Jeffrey Barahona
College of Engineering
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Works (3)
Updated: April 5th, 2024 15:16
2023 article
Adolescent Asthma Monitoring: A Preliminary Study of Audio and Spirometry Modalities
2023 45TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY, EMBC.
TL;DR:
Deep learning techniques are explored to improve forecasting of forced expiratory volume in one second (FEV1) by using audio data from participants and test whether auditory sleep disturbances are correlated with poorer asthma outcomes.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: February 26, 2024
2023 journal article
Robust Cough Detection With Out-of-Distribution Detection
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 27(7), 3210–3221.
author keywords: Cough detection; audio classification; out-of-distribution; bio-signal processing; machine learning
TL;DR:
The incorporation of OOD detection techniques improves cough detection performance by a significant margin and provides a valuable solution to real-world acoustic cough detection problems.
(via Semantic Scholar)
doi.org (repository)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: April 6, 2023
2021 journal article
Toward Automated Analysis of Fetal Phonocardiograms: Comparing Heartbeat Detection from Fetal Doppler and Digital Stethoscope Signals
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 975–979.
Event: 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) at Mexico on November 1-5, 2021
MeSH headings : Female; Fetal Monitoring; Heart Rate; Humans; Neural Networks, Computer; Pregnancy; Signal Processing, Computer-Assisted; Stethoscopes
TL;DR:
This work implemented two neural network architectures for heartbeat detection on a set of fetal phonocardiogram signals captured using fetal Doppler and a digital stethoscope, and shows a Convolutional Neural Network is the most efficient at identifying the S1 waveforms in a heartbeat.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: April 25, 2022
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