Efficacy of Spectral Signatures for the Automatic Classification of Abnormal Ventricular Potentials in Substrate-Guided Mapping Procedures

Several peculiar spectral signatures of post-ischaemic ventricular tachycardia (VT) electrograms (EGMs) have been recently published in the scientific literature. However, despite they were claimed as potentially useful for the automatic identification of arrhythmogenic targets for the VT treatment by trans-catheter ablation, their exploitation in machine learning (ML) applications has been not assessed yet. The aim of this work is to investigate the impact of the information retrieved from these frequency-domain signatures in modelling supervised ML tools for the identification of physiological and abnormal ventricular potentials (AVPs). As such, 1504 bipolar intracardiac EGMs from nine electroanatomic mapping procedures of post-ischaemic VT patients were retrospectively labelled as AVPs or physiological by an expert electrophysiologist. In order to assess the efficacy of the proposed spectral features for AVPs recognition, two different classifiers were adopted in a 10-time 10-fold cross-validation scheme. In both classifiers, the adoption of spectral signatures led to recognition accuracy values above 81%, suggesting that the use of the frequency-domain characteristics of these signals can be successfully considered for the computer-aided recognition of AVPs in substrate-guided mapping procedures

Mapping human disease-associated enzymes into Reactome allows characterization of disease groups and their interactions

According to databases such as OMIM, Humsavar, Clinvar and Monarch, 1494 human enzymes are presently associated to 2539 genetic diseases, 75% of which are rare (with an Orphanet code). The Mondo ontology initiative allows a standardization of the disease name into specific codes, making it possible a computational association between genes, variants, diseases, and their effects on biological processes. Here, we tackle the problem of which biological processes enzymes can affect when the protein variant is disease-associated. We adopt Reactome to describe human biological processes, and by mapping disease-associated enzymes in the Reactome pathways, we establish a Reactome-disease association. This allows a novel categorization of human monogenic and polygenic diseases based on Reactome pathways and reactions. Our analysis aims at dissecting the complexity of the human genetic disease universe, highlighting all the possible links within diseases and Reactome pathways. The novel mapping helps understanding the biochemical/molecular biology of the disease and allows a direct glimpse on the present knowledge of other molecules involved. This is useful for a complete overview of the disease molecular mechanism/s and for planning future investigations. Data are collected in DAR, a database that is free for search and available at https://dar.biocomp.unibo.it

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

Abnormal ventricular potentials (AVPs) are frequently referred to as high-frequency defections in intracardiac electrograms (EGMs). However, no scientifc 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 subcategories of post-ischaemic physiological potentials were considered. All signals were acquired by the CARTO\uae 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

An unusual case of venous thoracic outlet syndrome in relation to the anatomical position of the subclavian vein valves in a young athlete

Venous Thoracic Outlet Syndrome (vTOS) consists of upper extremities oedema, sometimes with varicose dilation of the superficial veins of the arm in consequence of compression and/or thrombosis of the subclavian vein. More specific factors, such as muscle hypertrophy, have additionally been registered in athletes. The case focuses on a 20-year-old male student in medicine, with an intense training activity in body building. The subject has presented symptoms of upper limbs oedema he has also reported heaviness and paresthesia in the left arm and hand. Varicose dilation of a superficial vein close to the axillary fossa was visible at naked eye. Both Doppler ultrasound evaluation and Angio TC were negative for venous thrombosis and/or complete obstruction from external compression. These reports depict an uncommon clinical scenario, which correlate an intense upper body training activity with the presence of a second valve distally of the first valve into the subclavian vein

Exploring Transfer Learning for Ventricular Tachycardia Electrophysiology Studies

Arrhythmogenic sites in post-ischemic ventricular tachycardia (VT) are usually identified by looking for abnormal ventricular potentials (AVPs) in intracardiac electrograms (EGMs). Unfortunately, the accurate recognition of AVPs is a challenging problem for different reasons, including the intrinsic variability in the A VP waveform. Given the high performance of deep neural networks in several scenarios, in this work, we explored the use of transfer learning (TL) for AVPs detection in intracardiac electrophysiology. A balanced set of 1504 bipolar intracardiac EGMs was collected from nine post-ischemic VT patients. The time-frequency representation was generated for each EGM by computing the synchrosqueezed wavelet transform to be used in the re-training of the convolutional neural network. The proposed approach allows obtaining high recognition results, above 90% for all the investigated performance indexes, demonstrating the effectiveness of deep learning in the recognition of AVPs in post-ischemic VT EGMs and paving the way for its use in supporting clinicians in targeting arrhythmogenic sites. In addition, this study further confirms the efficacy of the TL approach even in case of limited dataset sizes

Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography

Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the computational burden of signal processing methods implemented for the fECG extraction from the multi-channel trans-abdominal recordings plays a non-negligible role. In this work, a supervised machine-learning approach for the automatic selection of the most informative raw abdominal recordings in terms of fECG content, i.e., those potentially leading to good-quality, non-invasive fECG signals from a low number of channels, is presented and evaluated.Methods: For this purpose, several signal quality indexes from the scientific literature were adopted as features to train an ensemble tree classifier, which was asked to perform a binary classification between informative and non-informative abdominal channels. To reduce the dimensionality of the classification problem, and to improve the performance, a feature selection approach was also implemented for the identification of a subset of optimal features. 10336 5-s long signal segments derived from a real dataset of multi-channel trans-abdominal recordings acquired from 55 voluntary pregnant women between the 21st and the 27th week of gestation, with healthy fetuses, were adopted to train and test the classification approach in a stratified 10-time 10-fold cross-validation scheme. Abdominal recordings were firstly pre-processed and then labeled as informative or non-informative, according to the signal-to-noise ratio exhibited by the extracted fECG, thus producing a balanced dataset of bad and good quality abdominal channels.Results and Discussion: Classification performance revealed an accuracy above 86%, and more than 88% of those channels labeled as informative were correctly identified. Furthermore, by applying the proposed method to 50 annotated 24-channel recordings from the NInFEA dataset, a significant improvement was observed in fetal QRS detection when only the channels selected by the proposed approach were considered, compared with the use of all the available channels. As such, our findings support the hypothesis that performing a channel selection by looking directly at the raw abdominal signals, regardless of the fetal presentation, can produce a reliable measurement of fetal heart rate with a lower computational burden

Cryptic BCR-ABL fusion gene as variant rearrangement in chronic myeloid leukemia: Molecular cytogenetic characterization and influence on TKIs therapy

At diagnosis, about 5% of Chronic Myeloid Leukemia (CML) patients lacks Philadelphia chromosome (Ph), despite the presence of the BCR/ABL rearrangement. Two mechanisms have been proposed about the occurrence of this rearrangement: the first one is a cryptic insertion between chromosomes 9 and 22; the second one involves two sequential translocations: a classic t(9;22) followed by a reverse translocation, which reconstitutes the normal morphology of the partner chromosomes. Out of 398 newly diagnosed CML patients, we selected 12 Ph-negative cases. Six Ph-negative patients treated with tyrosine kinase inhibitors (TKIs) were characterized, in order to study the mechanisms leading to the rearrangement and the eventual correlation with prognosis in treatment with TKIs. FISH analysis revealed cryptic insertion in 5 patients and classic translocation in the last one. In more detail, we observed 4 different patterns of rearrangement, suggesting high genetic heterogeneity of these patients. In our cases, the BCR/ABL rearrangement mapped more frequently on 9q34 region than on 22q11 region, in contrast to previous reports. Four patients, with low Sokal risk, achieved Complete Cytogenetic Response and/or Major Molecular Response after TKIs therapy. Therapy resistance was observed in one patient with duplication of BCR/ABL rearrangement and in another one with high risk. Even if the number patient is inevitably low, we can confirm that the rare Ph-negative CML patients do not constitute a "warning" category, meanwhile the presence of further cytogenetic abnormalities remains an adverse prognostic factor even in TKI era

The baseline comorbidity burden affects survival in elderly patients with acute myeloid leukemia receiving hypomethylating agents: Results from a multicentric clinical study

Background: In older patients with acute myeloid leukemia (AML), the definition of fitness, prognosis, and risk of death represents an open question. Methods: In the present study, we tested the impact on survival of disease- and patient-related parameters in a large cohort of elderly AML patients homogeneously assigned to treatment with hypomethylating agents (HMAs). Results: In 131 patients with a median age of 76 years, we confirmed that early response (<0.001) and biology-based risk classification (p = 0.003) can select patients with better-predicted survival. However, a full disease-oriented model had limitations in stratifying our patients, prompting us to investigate the impact of baseline comorbidities on overall survival basing on a comorbidity score. The albumin level (p = 0.001) and the presence of lung disease (p = 0.013) had a single-variable impact on prognosis. The baseline comorbidity burden was a powerful predictor of patients' frailty, correlating with increased incidence of adverse events, especially infections, and predicted overall survival (p < 0.001). Conclusion: The comorbidity burden may contribute to impact prognosis in addition to disease biology. While the therapeutic armamentarium of elderly AML is improving, a comprehensive approach that combines AML biology with tailored interventions to patients' frailty is likely to fully exploit the anti-leukemia potential of novel drugs

Novel and Rare Fusion Transcripts Involving Transcription Factors and Tumor Suppressor Genes in Acute Myeloid Leukemia.

Approximately 18% of acute myeloid leukemia (AML) cases express a fusion transcript. However, few fusions are recurrent across AML and the identification of these rare chimeras is of interest to characterize AML patients. Here, we studied the transcriptome of 8 adult AML patients with poorly described chromosomal translocation(s), with the aim of identifying novel and rare fusion transcripts. We integrated RNA-sequencing data with multiple approaches including computational analysis, Sanger sequencing, fluorescence in situ hybridization and in vitro studies to assess the oncogenic potential of the ZEB2-BCL11B chimera. We detected 7 different fusions with partner genes involving transcription factors (OAZ-MAFK, ZEB2-BCL11B), tumor suppressors (SAV1-GYPB, PUF60-TYW1, CNOT2-WT1) and rearrangements associated with the loss of NF1 (CPD-PXT1, UTP6-CRLF3). Notably, ZEB2-BCL11B rearrangements co-occurred with FLT3 mutations and were associated with a poorly differentiated or mixed phenotype leukemia. Although the fusion alone did not transform murine c-Kit+ bone marrow cells, 45.4% of 14q32 non-rearranged AML cases were also BCL11B-positive, suggesting a more general and complex mechanism of leukemogenesis associated with BCL11B expression. Overall, by combining different approaches, we described rare fusion events contributing to the complexity of AML and we linked the expression of some chimeras to genomic alterations hitting known genes in AML

Advanced signal processing and machine learning tools for non-invasive foetal electrocardiography and intracardiac electrophysiology

In the last decades, bioengineering research promoted the improvement in human health and wellbeing through the development, optimization and evaluation of innovative technologies and medical devices for both diagnosis and therapy. In this context, the exploitation of biomedical technology advances plays a key role in the study and treatment of heart disorders. This PhD thesis focuses on two main application areas: on one hand, foetal cardiac physiology and electrocardiography and, on the other, intracardiac electrophysiology, substrate mapping and radiofrequency ablation. There, it aims at providing new instruments and insights to improve the knowledge and go beyond the current state of the art by the development of novel signal processing and machine learning tools that aim at supporting the diagnosis and treatment of cardiac diseases. Non-invasive foetal ECG (fECG) is a long-standing niche research topic characterized by the continuous demand of improved solutions to solve the problem of recovering high-quality fECG signals from non-invasive trans-abdominal recordings. This PhD thesis focused on the development of algorithms for non-invasive fECG extraction and enhancement. Specifically, in collaboration with the Prof. Hau-Tieng Wu (Department of Mathematics and Statistical Science, Duke University, Durham, NC, USA), a novel algorithm for the extraction of morphologically preserved multi-channel fECG signals was conceived. Furthermore, wavelet denoising was deeply investigated for the post-processing of the fECG recordings, to quantitatively evaluate the noise-removal and morphology-preservation effects of different wavelet denoising approaches, expressly tailored for this application domain. Intracardiac electrophysiology is a branch of interventional cardiology aimed at the diagnosis and treatment of arrhythmias by catheter-based techniques exploiting electroanatomic substrate mapping and ablation. In this exciting scenario, this PhD thesis focused on post-ischemic ventricular tachycardia, which is a life-threatening arrhythmia. Being the electrophysiological studies and ablations very time-consuming and operator-dependent, the first applied-research goal was the development of an effective tool able to support clinical experts in the recognition of the ablation targets during clinical procedures. Moreover, a detailed spectral characterization of post-ischaemic signals was performed, thus paving the way to the development of novel approaches in terms of advanced signal analysis, automatic recognition of the arrhythmogenic substrates, study of the substrate and, in general, to a deeper understanding of the arrhythmogenic mechanisms. Beyond the scientific content, this PhD thesis gives an important contribution from an industrial perspective in both fields. In fact, automated signal processing tools for the non-invasive fECG signals can improve the detection capabilities of current tools, to be clinically exploited for low-cost antenatal screening. At the same time, novel methods for ablation targets recognition in cardiac electrophysiology could be embedded in future medical electroanatomic mapping systems as plug-in to enhance current computer-aided methods