Estimation of cerebral blood flow velocity during breath-hold challenge using artificial neural networks
Abstract
The effect of untreated Obstructive Sleep Apnoea (OSA) on cerebral haemodynamics and CA impairment is an active field of research interest. A breath-hold challenge is usually used in clinical and research settings to simulate cardiovascular and cerebrovascular changes that mimic OSA events. This work utilises temporal arterial oxygen saturation (SpO2) and photoplethysmography (PPG) signals to estimate the temporal cerebral blood flow velocity (CBFv) waveform. Measurements of CBFv, SpO2, and PPG, were acquired concurrently from volunteers performing two different protocols of breath-hold challenge in the supine position. Past values of the SpO2 and PPG signals were used to estimate the current values of CBFv using different permutations and topologies of supervised learning with shallow artificial neural networks (ANNs). Measurements from one protocol were used to train the ANNs and find the optimum topologies, which in turn were tested using the other protocol. Data collected from 10 normotensive, healthy subjects (four females, age 28.5 ± 6.1 years, Body Mass Index (BMI) 24.0 ± 4.7 kg/m2) were used in this study. The results show that different subjects have different optimum topologies for ANNs, thus indicating the effects of inter-subject variability on ANNs. Successfully reconstructed blind waveforms for the same subject group in themore »
- Authors:
-
- The Hashemite University, Zarqa (Jordan)
- Jordan Univ. of Science and Technology, Ar Ramtha (Jordan)
- Yarmouk University, Irbid (Jordan)
- Wayne State Univ., Detroit, MI (United States)
- Univ. of Texas Southwestern Medical Center, Dallas, TX (United States)
- Univ. of Texas, Arlington, TX (United States)
- Publication Date:
- Research Org.:
- Univ. of Texas, Arlington, TX (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1800233
- Alternate Identifier(s):
- OSTI ID: 1575935
- Grant/Contract Number:
- FG02-05CH11280
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Computers in Biology and Medicine
- Additional Journal Information:
- Journal Volume: 115; Journal Issue: C; Journal ID: ISSN 0010-4825
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; Life Sciences & Biomedicine; Computer Science; Engineering; Mathematical & Computational Biology; Cerebral blood flow velocity; Blood oxygen saturation; Photoplethysmography; Machine learning; Time series estimation; Biomedical signal processing; Obstructive sleep apnea; Breath-hold challenge
Citation Formats
Al-Abed, Mohammad A., Al-Bashir, Areen K., Al-Rawashdeh, Ayman, Alex, Raichel M., Zhang, Rong, Watenpaugh, Donald E., and Behbehani, Khosrow. Estimation of cerebral blood flow velocity during breath-hold challenge using artificial neural networks. United States: N. p., 2019.
Web. doi:10.1016/j.compbiomed.2019.103508.
Al-Abed, Mohammad A., Al-Bashir, Areen K., Al-Rawashdeh, Ayman, Alex, Raichel M., Zhang, Rong, Watenpaugh, Donald E., & Behbehani, Khosrow. Estimation of cerebral blood flow velocity during breath-hold challenge using artificial neural networks. United States. https://doi.org/10.1016/j.compbiomed.2019.103508
Al-Abed, Mohammad A., Al-Bashir, Areen K., Al-Rawashdeh, Ayman, Alex, Raichel M., Zhang, Rong, Watenpaugh, Donald E., and Behbehani, Khosrow. Thu .
"Estimation of cerebral blood flow velocity during breath-hold challenge using artificial neural networks". United States. https://doi.org/10.1016/j.compbiomed.2019.103508. https://www.osti.gov/servlets/purl/1800233.
@article{osti_1800233,
title = {Estimation of cerebral blood flow velocity during breath-hold challenge using artificial neural networks},
author = {Al-Abed, Mohammad A. and Al-Bashir, Areen K. and Al-Rawashdeh, Ayman and Alex, Raichel M. and Zhang, Rong and Watenpaugh, Donald E. and Behbehani, Khosrow},
abstractNote = {The effect of untreated Obstructive Sleep Apnoea (OSA) on cerebral haemodynamics and CA impairment is an active field of research interest. A breath-hold challenge is usually used in clinical and research settings to simulate cardiovascular and cerebrovascular changes that mimic OSA events. This work utilises temporal arterial oxygen saturation (SpO2) and photoplethysmography (PPG) signals to estimate the temporal cerebral blood flow velocity (CBFv) waveform. Measurements of CBFv, SpO2, and PPG, were acquired concurrently from volunteers performing two different protocols of breath-hold challenge in the supine position. Past values of the SpO2 and PPG signals were used to estimate the current values of CBFv using different permutations and topologies of supervised learning with shallow artificial neural networks (ANNs). Measurements from one protocol were used to train the ANNs and find the optimum topologies, which in turn were tested using the other protocol. Data collected from 10 normotensive, healthy subjects (four females, age 28.5 ± 6.1 years, Body Mass Index (BMI) 24.0 ± 4.7 kg/m2) were used in this study. The results show that different subjects have different optimum topologies for ANNs, thus indicating the effects of inter-subject variability on ANNs. Successfully reconstructed blind waveforms for the same subject group in the second protocol showed a reasonable accuracy of 60–80% estimation compared to the measured waveforms.},
doi = {10.1016/j.compbiomed.2019.103508},
journal = {Computers in Biology and Medicine},
number = C,
volume = 115,
place = {United States},
year = {Thu Oct 17 00:00:00 EDT 2019},
month = {Thu Oct 17 00:00:00 EDT 2019}
}
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