1,721,102 research outputs found
"Introduzione" in Merenda A, Mainardi, L., Tura, G (a cura di) La cinoterapia e le relazioni di cura
No full textDistance and Similarity Measurements of P Waves before and after Pulmonary Vein Isolation in Patients with Atrial Fibrillation
No full textThis study aimed to assess electric markers obtained from the surface electrocardiogram in order to analyse significant differences before and after pulmonary vein isolation in patients who suffered from paroxysmal atrial fibrillation. 30 patients who underwent catheter ablation (in order to permanently restore sinus rhythm and stop atrial fibrillation episodes) were included in the study. Both surface electrocardiogram and intracavitary recordings were simultaneously acquired starting some minutes before catheter ablation began until the whole procedure successfully ended. P-waves have been delineated on V1 lead, and measurements of distances and similarities between them have been obtained to compare the recordings. It has been found that distances between P-waves significatively decrease (about 14%) whereas similarities significatively increase (about 3%) after catheter ablation. The use of these features would help to identify the success of the catheter ablation procedure, which is the main objective of this preliminary study: the non-invasive identification of spontaneous reconnection of pulmonary veins, the main cause of the arrhythmia recurrences
A Poincaré Image-Based Detector of ECG Segments Containing Atrial and Ventricular Beats
Full text linkAn electrocardiogram (ECG) classifier for the detection of ECG segments containing atrial or ventricular (A/V) beats could ease in the detection of premature atrial complexes (PACs) and by so, the study of their relationship with atrial fibrillation (AF) and stroke. In this work such a classifier is presented based on convolutional neural networks (CNN) and the RR and dRR interval representation on Poincaré Images. Two PhysioNet open-source databases containing beat annotations were used. ECG signals were divided into 30-beat segments with a 50% overlap. Each segment was then transformed into a Poincaré Image. A total of 381151 and 62142 Poincaré Images were computed for normal (N) and A/V segments. RR, dRR and both types of Poincaré Images combined were evaluated as inputs to the CNN. The CNN was trained following a patient-wise train-test division (i.e., no patient was included both in the train and test set) in a 10-fold cross-validation. The patient-wise median and interquartile range accuracy, sensitivity and positive predictive values were 97.90 (94.49 - 99.28), 96.03 (89.67 - 98.76) and 91.91 (70.87 - 99.24), respectively for RR input. No statistical significant differences in performance were found among the three types of Poincaré Images input. Results suggest the present methodology manages to distinguish among N and A/V with high precision
Assessment of the effect of intensity standardization on the reliability of T1-weighted MRI radiomic features: Experiment on a virtual phantom
No full textThe effect of time of repetition (TR) and time of echo (TE) on radiomic features was evaluated using a virtual phantom. Forty-two T1-weighted MRI images of the same virtual phantom were simulated with TR and TE in a range used in clinical practice. Fifty-eight radiomic features were considered for this analysis. Features were extracted from 3 different regions of interest (ROIs) from the original images and from images that underwent intensity standardization (linear intensity standardization, Z-score standardization and histogram matching). Intraclass correlation coefficient (ICC) was used to assess the reliability of the radiomic features and a threshold of 0.75 was used to discriminate features with good or bad reliability. The coefficient of determination R2 was used to quantify correlation between features and image acquisition parameters. The majority of radiomic features (76%) had good reliability (ICC>0.75) and 66% of the features were uncorrelated with TR and TE (R2<0.5). Intensity standardization (in particular histogram matching) significantly reduced the correlation. Intensity standardization also increased the reliability of FOS features, but histogram matching significantly reduced the reliability of GLCM features
Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI-radiomics of the brain
No full textEnsemble Learning of Modified Residual Networks for Classifying ECG with Different Set of Leads
No full textThe automatic detection and classification of cardiac abnormalities can assist physicians in making diagnoses, saving costs in modern healthcare systems. In this study we present an automatic algorithm for classification of cardiac abnormalities included in the CinC's challenge 2021 dataset consisting of twelve-lead, six-lead, three-lead, and two-lead ECGs (team: Polimi1). For each set of leads an ensemble of three deep learning models, trained on three different subsets, was developed. These subsets, obtained by splitting the recordings with the most frequent classes, had more balanced distributions for training and were used to train the 3 classifiers. The trained models were modified Residual Networks with a Squeeze-and-Excitation module. This module is based on the intuition of channel attention: the basic idea of this approach is to apply a weight to the Convolutional channels based on their relevance in learning before propagating to the next layer. For evaluation, we submitted our model to the official phase of the PhysioNet/Computing in Cardiology Challenge 2021. The model received scores of 0.47, 0.46, 0.45, 0.48 and 0.45 (ranked 14th, 13th, 15th, 10th, and 13th out of 39 teams) on 12-lead, 6-lead, 4-lead, 3-lead, 2-lead hidden test set, respectively; placing us in the 11th position for the mean of the 12-lead, 3-lead, and 2-lead scores
EEG Analysis of Selective Attention during Error Potential BCI experiments
No full textBrain Computer Interfaces (BCI) permit to control external devices through the detection and classification of brain activity. Electroencephalographic (EEG) signal is recorded to interpret this activity and the cerebral responses to specific stimuli can be used as drivers for the BCI system. During BCI tasks the attention of the subject plays a key role on the good performance of the system and engaging protocols are crucial for obtaining reliable results. Attention is particularly related to the Error Potential (ErrP), a specific EEG Evoked Potential (EP) that is elicited whenever an error is detected by the subject, either if it is performed by the subject itself or by another person or by a machine.In this paper an analysis of EEG features related to attention during ErrP-based BCI tasks is presented in order to assess how attention varies during a BCI experiment and how this affects the performance of the final system. The Power Spectral Density (PSD) in a band and the ratio of the PSD in beta and theta bands have been chosen as attention descriptors.The obtained results suggest that two subsets of subjects can be distinguished one more focused than the other in terms of attention related EEG features. The more attentive subset also resulted in better performance when in terms of balanced accuracy, using a Convolutional Neural Network for classifying between ErrP and Non-ErrP epochs.These results confirm how crucial subject's attention is during BCI experiments to obtain good performances. Moreover, the differences in ErrP and Non-ErrP epochs in terms of attention related EEG features, suggest that they can be useful descriptors for machine learning algorithms for classifying this EP in BCI application
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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