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.

Ventricular abnormal potentials are low-amplitude electrical signals that appear in intracardiac electrograms during a QRS or with an unpredictable delay with respect to it. Their spatial localization can be exploited by cardiologists for the identification of the ablation targets in substrate-guided mapping and ablation procedures. In this work, an automatic approach for a reliable detection of such potentials in intracardiac electrograms is proposed. To this aim, 86 intracardiac electrograms from five patients with post-ischemic ventricular tachycardia, acquired by the CARTO3 System, were retrospectively annotated by an expert cardiologist, to be used for a supervised classifier training and test. The automatic detection was based on a non-linear denoising followed by a timescale decomposition based on the continuous wavelet transform. Then, different morphological features were extracted from both the timescale domain and the time domain, and used to feed a support vector machine trained to discriminate between physiological and abnormal potentials. The recognition accuracy exceeded 93%, paving the way to further developments and more extensive studies.

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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.