A Skewness-Based Harmonic Filter for Harmonic Attenuation of Wearable Functional Near-Infrared Spectroscopy Signals
Ferdinando, Hany; Ilvesmäki, Martti; Kananen, Janne; Moradi, Sadegh; Myllylä, Teemu (2024-05-05)
Springer
05.05.2024
Ferdinando, H., Ilvesmäki, M., Kananen, J., Moradi, S., Myllylä, T. (2024). A Skewness-Based Harmonic Filter for Harmonic Attenuation of Wearable Functional Near-Infrared Spectroscopy Signals. In: Särestöniemi, M., et al. Digital Health and Wireless Solutions. NCDHWS 2024. Communications in Computer and Information Science, vol 2084. Springer, Cham. https://doi.org/10.1007/978-3-031-59091-7_11
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© 2024 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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© 2024 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405304104
https://urn.fi/URN:NBN:fi:oulu-202405304104
Tiivistelmä
Abstract
Harmonics is an unavoidable phenomenon, even before we knew about digital circuits. In our sleep study, we found harmonic artefacts (HA) in our functional near-infrared spectroscopy (fNIRS) signal. Interestingly, it was neither device- nor subject-dependent. The fundamental frequency was around either 0.5 Hz or 1 Hz. It appeared to be very sharp peaks and they were within the band of interest, i.e., respiratory (0.1–0.6 Hz) and cardiac (0.6–5 Hz) bands. Since the exact location might change, we proposed a skewness-based harmonic filter (sbHF) to identify the fundamental frequency and attenuate HA. Since suppressing certain frequencies may change signal characteristic, spectral entropy was used to evaluate it based on Wilcoxon-test at a 0.05 significant level. 25 controls (6 females, age: 39.0 ± 8.5 years, height: 175.6 ± 8.0 cm, weight: 80.3 ± 10.8 kg) and 16 sleep apnea patients (1 female, age: 48.3 ± 12.4 years, height: 177.3 ± 6.0 cm, weight: 93.6 ± 17.1 kg) were recruited for our sleep study. sbHF showed good performance to identify fundamental frequency and attenuate HA from our raw fNIRS signals and 5% of the signal experienced changes in signal characteristics based on the spectral entropy analysis. Combining sbHF with a certain motion artefact reduction, we found that specific order of operation to get appropriate chromophore concentration was needed. This method is not only for problems in wearable fNIRS, but also can be modified for other problems by adjusting the suspected area or sweeping the frequency range to identify a fundamental frequency.
Harmonics is an unavoidable phenomenon, even before we knew about digital circuits. In our sleep study, we found harmonic artefacts (HA) in our functional near-infrared spectroscopy (fNIRS) signal. Interestingly, it was neither device- nor subject-dependent. The fundamental frequency was around either 0.5 Hz or 1 Hz. It appeared to be very sharp peaks and they were within the band of interest, i.e., respiratory (0.1–0.6 Hz) and cardiac (0.6–5 Hz) bands. Since the exact location might change, we proposed a skewness-based harmonic filter (sbHF) to identify the fundamental frequency and attenuate HA. Since suppressing certain frequencies may change signal characteristic, spectral entropy was used to evaluate it based on Wilcoxon-test at a 0.05 significant level. 25 controls (6 females, age: 39.0 ± 8.5 years, height: 175.6 ± 8.0 cm, weight: 80.3 ± 10.8 kg) and 16 sleep apnea patients (1 female, age: 48.3 ± 12.4 years, height: 177.3 ± 6.0 cm, weight: 93.6 ± 17.1 kg) were recruited for our sleep study. sbHF showed good performance to identify fundamental frequency and attenuate HA from our raw fNIRS signals and 5% of the signal experienced changes in signal characteristics based on the spectral entropy analysis. Combining sbHF with a certain motion artefact reduction, we found that specific order of operation to get appropriate chromophore concentration was needed. This method is not only for problems in wearable fNIRS, but also can be modified for other problems by adjusting the suspected area or sweeping the frequency range to identify a fundamental frequency.
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