Exploring the impact and influence of melanin on frequency-domain near-infrared spectroscopy measurements.


Journal

Journal of biomedical optics
ISSN: 1560-2281
Titre abrégé: J Biomed Opt
Pays: United States
ID NLM: 9605853

Informations de publication

Date de publication:
Jun 2024
Historique:
received: 30 04 2024
revised: 12 08 2024
accepted: 30 08 2024
medline: 26 9 2024
pubmed: 26 9 2024
entrez: 26 9 2024
Statut: ppublish

Résumé

Near-infrared spectroscopy (NIRS) is a non-invasive optical method that measures changes in hemoglobin concentration and oxygenation. The measured light intensity is susceptible to reduced signal quality due to the presence of melanin. We quantify the influence of melanin concentration on NIRS measurements taken with a frequency-domain near-infrared spectroscopy system using 690 and 830 nm. Using a forehead NIRS probe, we measured 35 healthy participants and investigated the correlation between melanin concentration indices, which were determined using a colorimeter, and several key metrics from the NIRS signal. These metrics include signal-to-noise ratio (SNR), two measurements of oxygen saturation (arterial oxygen saturation, We found a significant negative correlation between the melanin index and the SNR estimated in oxy-hemoglobin signals ( We found that estimated SNR and

Identifiants

pubmed: 39323492
doi: 10.1117/1.JBO.29.S3.S33310
pii: 240123SSR
pmc: PMC11423252
doi:

Substances chimiques

Melanins 0
Oxygen S88TT14065
Oxyhemoglobins 0
Hemoglobins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

S33310

Informations de copyright

© 2024 The Authors.

Auteurs

Shidhartho Roy (S)

Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.
Carnegie Mellon University, Department of Electrical and Computer Engineering, Pittsburgh, Pennsylvania, United States.

Jingyi Wu (J)

Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.

Jiaming Cao (J)

Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.

Joel Disu (J)

Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.

Sharadhi Bharadwaj (S)

Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.

Elizabeth Meinert-Spyker (E)

Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.

Pulkit Grover (P)

Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.
Carnegie Mellon University, Department of Electrical and Computer Engineering, Pittsburgh, Pennsylvania, United States.
Carnegie Mellon University, Neuroscience Institute, Pittsburgh, Pennsylvania, United States.

Jana M Kainerstorfer (JM)

Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.
Carnegie Mellon University, Department of Electrical and Computer Engineering, Pittsburgh, Pennsylvania, United States.
Carnegie Mellon University, Neuroscience Institute, Pittsburgh, Pennsylvania, United States.

Sossena Wood (S)

Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.
Carnegie Mellon University, Department of Electrical and Computer Engineering, Pittsburgh, Pennsylvania, United States.
Carnegie Mellon University, Neuroscience Institute, Pittsburgh, Pennsylvania, United States.

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Classifications MeSH