Robust automated cardiac arrhythmia detection in ECG beat signals

Albuquerque, Victor Hugo C. de; Nunes, Thiago; Pereira, Danillo Roberto; Luz, Eduardo; Menotti, David; Papa, João P.; Tavares, João Manuel R. S.

doi:10.1007/s00521-016-2472-8

Cited by 64 publications

(25 citation statements)

References 27 publications

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“…Regarding the classifiers, the support vector machine (SVM) [8, 20, 22–24], nearest neighbors (NN) [25, 26], artificial neural networks (ANN) [13, 27], optimum-path forest (OPF) [28], linear discriminants (LD) [3], conditional random field [11], and reservoir computing with logistic regression [29] are common choices for the heartbeat classification problem. However, using a single classifier can bias the classification and lead to a relatively low generalization performance.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, the misclassified N beats lead to a decrease of the positive predictive value of the S beats. However, as the heartbeat classification plays an important role toward identifying the cardiac arrhythmia, the accuracy over the class S is considered most important [28]. From an overall point of view, the nsDispatcher does a decent job.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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A pyramid-like model for heartbeat classification from ECG recordings

Sun

Rong

et al. 2018

PLoS ONE

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Heartbeat classification is an important step in the early-stage detection of cardiac arrhythmia, which has been identified as a type of cardiovascular diseases (CVDs) affecting millions of people around the world. The current progress on heartbeat classification from ECG recordings is facing a challenge to achieve high classification sensitivity on disease heartbeats with a satisfied overall accuracy. Most of the work take individual heartbeats as independent data samples in processing. Furthermore, the use of a static feature set for classification of all types of heartbeats often causes distractions when identifying supraventricular (S) ectopic beats. In this work, a pyramid-like model is proposed to improve the performance of heartbeat classification. The model distinguishes the classification of normal and S beats and takes advantage of the neighbor-related information to assist identification of S bests. The proposed model was evaluated on the benchmark MIT-BIH-AR database and the St. Petersburg Institute of Cardiological Technics(INCART) database for generalization performance measurement. The results reported prove that the proposed pyramid-like model exhibits higher performance than the state-of-the-art rivals in the identification of disease heartbeats as well as maintains a reasonable overall classification accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

A pyramid-like model for heartbeat classification from ECG recordings

Sun

Rong

et al. 2018

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…de Albuquerque [13] illustrated the arrhythmia identification in ECG signal using supervised machine learning methods of Optimum Path Forest (OPF) classifier. The proposed method's efficiency and effectiveness are compared with the different feature extraction methods and classifiers.…”

Section: Literature Reviewmentioning

confidence: 99%

Arrhythmia Detection Based on Hybrid Features of T-wave in Electrocardiogram

Raghu¹,

Meshram²

2018

IJIES

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An Electrocardiogram (ECG) is used as one of the important diagnostic tools for the detection of the health of a heart. An automatic heart abnormality identification methods sense numerous abnormalities or arrhythmia and decrease the physician's pressure as well as share their work load. In ECG analysis, the main focus is to enhance degree of accuracy and include a number of heart diseases that can be classified. In this research paper, arrhythmia classification is proposed using Hybrid features of T-wave in ECG. The classification system consists of majorly three phases, windowing technique, feature extraction and classification. In feature extraction phase various features are used such as Differential Entropy (DE), Peak-Magnitude RMS ratio, Auto Regressive feature based Yule Walker, Burgs method. In classification phase Deep Neural Network (DNN) classifier is used. This classifier categorizes the normal and abnormal signals efficiently. The experimental analysis showed that, the Hybrid features Arrhythmia classification performance of accuracy approximately 98.3%, Specificity 98.0% and Sensitivity 98.6% using MIT-BIH database.

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“…The vast viability of the utilization and easiness of the patient’s physiological data acquisition characterize this method [ 13 , 16 ]. Compared to the electrocardiogram (ECG) signal [ 17 , 18 ], the PPG signal does not have a complex hardware implementation. It also does not have the requirement of a reference signal, hence the PPG sensors can be incorporated into wristbands.…”

Section: Introductionmentioning

confidence: 99%

Advances in Photopletysmography Signal Analysis for Biomedical Applications

Moraes

Rocha

Vasconcelos

et al. 2018

Sensors

222

131

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Heart Rate Variability (HRV) is an important tool for the analysis of a patient’s physiological conditions, as well a method aiding the diagnosis of cardiopathies. Photoplethysmography (PPG) is an optical technique applied in the monitoring of the HRV and its adoption has been growing significantly, compared to the most commonly used method in medicine, Electrocardiography (ECG). In this survey, definitions of these technique are presented, the different types of sensors used are explained, and the methods for the study and analysis of the PPG signal (linear and nonlinear methods) are described. Moreover, the progress, and the clinical and practical applicability of the PPG technique in the diagnosis of cardiovascular diseases are evaluated. In addition, the latest technologies utilized in the development of new tools for medical diagnosis are presented, such as Internet of Things, Internet of Health Things, genetic algorithms, artificial intelligence and biosensors which result in personalized advances in e-health and health care. After the study of these technologies, it can be noted that PPG associated with them is an important tool for the diagnosis of some diseases, due to its simplicity, its cost–benefit ratio, the easiness of signals acquisition, and especially because it is a non-invasive technique.

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Robust automated cardiac arrhythmia detection in ECG beat signals

Cited by 64 publications

References 27 publications

A pyramid-like model for heartbeat classification from ECG recordings

A pyramid-like model for heartbeat classification from ECG recordings

Arrhythmia Detection Based on Hybrid Features of T-wave in Electrocardiogram

Advances in Photopletysmography Signal Analysis for Biomedical Applications

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