correlation characteristics of the electrocardiogram (ECG) signals. At first, the original ECG signal is denoised and preprocessed and then segmented into heartbeats. After that, the heartbeat sequence is sent to the deep neural network training model to learn the classification. Lastly, the Physikalisch-Technische Bundesanstalt (PTB) ECG database is used to verify the multi-lead BiGRU algorithm. The verification results demonstrate that the accuracy of the algorithm for MI localization is 99.84%, which outperform the other algorithms. The experimental results also show that the algorithm is obviously superior to the traditional localization algorithm in improving the localization accuracy, which is of great significance for improving the correct diagnosis rate of MI. Electrocardiogram, myocardial infarction, multi-lead, Bi-GRU. INDEX TERMS

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ECG feature extraction and classification of anteroseptal myocardial infarction and normal subjects using discrete wavelet transform

Cited by 23 publications

References 22 publications

F4: An All-Purpose Tool for Multivariate Time Series Classification

F4: An All-Purpose Tool for Multivariate Time Series Classification

Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors

Localization of Myocardial Infarction With Multi-Lead Bidirectional Gated Recurrent Unit Neural Network

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