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

Syed Ghufran Khalid, Syed Mehmood Ali, Haipeng Liu, Aisha Ghazal Qurashi, Uzma Ali

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    17 Citations (Scopus)
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    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). 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. [Abstract copyright: © 2022. The Author(s).]
    Original languageEnglish
    Pages (from-to)3057-3068
    Number of pages12
    JournalMedical & biological engineering & computing
    Issue number11
    Early online date5 Sept 2022
    Publication statusPublished - Nov 2022

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    • Queensland database
    • MIMIC II database
    • Anesthesia depth
    • K-nearest neighbor
    • Photoplethysmography


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