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http://hdl.handle.net/1893/26788
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DC Field | Value | Language |
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dc.contributor.author | Abdullah, Ahsan | en_UK |
dc.contributor.author | Hussain, Amir | en_UK |
dc.contributor.author | Khan, Imtiaz Hussain | en_UK |
dc.date.accessioned | 2018-02-22T23:34:50Z | - |
dc.date.available | 2018-02-22T23:34:50Z | - |
dc.date.issued | 2018-08-31 | en_UK |
dc.identifier.uri | http://hdl.handle.net/1893/26788 | - |
dc.description.abstract | Globally, there has been a dramatic increase in obesity, with prevalence in males and females expected to increase to 18 and 21%, respectively (NCD Risk Factor Collaboration, Lancet 387(10026):1377–96, 2016). However, there are hardly any data-analytic calorie-based cognitive studies, especially using non-invasive near infrared spectroscopy (NIRS) data that predict obesity using predictive data mining. Obesity is linked with neurodegenerative diseases, diabetes, and cardiovascular diseases. Thus, understanding, predicting, preventing, and managing obesity have the potential to save the lives of millions. Behavioral studies suggest that overeating in obese individuals is triggered by exaggerated brain reward center (BRC) activity to high-calorie food stimuli (Shefer et al., Neurosci Biobehav Rev 37(10):2489–503, 2013). In this paper, details of a novel research methodology are presented for a 24-month longitudinal study using a 44-channel NIRS device with the subjects in a natural environment. The proposed methodology consists of using visual stimuli of low/high calorie food items under fasting and satiated conditions for three types of subjects. The experiments consist of block design, longitudinal plan, data smoothing, BRC activation mapping, stereotactic normalization, generating paired t-test maps under fasting and non-fasting conditions and subsequently using Naïve Bayes modeling to generate obesity prediction maps for the control subjects. The simulated results consist of generation of Bayesian prediction maps using layers of paired t-test cerebral activity maps for the four BRC functional regions considered for three types of subjects, i.e., obese, control, and control subjects fed high calorie diet. We have demonstrated how cerebral functional activity data in response to visual food stimuli can be used to predict obesity in the non-obese, thus offering a non-invasive preventive measure. | en_UK |
dc.language.iso | en | en_UK |
dc.publisher | Springer | en_UK |
dc.relation | Abdullah A, Hussain A & Khan IH (2018) A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Data. Cognitive Computation, 10 (4), pp. 591-609. https://doi.org/10.1007/s12559-017-9541-x | en_UK |
dc.rights | This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. The final publication is available at Springer via https://doi.org/10.1007/s12559-017-9541-x | en_UK |
dc.subject | Prediction | en_UK |
dc.subject | Data mining | en_UK |
dc.subject | Noise | en_UK |
dc.subject | Preventing obesity | en_UK |
dc.subject | NIRS | en_UK |
dc.subject | Naïve Bayes | en_UK |
dc.subject | Paired t-test | en_UK |
dc.subject | Calorie | en_UK |
dc.title | A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Data | en_UK |
dc.type | Journal Article | en_UK |
dc.rights.embargodate | 2019-09-01 | en_UK |
dc.rights.embargoreason | [NIRS obesity AA AHU IHK-f.pdf] Publisher requires embargo of 12 months after formal publication. | en_UK |
dc.identifier.doi | 10.1007/s12559-017-9541-x | en_UK |
dc.citation.jtitle | Cognitive Computation | en_UK |
dc.citation.issn | 1866-9964 | en_UK |
dc.citation.issn | 1866-9956 | en_UK |
dc.citation.volume | 10 | en_UK |
dc.citation.issue | 4 | en_UK |
dc.citation.spage | 591 | en_UK |
dc.citation.epage | 609 | en_UK |
dc.citation.publicationstatus | Published | en_UK |
dc.citation.peerreviewed | Refereed | en_UK |
dc.type.status | AM - Accepted Manuscript | en_UK |
dc.contributor.funder | Engineering and Physical Sciences Research Council | en_UK |
dc.author.email | ahu@cs.stir.ac.uk | en_UK |
dc.contributor.affiliation | Foundation University, Islamabad | en_UK |
dc.contributor.affiliation | Computing Science | en_UK |
dc.contributor.affiliation | King Abdulaziz University, Saudi Arabia | en_UK |
dc.identifier.isi | WOS:000441015100005 | en_UK |
dc.identifier.scopusid | 2-s2.0-85041115072 | en_UK |
dc.identifier.wtid | 498436 | en_UK |
dc.contributor.orcid | 0000-0002-8080-082X | en_UK |
dc.date.accepted | 2017-12-28 | en_UK |
dcterms.dateAccepted | 2017-12-28 | en_UK |
dc.date.filedepositdate | 2018-02-22 | en_UK |
dc.relation.funderproject | Towards visually-driven speech enhancement for cognitively-inspired multi-modal hearing-aid devices | en_UK |
dc.relation.funderref | EP/M026981/1 | en_UK |
rioxxterms.apc | not required | en_UK |
rioxxterms.type | Journal Article/Review | en_UK |
rioxxterms.version | AM | en_UK |
local.rioxx.author | Abdullah, Ahsan| | en_UK |
local.rioxx.author | Hussain, Amir|0000-0002-8080-082X | en_UK |
local.rioxx.author | Khan, Imtiaz Hussain| | en_UK |
local.rioxx.project | EP/M026981/1|Engineering and Physical Sciences Research Council|http://dx.doi.org/10.13039/501100000266 | en_UK |
local.rioxx.freetoreaddate | 2019-09-01 | en_UK |
local.rioxx.licence | http://www.rioxx.net/licenses/under-embargo-all-rights-reserved||2019-08-31 | en_UK |
local.rioxx.licence | http://www.rioxx.net/licenses/all-rights-reserved|2019-09-01| | en_UK |
local.rioxx.filename | NIRS obesity AA AHU IHK-f.pdf | en_UK |
local.rioxx.filecount | 1 | en_UK |
local.rioxx.source | 1866-9956 | en_UK |
Appears in Collections: | Computing Science and Mathematics Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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NIRS obesity AA AHU IHK-f.pdf | Fulltext - Accepted Version | 7.71 MB | Adobe PDF | View/Open |
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