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Salient Facets in Artificial Intelligence for Cleansing Pulsatile Physiological Signals: Knowledge Incorporation, Real-Time Dynamics, Assessment Entities, and Technology Acceptance
  • +5
  • Kyoungsuk Park,
  • Kyoung-Sun Kim,
  • Dong-In Kim,
  • Kanghee Kim,
  • Woo-Young Seo,
  • Jae-Man Shin,
  • Juntae Kim,
  • Sung-Hoon Kim
Kyoungsuk Park
Kyoung-Sun Kim
Dong-In Kim
Kanghee Kim
Woo-Young Seo
Jae-Man Shin
Juntae Kim

Corresponding Author:[email protected]

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Sung-Hoon Kim

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Abstract

Pulsatile physiological signals, characterized by periodic waveform traits, are essential for patient monitoring. However, artifacts considerably compromise signal fidelity, thereby rendering the signal cleansing process essential. Although previous studies have focused on algorithmic advancements, domainspecific factors in this field are yet to be comprehensively understood. Therefore, this study conducts an empirical investigation into four critical signal cleansing aspects in the medical field. First, we verified the performance enhancement of incorporating domain knowledge into cleansing techniques. Second, we emulated real-time cleansing requirements from clinical environments. Third, we explored the similarities and differences among three evaluation approaches: quantitative assessment of the cleansing performance, qualitative assessment of the same by clinical experts, and a cleansing effect assessment on downstream tasks. Finally, we evaluated whether the willingness to accept these techniques varied according to the knowledge incorporation mode and professional groups comprising clinicians and engineers. The results revealed that domain knowledge incorporation enhances cleansing performance, visually appealing cleansing results can mislead experts, and the willingness to accept cleansing techniques varies depending on the knowledge incorporation method, professional group, and experience level.
04 Jun 2024Submitted to TechRxiv
07 Jun 2024Published in TechRxiv