Complex-Valued Wavelet Spectrum Analysis of Respiratory Conditions and Its Feasibility in the Detection of Low-Functional Respiration.

Yoshikazu Nakajima,Yohei Wada,Kenichi Okubo,Katsunori Suzuki,Yasuro Okumiya,Koji Yataka,Toshihiro Kawase,Yasuhiro Nakashima,Takaaki Sugino,Masashi Kobayashi,Shinya Onogi

doi:10.3390/healthcare9080981

Yoshikazu Nakajima, Yohei Wada + Show 9 more

Open Access

https://doi.org/10.3390/healthcare9080981

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Abstract

Respiratory monitoring is a significant issue to reduce patient risks and medical staff labor in postoperative care and epidemic infection, particularly after the COVID-19 pandemic. Oximetry is widely used for respiration monitoring in the clinic, but it sometimes fails to capture a low-functional respiratory condition even though a patient has breathing difficulty. Another approach is breathing-sound monitoring, but this is unstable due to the indirect measurement of lung volume. Kobayashi in our team is developing a sensor measuring temporal changes in lung volume with a displacement sensor attached across the sixth and eighth ribs. For processing these respiratory signals, we propose the combination of complex-valued wavelet transform and the correlation among spectrum sequences. We present the processing results and discuss its feasibility to detect a low-functional condition in respiration. The result for detecting low-functional respiration showed good performance with a sensitivity of 0.88 and specificity of 0.88 to 1 in its receiver operating characteristic (ROC) curve.

Highlights

The monitoring of respiratory conditions is a common concern in patient care in postoperative treatments and epidemic infections such as COVID-19
Frequent checks for a patient’s condition are important to start speedy treatment. Another case that requires respiratory monitoring is COVID-19, which poses a threat to the world and could cause widespread infections
In this study, we evaluate respiratory signals with complex-based wavelet transform to clarify the analytical capability of the complex-valued wavelet transform and show a criterion with the frequency domain to detect low-functional conditions in respiration

Summary

Introduction

The monitoring of respiratory conditions is a common concern in patient care in postoperative treatments and epidemic infections such as COVID-19. Frequent checks for a patient’s condition are important to start speedy treatment. Another case that requires respiratory monitoring is COVID-19, which poses a threat to the world and could cause widespread infections. This can be submerged and incubate for up to a couple of weeks, but possibly makes the respiration activity of affected people severe in a couple of hours. Medical staff are burdened with frequently checking patients’ respiratory conditions, such as every hour for a long period of around two weeks. The automatic monitoring of respiratory activation is required to reduce patient risks and the difficulty of medical staff labor. Oximetry is widely used in automatic respiratory monitoring, but it does not directly capture respiratory motion

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