Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

doi:10.3390/s16101744

Cited by 75 publications

(40 citation statements)

References 40 publications

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“…In [ 14 ], a false arrhythmia alarm reduction framework is proposed using machine learning. In [ 15 ], ECG-based automatic recognition of arrhythmias is proposed for the diagnosis of heart diseases. Usually, remote monitoring of a person becomes a challenging job, if a person is engaged in activities such as motor racing, cycling, car racing and on field military service.…”

Section: Related Workmentioning

confidence: 99%

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals

Sahoo

Thakkar

Lin

et al. 2018

Sensors

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Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the standalone use of ECG and SCG.

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Section: Related Workmentioning

confidence: 99%

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals

Sahoo

Thakkar

Lin

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Conventional machine learning algorithms have been utilized in previous studies for the classification of different arrhythmia types. Support Vector Machine (SVM) [13], Random Forest (RF) [14], Artificial Neural Networks (ANN) [15] have been used. In [16] arrhythmias.…”

Section: Introductionmentioning

confidence: 99%

ECG Heartbeat Classification Based on Signal-to-Image Transformation Using Convolutional Neural Networks

Degirmenci¹,

Özdemir²,

Izci³

et al. 2020

Preprint

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Background: Electrocardiogram (ECG) is a method of recording the electrical activity of the heart and provides a diagnostic mean for heart-related diseases. An arrhythmia is any irregularity of heartbeat that causes an abnormality in one’s heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis of ECG signal is not sufficient for quickly identifying arrhythmias that may cause sudden deaths. Hence, many studies have been presented to developed computer-aided diagnosis (CAD) systems to automatically identify arrhythmias.Methods: This paper proposes a novel deep learning approach to identify arrhythmias in ECG signals. The signals are obtained from MIT-BIH arrhythmia database and are categorized according to five arrhythmia types. The proposed approach identifies arrhythmia classes by using Convolutional Neural Network (CNN) architecture trained by two-dimensional (2D) ECG beat images. CNN architecture is selected due to high image recognition performance. ECG signals are segmented into heartbeats, then each heartbeat is transformed into a 2D grayscale image. The heartbeat images are used as input for the CNN. Results: The proposed CNN model is compared to other common CNN architectures such as LeNet and ResNet-50 to evaluate the performance of our study. Overall, the proposed study achieved 99.7% test accuracy in the classification of five different ECG arrhythmias.Conclusions: Testing results demonstrate that CNN trained by ECG image representations provide outstanding classification performance of arrhythmic ECG signals and outperforms similar network architectures. Hence, the proposed approach provides a robust method for the classification of ECG arrhythmias.

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“…Cardiac arrhythmias are often first detected with electrocardiogram (ECG) monitoring, which consists of recording the electrical potential produced by the heart in the skin [ 1 ]. ECG interpretation is crucial for arrhythmia diagnosis, and in recent years, different techniques have been studied to improve the ECG signal quality, including the search for new materials for the electrodes [ 2 ], the design of cardiac monitoring systems [ 3 , 4 ] combining the analysis of ECG signals and other non-invasive signals such as the seismocardiogram [ 5 ], the estimation of fetal ECG [ 6 ], the impact of noise and improvement of signal preprocessing techniques [ 7 , 8 ], or the study of ECG recognition systems for arrhythmia classification [ 9 – 11 ]. However, the arrhythmia treatment often requires additional bioelectric sources, and invasive methods based on catheters have been used in cardiac electrophysiology to find the arrhythmia mechanisms and to suppress them with intracardiac catheter ablation.…”

Section: Introductionmentioning

confidence: 99%

A new approach to the intracardiac inverse problem using Laplacian distance kernel

et al. 2018

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BackgroundThe inverse problem in electrophysiology consists of the accurate estimation of the intracardiac electrical sources from a reduced set of electrodes at short distances and from outside the heart. This estimation can provide an image with relevant knowledge on arrhythmia mechanisms for the clinical practice. Methods based on truncated singular value decomposition (TSVD) and regularized least squares require a matrix inversion, which limits their resolution due to the unavoidable low-pass filter effect of the Tikhonov regularization techniques.MethodsWe propose to use, for the first time, a Mercer’s kernel given by the Laplacian of the distance in the quasielectrostatic field equations, hence providing a Support Vector Regression (SVR) formulation by following the principles of the Dual Signal Model (DSM) principles for creating kernel algorithms.ResultsSimulations in one- and two-dimensional models show the performance of our Laplacian distance kernel technique versus several conventional methods. Firstly, the one-dimensional model is adjusted for yielding recorded electrograms, similar to the ones that are usually observed in electrophysiological studies, and suitable strategy is designed for the free-parameter search. Secondly, simulations both in one- and two-dimensional models show larger noise sensitivity in the estimated transfer matrix than in the observation measurements, and DSM−SVR is shown to be more robust to noisy transfer matrix than TSVD.ConclusionThese results suggest that our proposed DSM−SVR with Laplacian distance kernel can be an efficient alternative to improve the resolution in current and emerging intracardiac imaging systems.

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Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

Cited by 75 publications

References 40 publications

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals

ECG Heartbeat Classification Based on Signal-to-Image Transformation Using Convolutional Neural Networks

A new approach to the intracardiac inverse problem using Laplacian distance kernel

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