Automatic Recognition of Ventricular Abnormal Potentials in Intracardiac Electrograms

Baldazzi, Giulia; Orrù, Marco; Matraxia, Mirko; Viola, Graziana; Pani, Danilo

doi:10.22489/cinc.2019.343

Cited by 5 publications

(8 citation statements)

References 7 publications

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“…In the first method [11], four features (Pearson's correlation coefficient below 0.83) from the time-scale and time domain were identified for the automatic recognition of VAPs.…”

Section: Svm-based Approachmentioning

confidence: 99%

“…Initially, in order to remove noisy oscillations of the signal that could affect the subsequent feature extraction, a translation-invariant wavelet denoising was performed on each intracardiac electrogram [11]. Then, from each denoised electrogram, a 350 ms window, including 50 ms before and 300 ms after the reference annotation, was selected.…”

Section: Svm-based Approachmentioning

confidence: 99%

“…As in [11], only the ending portion of each 2.5 s-long recording was exploited for the classification. In this regard, the annotation of the reference point, automatically saved by the CARTO3 acquisition system, easily allowed the identification of each beat of interest.…”

Section: Datasetmentioning

confidence: 99%

“…On the basis of our previous findings [11], in this work a comparison of two different approaches to the supervised classification of VAPs and physiological potentials is presented, aiming to support operators in the identification of VAPs during ablation procedures. Both approaches were evaluated on a dataset composed of 954 beats from six subjects affected by post-ischemic VT.…”

Section: Introductionmentioning

confidence: 99%

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Supervised Classification of Ventricular Abnormal Potentials in Intracardiac Electrograms

Baldazzi¹,

Orrù²,

Matraxia³

et al. 2020

Computing in Cardiology Conference (CinC)

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Ventricular abnormal potentials (VAPs) identification is a challenging issue, since they constitute the ablation targets in substrate-guided mapping and ablation procedures for ventricular tachycardia (VT) treatment. In this work, two approaches for the supervised classification of VAPs in bipolar intracardiac electrograms are evaluated and compared. To this aim, 954 bipolar electrograms were retrospectively annotated by an expert cardiologist. All signals were acquired from six patients affected by post-ischemic VT by the CARTO3 system at the San Francesco Hospital (Nuoro, Italy) during routine procedures. The first classification approach was based on a support vector machine trained and tested on four different features, extracted from both the time and timescale domain, to identify physiological and abnormal potentials. Conversely, in order to assess the significance of the first approach and its features, in the second approach all the samples constituting a time-domain segment of each bipolar electrogram were given as input to a feed-forward artificial neural network. In both cases, the accuracy in VAPs and physiological potentials identification exceeded 79%, suggesting their efficacy and the possibility of VAPs automatic recognition without identifying peculiar features.

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“…In the first method [11], four features (Pearson's correlation coefficient below 0.83) from the time-scale and time domain were identified for the automatic recognition of VAPs.…”

Section: Svm-based Approachmentioning

confidence: 99%

Section: Svm-based Approachmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Supervised Classification of Ventricular Abnormal Potentials in Intracardiac Electrograms

Baldazzi¹,

Orrù²,

Matraxia³

et al. 2020

Computing in Cardiology Conference (CinC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…All intracardiac EGMs were manually labelled by an experienced cardiologist using an ad hoc MATLAB graphical user interface implemented for this purpose. Specifically, by exploiting the corresponding simultaneous surface ECG leads, we considered all abnormal potentials occurring after or during the corresponding QRS complexes, as in other works 31 , 49 , 50 , and all physiological potentials from post-ischaemic damaged substrates, as detailed hereafter.…”

Section: Methodsmentioning

confidence: 99%

Spectral characterisation of ventricular intracardiac potentials in human post-ischaemic bipolar electrograms

Baldazzi

Orrù

Solinas

et al. 2022

Sci Rep

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Abnormal ventricular potentials (AVPs) are frequently referred to as high-frequency deflections in intracardiac electrograms (EGMs). However, no scientific study performed a deep spectral characterisation of AVPs and physiological potentials in real bipolar intracardiac recordings across the entire frequency range imposed by their sampling frequency. In this work, the power contributions of post-ischaemic physiological potentials and AVPs, along with some spectral features, were evaluated in the frequency domain and then statistically compared to highlight specific spectral signatures for these signals. To this end, 450 bipolar EGMs from seven patients affected by post-ischaemic ventricular tachycardia were retrospectively annotated by an experienced cardiologist. Given the high variability of the morphologies observed, three different sub-classes of AVPs and two sub-categories of post-ischaemic physiological potentials were considered. All signals were acquired by the CARTO® 3 system during substrate-guided catheter ablation procedures. Our findings indicated that the main frequency contributions of physiological and pathological post-ischaemic EGMs are found below 320 Hz. Statistical analyses showed that, when biases due to the signal amplitude influence are eliminated, not only physiological potentials show greater contributions below 20 Hz whereas AVPs demonstrate higher spectral contributions above ~ 40 Hz, but several finer differences may be observed between the different AVP types.

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Arrhythmogenic sites identification in post-ischemic ventricular tachycardia electrophysiological studies by explainable deep learning

Pitzus,

Baldazzi,

Orrù

et al. 2025

Biomedical Signal Processing and Control

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Automatic Recognition of Ventricular Abnormal Potentials in Intracardiac Electrograms

Cited by 5 publications

References 7 publications

Supervised Classification of Ventricular Abnormal Potentials in Intracardiac Electrograms

Supervised Classification of Ventricular Abnormal Potentials in Intracardiac Electrograms

Spectral characterisation of ventricular intracardiac potentials in human post-ischaemic bipolar electrograms

Arrhythmogenic sites identification in post-ischemic ventricular tachycardia electrophysiological studies by explainable deep learning

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