Literature DB >> 32026257

Photoplethysmography-derived approximate entropy and sample entropy as measures of analgesia depth during propofol-remifentanil anesthesia.

Wanlin Chen1,2,3, Feng Jiang1,2,3, Xinzhong Chen3,4, Ying Feng4, Jiajun Miao1,2,3, Shali Chen1,2,3, Cuicui Jiao4, Hang Chen5,6,7,8.   

Abstract

The ability to monitor the physiological effect of the analgesic agent is of interest in clinical practice. Nonstationary changes would appear in photoplethysmography (PPG) during the analgesics-driven transition to analgesia. The present work studied the properties of nonlinear methods including approximate entropy (ApEn) and sample entropy (SampEn) derived from PPG responding to a nociceptive stimulus under various opioid concentrations. Forty patients with ASA I or II were randomized to receive one of the four possible remifentanil effect-compartment target concentrations (Ceremi) of 0, 1, 3, and 5 ng·ml-1 and a propofol effect-compartment target-controlled infusion to maintain the state entropy (SE) at 50 ± 10. Laryngeal mask airway (LMA) insertion was applied as a standard noxious stimulation. To optimize the performance of ApEn and SampEn, different coefficients were carefully evaluated. The monotonicity of ApEn and SampEn changing from low Ceremi to high Ceremi was assessed with prediction probabilities (PK). The result showed that low Ceremi (0 and 1 ng·ml-1) could be differentiated from high Ceremi (3 and 5 ng·ml-1) by ApEn and SampEn. Depending on the coefficient employed in algorithm: ApEn with k = 0.15 yielded the largest PK value (0.875) whereas SampEn gained its largest PK of 0.867 with k = 0.2. Thus, PPG-based ApEn and SampEn with appropriate k values have the potential to offer good quantification of analgesia depth under general anesthesia.

Entities:  

Keywords:  Analgesia depth; Approximate entropy; Nonlinear methods; Photoplethysmography; Sample entropy

Year:  2020        PMID: 32026257     DOI: 10.1007/s10877-020-00470-6

Source DB:  PubMed          Journal:  J Clin Monit Comput        ISSN: 1387-1307            Impact factor:   2.502


  3 in total

1.  A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy.

Authors:  Haikun Shang; Yucai Li; Junyan Xu; Bing Qi; Jinliang Yin
Journal:  Entropy (Basel)       Date:  2020-09-17       Impact factor: 2.524

2.  Cerebral Tissue Oxygen Saturation Correlates with Emergence from Propofol-Remifentanil Anesthesia: An Observational Cohort Study.

Authors:  Jianxi Zhang; Zhigang Cheng; Ying Tian; Lili Weng; Yiying Zhang; Xin Yang; Michael K E Schäfer; Qulian Guo; Changsheng Huang
Journal:  J Clin Med       Date:  2022-08-19       Impact factor: 4.964

3.  Photoplethysmography temporal marker-based machine learning classifier for anesthesia drug detection.

Authors:  Syed Ghufran Khalid; Syed Mehmood Ali; Haipeng Liu; Aisha Ghazal Qurashi; Uzma Ali
Journal:  Med Biol Eng Comput       Date:  2022-09-05       Impact factor: 3.079

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.