Application of Dense Neural Networks for Detection of Atrial Fibrillation and Ranking of Augmented ECG Feature Set

Krasteva, Vessela; Christov, Ivaylo; Naydenov, Stefan; Stoyanov, Todor; Jekova, Irena

doi:10.3390/s21206848

Cited by 26 publications

(16 citation statements)

References 60 publications

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“…PR interval refers to the time interval from the starting point of the P wave to the starting point of the QRS complex on ECG. Some studies have used and proved the effectiveness of PR interval for ECG classification [ 3 , 27 , 28 ]. To get the PR interval, the P wave of the ECG recording should be located.…”

Section: Methodsmentioning

confidence: 99%

“…Most of the current studies on AF automatic analysis do not focus on recognizing the noisy ECG recordings with low SNR. Krasteva et al [ 3 ] used the limited feature set and combined with the optimized artificial neural network to conduct four-classification research on the CinC 2017 database. Goodfellow et al [ 4 ] extracted three types of features, that is, template features, RRI features, and full waveform features using step-by-step machine and classified the CinC 2017 database into four categories.…”

Section: Introductionmentioning

confidence: 99%

“…Traditional machine learning algorithms based on statistics were extensively used for data analysis [3][4][5][6]. Most of the current studies on AF automatic analysis do not focus on recognizing the noisy ECG recordings with low SNR.…”

Section: Introductionmentioning

confidence: 99%

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Atrial Fibrillation Detection with Low Signal-to-Noise Ratio Data Using Artificial Features and Abstract Features

Bao

Shou-fen

et al. 2023

Journal of Healthcare Engineering

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Detecting atrial fibrillation (AF) of short single-lead electrocardiogram (ECG) with low signal-to-noise ratio (SNR) is a key of the wearable heart monitoring system. This study proposed an AF detection method based on feature fusion to identify AF rhythm (A) from other three categories of ECG recordings, that is, normal rhythm (N), other rhythm (O), and noisy (∼) ECG recordings. So, the four categories, that is, N, A, O, and ∼ were identified from the database provided by PhysioNet/CinC Challenge 2017. The proposed method first unified the 9 to 60 seconds unbalanced ECG recordings into 30 s segments by copying, cutting, and symmetry. Then, 24 artificial features including waveform features, interval features, frequency-domain features, and nonlinear feature were extracted relying on prior knowledge. Meanwhile, a 13-layer one-dimensional convolutional neural network (1-D CNN) was constructed to yield 38 abstract features. Finally, 24 artificial features and 38 abstract features were fused to yield the feature matrix. Random forest was employed to classify the ECG recordings. In this study, the mean accuracy (Acc) of the four categories reached 0.857. The F1 of N, A, and O reached 0.837. The results exhibited the proposed method had relatively satisfactory performance for identifying AF from short single-lead ECG recordings with low SNR.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Atrial Fibrillation Detection with Low Signal-to-Noise Ratio Data Using Artificial Features and Abstract Features

Bao

Shou-fen

et al. 2023

Journal of Healthcare Engineering

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“…Due to different distributions of HRV-derived parameters in AF and SR [ 8 ], HRV has gained new interest in AF detection in ECG [ 9 , 10 , 11 ], as well as from wearable devices [ 12 , 13 ]. Several authors used feature selection methods to find the most relevant HRV parameters for AF detection [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Statistical and Diagnostic Properties of pRRx Parameters in Atrial Fibrillation Detection

Buś

Jędrzejewski

Guzik

2022

JCM

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Background: We studied the diagnostic properties of the percentage of successive RR intervals differing by at least x ms (pRRx) as functions of the threshold value x in a range of 7 to 195 ms for the differentiation of atrial fibrillation (AF) from sinus rhythm (SR). Methods: RR intervals were measured in 60-s electrocardiogram (ECG) segments with either AF (32,141 segments) or SR (32,769 segments) from the publicly available Physionet Long-Term Atrial Fibrillation Database (LTAFDB). For validation, we have used ECGs from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT–BIH) Atrial Fibrillation Database. The pRRx distributions in AF and SR in relation to x were studied by histograms, along with the mutual association by the nonparametric Spearman correlations for all pairs of pRRx, and separately for AF or SR. The optimal cutoff values for all pRRx were determined using the receiver operator curve characteristic. A nonparametric bootstrap with 5000 samples was used to calculate a 95% confidence interval for several classification metrics. Results: The distributions of pRRx for x in the 7–195 ms range are significantly different in AF than in SR. The sensitivity, specificity, accuracy, and diagnostic odds ratios differ for pRRx, with the highest values for x = 31 ms (pRR31) rather than x = 50 (pRR50), which is most commonly applied in studies on heart rate variability. For the optimal cutoff of pRR31 (68.79%), the sensitivity is 90.42%, specificity 95.37%, and the diagnostic odds ratio is 194.11. Validation with the ECGs from the MIT–BIH Atrial Fibrillation Database confirmed our findings. Conclusions: We demonstrate that the diagnostic properties of pRRx depend on x, and pRR31 outperforms pRR50, at least for ECGs of 60-s duration.

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“…Unfortunately, the detailed results of selecting features were not included in these papers. In a study conducted by Krasteva et al (2020), signals from the PhysioNet/CinC Challenge 2017 database were classified using features from HRV, morphology analysis, heartbeat classification, principal component analysis (PCA) of PQRST and TQ, P-wave analysis, TQ-segment analysis and noise correction [ 25 ]. The HRV parameters included the percentage of successive RR intervals differing by at least 50 ms (pRR50) and the ratio of standard deviations of points across (SD1) and along the identity line (SD2) of the Poincare plots, i.e., SD1/SD2.…”

Section: Introductionmentioning

confidence: 99%

Using Minimum Redundancy Maximum Relevance Algorithm to Select Minimal Sets of Heart Rate Variability Parameters for Atrial Fibrillation Detection

Buś

Jędrzejewski

Guzik

2022

JCM

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Heart rate is quite regular during sinus (normal) rhythm (SR) originating from the sinus node. In contrast, heart rate is usually irregular during atrial fibrillation (AF). Complete atrioventricular block with an escape rhythm, ventricular pacing, or ventricular tachycardia are the most common exceptions when heart rate may be regular in AF. Heart rate variability (HRV) is the variation in the duration of consecutive cardiac cycles (RR intervals). We investigated the utility of HRV parameters for automated detection of AF with machine learning (ML) classifiers. The minimum redundancy maximum relevance (MRMR) algorithm, one of the most effective algorithms for feature selection, helped select the HRV parameters (including five original), best suited for distinguishing AF from SR in a database of over 53,000 60 s separate electrocardiogram (ECG) segments cut from longer (up to 24 h) ECG recordings. HRV parameters entered the ML-based classifiers as features. Seven different, commonly used classifiers were trained with one to six HRV-based features with the highest scores resulting from the MRMR algorithm and tested using the 5-fold cross-validation and blindfold validation. The best ML classifier in the blindfold validation achieved an accuracy of 97.2% and diagnostic odds ratio of 1566. From all studied HRV features, the top three HRV parameters distinguishing AF from SR were: the percentage of successive RR intervals differing by at least 50 ms (pRR50), the ratio of standard deviations of points along and across the identity line of the Poincare plots, respectively (SD2/SD1), and coefficient of variation—standard deviation of RR intervals divided by their mean duration (CV). The proposed methodology and the presented results of the selection of HRV parameters have the potential to develop practical solutions and devices for automatic AF detection with minimal sets of simple HRV parameters. Using straightforward ML classifiers and the extremely small sets of simple HRV features, always with pRR50 included, the differentiation of AF from sinus rhythms in the 60 s ECGs is very effective.

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Application of Dense Neural Networks for Detection of Atrial Fibrillation and Ranking of Augmented ECG Feature Set

Cited by 26 publications

References 60 publications

Atrial Fibrillation Detection with Low Signal-to-Noise Ratio Data Using Artificial Features and Abstract Features

Atrial Fibrillation Detection with Low Signal-to-Noise Ratio Data Using Artificial Features and Abstract Features

Statistical and Diagnostic Properties of pRRx Parameters in Atrial Fibrillation Detection

Using Minimum Redundancy Maximum Relevance Algorithm to Select Minimal Sets of Heart Rate Variability Parameters for Atrial Fibrillation Detection

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