Photoplethysmography temporal marker-based machine learning classifier for anesthesia drug detection.
Anesthesia drug overdose hazards and lack of gold standards in anesthesia monitoring lead to an urgent need for accurate anesthesia drug detection. To investigate the PPG waveform features affected by anesthesia drugs and develop a machine-learning classifier with high anesthesia drug sensitivity. T...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 11; pp. 3057 - 3069 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159531221&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159531221 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2022 vid: 60 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159531221 158908253 159531221 NLM36063352 159531221 10.1007/s11517-022-02658-1 NLM36063352 159531221 ppf: 3057 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Photoplethysmography temporal marker-based machine learning classifier for anesthesia drug detection. aug: au: Khalid, Syed Ghufran Ali, Syed Mehmood Liu, Haipeng Qurashi, Aisha Ghazal Ali, Uzma affil: Faculty of Health, Education and Life Sciences, Birmingham City University, B15 3TN, Birmingham, UK sug: subj: Algorithms Plethysmography Methods Monitoring, Physiologic Methods ab: Anesthesia drug overdose hazards and lack of gold standards in anesthesia monitoring lead to an urgent need for accurate anesthesia drug detection. To investigate the PPG waveform features affected by anesthesia drugs and develop a machine-learning classifier with high anesthesia drug sensitivity. This study used 64 anesthesia and non-anesthesia patient data (32 cases each), extracted from Queensland and MIMIC-II databases, respectively. The key waveform features (total area, rising time, width 75%, 50%, and 25%) were extracted from 16,310 signal recordings (5-s duration). Discriminant analysis, support vector machine (SVM), and K-nearest neighbor (KNN) were evaluated by splitting the dataset into halve training (11 patients, 8570 segments) and halve testing dataset (11 patients, 7740 segments). Significant differences exist between PPG waveform features of anesthesia and non-anesthesia groups (p < 0.05) except total area feature (p > 0.05). The KNN classifier achieved 91.7% (AUC = 0.95) anesthesia detection accuracy with the highest sensitivity (0.88) and specificity (0.90) as compared to other classifiers. Kohen's kappa also shows almost perfect agreement (0.79) with the KNN classifier. The KNN classifier trained with significant PPG features has the potential to be used as a reliable, non-invasive, and low-cost method for the detection of anesthesia drugs for depth analysis during surgical operations and postoperative monitoring. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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