Metadata
Title
TRAINING STRATEGIES FOR COVID-19 SEVERITY CLASSIFICATION
作者
PORDEUS, DANIEL | RIBEIRO, PEDRO | ZACARIAS, LAÍLA | DE OLIVEIRA, ADRIEL | MARQUES, JOÃO ALEXANDRE LOBO | RODRIGUES, PEDRO MIGUEL | LEITE, CAMILA | NETO, MANOEL ALVES | PEIXOTO, ARNALDO AIRES | DO VALE MADEIRO, JOÃO PAULO
PUBLISH YEAR
2023
FACULTY / RESEARCH UNIT
摘要
THE COVID-19 PANDEMIC HAS POSED A SIGNIFICANT PUBLIC HEALTH CHALLENGE ON A GLOBAL SCALE. IT IS IMPERATIVE THAT WE CONTINUE TO UNDERTAKE RESEARCH IN ORDER TO IDENTIFY EARLY MARKERS OF DISEASE PROGRESSION, ENHANCE PATIENT CARE THROUGH PROMPT DIAGNOSIS, IDENTIFICATION OF HIGH-RISK PATIENTS, EARLY PREVENTION, AND EFFICIENT ALLOCATION OF MEDICAL RESOURCES. IN THIS PARTICULAR STUDY, WE OBTAINED 100 5-MIN ELECTROCARDIOGRAMS (ECGS) FROM 50 COVID-19 VOLUNTEERS IN TWO DIFFERENT POSITIONS, NAMELY UPRIGHT AND SUPINE, WHO WERE CATEGORIZED AS EITHER MODERATELY OR CRITICALLY ILL. WE USED CLASSIFICATION ALGORITHMS TO ANALYZE HEART RATE VARIABILITY (HRV) METRICS DERIVED FROM THE ECGS OF THE VOLUNTEERS WITH THE GOAL OF PREDICTING THE SEVERITY OF ILLNESS. OUR STUDY CHOOSE A CONFIGURATION PRO SVC THAT ACHIEVED 76% OF ACCURACY, AND 0.84 ON F1 SCORE IN PREDICTING THE SEVERITY OF COVID-19 BASED ON HRV METRICS.
SUBJECTS
COVID-19,SIGNAL PROCESSING,DISEASE SEVERITY CLASSIFICATION,ELECTROCARDIOGRAM (ECG),HEART RATE VARIABILITY (HRV)
DOCUMENT TYPE
SDG Category
Part of
BIOINFORMATICS AND BIOMEDICAL ENGINEERING
DOI
10.1007/978-3-031-34953-9_40
ISBN
978-3-031-34953-9