Symbolic Recurrence Analysis of RR Interval to Detect Atrial Fibrillation

Atrial fibrillation (AF) is a sustained cardiac arrhythmia associated with stroke, heart failure, and related health conditions. Though easily diagnosed upon presentation in a clinical setting, the transient and/or intermittent emergence of AF episodes present diagnostic and clinical monitoring challenges that would ideally be met with automated ambulatory monitoring and detection. Current approaches to address these needs, commonly available both in smartphone applications and dedicated technologies, combine electrocardiogram (ECG) sensors with predictive algorithms to detect AF. These methods typically require extensive preprocessing, preliminary signal analysis, and the integration of a wide and complex array of features for the detection of AF events, and are consequently vulnerable to over-fitting. In this paper, we introduce the application of symbolic recurrence quantification analysis (SRQA) for the study of ECG signals and detection of AF events, which requires minimal pre-processing and allows the construction of highly accurate predictive algorithms from relatively few features. In addition, this approach is robust against commonly-encountered signal processing challenges that are expected in ambulatory monitoring contexts, including noisy and non-stationary data. We demonstrate the application of this method to yield a highly accurate predictive algorithm, which at optimal threshold values is 97.9% sensitive, 97.6% specific, and 97.7% accurate in classifying AF signals. To confirm the robust generalizability of this approach, we further evaluated its performance in the implementation of a 10-fold cross-validation paradigm, yielding 97.4% accuracy. In sum, these findings emphasize the robust utility of SRQA for the analysis of ECG signals and detection of AF. To the best of our knowledge, the proposed model is the first to incorporate symbolic analysis for AF beat detection.

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A SCALABLE HYBRID MODEL FOR ATRIAL FIBRILLATION DETECTION

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519421400212 ◽

2021 ◽

pp. 2140021

Author(s):

HAO WEN ◽

WENJIAN YU ◽

YUANQING WU ◽

SHUAI YANG ◽

XIAOLONG LIU

Keyword(s):

Neural Network ◽

Atrial Fibrillation ◽

Hybrid Model ◽

Continuous Monitoring ◽

Processing Stage ◽

Ecg Signals ◽

Proposed Model ◽

Pr Interval ◽

Interval Sequence ◽

Electrocardiogram Ecg

In this work, a scalable hybrid model is proposed for the purpose of screening and continuous monitoring of atrial fibrillation (AF) using electrocardiogram (ECG) signals collected from wearable ECG devices. The time series of RR intervals (with units in seconds) extracted from the ECG signal is fed into a recurrent neural network (RNN), and the bandpass filtered and scaled signal itself is fed into a convolutional neural network (CNN). At the post-processing stage, these two predictions are merged. An additional logistic regression model using statistical features of “pseudo” PR interval sequence is applied to aid making the final prediction. The proposed model is trained and validated on several datasets from PhysioNet and achieves a precision of 98.28% and a specificity of 99.82% on a dataset collected from several PhysioNet databases. This hybrid model has already been deployed through a WeChat applet, providing services those using wearable ECG devices, thus helping the screening and continuous out-of-hospital monitoring of the disease of AF.

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ARRHYTHMIA DIAGNOSIS FROM ECG SIGNAL ANALYSIS USING STATISTICAL FEATURES AND NOVEL CLASSIFICATION METHOD

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519421500251 ◽

2021 ◽

pp. 2150025

Author(s):

SAURAV MANDAL ◽

NABANITA SINHA

Keyword(s):

Cardiac Arrhythmia ◽

Present Model ◽

Fluctuation Analysis ◽

Statistical Features ◽

Multifractal Detrended Fluctuation Analysis ◽

Ecg Signals ◽

Proposed Model ◽

Electrocardiogram Ecg ◽

Detrended Fluctuation ◽

Heart Condition

This study aims to present an efficient model for autodetection of cardiac arrhythmia by the diagnosis of self-affinity and identification of governing processes of a number of Electrocardiogram (ECG) signals taken from MIT-BIH database. In this work, the proposed model includes statistical methods to find the diagnosis pattern for detecting cardiac abnormalities which is useful for the computer aided system for arrhythmia detection. First, the Rescale Range (R/S) analysis has been employed for ECG signals to understand the scaling property of ECG signals. The value of Hurst exponent identifies the presence of abnormality in ECG signals taken for consideration with 92.58% accuracy. In this study, Higuchi method which deals with unifractality or monofractality of signals has been applied and it is found that unifractality is sufficient to detect arrhythmia with 91.61% accuracy. The Multifractal Detrended Fluctuation Analysis (MFDFA) has been used over the present signals to identify and confirm the multifractality. The nature of multifractality is different for arrhythmia patients and normal heart condition. The multifractal analysis is useful to detect abnormalities with 93.75% accuracy. Finally, the autocorrelation analysis has been used to identify the prevalent governing process in the present arrhythmic ECG signals and study confirms that all the signals are governed by stationary autoregressive methods of certain orders. In order to increase the overall efficiency, this present model deals with analyzing all the statistical features extracted from different statistical techniques for a large number of ECG signals of normal and abnormal heart condition. Finally, the result of present analysis altogether possibly indicates that the proposed model is efficient to detect cardiac arrhythmia with 99.3% accuracy.

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Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques

Mathematical Problems in Engineering ◽

10.1155/2018/7354081 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Jia Li ◽

Yujuan Si ◽

Tao Xu ◽

Saibiao Jiang

Keyword(s):

Cardiovascular Disease ◽

Adaptive Learning ◽

Portable Devices ◽

Ecg Signals ◽

Automatic Feature Extraction ◽

Average Accuracy ◽

Proposed Model ◽

Home Health Monitoring ◽

Predictive Rate ◽

Electrocardiogram Ecg

Although convolutional neural networks (CNNs) can be used to classify electrocardiogram (ECG) beats in the diagnosis of cardiovascular disease, ECG signals are typically processed as one-dimensional signals while CNNs are better suited to multidimensional pattern or image recognition applications. In this study, the morphology and rhythm of heartbeats are fused into a two-dimensional information vector for subsequent processing by CNNs that include adaptive learning rate and biased dropout methods. The results demonstrate that the proposed CNN model is effective for detecting irregular heartbeats or arrhythmias via automatic feature extraction. When the proposed model was tested on the MIT-BIH arrhythmia database, the model achieved higher performance than other state-of-the-art methods for five and eight heartbeat categories (the average accuracy was 99.1% and 97%). In particular, the proposed system had better performance in terms of the sensitivity and positive predictive rate for V beats by more than 4.3% and 5.4%, respectively, and also for S beats by more than 22.6% and 25.9%, respectively, when compared to existing algorithms. It is anticipated that the proposed method will be suitable for implementation on portable devices for the e-home health monitoring of cardiovascular disease.

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Atrial Fibrillation Detection Directly from Compressed ECG with the Prior of Measurement Matrix

Information ◽

10.3390/info11090436 ◽

2020 ◽

Vol 11 (9) ◽

pp. 436

Author(s):

Yunfei Cheng ◽

Ying Hu ◽

Mengshu Hou ◽

Tongjie Pan ◽

Wenwen He ◽

...

Keyword(s):

Atrial Fibrillation ◽

Compressed Sensing ◽

Health Monitoring ◽

Prior Information ◽

Classification Performance ◽

Experimental Results ◽

Measurement Matrix ◽

Ecg Signals ◽

Proposed Model ◽

Atrial Fibrillation Detection

In the wearable health monitoring based on compressed sensing, atrial fibrillation detection directly from the compressed ECG can effectively reduce the time cost of data processing rather than classification after reconstruction. However, the existing methods for atrial fibrillation detection from compressed ECG did not fully benefit from the existing prior information, resulting in unsatisfactory classification performance, especially in some applications that require high compression ratio (CR). In this paper, we propose a deep learning method to detect atrial fibrillation directly from compressed ECG without reconstruction. Specifically, we design a deep network model for one-dimensional ECG signals, and the measurement matrix is used to initialize the first layer of the model so that the proposed model can obtain more prior information which benefits improving the classification performance of atrial fibrillation detection from compressed ECG. The experimental results on the MIT-BIH Atrial Fibrillation Database show that when the CR is 10%, the accuracy and F1 score of the proposed method reach 97.52% and 98.02%, respectively. Compared with the atrial fibrillation detection from original ECG, the corresponding accuracy and F1 score are only reduced by 0.88% and 0.69%. Even at a high CR of 90%, the accuracy and F1 score are still only reduced by 6.77% and 5.31%, respectively. All of the experimental results demonstrate that the proposed method is superior to other existing methods for atrial fibrillation detection from compressed ECG. Therefore, the proposed method is promising for atrial fibrillation detection in wearable health monitoring based on compressed sensing.

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A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection

Sensors ◽

10.3390/s20030606 ◽

2020 ◽

Vol 20 (3) ◽

pp. 606 ◽

Cited By ~ 3

Author(s):

Minggang Shao ◽

Zhuhuang Zhou ◽

Guangyu Bin ◽

Yanping Bai ◽

Shuicai Wu

Keyword(s):

Atrial Fibrillation ◽

Ecg Monitoring ◽

Web Browser ◽

Ecg Signals ◽

Cloud Server ◽

Android App ◽

Electrocardiogram Ecg ◽

The Web ◽

Ecg Data

In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor’s diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F1 score of 0.92 on the test set (n = 7270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection.

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Anticipating Atrial Fibrillation Signal Using Efficient Algorithm

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v17i02.19183 ◽

2021 ◽

Vol 17 (02) ◽

pp. 106

Author(s):

Mohand Lokman Ahmad Al-dabag ◽

Haider Th. Salim ALRikabi ◽

Raid Rafi Omar Al-Nima

Keyword(s):

Atrial Fibrillation ◽

Computational Cost ◽

Extraction Methods ◽

Support Vector ◽

Ecg Signal ◽

Heart Problem ◽

Ecg Signals ◽

The Right ◽

Electrocardiogram Ecg ◽

Low Computational Cost

One of the common types of arrhythmia is Atrial Fibrillation (AF), it may cause death to patients. Correct diagnosing of heart problem through examining the Electrocardiogram (ECG) signal will lead to prescribe the right treatment for a patient. This study proposes a system that distinguishes between the normal and AF ECG signals. First, this work provides a novel algorithm for segmenting the ECG signal for extracting a single heartbeat. The algorithm utilizes low computational cost techniques to segment the ECG signal. Then, useful pre-processing and feature extraction methods are suggested. Two classifiers, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), are separately used to evaluate the two proposed algorithms. The performance of the last proposed method with the two classifiers (SVM and MLP) show an improvement of about (19% and 17%, respectively) after using the proposed segmentation method so it became 96.2% and 97.5%, respectively.

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ECG Heartbeat Classification Based on an Improved ResNet-18 Model

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6649970 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Enbiao Jing ◽

Haiyang Zhang ◽

ZhiGang Li ◽

Yazhi Liu ◽

Zhanlin Ji ◽

...

Keyword(s):

State Of The Art ◽

Classification Performance ◽

Classification Models ◽

Heartbeat Classification ◽

Ecg Signals ◽

Residual Structure ◽

Proposed Model ◽

Model Training ◽

Electrocardiogram Ecg

Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better classification performance. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrate that the model achieves higher accuracy (96.50%) compared to other state-of-the-art classification models, while specifically for the ventricular ectopic heartbeat class, its sensitivity is 93.83% and the precision is 97.44%.

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An ECG signal model based on a parametric description of the characteristic waves

ACTA IMEKO ◽

10.21014/acta_imeko.v9i2.760 ◽

2020 ◽

Vol 9 (2) ◽

pp. 3

Author(s):

Pavol Dolinsky ◽

Imrich Andras ◽

Linus Michaeli ◽

Jan Saliga

Keyword(s):

Model Parameters ◽

Ecg Signal ◽

Elementary Functions ◽

Signal Model ◽

Ecg Signals ◽

Model Based ◽

Proposed Model ◽

Base Functions ◽

Parametric Description ◽

Electrocardiogram Ecg

This article introduces a new electrocardiogram (ECG) signal model based on geometric signal properties. Instead of the artificial functions used in common ECG models, the proposed model is based on the modelling of real ECG signals divided into time segments. Each segment has been modelled using simple geometrical forms. The final ECG signal model is represented by the sequence of parameters of the base functions. Parameter variations allow for the generation of different waveforms for each subsequent heartbeat without mixing up the PQRST waves order. Two basic models utilize slightly modified elementary functions, which are computationally simple. A combination of both models allows for the modelling of irregularities in the consecutive heartbeats of the specific ECG waveforms. Respiratory, noise, and powerline interference can be added in order to make the generated ECG signal more realistic. The model parameters are estimated by differential evolution optimization and a comparison between the modelled ECG and the acquired signal. The proposed models are tested by the database included in the LabVIEW Biomedical Toolkit and ECG records in the MIT-BIH arrhythmia database.

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Automatic Detection of Arrhythmias Using a YOLO-Based Network with Long-Duration ECG Signals

Engineering Proceedings ◽

10.3390/ecsa-7-08229 ◽

2020 ◽

Vol 2 (1) ◽

pp. 84

Author(s):

Won Hee Hwang ◽

Chan Hee Jeong ◽

Dong Hyun Hwang ◽

Young Chang Jo

Keyword(s):

Embedded System ◽

High Performance ◽

Detection Algorithm ◽

Wearable Device ◽

Arrhythmia Detection ◽

Ecg Signals ◽

Long Duration ◽

Proposed Model ◽

Different Types ◽

Electrocardiogram Ecg

Early detection of arrhythmias is very important. Recently, wearable devices are being used to monitor the patient’s heartbeat to detect an arrhythmia. However, there are not satisfactory algorithms for real-time monitoring of arrhythmias in a wearable device. In this work, a novel fast and simple arrhythmia detection algorithm based on YOLO is proposed. The algorithm can detect each heartbeat on long-duration electrocardiogram (ECG) signals without R-peak detection and can classify an arrhythmia simultaneously. The model replaces the 2D Convolutional Neural networks (CNN) with a 1D CNN and the bounding box with a bounding window to utilize raw ECG signals. Results demonstrate that the proposed algorithm has high performance in speed and mean average precisionin detecting an arrhythmia. Furthermore, the bounding window can predict different window lengths on different types of arrhythmia. Therefore, the model can choose an optimal heartbeat window length for arrhythmia classification. Since the proposed model is a compact 1D CNN model based on YOLO, it can be used in a wearable device and embedded system.

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Automatic Detection of Atrial Fibrillation Based on CNN-LSTM and Shortcut Connection

Healthcare ◽

10.3390/healthcare8020139 ◽

2020 ◽

Vol 8 (2) ◽

pp. 139

Author(s):

Yongjie Ping ◽

Chao Chen ◽

Lu Wu ◽

Yinglong Wang ◽

Minglei Shu

Keyword(s):

Atrial Fibrillation ◽

Neural Networks ◽

Short Term Memory ◽

Segment Length ◽

Practical Applications ◽

Multi Scale ◽

Long Short Term Memory ◽

Electrocardiogram Ecg ◽

Fold Cross Validation

Atrial fibrillation (AF) is one of the most common persistent arrhythmias, which has a close connection to a large number of cardiovascular diseases. However, if spotted early, the diagnosis of AF can improve the effectiveness of clinical treatment and effectively prevent serious complications. In this paper, a combination of an 8-layer convolutional neural network (CNN) with a shortcut connection and 1-layer long short-term memory (LSTM), named 8CSL, was proposed for the Electrocardiogram (ECG) classification task. Compared with recurrent neural networks (RNN) and multi-scale convolution neural networks (MCNN), not only can 8CSL extract features skillfully, but also deal with long-term dependency between data. In particular, 8CSL includes eight shortcut connections that can improve the speed of the data transmission and processing as a result of the shortcut connections. The model was evaluated on the base of the test set of the Computing in Cardiology Challenge 2017 dataset with the F1 score. The ECG recordings were cropped or padded to the same length. After 10-fold cross-validation, the average test F1 score was 84.89%, 89.55%, and 85.64% when the segment length was 5, 10, 20 s, respectively. The experiment results demonstrate excellent performance with potential practical applications.

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