Stress Management Using Physiological Signals and Audio Stimulation
A Master of Science thesis in Biomedical Engineering by Rateb Majd Katmah entitled, “Stress Management Using Physiological Signals and Audio Stimulation”, submitted in November 2021. Thesis advisor is Dr. Hasan Al-Nashash and thesis co-advisors are Dr. Usman Tariq and Dr. Fares Yahya. Soft copy is a...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | doctoralThesis |
| منشور في: |
2021
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/21592 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513436625403904 |
|---|---|
| author | Katmah, Rateb Majd |
| author_facet | Katmah, Rateb Majd |
| author_role | author |
| dc.contributor.none.fl_str_mv | Al Nashash, Hasan Tariq, Usman Yahya, Fares |
| dc.creator.none.fl_str_mv | Katmah, Rateb Majd |
| dc.date.none.fl_str_mv | 2021-11 2022-01-24T07:44:23Z 2022-01-24T07:44:23Z |
| dc.format.none.fl_str_mv | application/pdf application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2021.45 http://hdl.handle.net/11073/21592 |
| dc.language.none.fl_str_mv | en_US |
| dc.subject.none.fl_str_mv | Mental stress Vigilance enhancement Stress mitigation Stroop Color-Word Task (SCWT) Functional connectivity Cortisol level Partial Directed Coherence (PDC) Convolutional Neural Network (CNN) Functional near infrared spectroscopy (fNIRS) |
| dc.title.none.fl_str_mv | Stress Management Using Physiological Signals and Audio Stimulation |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | A Master of Science thesis in Biomedical Engineering by Rateb Majd Katmah entitled, “Stress Management Using Physiological Signals and Audio Stimulation”, submitted in November 2021. Thesis advisor is Dr. Hasan Al-Nashash and thesis co-advisors are Dr. Usman Tariq and Dr. Fares Yahya. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form). |
| format | doctoralThesis |
| id | aus_33f2ed747e9195f83b4507a33f87777c |
| identifier_str_mv | 35.232-2021.45 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/21592 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Stress Management Using Physiological Signals and Audio StimulationKatmah, Rateb MajdMental stressVigilance enhancementStress mitigationStroop Color-Word Task (SCWT)Functional connectivityCortisol levelPartial Directed Coherence (PDC)Convolutional Neural Network (CNN)Functional near infrared spectroscopy (fNIRS)A Master of Science thesis in Biomedical Engineering by Rateb Majd Katmah entitled, “Stress Management Using Physiological Signals and Audio Stimulation”, submitted in November 2021. Thesis advisor is Dr. Hasan Al-Nashash and thesis co-advisors are Dr. Usman Tariq and Dr. Fares Yahya. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Stress has a significant role in the development of a wide variety of mental, psychological, emotional, behavioral, and physical illnesses. Additionally, there is substantial evidence in the literature that stress impairs vigilance. Thus, early stress detection, vigilance enhancement, and stress mitigation may aid in the prevention of a wide range of diseases and improve human health. The purpose of this thesis is to examine the effects of binaural beat stimulation (BBs) on increasing alertness and reducing mental stress in the workplace. We devised an experiment in which participants were subjected to time pressure and negative feedback while completing the Stroop Color-Word Task (SCWT). Then, we used 16 Hz BBs to improve vigilance and reduce stress levels. Functional Near-Infrared Spectroscopy (fNIRS), salivary alpha-amylase, behavioral data, and subjective reactions were used to determine the levels of stress. We quantified the level of stress using statistical analysis, functional connectivity based on Partial Directed Coherence (PDC), Graph Theory Analysis (GTA) and Convolution Neural Network (CNN). We discovered that BBs substantially increased target detection accuracy by 11.05% (p<0.001), decreased effort and temporal demand, boosted performance, and decreased cortisol levels. The deep learning results indicated that the CNN technique combined with PDC features is capable of discriminating between four distinct mental states (vigilance, enhancement, stress, and mitigation) with an average accuracy of 70.62%, a sensitivity of 68.39%, and a specificity of 90.76%.College of EngineeringMultidisciplinary ProgramsMaster of Science in Biomedical Engineering (MSBME)Al Nashash, HasanTariq, UsmanYahya, Fares2022-01-24T07:44:23Z2022-01-24T07:44:23Z2021-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfapplication/pdf35.232-2021.45http://hdl.handle.net/11073/21592en_USoai:repository.aus.edu:11073/215922025-06-26T12:30:45Z |
| spellingShingle | Stress Management Using Physiological Signals and Audio Stimulation Katmah, Rateb Majd Mental stress Vigilance enhancement Stress mitigation Stroop Color-Word Task (SCWT) Functional connectivity Cortisol level Partial Directed Coherence (PDC) Convolutional Neural Network (CNN) Functional near infrared spectroscopy (fNIRS) |
| status_str | publishedVersion |
| title | Stress Management Using Physiological Signals and Audio Stimulation |
| title_full | Stress Management Using Physiological Signals and Audio Stimulation |
| title_fullStr | Stress Management Using Physiological Signals and Audio Stimulation |
| title_full_unstemmed | Stress Management Using Physiological Signals and Audio Stimulation |
| title_short | Stress Management Using Physiological Signals and Audio Stimulation |
| title_sort | Stress Management Using Physiological Signals and Audio Stimulation |
| topic | Mental stress Vigilance enhancement Stress mitigation Stroop Color-Word Task (SCWT) Functional connectivity Cortisol level Partial Directed Coherence (PDC) Convolutional Neural Network (CNN) Functional near infrared spectroscopy (fNIRS) |
| url | http://hdl.handle.net/11073/21592 |