Dynamic ECG features for atrial fibrillation recognition

Abdul-Kadir, Nurul Ashikin; Safri, Norlaili Mat; Othman, Mohamed

doi:10.1016/j.cmpb.2016.08.021

Cited by 24 publications

(10 citation statements)

References 31 publications

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“…A study by Mohebbi et al investigated AF by applying the feature dimension reduction technique with SVM classifier [ 15 ]. Abdul-Kadir et al used dynamic ECG system according to second order differential equation of ECG behavior with cross validation technique by utilizing SVM and ANN as predictors [ 16 ]. Recently, Rajpurkar et al have utilized one of the deep learning techniques, namely a 34-layer convolutional neural network for detecting arrhythmia, including AF [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Arrhythmia Evaluation in Wearable ECG Devices

Sadrawi

Lin²,

Lin³

et al. 2017

Sensors

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This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation.

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Section: Introductionmentioning

confidence: 99%

Arrhythmia Evaluation in Wearable ECG Devices

Sadrawi

Lin²,

Lin³

et al. 2017

Sensors

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“…For instance, Abdul-Kadir et al . 57 used a second-order dynamic system to extract features form ECG recordings; Ghosh et al . 58 extracted features from single-lead ECG recordings using a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different sub-bands; a DeepNN algorithm known as Hierarchical Extreme Learning Machine used the extracted features to detect AF; and Kisohara et al .…”

Section: ML For Detecting Afmentioning

confidence: 99%

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

Olier

Ortega‐Martorell

Pieroni

et al. 2021

Cardiovascular Research

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There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centered on applying ML for detecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modeling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centered in aims such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g., blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.

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“…Hence, they do not provide all the available information. Examples of time-domain biomarker algorithms are differential equations [102], Principal Component Analysis (PCA) [103,104], the Hadamard transform [105,106], Linear Discriminant Analysis (LDA) [107], and Independent Component Analysis (ICA) [108].…”

Section: Digital Biomarkersmentioning

confidence: 99%

A Review of Atrial Fibrillation Detection Methods as a Service

Faust

Ciaccio

Acharya

2020

IJERPH

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Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals.

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Dynamic ECG features for atrial fibrillation recognition

Cited by 24 publications

References 31 publications

Arrhythmia Evaluation in Wearable ECG Devices

Arrhythmia Evaluation in Wearable ECG Devices

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

A Review of Atrial Fibrillation Detection Methods as a Service

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