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A deep learning method for predicting the COVID-19 ICU patient outcome fusing X-rays, respiratory sounds, and ICU parameters., , , , , , , , , and 6 other author(s). Expert Syst. Appl., (January 2024)Automatic Wheeze and Respiratory Phase Detectors to Evaluate Respiratory Physiotherapy in LRTI - A Preliminary Study., , , , , and . HEALTHINF, page 233-238. SciTePress, (2013)Automatic Crackle Detection Algorithm Based on Fractal Dimension and Box Filtering., , , , and . CENTERIS/ProjMAN/HCist, volume 64 of Procedia Computer Science, page 705-712. Elsevier, (2015)Home telemonitoring in COPD: A systematic review of methodologies and patients' adherence., , and . Int. J. Medical Informatics, 83 (4): 249-263 (2014)Detection of squawks in respiratory sounds of mechanically ventilated COVID-19 patients., , , , , , , , , and . EMBC, page 512-516. IEEE, (2021)Automatic Wheeze Segmentation Using Harmonic-Percussive Source Separation and Empirical Mode Decomposition., , , , and . IEEE J. Biomed. Health Informatics, 27 (4): 1926-1934 (April 2023)Automatic Classification of Adventitious Respiratory Sounds: A (Un)Solved Problem?, , , , and . Sensors, 21 (1): 57 (2021)IMU-based monitoring of discharged patients with COVID-19 for the assessment of in-home recovering., , , , , , , and . MeMeA, page 1-2. IEEE, (2022)Influence of Event Duration on Automatic Wheeze Classification., , , , and . ICPR, page 7462-7469. IEEE, (2020)Usability of Computerized Lung Auscultation-Sound Software (CLASS) for learning pulmonary auscultation., , , , , , , , and . Medical Biol. Eng. Comput., 56 (4): 623-633 (2018)