Optimizing the Automated Detection of Atrial Fibrillation Episodes in Long-term Recording Instrumentation

Wróbel, Janusz; Horoba, K.; Matonia, A.; Kupka, T.; Henzel, N.; Sobotnicka, Ewelina

doi:10.23919/mixdes.2018.8436835

Cited by 5 publications

(9 citation statements)

References 29 publications

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“…Especially, significant increase of the PPV value was noted a lower number of false AF detections. Obtained performance is higher than that provided by previously developed classification method based on linear classifier, where Se = 95.42% and PPV = 94.97% [53]. Thus, the proposed more advanced method has better ability to detect the true occurrences of AF and provides lower number of false arrhythmias.…”

Section: Discussionmentioning

confidence: 78%

“…Considering on-line detection of AF and limited computational power of the developed mobile monitor, we applied a simple linear classifier which recognizes the AF heartbeats basing on easily accessible information about heart rhythm and HR features [52,53]. Apart from the HR value, other four input features have been selected in a series of preliminary investigations carried out among larger feature set [54].…”

Section: Hr Irregularity Featuresmentioning

confidence: 99%

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Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine

Czabański

Horoba

Wróbel

et al. 2020

Sensors

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Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.

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Section: Discussionmentioning

confidence: 78%

Section: Hr Irregularity Featuresmentioning

confidence: 99%

Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine

Czabański

Horoba

Wróbel

et al. 2020

Sensors

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“…One of them is based on estimating the changes in the interval between two consecutive R-waves (RR) with various irregularity measures. The others are based on observing a lack of or abnormal P-waves (replaced by rapid, irregular, and disordered fibrillatory waves, called F-waves) [2,32]. It can be concluded that the absence of P-waves is a crucial indicator of the presence of AF [6].…”

Section: Review Of Existing Literaturementioning

confidence: 99%

An Improved Approach for Atrial Fibrillation Detection in Long-Term ECG Using Decomposition Transforms and Least-Squares Support Vector Machine

Pander

2023

Applied Sciences

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Atrial fibrillation is a common heart rhythm disorder that is now becoming a significant healthcare challenge as it affects more and more people in developed countries. This paper proposes a novel approach for detecting this disease. For this purpose, we examined the ECG signal by detecting QRS complexes and then selecting 30 successive R-peaks and analyzing the atrial activity segment with a variety of indices, including the entropy change, the variance of the wavelet transform indices, and the distribution of energy in bands determined by the dual-Q tunable Q-factor wavelet transform and coefficients of the Hilbert transform of ensemble empirical mode decomposition. These transformations provided a vector of 21 features that characterized the relevant part of the electrocardiography signal. The MIT-BIH Atrial Fibrillation Database was used to evaluate the proposed method. Then, using the K-fold cross-validation method, the sets of features were fed into the LS-SVM and SVM classifiers and a trilayered neural network classifier. Training and test subsets were set up to avoid sampling from a single participant and to maintain the balance between classes. In addition, individual classification quality scores were analyzed for each signal to determine the dependencies of the classification quality on the subject. The results obtained during the testing procedure showed a sensitivity of 98.86%, a positive predictive value of 99.04%, and a classification accuracy of 98.95%.

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“…Many subsequent studies considered this detection problem as a classification problem and focused on the extraction of various features and the design of classifiers. These features include entropy [9][10][11][12][13], mean and/or median (with or without normalization), root mean square and/or variance [14][15][16], quantiles [16,17], median absolute deviation [10,16,17], coefficients of wavelet transformation [12,13], Markov score [18] of RRI and/or ΔRRI, or a combination of several features [10,11,16,19,20]. In recent studies, deep learning algorithms such as long short-term memory (LSTM) [21,22], and others [20,[23][24][25] have been used to process original signals without feature extraction.…”

Section: Plos Onementioning

confidence: 99%

Accurate detection of atrial fibrillation events with R-R intervals from ECG signals

et al. 2022

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Atrial fibrillation (AF) is a typical category of arrhythmia. Clinical diagnosis of AF is based on the detection of abnormal R-R intervals (RRIs) with an electrocardiogram (ECG). Previous studies considered this detection problem as a classification problem and focused on extracting a number of features. In this study we demonstrate that instead of using any specific numerical characteristic as the input feature, the probability density of RRIs from ECG conserves comprehensive statistical information; hence, is a natural and efficient input feature for AF detection. Incorporated with a support vector machine as the classifier, results on the MIT-BIH database indicates that the proposed method is a simple and accurate approach for AF detection in terms of accuracy, sensitivity, and specificity.

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Optimizing the Automated Detection of Atrial Fibrillation Episodes in Long-term Recording Instrumentation

Cited by 5 publications

References 29 publications

Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine

Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine

An Improved Approach for Atrial Fibrillation Detection in Long-Term ECG Using Decomposition Transforms and Least-Squares Support Vector Machine

Accurate detection of atrial fibrillation events with R-R intervals from ECG signals

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