Using Signal Features of Functional Near-Infrared Spectroscopy for Acute Physiological Score Estimation in ECMO Patients

Hsiao-Huang Chang; Kai-Hsiang Hou; Ting-Wei Chiang; Yi-Min Wang; Chia-Wei Sun

doi:10.3390/bioengineering11010026

Using Signal Features of Functional Near-Infrared Spectroscopy for Acute Physiological Score Estimation in ECMO Patients

Bioengineering (Basel). 2023 Dec 26;11(1):26. doi: 10.3390/bioengineering11010026.

Authors

Hsiao-Huang Chang^{1

2}, Kai-Hsiang Hou³, Ting-Wei Chiang³, Yi-Min Wang³, Chia-Wei Sun^{3

4

5}

Affiliations

¹ Division of Cardiovascular Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei 11217, Taiwan.
² Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan.
³ Biomedical Optical Imaging Lab, Department of Photonics, Institute of Electro-Optical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
⁴ Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
⁵ Medical Device Innovation and Translation Center, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan.

Abstract

Extracorporeal membrane oxygenation (ECMO) is a vital emergency procedure providing respiratory and circulatory support to critically ill patients, especially those with compromised cardiopulmonary function. Its use has grown due to technological advances and clinical demand. Prolonged ECMO usage can lead to complications, necessitating the timely assessment of peripheral microcirculation for an accurate physiological evaluation. This study utilizes non-invasive near-infrared spectroscopy (NIRS) to monitor knee-level microcirculation in ECMO patients. After processing oxygenation data, machine learning distinguishes high and low disease severity in the veno-venous (VV-ECMO) and veno-arterial (VA-ECMO) groups, with two clinical parameters enhancing the model performance. Both ECMO modes show promise in the clinical severity diagnosis. The research further explores statistical correlations between the oxygenation data and disease severity in diverse physiological conditions, revealing moderate correlations with the acute physiologic and chronic health evaluation (APACHE II) scores in the VV-ECMO and VA-ECMO groups. NIRS holds the potential for assessing patient condition improvements.

Keywords: acute physiologic and chronic health evaluation II (APACHE II) scoring system; extracorporeal membrane oxygenation (ECMO); microcirculation; near-infrared spectroscopy (NIRS); support vector machine (SVM).

Grants and funding

This work was supported in part by the Ministry of Science and Technology, Grant/Award Numbers: 112-2221-E-A49-047-MY3, 112-2811-E-A49-535-MY2, 110-2221-E-075-002, 109-2221-E-009-018-MY3, 109-2811-E-009-532-MY3, and 110-2221-E-075-001-MY3.