Sudden cardiac death multiparametric classification system for Chagas heart disease's patients based on clinical data and 24-hours ECG monitoring.

About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ > million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximately 65% of ChHD patients at a rate of 24 per 1000 patient-years, much greater than the SCD rate in the general population. Its occurrence in the specific context of ChHD needs to be better exploited. This paper provides the first evidence supporting the use of machine learning (ML) methods within non-invasive tests: patients' clinical data and cardiac restitution metrics (CRM) features extracted from ECG-Holter recordings as an adjunct in the SCD risk assessment in ChHD. The feature selection (FS) flows evaluated 5 different groups of attributes formed from patients' clinical and physiological data to identify relevant attributes among 57 features reported by 315 patients at HUCFF-UFRJ. The FS flow with FS techniques (variance, ANOVA, and recursive feature elimination) and Naive Bayes (NB) model achieved the best classification performance with 90.63% recall (sensitivity) and 80.55% AUC. The initial feature set is reduced to a subset of 13 features (4 Classification; 1 Treatment; 1 CRM; and 7 Heart Tests). The proposed method represents an intelligent diagnostic support system that predicts the high risk of SCD in ChHD patients and highlights the clinical and CRM data that most strongly impact the final outcome.

Oral biopsies in a Portuguese population: A 20-year clinicopathological study in a university clinic.

Background: Performing a biopsy is very important in oral medicine and the anatomopathological examination is fundamental to obtain or to confirm the diagnosis in oral and maxillofacial pathology. The purpose of this study is to analyse the frequency and characteristic patterns of biopsied oromaxillofacial lesions in a Portuguese population. Material and Methods: A descriptive statistical analysis of the data from the anatomopathological reports of the biopsies performed between 1999 and 2019 at the university clinic of the Faculty of Dental Medicine of the University of Lisbon was performed, regarding the patient's gender and age, type of biopsy, location of lesions, clinical and histological diagnosis, and the results were obtained. Association relationships were studied using the chi-square test and the Kruskal-Wallis test to correlate variables. P<0.05 was considered statistically significant. Results: From a total sample of 1448 patients, 826 (57.1%) were female, 610 (42.1%) were male, and 12 (0.8%) had no gender information, with a mean age of 50.14 years (standard deviation ± 17.61). The preferred location was the buccal mucosa, vestibule fundus and alveolar mucosa (20.7%). Benign lesions (BL) were the most common, in 82,8% of the cases, followed by oral potentially malignant disorders (OPMD) in 15,5%, and finally, malignant lesions (ML) in 1.7%. Focal fibrous hyperplasia was the most frequent diagnosis in the total sample (25.6%). In the young group, the most common entity was mucocele (34.0%), with a predominance of the lower lip (32.9%). In OPMD, leukoplakia was the most frequently diagnosed (48,7%). The most common ML was squamous cell carcinoma (92.0%), appearing mainly in the tongue (34.8%). A statistically significant relation between ML and older age was found. Conclusions: This study included biopsies analysed over a period of 20 years, being BL the main pathology to affect the oral cavity. Although less frequent, OPMD and ML should not be neglected and must be correctly diagnosed and treated. Key words:Oral biopsies, Oral and maxillofacial pathology, Oral medicine, Clinicopathological analysis, Epidemiological study, University clinic.

New approach for T-wave peak detection and T-wave end location in 12-lead paced ECG signals based on a mathematical model.

This paper presents an innovative approach for T-wave peak detection and subsequent T-wave end location in 12-lead paced ECG signals based on a mathematical model of a skewed Gaussian function. Following the stage of QRS segmentation, we establish search windows using a number of the earliest intervals between each QRS offset and subsequent QRS onset. Then, we compute a template based on a Gaussian-function, modified by a mathematical procedure to insert asymmetry, which models the T-wave. Cross-correlation and an approach based on the computation of Trapezium's area are used to locate, respectively, the peak and end point of each T-wave throughout the whole raw ECG signal. For evaluating purposes, we used a database of high resolution 12-lead paced ECG signals, recorded from patients with ischaemic cardiomyopathy (ICM) in the University Hospitals of Leicester NHS Trust, UK, and the well-known QT database. The average T-wave detection rates, sensitivity and positive predictivity, were both equal to 99.12%, for the first database, and, respectively, equal to 99.32% and 99.47%, for QT database. The average time errors computed for T-wave peak and T-wave end locations were, respectively, -0.38±7.12 ms and -3.70±15.46 ms, for the first database, and 1.40±8.99 ms and 2.83±15.27 ms, for QT database. The results demonstrate the accuracy, consistency and robustness of the proposed method for a wide variety of T-wave morphologies studied.

An innovative approach of QRS segmentation based on first-derivative, Hilbert and Wavelet Transforms.

The QRS detection and segmentation processes constitute the first stages of a greater process, e.g., electrocardiogram (ECG) feature extraction. Their accuracy is a prerequisite to a satisfactory performance of the P and T wave segmentation, and also to the reliability of the heart rate variability analysis. This work presents an innovative approach of QRS detection and segmentation and the detailed results of the proposed algorithm based on First-Derivative, Hilbert and Wavelet Transforms, adaptive threshold and an approach of surface indicator. The method combines the adaptive threshold, Hilbert and Wavelet Transforms techniques, avoiding the whole ECG signal preprocessing. After each QRS detection, the computation of an indicator related to the area covered by the QRS complex envelope provides the detection of the QRS onset and offset. The QRS detection proposed technique is evaluated based on the well-known MIT-BIH Arrhythmia and QT databases, obtaining the average sensitivity of 99.15% and the positive predictability of 99.18% for the first database, and 99.75% and 99.65%, respectively, for the second one. The QRS segmentation approach is evaluated on the annotated QT database with the average segmentation errors of 2.85±9.90ms and 2.83±12.26ms for QRS onset and offset, respectively. Those results demonstrate the accuracy of the developed algorithm for a wide variety of QRS morphology and the adaptation of the algorithm parameters to the existing QRS morphological variations within a single record.