Evaluation of Morlet Wavelet Analysis for Artifact Detection in Low-Frequency Commercial Near-Infrared Spectroscopy Systems
Regional cerebral oxygen saturation (rSO<sub>2</sub>), a method of cerebral tissue oxygenation measurement, is recorded using non-invasive near-infrared Spectroscopy (NIRS) devices. A major limitation is that recorded signals often contain artifacts. Manually removing these artifacts is...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/11/1/33 |
| _version_ | 1797344650976034816 |
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| author | Tobias Bergmann Logan Froese Alwyn Gomez Amanjyot Singh Sainbhi Nuray Vakitbilir Abrar Islam Kevin Stein Izzy Marquez Fiorella Amenta Kevin Park Younis Ibrahim Frederick A. Zeiler |
| author_facet | Tobias Bergmann Logan Froese Alwyn Gomez Amanjyot Singh Sainbhi Nuray Vakitbilir Abrar Islam Kevin Stein Izzy Marquez Fiorella Amenta Kevin Park Younis Ibrahim Frederick A. Zeiler |
| author_sort | Tobias Bergmann |
| collection | DOAJ |
| description | Regional cerebral oxygen saturation (rSO<sub>2</sub>), a method of cerebral tissue oxygenation measurement, is recorded using non-invasive near-infrared Spectroscopy (NIRS) devices. A major limitation is that recorded signals often contain artifacts. Manually removing these artifacts is both resource and time consuming. The objective was to evaluate the applicability of using wavelet analysis as an automated method for simple signal loss artifact clearance of rSO<sub>2</sub> signals obtained from commercially available devices. A retrospective observational study using existing populations (healthy control (HC), elective spinal surgery patients (SP), and traumatic brain injury patients (TBI)) was conducted. Arterial blood pressure (ABP) and rSO<sub>2</sub> data were collected in all patients. Wavelet analysis was determined to be successful in removing simple signal loss artifacts using wavelet coefficients and coherence to detect signal loss artifacts in rSO<sub>2</sub> signals. The removal success rates in HC, SP, and TBI populations were 100%, 99.8%, and 99.7%, respectively (though it had limited precision in determining the exact point in time). Thus, wavelet analysis may prove to be useful in a layered approach NIRS signal artifact tool utilizing higher-frequency data; however, future work is needed. |
| first_indexed | 2024-03-08T11:05:49Z |
| format | Article |
| id | doaj.art-ec3a807294f74e958595cfb6e0ea8a0f |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-08T11:05:49Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-ec3a807294f74e958595cfb6e0ea8a0f2024-01-26T15:06:12ZengMDPI AGBioengineering2306-53542023-12-011113310.3390/bioengineering11010033Evaluation of Morlet Wavelet Analysis for Artifact Detection in Low-Frequency Commercial Near-Infrared Spectroscopy SystemsTobias Bergmann0Logan Froese1Alwyn Gomez2Amanjyot Singh Sainbhi3Nuray Vakitbilir4Abrar Islam5Kevin Stein6Izzy Marquez7Fiorella Amenta8Kevin Park9Younis Ibrahim10Frederick A. Zeiler11Biosystems Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, CanadaBiomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, CanadaSection of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3A 1R9, CanadaBiomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, CanadaBiomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, CanadaBiomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, CanadaBiomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, CanadaBiosystems Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, CanadaBiosystems Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, CanadaUndergraduate Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P5, CanadaBiomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, CanadaBiomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, CanadaRegional cerebral oxygen saturation (rSO<sub>2</sub>), a method of cerebral tissue oxygenation measurement, is recorded using non-invasive near-infrared Spectroscopy (NIRS) devices. A major limitation is that recorded signals often contain artifacts. Manually removing these artifacts is both resource and time consuming. The objective was to evaluate the applicability of using wavelet analysis as an automated method for simple signal loss artifact clearance of rSO<sub>2</sub> signals obtained from commercially available devices. A retrospective observational study using existing populations (healthy control (HC), elective spinal surgery patients (SP), and traumatic brain injury patients (TBI)) was conducted. Arterial blood pressure (ABP) and rSO<sub>2</sub> data were collected in all patients. Wavelet analysis was determined to be successful in removing simple signal loss artifacts using wavelet coefficients and coherence to detect signal loss artifacts in rSO<sub>2</sub> signals. The removal success rates in HC, SP, and TBI populations were 100%, 99.8%, and 99.7%, respectively (though it had limited precision in determining the exact point in time). Thus, wavelet analysis may prove to be useful in a layered approach NIRS signal artifact tool utilizing higher-frequency data; however, future work is needed.https://www.mdpi.com/2306-5354/11/1/33artifact managementcerebral bio-signal analysistraumatic brain injuryhemodynamic monitoringnear-infrared spectroscopybrain tissue oxygen saturation |
| spellingShingle | Tobias Bergmann Logan Froese Alwyn Gomez Amanjyot Singh Sainbhi Nuray Vakitbilir Abrar Islam Kevin Stein Izzy Marquez Fiorella Amenta Kevin Park Younis Ibrahim Frederick A. Zeiler Evaluation of Morlet Wavelet Analysis for Artifact Detection in Low-Frequency Commercial Near-Infrared Spectroscopy Systems Bioengineering artifact management cerebral bio-signal analysis traumatic brain injury hemodynamic monitoring near-infrared spectroscopy brain tissue oxygen saturation |
| title | Evaluation of Morlet Wavelet Analysis for Artifact Detection in Low-Frequency Commercial Near-Infrared Spectroscopy Systems |
| title_full | Evaluation of Morlet Wavelet Analysis for Artifact Detection in Low-Frequency Commercial Near-Infrared Spectroscopy Systems |
| title_fullStr | Evaluation of Morlet Wavelet Analysis for Artifact Detection in Low-Frequency Commercial Near-Infrared Spectroscopy Systems |
| title_full_unstemmed | Evaluation of Morlet Wavelet Analysis for Artifact Detection in Low-Frequency Commercial Near-Infrared Spectroscopy Systems |
| title_short | Evaluation of Morlet Wavelet Analysis for Artifact Detection in Low-Frequency Commercial Near-Infrared Spectroscopy Systems |
| title_sort | evaluation of morlet wavelet analysis for artifact detection in low frequency commercial near infrared spectroscopy systems |
| topic | artifact management cerebral bio-signal analysis traumatic brain injury hemodynamic monitoring near-infrared spectroscopy brain tissue oxygen saturation |
| url | https://www.mdpi.com/2306-5354/11/1/33 |
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