PFT: A Novel Time-Frequency Decomposition of BOLD fMRI Signals for Autism Spectrum Disorder Detection
<p dir="ltr">Diagnosing Autism spectrum disorder (ASD) is a challenging task for clinicians due to the inconsistencies in existing medical tests. The Internet of things (IoT) has been used in several medical applications to realize advancements in the healthcare industry. Using machi...
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2023
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| _version_ | 1864513507123265536 |
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| author | Samir Brahim Belhaouari (9427347) |
| author2 | Abdelhamid Talbi (19437823) Saima Hassan (14918003) Dena Al-Thani (16864245) Marwa Qaraqe (10135172) |
| author2_role | author author author author |
| author_facet | Samir Brahim Belhaouari (9427347) Abdelhamid Talbi (19437823) Saima Hassan (14918003) Dena Al-Thani (16864245) Marwa Qaraqe (10135172) |
| author_role | author |
| dc.creator.none.fl_str_mv | Samir Brahim Belhaouari (9427347) Abdelhamid Talbi (19437823) Saima Hassan (14918003) Dena Al-Thani (16864245) Marwa Qaraqe (10135172) |
| dc.date.none.fl_str_mv | 2023-02-23T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/su15054094 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/PFT_A_Novel_Time-Frequency_Decomposition_of_BOLD_fMRI_Signals_for_Autism_Spectrum_Disorder_Detection/26771878 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Neurosciences Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning progressive Fourier transform BOLD signal resting state default-mode network fMRI data CNN SVM KNN |
| dc.title.none.fl_str_mv | PFT: A Novel Time-Frequency Decomposition of BOLD fMRI Signals for Autism Spectrum Disorder Detection |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Diagnosing Autism spectrum disorder (ASD) is a challenging task for clinicians due to the inconsistencies in existing medical tests. The Internet of things (IoT) has been used in several medical applications to realize advancements in the healthcare industry. Using machine learning in tandem IoT can enhance the monitoring and detection of ASD. To date, most ASD studies have relied primarily on the operational connectivity and structural metrics of fMRI data processing while neglecting the temporal dynamics components. Our research proposes Progressive Fourier Transform (PFT), a novel time-frequency decomposition, together with a Convolutional Neural Network (CNN), as a preferred alternative to available ASD detection systems. We use the Autism Brain Imaging Data Exchange dataset for model validation, demonstrating better results of the proposed PFT model compared to the existing models, including an increase in accuracy to 96.7%. These results show that the proposed technique is capable of analyzing rs-fMRI data from different brain diseases of the same type.</p><h2>Other Information</h2><p dir="ltr">Published in: Sustainability<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/su15054094" target="_blank">https://dx.doi.org/10.3390/su15054094</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8e0a5a131781391c6a16ecc44ba1fd7f |
| identifier_str_mv | 10.3390/su15054094 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26771878 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | PFT: A Novel Time-Frequency Decomposition of BOLD fMRI Signals for Autism Spectrum Disorder DetectionSamir Brahim Belhaouari (9427347)Abdelhamid Talbi (19437823)Saima Hassan (14918003)Dena Al-Thani (16864245)Marwa Qaraqe (10135172)Biomedical and clinical sciencesNeurosciencesEngineeringBiomedical engineeringHealth sciencesHealth services and systemsInformation and computing sciencesMachine learningprogressive Fourier transformBOLD signalresting statedefault-mode networkfMRI dataCNNSVMKNN<p dir="ltr">Diagnosing Autism spectrum disorder (ASD) is a challenging task for clinicians due to the inconsistencies in existing medical tests. The Internet of things (IoT) has been used in several medical applications to realize advancements in the healthcare industry. Using machine learning in tandem IoT can enhance the monitoring and detection of ASD. To date, most ASD studies have relied primarily on the operational connectivity and structural metrics of fMRI data processing while neglecting the temporal dynamics components. Our research proposes Progressive Fourier Transform (PFT), a novel time-frequency decomposition, together with a Convolutional Neural Network (CNN), as a preferred alternative to available ASD detection systems. We use the Autism Brain Imaging Data Exchange dataset for model validation, demonstrating better results of the proposed PFT model compared to the existing models, including an increase in accuracy to 96.7%. These results show that the proposed technique is capable of analyzing rs-fMRI data from different brain diseases of the same type.</p><h2>Other Information</h2><p dir="ltr">Published in: Sustainability<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/su15054094" target="_blank">https://dx.doi.org/10.3390/su15054094</a></p>2023-02-23T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/su15054094https://figshare.com/articles/journal_contribution/PFT_A_Novel_Time-Frequency_Decomposition_of_BOLD_fMRI_Signals_for_Autism_Spectrum_Disorder_Detection/26771878CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/267718782023-02-23T09:00:00Z |
| spellingShingle | PFT: A Novel Time-Frequency Decomposition of BOLD fMRI Signals for Autism Spectrum Disorder Detection Samir Brahim Belhaouari (9427347) Biomedical and clinical sciences Neurosciences Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning progressive Fourier transform BOLD signal resting state default-mode network fMRI data CNN SVM KNN |
| status_str | publishedVersion |
| title | PFT: A Novel Time-Frequency Decomposition of BOLD fMRI Signals for Autism Spectrum Disorder Detection |
| title_full | PFT: A Novel Time-Frequency Decomposition of BOLD fMRI Signals for Autism Spectrum Disorder Detection |
| title_fullStr | PFT: A Novel Time-Frequency Decomposition of BOLD fMRI Signals for Autism Spectrum Disorder Detection |
| title_full_unstemmed | PFT: A Novel Time-Frequency Decomposition of BOLD fMRI Signals for Autism Spectrum Disorder Detection |
| title_short | PFT: A Novel Time-Frequency Decomposition of BOLD fMRI Signals for Autism Spectrum Disorder Detection |
| title_sort | PFT: A Novel Time-Frequency Decomposition of BOLD fMRI Signals for Autism Spectrum Disorder Detection |
| topic | Biomedical and clinical sciences Neurosciences Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning progressive Fourier transform BOLD signal resting state default-mode network fMRI data CNN SVM KNN |