Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine

Asgari, Shadnaz; Mehrnia, Alireza; Moussavi, Maryam

doi:10.1016/j.compbiomed.2015.03.005

Cited by 210 publications

(113 citation statements)

References 34 publications

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“…Additionally, current AF diagnosis is mainly based on the presence of typical symptoms, such as dyspnea, chest pain, dizziness and palpitations [17], but not every single patient always presents these signs. Indeed, previous works have reported that up to 90% of PAF episodes may be asymptomatic [18].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms

Rodenas

García

Alcaraz

et al. 2015

Entropy

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This work introduces for the first time the application of wavelet entropy (WE) to detect episodes of the most common cardiac arrhythmia, atrial fibrillation (AF), automatically from the electrocardiogram (ECG). Given that AF is often asymptomatic and usually presents very brief initial episodes, its early automatic detection is clinically relevant to improve AF treatment and prevent risks for the patients. After discarding noisy TQ intervals from the ECG, the WE has been computed over the median TQ segment obtained from the 10 previous noise-free beats under study. In this way, the P-waves or the fibrillatory waves present in the recording were highlighted or attenuated, respectively, thus enabling the patient's rhythm identification (sinus rhythm or AF). Results provided a discriminant ability of about 95%, which is comparable to previous works. However, in contrast to most of them, which are mainly based on quantifying RR series variability, the proposed algorithm is able to deal with patients under rate-control therapy or with a reduced heart rate variability during AF. Additionally, it also presents interesting properties, such as the lowest delay in detecting AF or sinus rhythm, the ability to detect episodes as brief as five beats in length or its integration facilities under real-time beat-by-beat ECG monitoring systems. Consequently, this tool may help clinicians in the automatic detection of a wide variety of AF episodes, thus gaining further knowledge about the mechanisms initiating this arrhythmia.

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Section: Introductionmentioning

confidence: 99%

Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms

Rodenas

García

Alcaraz

et al. 2015

Entropy

View full text Add to dashboard Cite

show abstract

“…Many of the algorithms (Moody and Mark, 1983; Cerutti et al, 1997; Tateno and Glass, 2001; Logan and Healey, 2005; Couceiro et al, 2008; Babaeizadeh et al, 2009; Dash et al, 2009; Huang et al, 2011; Lake and Moorman, 2011; Asgari et al, 2015; Ladavich and Ghoraani, 2015; García et al, 2016; Xia et al, 2018) were chosen for comparison as the best performing results for various methods.…”

Section: Resultsmentioning

confidence: 99%

Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks

et al. 2018

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Atrial fibrillation (AF) is the most common cardiac arrhythmias causing morbidity and mortality. AF may appear as episodes of very short (i.e., proximal AF) or sustained duration (i.e., persistent AF), either form of which causes irregular ventricular excitations that affect the global function of the heart. It is an unmet challenge for early and automatic detection of AF, limiting efficient treatment strategies for AF. In this study, we developed a new method based on continuous wavelet transform and 2D convolutional neural networks (CNNs) to detect AF episodes. The proposed method analyzed the time-frequency features of the electrocardiogram (ECG), thus being different to conventional AF detecting methods that implement isolating atrial or ventricular activities. Then a 2D CNN was trained to improve AF detection performance. The MIT-BIH Atrial Fibrillation Database was used for evaluating the algorithm. The efficacy of the proposed method was compared with those of some existing methods, most of which implemented the same dataset. The newly developed algorithm using CNNs achieved 99.41, 98.91, 99.39, and 99.23% for the sensitivity, specificity, positive predictive value, and overall accuracy (ACC) respectively. As the proposed algorithm targets the time-frequency feature of ECG signals rather than isolated atrial or ventricular activity, it has the ability to detect AF episodes for using just five beats, suggesting practical applications in the future.

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“…In Asgari et al, [33] it is able to detect AFIB with 97% of sensitivity and 97.1% of specificity. It is also found that an algorithm is able to produce approximately 100% of accuracy.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Artificial intelligence classification methods of atrial fibrillation with implementation technology

Lim

Hau

Lim

et al. 2016

Computer Assisted Surgery

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Background: Atrial fibrillation (AFIB) is one of the most common types of arrhythmia, which leads to heart failure and stroke to public. As AFIB has the high potential to cause permanent disability in patients, its early detection is extremely important. There are different types of AFIB classification algorithm that have been proposed by researchers in recent years. Methods: This paper reviews the features of AFIB in terms of ECG morphological features and heart rate variability (HRV) analysis on different methods. The existing classification method, particularly focusing on Artificial Intelligence technique, is also comprehensively described. Other than that, the existing implementation technology of arrhythmia detection platforms such as smart phone and System-on-Chip-based embedded device are also elaborated in terms of their design trade-offs. Conclusion: Current existing AFIB detection algorithm cannot compromise for high accuracy and low complexity. Due to the limitation of embedded system, design trade off should be considered to strike the balance between the performance of algorithm and the limitation.

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Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine

Cited by 210 publications

References 34 publications

Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms

Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms

Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks

Artificial intelligence classification methods of atrial fibrillation with implementation technology

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