Heartbeat Classification using discrete wavelet transform and kernel principal component analysis

Yang, Sheng-Kai; Shen, Haibin

doi:10.1109/tenconspring.2013.6584412

Cited by 22 publications

(13 citation statements)

References 22 publications

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“…In the past few years, numerous detection algorithms of cardiac arrhythmias have been proposed. These algorithms mainly consist of four main procedures including denoising [6]- [10], waveform detection [11]- [13], feature extraction and arrhythmia classification [14]- [28]. Among these four steps, feature extraction transforms the input ECG signal into a variety of features that play an important role in detecting most of cardiac arrhythmias.…”

Section: Introductionmentioning

confidence: 99%

Automatic Cardiac Arrhythmia Classification Using Combination of Deep Residual Network and Bidirectional LSTM

et al. 2019

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Cardiac arrhythmia is associated with abnormal electrical activities of the heart, which can be reflected by altered characteristics of electrocardiogram (ECG). Due to the simplicity and non-invasive nature, the ECG has been widely used for detecting arrhythmias and there is an urgent need for automatic ECG detection. Up to date, some algorithms have been proposed for automatic classification of cardiac arrhythmias based on the features of the ECG; however, their stratification rate is still poor due to unreliable features of signal characteristics or limited generalization capability of the classifier, and therefore, it remains a challenge for automatic diagnosis of arrhythmias. In this paper, we propose a new method for automatic classification of arrhythmias based on deep neural networks (DNNs). The two DNN models constitutive of residual convolutional modules and bidirectional long short-term memory (LSTM) layers are trained to extract features from raw ECG signals. The extracted features are concatenated to form a feature vector which is trained to do the final classification. The algorithm is evaluated based on the test set of China Physiological Signal Challenge (CPSC) dataset with F 1 measure regarded as the harmonic mean between the precision and recall. The resulting overall F 1 score is 0.806, F AF score is 0.914 for atrial fibrillation (AF), F Block score is 0.879 for block, F PC and F ST scores are 0.801 and 0.742 for premature contraction and ST-segment change, which demonstrates a good performance that may have potential practical applications. INDEX TERMSCardiac arrhythmia, electrocardiogram (ECG), deep neural networks (DNNs), deep residual network, bidirectional long short-term memory (LSTM).

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Section: Introductionmentioning

confidence: 99%

Automatic Cardiac Arrhythmia Classification Using Combination of Deep Residual Network and Bidirectional LSTM

et al. 2019

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“…However, because the P and T wave detection works well with normal heartbeats, but not for many abnormal heartbeat types. Many researchers choose manual annotation, such as [71], or a fixed window, such as [32,33,38,71,72], for their heartbeat segmentation.…”

Section: Heartbeat Detection and Segmentationmentioning

confidence: 99%

A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG)

Boulanger

2020

Sensors

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Cardiovascular diseases (CVDs) are the number one cause of death globally. An estimated 17.9 million people die from CVDs each year, representing 31% of all global deaths. Most cardiac patients require early detection and treatment. Therefore, many products to monitor patient’s heart conditions have been introduced on the market. Most of these devices can record a patient’s bio-metric signals both in resting and in exercising situations. However, reading the massive amount of raw electrocardiogram (ECG) signals from the sensors is very time-consuming. Automatic anomaly detection for the ECG signals could act as an assistant for doctors to diagnose a cardiac condition. This paper reviews the current state-of-the-art of this technology discusses the pros and cons of the devices and algorithms found in the literature and the possible research directions to develop the next generation of ambulatory monitoring systems.

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“…In the clinical setting, the arrhythmia is usually diagnosed by analyzing the heartbeat of an electrocardiogram (ECG) signal. An ECG signal consists of a series of heartbeats (or called waves) that repeat periodically in time and represents the electrical activity of the heart over time [ 3 ]. The doctor checks these heartbeats to diagnose the presence of arrhythmia, while the process is time-consuming and labor-intensive.…”

Section: Introductionmentioning

confidence: 99%

Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Wang

Sun

et al. 2021

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184

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Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

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Heartbeat Classification using discrete wavelet transform and kernel principal component analysis

Cited by 22 publications

References 22 publications

Automatic Cardiac Arrhythmia Classification Using Combination of Deep Residual Network and Bidirectional LSTM

Automatic Cardiac Arrhythmia Classification Using Combination of Deep Residual Network and Bidirectional LSTM

A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG)

Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

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