scholarly journals Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine Learning Techniques

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5302
Author(s):  
Yaru Yue ◽  
Chengdong Chen ◽  
Pengkun Liu ◽  
Ying Xing ◽  
Xiaoguang Zhou
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Wavelet Transform ◽  
Automatic Detection ◽  
Gaussian Kernel ◽  
Two Dimensional ◽  
Short Term ◽  
Time Frequency ◽  
Ecg Signals ◽  
Ventricular Activity

Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection of R peak. In this paper, to utilize the information of the P-QRS-T waveform generated by atrial and ventricular activity, frequency slice wavelet transform (FSWT) is adopted to conduct time-frequency analysis on short-term ECG segments from the MIT-BIH Atrial Fibrillation Database. The two-dimensional time-frequency matrices are obtained. Furthermore, an average sliding window is used to convert the two-dimensional time-frequency matrices to the one-dimensional feature vectors, which are classified using five machine learning (ML) techniques. The experimental results show that the classification performance of the Gaussian-kernel support vector machine (GKSVM) based on the Bayesian optimizer is better. The accuracy of the training set and validation set are 100% and 93.4%. The accuracy, sensitivity, and specificity of the test set without training are 98.15%, 96.43%, and 100%, respectively. Compared with previous research results, our proposed FSWT-GKSVM model shows stability and robustness, and it could achieve the purpose of automatic detection of AF.

scholarly journals SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hongpo Zhang ◽  
Renke He ◽  
Honghua Dai ◽  
Mingliang Xu ◽  
Zongmin Wang
Keyword(s):  
Atrial Fibrillation ◽  
Wavelet Transform ◽  
Morbidity And Mortality ◽  
High Morbidity ◽  
Ecg Signal ◽  
Time Frequency ◽  
Ecg Signals ◽  
Convolution Process ◽  
The Time Domain ◽  
Atrial Fibrillation Detection

Atrial fibrillation is the most common arrhythmia and is associated with high morbidity and mortality from stroke, heart failure, myocardial infarction, and cerebral thrombosis. Effective and rapid detection of atrial fibrillation is critical to reducing morbidity and mortality in patients. Screening atrial fibrillation quickly and efficiently remains a challenging task. In this paper, we propose SS-SWT and SI-CNN: an atrial fibrillation detection framework for the time-frequency ECG signal. First, specific-scale stationary wavelet transform (SS-SWT) is used to decompose a 5-s ECG signal into 8 scales. We select specific scales of coefficients as valid time-frequency features and abandon the other coefficients. The selected coefficients are fed to the scale-independent convolutional neural network (SI-CNN) as a two-dimensional (2D) matrix. In SI-CNN, a convolution kernel specifically for the time-frequency characteristics of ECG signals is designed. During the convolution process, the independence between each scale of coefficient is preserved, and the time domain and the frequency domain characteristics of the ECG signal are effectively extracted, and finally the atrial fibrillation signal is quickly and accurately identified. In this study, experiments are performed using the MIT-BIH AFDB data in 5-s data segments. We achieve 99.03% sensitivity, 99.35% specificity, and 99.23% overall accuracy. The SS-SWT and SI-CNN we propose simplify the feature extraction step, effectively extracts the features of ECG, and reduces the feature redundancy that may be caused by wavelet transform. The results shows that the method can effectively detect atrial fibrillation signals and has potential in clinical application.

A method for automatic detection of atrial fibrillation: based on CNN combined with BLSTM (Preprint)

2020 ◽  
Author(s):  
Wei Liang ◽  
Liang Tao ◽  
Baoning Liu
Keyword(s):  
Atrial Fibrillation ◽  
Short Term Memory ◽  
Automatic Detection ◽  
Effective Solution ◽  
Short Term ◽  
Accurate Analysis ◽  
Ecg Signals ◽  
Long Short Term Memory ◽  
Fully Connected ◽  
Deep Learning Model

BACKGROUND Atrial fibrillation (AF) is the most common arrhythmia harmful to human health. The morbidity and mortality of AF increase with age. However, due to its small amplitude and short duration, as well as its complexity and nonlinearity, it is very difficult to carry out accurate analysis only through expert experience. Therefore, the automatic detection of AF becomes extremely important. OBJECTIVE The purpose of this paper is to develop a method of automatic detection of AF, more accurate and efficient automatic detection of the occurrence of AF. METHODS In this paper, a deep learning model consisting of a convolutional neural network (CNN) and a bi-directional long short-term memory (BLSTM) network is proposed to detect AF automatically. The model is mainly composed of six convolutional layers, three pooling layers, a BLSTM layer and a fully connected layer. In addition, the data of MIT-BIH AFDB database is divided into different lengths of ECG segments, which are used as input to the network to verify the effect of different lengths of ECG segments on the final result. RESULTS The method proposed also achieved excellent results on ECG signals of 1 second. Most importantly, our proposed method achieved the best performances on ECG signals of 10 seconds, with an accuracy of 99.57%，a sensitivity of 99.65%,and a specificity of 99.49%. CONCLUSIONS This method requires no complex pretreatment and retains the characteristics of the original ECG to the greatest extent. Compared with the existing studies, the proposed method has higher accuracy and provides an effective solution for the automatic detection of AF.

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

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.

High Accuracy of Automatic Detection of Atrial Fibrillation Using Wavelet Transform of Heart Rate Intervals

2002 ◽  
Vol 25 (4) ◽  
pp. 457-462 ◽  
Author(s):  
DAVID DUVERNEY ◽  
JEAN-MICHEL GASPOZ ◽  
VINCENT PICHOT ◽  
FREDERIC ROCHE ◽  
RICHARD BRION ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Wavelet Transform ◽  
Automatic Detection ◽  
High Accuracy

scholarly journals Wavelet Transform in Vibration Analysis for Mechanical Fault Diagnosis

1996 ◽  
Vol 3 (1) ◽  
pp. 17-26 ◽  
Author(s):  
W.J. Wang
Keyword(s):  
Wavelet Transform ◽  
Time Scale ◽  
Vibration Analysis ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Vibration Signals ◽  
Time Frequency ◽  
Special Cases ◽  
Mechanical Faults ◽  
Short Time

The wavelet transform is introduced to indicate short-time fault effects in associated vibration signals. The time-frequency and time-scale representations are unified in a general form of a three-dimensional wavelet transform, from which two-dimensional transforms with different advantages are treated as special cases derived by fixing either the scale or frequency variable. The Gaussian enveloped oscillating wavelet is recommended to extract different sizes of features from the signal. It is shown that the time-frequency and time-scale distributions generated by the wavelet transform are effective in identifying mechanical faults.

scholarly journals Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification

2020 ◽  
Vol 17 (2) ◽  
pp. 498 ◽  
Author(s):  
César A. Millán ◽  
Nathalia A. Girón ◽  
Diego M. Lopez
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Systematic Review ◽  
Meta Analysis ◽  
Test Accuracy ◽  
Time Frequency ◽  
Frequency Domains ◽  
Two Stages ◽  
Electrocardiogram Ecg ◽  
Diagnostic Criterion

Atrial Fibrillation (AF) is the most common cardiac arrhythmia found in clinical practice. It affects an estimated 33.5 million people, representing approximately 0.5% of the world’s population. Electrocardiogram (ECG) is the main diagnostic criterion for AF. Recently, photoplethysmography (PPG) has emerged as a simple and portable alternative for AF detection. However, it is not completely clear which are the most important features of the PPG signal to perform this process. The objective of this paper is to determine which are the most relevant features for PPG signal analysis in the detection of AF. This study is divided into two stages: (a) a systematic review carried out following the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) statement in six databases, in order to identify the features of the PPG signal reported in the literature for the detection of AF, and (b) an experimental evaluation of them, using machine learning, in order to determine which have the greatest influence on the process of detecting AF. Forty-four features were found when analyzing the signal in the time, frequency, or time–frequency domains. From those 44 features, 27 were implemented, and through machine learning, it was found that only 11 are relevant in the detection process. An algorithm was developed for the detection of AF based on these 11 features, which obtained an optimal performance in terms of sensitivity (98.43%), specificity (99.52%), and accuracy (98.97%).

scholarly journals Towards the Development of Nonlinear Approaches to Discriminate AF from NSR Using a Single-Lead ECG

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 531
Author(s):  
Jieun Lee ◽  
Yugene Guo ◽  
Vasanth Ravikumar ◽  
Elena G. Tolkacheva
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Multiscale Entropy ◽  
Short Term ◽  
Embedding Method ◽  
Ecg Signals ◽  
Normal Sinus ◽  
The Right ◽  
Electrocardiogram Ecg ◽  
Insight Into

Paroxysmal atrial fibrillation (Paro. AF) is challenging to identify at the right moment. This disease is often undiagnosed using currently existing methods. Nonlinear analysis is gaining importance due to its capability to provide more insight into complex heart dynamics. The aim of this study is to use several recently developed nonlinear techniques to discriminate persistent AF (Pers. AF) from normal sinus rhythm (NSR), and more importantly, Paro. AF from NSR, using short-term single-lead electrocardiogram (ECG) signals. Specifically, we adapted and modified the time-delayed embedding method to minimize incorrect embedding parameter selection and further support to reconstruct proper phase plots of NSR and AF heart dynamics, from MIT-BIH databases. We also examine information-based methods, such as multiscale entropy (MSE) and kurtosis (Kt) for the same purposes. Our results demonstrate that embedding parameter time delay ( τ ), as well as MSE and Kt values can be successfully used to discriminate between Pers. AF and NSR. Moreover, we demonstrate that τ and Kt can successfully discriminate Paro. AF from NSR. Our results suggest that nonlinear time-delayed embedding method and information-based methods provide robust discriminating features to distinguish both Pers. AF and Paro. AF from NSR, thus offering effective treatment before suffering chaotic Pers. AF.

scholarly journals ECG-based machine-learning algorithms for heartbeat classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saira Aziz ◽  
Sajid Ahmed ◽  
Mohamed-Slim Alouini
Keyword(s):  
Machine Learning ◽  
Fractional Fourier Transform ◽  
Heart Diseases ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Heartbeat Classification ◽  
Time Frequency ◽  
Ecg Signals ◽  
Signal Features ◽  
Machine Learning Model

AbstractElectrocardiogram (ECG) signals represent the electrical activity of the human hearts and consist of several waveforms (P, QRS, and T). The duration and shape of each waveform and the distances between different peaks are used to diagnose heart diseases. In this work, to better analyze ECG signals, a new algorithm that exploits two-event related moving-averages (TERMA) and fractional-Fourier-transform (FrFT) algorithms is proposed. The TERMA algorithm specifies certain areas of interest to locate desired peak, while the FrFT rotates ECG signals in the time-frequency plane to manifest the locations of various peaks. The proposed algorithm’s performance outperforms state-of-the-art algorithms. Moreover, to automatically classify heart disease, estimated peaks, durations between different peaks, and other ECG signal features were used to train a machine-learning model. Most of the available studies uses the MIT-BIH database (only 48 patients). However, in this work, the recently reported Shaoxing People’s Hospital (SPH) database, which consists of more than 10,000 patients, was used to train the proposed machine-learning model, which is more realistic for classification. The cross-database training and testing with promising results is the uniqueness of our proposed machine-learning model.

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