Localizing Atrial Flutter Circuit Using Variability in the Vectorcardiographic Loop Parameters

Azman, Muhammad Haziq Kamarul; Meste, Olivier; Kadir, Kushsairy; Laţcu, Decebal Gabriel

doi:10.22489/cinc.2018.355

Cited by 6 publications

(9 citation statements)

References 8 publications

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“…F waves from each recording were detected, segmented, synchronized, transformed into VCG and removed of respiratory motion using a technique described previously [1]. Only F waves not overlapped within T waves were considered.…”

Section: Dataset and Preprocessingmentioning

confidence: 99%

“…Only F waves not overlapped within T waves were considered. For this study, only the statistics Mean(·), Var(·), Skewness(·) and Kurtosis(·) were considered for the 4 loop parameters (total of 16 features, see [1] for details).…”

Section: Dataset and Preprocessingmentioning

confidence: 99%

“…To investigate this matter, classifier performance results using the PLSV transform were also obtained. The processing scheme is similar to the one detailed in [1], except that the IDT is replaced by the PLSV transform.…”

Section: 2mentioning

confidence: 99%

“…The rotating circular depolarization generates a pseudo-periodic sawtooth waveform on the electrocardiogram (ECG); a full wave representing one complete cycle is known as an F wave. Our previous study showed that by using serial beat-tobeat approach, variability features extracted from the vectorcardiographic (VCG) F loops obtained from the 12-lead ECG using the Inverse Dower Transform (IDT) allowed non-invasive localization of AFL circuits [1].…”

Section: Introductionmentioning

confidence: 99%

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Improving Flutter Localization Performance by Optimizing the Inverse Dower Transform

Azman¹,

Meste²,

Laţcu³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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A previous study showed the possibility to localize right or left flutter circuit origin using variability contained in vectorcardiographic loop parameters. The Inverse Dower Transform, used to obtain the vectorcardiograms is based on a very simplistic torso conductor model, and hence not optimized. The present study aims to optimize the transform to maximize classifier accuracy. A parametric optimization model was proposed, as well as an optimization scheme. Model parameters were obtained by iteratively optimizing the linear SVM classifier accuracy until convergence. The goal can be shown to be multimodal and non-smooth. Therefore, a multi-instance and derivativefree method was considered. Previous dataset of 56 flutter recordings (31 right, 25 left) was used, considering only non-overlapped and respiratory motion-corrected F loops. For the SVM classifier, a 3.8% increase in accuracy was observed (max 0.95). When the logistic regression classifier was used, an increase of 7.8% was observed (max 0.98). Comparison to a targeted transform previously developed showed an improvement by 17−19%. Observation of the model parameter values showed amplitude reduction applied to Lead X and rotation applied to Lead Z.

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Section: Dataset and Preprocessingmentioning

confidence: 99%

Section: Dataset and Preprocessingmentioning

confidence: 99%

Section: 2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving Flutter Localization Performance by Optimizing the Inverse Dower Transform

Azman¹,

Meste²,

Laţcu³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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“…It has been shown in [3] that the spatial variability of the F-waves could unveils from which chamber the AFL originates. The spatial variability is far from being addressed by a simple 12-leads ECG visual inspection used by practitioners.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach and Waves Synchronization Improvement for the Localization of Atrial Flutter Circuit Based on the 12-Leads ECG

Meste¹,

Azman²,

Laţcu³

2019

2019 Computing in Cardiology Conference (CinC)

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The localization of the Atrial flutter (AFL) is of great interest for ablation planification. Regardless the direction of rotation of the corresponding reentry loop, its left or right atrium origin needs to be known beforehand. This localization is usually performed by using visual inspection of the 12-leads standard ECG that could be computerized. The aim of the study is to automatically classify the corresponding averaged F-waves by using one to five simple features. The averaged F-wave is computed by introducing a new multi-lead extension of a SVD based method for the wave resynchronization.A dataset of ECG recorded from 56 subjects and comprising 25 left AFL and 31 right AFL will train the classifier. From the averaged 12 leads F-wave, 3 groups (Gi) of features were extracted: G1-(min, max), G2-(integral of the negative, of the positive part), G3-(integral of the wave, integral of the absolute value of the wave). The logistic regression (LR) model is used for the supervised classifications.The mean accuracy ranges for the three groups, without validations, are G1:[0.69-0.83], G2:[0.68-0.81], G3:[0.68-0.80] for one feature up to five. The maximum accuracy comes from G1 with five features and is equal to 93%. The corresponding selected features are [max(I), max(III), max(V3), min(aVL), min(V5)].

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Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG

2019

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Objective: This study proposes a reference database, composed of a large number of simulated ECG signals in atrial fibrillation (AF), for investigating the performance of methods for extraction of atrial fibrillatory waves (f-waves). Approach: The simulated signals are produced using a recently published and validated model of 12-lead ECGs in AF. The database is composed of eight signal sets which together account for a wide range of characteristics known to represent major challenges in f-wave extraction, including high heart rates, high morphological QRST variability, and the presence of ventricular premature beats. Each set contains 30 5-min signals with different f-wave amplitudes. The database is used for the purpose of investigating the statistical association between different indices, designed either for use with real or simulated signals. Main results: Using the database, which is made available at the Physionet repository of physiological signals, the performance indices unnormalized ventricular residue (uVR), designed for real signals, and the root mean square error, designed for simulated signals, were found to exhibit the strongest association, leading to the recommendation that uVR should be used when characterizing performance in real signals. Significance: The proposed database facilitates the comparison of performance of different f-wave extraction methods and makes it possible to express performance in terms of the error between simulated and extracted f-wave signals.

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Localizing Atrial Flutter Circuit Using Variability in the Vectorcardiographic Loop Parameters

Cited by 6 publications

References 8 publications

Improving Flutter Localization Performance by Optimizing the Inverse Dower Transform

Improving Flutter Localization Performance by Optimizing the Inverse Dower Transform

Machine Learning Approach and Waves Synchronization Improvement for the Localization of Atrial Flutter Circuit Based on the 12-Leads ECG

Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG

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