ArticleMedical & biological engineering & computing2022
Photoplethysmography temporal marker-based machine learning classifier for anesthesia drug detection.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 27 citations in OpenAlex.
- Comparison of Cerebral Blood Flow During General Anesthesia in Elderly Patients with and Without Dementia: A Prospective Controlled Clinical Trial.Journal of clinical medicine · 2025Article
- Predictive effect of postoperative recovery in general anesthesia patients using interpretable models based on swarm intelligence machine learning.Frontiers in physiology · 2025Article
- A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer.Scientific reports · 2024Article
- A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model.Diagnostics (Basel, Switzerland) · 2024Article
- Predictors of Nocturnal Hypoxemic Burden in Patients Undergoing Elective Coronary Artery Bypass Grafting Surgery.Biomedicines · 2023Article
- Implantable Intracranial Pressure Sensor with Continuous Bluetooth Transmission via Mobile Application.Journal of personalized medicine · 2023Article
- Wearable Multisensor Ring-Shaped Probe for Assessing Stress and Blood Oxygenation: Design and Preliminary Measurements.Biosensors · 2023Article
- Filtering-induced changes of pulse transmit time across different ages: a neglected concern in photoplethysmography-based cuffless blood pressure measurement.Frontiers in physiology · 2023Article
- Forehead monitoring of heart rate in neonatal intensive care.Frontiers in physiology · 2023Article
- Effect of intracranial pressure on photoplethysmographic waveform in different cerebral perfusion territories: A computational study.Frontiers in physiology · 2023Article
- Wavelet analysis of laser Doppler microcirculatory signals: Current applications and limitations.Frontiers in physiology · 2022Review
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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