Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet
An aim of the analysis of biomedical signals such as heart rate variability signals, brain signals, oxygen saturation variability (OSV) signals, etc., is for the design and development of tools to extract information about the underlying complexity of physiological systems, to detect physiological s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/11/16/2280 |
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| author | Madini O. Alassafi Ishtiaq Rasool Khan Rayed AlGhamdi Wajid Aziz Abdulrahman A. Alshdadi Mohamed M. Dessouky Adel Bahaddad Ali Altalbe Nabeel Albishry |
| author_facet | Madini O. Alassafi Ishtiaq Rasool Khan Rayed AlGhamdi Wajid Aziz Abdulrahman A. Alshdadi Mohamed M. Dessouky Adel Bahaddad Ali Altalbe Nabeel Albishry |
| author_sort | Madini O. Alassafi |
| collection | DOAJ |
| description | An aim of the analysis of biomedical signals such as heart rate variability signals, brain signals, oxygen saturation variability (OSV) signals, etc., is for the design and development of tools to extract information about the underlying complexity of physiological systems, to detect physiological states, monitor health conditions over time, or predict pathological conditions. Entropy-based complexity measures are commonly used to quantify the complexity of biomedical signals; however novel complexity measures need to be explored in the context of biomedical signal classification. In this work, we present a novel technique that used Haar wavelets to analyze the complexity of OSV signals of subjects during COVID-19 infection and after recovery. The data used to evaluate the performance of the proposed algorithms comprised recordings of OSV signals from 44 COVID-19 patients during illness and after recovery. The performance of the proposed technique was compared with four, scale-based entropy measures: multiscale entropy (MSE); multiscale permutation entropy (MPE); multiscale fuzzy entropy (MFE); multiscale amplitude-aware permutation entropy (MAMPE). Preliminary results of the pilot study revealed that the proposed algorithm outperformed MSE, MPE, MFE, and MMAPE in terms of better accuracy and time efficiency for separating during and after recovery the OSV signals of COVID-19 subjects. Further studies are needed to evaluate the potential of the proposed algorithm for large datasets and in the context of other biomedical signal classifications. |
| first_indexed | 2024-03-10T23:54:26Z |
| format | Article |
| id | doaj.art-7ffdc28d33544304aad1846f0d1880c9 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-10T23:54:26Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-7ffdc28d33544304aad1846f0d1880c92023-11-19T01:18:32ZengMDPI AGHealthcare2227-90322023-08-011116228010.3390/healthcare11162280Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar WaveletMadini O. Alassafi0Ishtiaq Rasool Khan1Rayed AlGhamdi2Wajid Aziz3Abdulrahman A. Alshdadi4Mohamed M. Dessouky5Adel Bahaddad6Ali Altalbe7Nabeel Albishry8Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaCollege of Computer Science and Engineering, University of Jeddah, Jeddah 21725, Saudi ArabiaFaculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu and Kashmir Muzaffarabad (AK), Azad Jammu and Kashmir 13100, PakistanCollege of Computer Science and Engineering, University of Jeddah, Jeddah 21725, Saudi ArabiaCollege of Computer Science and Engineering, University of Jeddah, Jeddah 21725, Saudi ArabiaFaculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaFaculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaFaculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaAn aim of the analysis of biomedical signals such as heart rate variability signals, brain signals, oxygen saturation variability (OSV) signals, etc., is for the design and development of tools to extract information about the underlying complexity of physiological systems, to detect physiological states, monitor health conditions over time, or predict pathological conditions. Entropy-based complexity measures are commonly used to quantify the complexity of biomedical signals; however novel complexity measures need to be explored in the context of biomedical signal classification. In this work, we present a novel technique that used Haar wavelets to analyze the complexity of OSV signals of subjects during COVID-19 infection and after recovery. The data used to evaluate the performance of the proposed algorithms comprised recordings of OSV signals from 44 COVID-19 patients during illness and after recovery. The performance of the proposed technique was compared with four, scale-based entropy measures: multiscale entropy (MSE); multiscale permutation entropy (MPE); multiscale fuzzy entropy (MFE); multiscale amplitude-aware permutation entropy (MAMPE). Preliminary results of the pilot study revealed that the proposed algorithm outperformed MSE, MPE, MFE, and MMAPE in terms of better accuracy and time efficiency for separating during and after recovery the OSV signals of COVID-19 subjects. Further studies are needed to evaluate the potential of the proposed algorithm for large datasets and in the context of other biomedical signal classifications.https://www.mdpi.com/2227-9032/11/16/2280biomedical signal processingCOVID-19Haar waveletoxygen saturation variabilityphysiological systems |
| spellingShingle | Madini O. Alassafi Ishtiaq Rasool Khan Rayed AlGhamdi Wajid Aziz Abdulrahman A. Alshdadi Mohamed M. Dessouky Adel Bahaddad Ali Altalbe Nabeel Albishry Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet Healthcare biomedical signal processing COVID-19 Haar wavelet oxygen saturation variability physiological systems |
| title | Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet |
| title_full | Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet |
| title_fullStr | Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet |
| title_full_unstemmed | Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet |
| title_short | Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet |
| title_sort | studying dynamical characteristics of oxygen saturation variability signals using haar wavelet |
| topic | biomedical signal processing COVID-19 Haar wavelet oxygen saturation variability physiological systems |
| url | https://www.mdpi.com/2227-9032/11/16/2280 |
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