If you have used Vital Recorder in your research and would like to describe the program and the data obtained in the Methods section, cite the following paper to reduce the inconvenience of describing in detail how to obtain the data.

  • Lee HC, Jung CW. Vital Recorder-a free research tool for automatic recording of high-resolution time-synchronised physiological data from multiple anaesthesia devices. Sci Rep. 2018 Jan 24;8(1):1527. doi: 10.1038/s41598-018-20062-4

If you are working with VitalDB datasets, the following paper will alleviate the inconvenience of describing in detail the data structure, content, and how to obtain it.

  • Lee HC, Jung CW. Vital Recorder-a free research tool for automatic recording of high-resolution time-synchronised physiological data from multiple anaesthesia devices. Sci Rep. 2018 Jan 24;8(1):1527. doi: 10.1038/s41598-018-20062-4


The following articles were conducted using Vital Recorder or cited Vital Recorder papers in the text.


  • Park et al. Intraoperative Arterial Pressure Variability and Postoperative Acute Kidney Injury. CJASN 2020.
  • Kang et al. Development of a prediction model for hypotension after induction of anesthesia using machine learning. PLOS one 2020 April doi: 10.1371/journal.pone.0231172
  • Yoon et al. Discovering hidden information in biosignals from patients by artificial intelligence. Korean Journal of Anesthesiology. 2020
  • Goodwin et al. A practical approach to storage and retrieval of high-frequency physiological signals. Physiological Measurement. 2020
  • Sun et al. INSMA: An integrated system for multimodal data acquisition and analysis in the intensive care unit. Journal of Biomedical Informatics. 2020.
  • Park et al. Comparison of electroencephalogram between propofol- and thiopental-induced anesthesia for awareness risk in pregnant women. Scientific Report 2020. doi: 10.1038/s41598-020-62999-5
  • Kim et al. Prediction of Post-Intubation Tachycardia Using Machine-Learning Models. Applied Scinces 2020.
  • Yoon et al. Predictive factors for hypotension associated with supine-to-prone positional change in patients undergoing spine surgery. Journal of neurosurgical anesthesiology. 2020
  • Kim et al. Duty cycle of 33% increases cardiac output during cardiopulmonary resuscitation. PLOS one 2020
  • Park et al. Real-time monitoring of blood pressure using digitized pulse arrival time calculation technology for prompt detection of sudden hypertensive episodes during laryngeal microsurgery: retrospective observational study. JMIR. May 2020


  • Koo et al. Microvolt T-wave alternans at the end of surgery is associated with postoperative mortality in cardiac surgery patients. Scientific reports 2019.
  • Jeong et al. Prediction of Blood Pressure after Induction of Anesthesia Using Deep Learning: A Feasibility Study. Applied Sciences. 2019.
  • Lee et al. Is dynamic arterial elastance a predictor of an increase in blood pressure after fluid administration in pediatric patients with hypotension? Reanalysis of prospective observational studies. Pediatric Anesthesia. 2019
  • Cho et al. Effects of ischaemic conditioning on tissue oxygen saturation and heart rate variability: an observational study. JIMR. 2019
  • Lee et al. Analysis of Pulse Arrival Time as an Indicator of Blood Pressure in a Large Surgical Biosignal Database: Recommendations for Developing Ubiquitous Blood Pressure Monitoring Methods. J Clin Med. 2019 Oct 24;8(11). pii: E1773. doi: 10.3390/jcm8111773
  • Ji et al. Comparison of pulse pressure variation and pleth variability index in the prone position in pediatric patients under 2 years old. Korean J Anesthesiol. 2019 Oct;72(5):466-471. doi: 10.4097/kja.19128
  • Lee et al. Data Driven Investigation of Bispectral Index Algorithm. Sci Rep. 2019 Sep 24;9(1):13769. doi: 10.1038/s41598-019-50391-x
  • Moon et al. Deep Learning-Based Stroke Volume Estimation Outperforms Conventional Arterial Contour Method in Patients with Hemodynamic Instability. J Clin Med. 2019 Sep 9;8(9). pii: E1419. doi: 10.3390/jcm8091419.
  • Jun et al. The tidal volume challenge improves the reliability of dynamic preload indices during robot-assisted laparoscopic surgery in the Trendelenburg position with lung-protective ventilation. BMC Anesthesiol. 2019 Aug 7;19(1):142. doi: 10.1186/s12871-019-0807-6 
  • Chung et al. Sustained erroneous near-infrared cerebral oxygen saturation in alert icteric patient with vanishing bile duct syndrome during and after liver transplantation - A case report. Anesth Pain Med 2019; 14(1):63-66. doi: 10.17085/apm.2019.14.1.63
  • Seo et al. Comparative Analysis of Phase Lag Entropy and Bispectral Index as Anesthetic Depth Indicators in Patients Undergoing Thyroid Surgery with Nerve Integrity Monitoring. J Korean Med Sci. 2019 May 27;34(20):e151. doi:10.3346/jkms.2019.34.e151
  • Lee et al. Prediction of Bispectral Index during target-controlled infusion of propofol and remifentanil: A deep learning approach. Anesthesiology. 2018 March doi:10.1097/ALN.0000000000001892
  • Park et al. The importance of sensor contacting force for predicting fluid responsiveness in children using respiratory variations in pulse oximetry plethysmographic waveform. J Clin Monit Comput. 2019 Jun;33(3):393-401. doi: 10.1007/s10877-018-0183-7
  • Choi et al. Sugammadex associated profound bradycardia and sustained hypotension in patient with the slow recovery of neuromuscular blockade: A case report. Anesthesia and Pain Medicine 2019


  • Lee and Jung. Anesthesia research in the artificial intelligence era. Anesth Pain Med 2018. doi: 10.1016/j.urolonc.2013.09.012
  • Lehavi et al. Effect of position and pneumoperitoneum on respiratory mechanics and transpulmonary pressure during laparoscopic surgery. Laparosc Surg 2018;2:60 doi:10.21037/ls.2018.10.13
  • Comparison of the effect of different infusion rates of sufentanil on surgical stress index during cranial pinning in children under general anaesthesia: a randomized controlled study. BMC Anesthesiology 2017;17:167. doi:10.1186/s12871-017-0448-6