7 documents found, page 1 of 1
A deep learning method for predicting the COVID-19 ICU patient outcome fusing X...
Wu, Yunan; Rocha, Bruno Machado; Kaimakamis, Evangelos; Cheimariotis, Grigorios-Aris; Petmezas, Georgios; Chatzis, Evangelos; Kilintzis, VassilisAssessing the health status of critically ill patients with COVID-19 and predicting their outcome are highly challenging problems and one of the reasons for poor management of ICU resources worldwide. A better pathophysiological understanding of patients’ state evolution in the ICU can enhance effective medical interventions. Therefore, there is a need to monitor and analyze the pulmonary function of a ICU pati...
Ensemble deep learning model for dimensionless respiratory airflow estimation u...
Pessoa, Diogo; Rocha, Bruno Machado; Gomes, Maria; Rodrigues, Guilherme; Petmezas, Georgios; Cheimariotis, Grigorios-Aris; Maglaveras, NicosIn recent years, computerized methods for analyzing respiratory function have gained increased attention within the scientific community. This study proposes a deep-learning model to estimate the dimensionless respiratory airflow using only respiratory sound without prior calibration. We developed hybrid deep learning models (CNN + LSTM) to extract features from the respiratory sound and model their temporal de...
Automatic wheeze segmentation using harmonic-percussive source separation and e...
Rocha, Bruno Machado; Pessoa, Diogo; Marques, Alda; Carvalho, Paulo de; Paiva, Rui PedroWheezes are adventitious respiratory sounds commonly present in patients with respiratory conditions. The presence of wheezes and their time location are relevant for clinical reasons, such as understanding the degree of bronchial obstruction. Conventional auscultation is usually employed to analyze wheezes, but remote monitoring has become a pressing need during recent years. Automatic respiratory sound analys...
BRACETS: bimodal repository of auscultation coupled with electrical impedance t...
Pessoa, Diogo; Rocha, Bruno Machado; Strodthoff, Claas; Gomes, Maria; Rodrigues, Guilherme; Petmezas, Georgios; Cheimariotis, Grigorios-ArisBackground and Objective: Respiratory diseases are among the most significant causes of morbidity and mortality worldwide, causing substantial strain on society and health systems. Over the last few decades, there has been increasing interest in the automatic analysis of respiratory sounds and electrical impedance tomography (EIT). Nevertheless, no publicly available databases with both respiratory sound and EI...
Automatic classification of adventitious respiratory sounds: a (un)solved problem?
Rocha, Bruno Machado; Pessoa, Diogo; Marques, Alda; Carvalho, Paulo; Paiva, Rui Pedro(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we studied the influence of event duration on automatic ARS classification, namely, how the creation of the Other class (negative class) affected the classifiers’ performance. (2) Methods: We conducted a set of experiments whe...
Detection of squawks in respiratory sounds of mechanically ventilated COVID-19 ...
Rocha, Bruno Machado; Pessoa, Diogo; Cheimariotis, Grigorios-Aris; Kaimakamis, Evangelos; Kotoulas, Serafeim-Chrysovalantis; Tzimou, MyrtoMechanically ventilated patients typically exhibit abnormal respiratory sounds. Squawks are short inspiratory adventitious sounds that may occur in patients with pneumonia, such as COVID-19 patients. In this work we devised a method for squawk detection in mechanically ventilated patients by developing algorithms for respiratory cycle estimation, squawk candidate identification, feature extraction, and clusteri...
Automatic Classification of Adventitious Respiratory Sounds: A (Un)Solved Problem?
Rocha, Bruno Machado; Pessoa, Diogo; Marques, Alda; Carvalho, Paulo de; Paiva, Rui Pedro(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we studied the influence of event duration on automatic ARS classification, namely, how the creation of the Other class (negative class) affected the classifiers' performance. (2) Methods: We conducted a set of experiments whe...