Jeffrey Barahona

College of Engineering

Works (4)

Updated: November 28th, 2024 05:01

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.

By: J. Barahona n, K. Mills *, M. Hernandez *, A. Bozkurt n, D. Carpenter * & E. Lobaton n 

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)
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Sources: Web Of Science, NC State University Libraries
Added: February 26, 2024

2023 review

Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic Review

[Review of ]. PHYSIOLOGIA, 4(1), 1–42.

By: R. Soleimani n, J. Barahona n, Y. Chen n , A. Bozkurt n, M. Daniele n, V. Pozdin*, E. Lobaton n 

author keywords: sleep stage classification; EEG; ECG; deep learning; contrastive learning; transfer learning; data augmentation; interpretability
TL;DR: An improvement in performance up to 12% on standard datasets over the last 5 years is identified and the causes for these trends seem to be caused by incorporating new architectural components, such as the use of transformers and contrastive learning. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: February 1, 2024

2023 journal article

Robust Cough Detection With Out-of-Distribution Detection

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 27(7), 3210–3221.

By: Y. Chen n , P. Attri  n, J. Barahona n, M. Hernandez *, D. Carpenter *, A. Bozkurt n, E. Lobaton n 

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)
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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.

By: Y. Chen n , M. Wilkins n, J. Barahona n, A. Rosenbaum n, M. Daniele n & E. Lobaton n 

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)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: April 25, 2022

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