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.


  • Kim et al. Frontal electroencephalogram activity during emergence from general anaesthesia in children with and without emergence delirium. British Journal of Anaesthesia 2021 January. DOI:10.1016/j.bja.2020.07.060
  • Shin et al. Joint Behavioral Cloning and Reinforvitaldb: fostering collaboration in anaesthesiacement Learning Method for Propofol and Remifentanil Infusion in Anesthesia. IEEE 2021 January. DOI: 10.1109/ICOIN50884.2021.9333933
  • Oh et al. Intraarterial catheter diameter and dynamic response of arterial pressure monitoring system: a randomized controlled trial. Sprinkal Link 2021 february.  DOI: 10.1007/s10877-021-00663-7
  • Seo et al. Changes in electroencephalographic power and bicoherence spectra according to depth of dexmedetomidine sedation in patients undergoing spinal anesthesia. IVYSPRING 2021 March.  DOI: 10.7150/ijms.54677
  • Carrier-Ruiz et al. Calcium imaging of adult-born neurons in freely moving mice. STAR Protoc. 2020 Dec 31;2(1):100238. DOI:10.1016/j.xpro.2020.100238
  • Shin et al. Complementary Photoplethysmogram Synthesis From Electrocardiogram Using Generative Adversarial Network. IEEE 2021 March.
  • Peng et al. Predicting Acute Kidney Injury via Interpretable Ensemble Learning and Attention Weighted Convoutional-Recurrent Neural Networks. IEEE 2021 March. DOI:10.1109/CISS50987.2021.9400242
  • Cho et al. Heart rate variability and oxygen reserve index during cardiorespiratory events in patients undergoing ophthalmic arterial chemotherapy: a prospective observational study. J Clin Monit Comput. 2021 Mar 18. DOI: 10.1007/s10877-021-00687-z
  • Kim EH, Lee JH, Jang YE, Ji SH, Kim HS, Cho SA, Kim JT. Prediction of fluid responsiveness using lung recruitment manoeuvre in paediatric patients receiving lung-protective ventilation: A prospective observational study. Eur J Anaesthesiol. 2021 May 1;38(5):452-458. doi: 10.1097/EJA.0000000000001387.
  • Yoon et al. Arrhythmia incidence and associated factors during volatile induction of general anesthesia with sevoflurane: a retrospective analysis of 950 adult patients. Anaesthesia Critical Care & Pain Medicine. 2021 june. DOI:10.1016/j.accpm.2021.100878
  • Zhang et al. Study of cuffless blood pressure estimation method based on multiple physiological parameters. Physiol Meas. 2021 Jun 17;42(5). DOI: 10.1088/1361-6579/abf889.
  • Kim et al. Dialysis adequacy predictions using a machine learning method. Scientific Reports 2021 July. DOI: 10.1038/s41598-021-94964-1  
  • Cho et al. The influence of propofol-based total intravenous anesthesia on postoperative outcomes in end-stage renal disease patients: A retrospective observation study. PLOS one 2021 July. DOI: 10.1371/journal.pone.0254014 
  • Kwon et al. Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network. Sensors 2021 july. DOI: 10.3390/s21155130
  • Yang et al. Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data. JMIR Publications 2021 August. DOI: 10.2196/24762
  • Lee et al. Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension. Sensors 2021 August. DOI: 10.3390/s20164575
  • Cho et al. A randomised controlled trial of 7.5-mm and 7.0-mm tracheal tubes vs. 6.5-mm and 6.0-mm tracheal tubes for men and women during laparoscopic surgery. Association of Anaesthetists 2021 August. DOI:10.1111/anae.15568
  • Jeong et al. The Prognostic Role of Right Ventricular Stroke Work Index during Liver Transplantation. J Clin Med. 2021 Sep 6;10(17):4022. DOI: 10.3390/jcm10174022.
  • Lee et al. Determining optimal positive end-expiratory pressure and tidal volume in children by intratidal compliance: a prospective observational study. Br J Anaesth. 2021 Oct 19:S0007-0912(21)00619-X. DOI:10.1016/j.bja.2021.09.024
  • Li et al. Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery. World J Clin Cases. 2021 Oct 16;9(29):8729-8739. doi: 10.12998/wjcc.v9.i29.8729.
  • Afshar et al. A Combinatorial Deep Learning Structure for Precise Depth of Anesthesia Estimation from EEG Signals. IEEE 2021. DOI: 10.1109/JBHI.2021.3068481
  • Zonca et al. Emergence and fragmentation of the alpha-band driven by neuronal 2 network dynamics. bioRxi 2021. DOI:10.1101/2021.07.19.452820  
  • Vistisen et al. VitalDB: fostering collaboration in anaesthesia research. Br J Anaesth. 2021 Aug;127(2):184-187. DOI: 10.1016/j.bja.2021.03.011
  • Ki et al. Verification of the performance of the Bispectral Index as a hypnotic depth indicator during dexmedetomidine sedation. Anesth Pain Med (Seoul). 2021 Oct 14. DOI:10.17085/apm.21065
  • shin et al. Joint Behavioral Cloning and Reinforcement Learning Method for Propofol and Remifentanil Infusion in Anesthesia. 2021 International Conference on Information Networking (ICOIN) 02 February. DOI: 10.1109/ICOIN50884.2021.9333933


  • Park et al. Intraoperative Arterial Pressure Variability and Postoperative Acute Kidney Injury. CJASN 2020. DOI: 10.2215/CJN.06620619
  • Kim et al. Prediction of Post-Intubation Tachycardia Using Machine-Learning Models. applied sciences 8 February. DOI:10.3390/app10031151
  • 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
  • Park et al. A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End. IEEE Trans Biomed Circuits Syst. 2020 Aug;14(4):825-837. DOI: 10.1109/TBCAS.2020.2998172.
  • Yoon et al. Discovering hidden information in biosignals from patients by artificial intelligence. Korean Journal of Anesthesiology. 2020. DOI: 10.4097/kja.19475
  • Goodwin et al. A practical approach to storage and retrieval of high-frequency physiological signals. Physiological Measurement. 2020. DOI: 10.1088/1361-6579/ab7cb5
  • Sun et al. INSMA: An integrated system for multimodal data acquisition and analysis in the intensive care unit. Journal of Biomedical Informatics. 2020. DOI: 10.1016/j.jbi.2020.103434
  • 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.  DOI: 10.1097/ANA.0000000000000565
  • Kim et al. Duty cycle of 33% increases cardiac output during cardiopulmonary resuscitation. PLOS one 2020. DOI: 10.1371/journal.pone.0228111
  • 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. DOI: 10.2196/13156
  • Yang et al. Estimation and Validation of Arterial Blood Pressure Using Photoplethysmogram Morphology Features in Conjunction With Pulse Arrival Time in Large Open Databases. IEEE July 2020. DOI: 10.1109/JBHI.2020.3009658
  • Park et al. Effect of depth of anesthesia on the phase lag entropy in patients undergoing general anesthesia by propofol: A STROBE-compliant study. Medicine (Baltimore). 2020 Jul 24;99(30):e21303. DOI:10.1097/MD.0000000000021303
  • Yoon et al. The cumulative duration of bispectral index less than 40 concurrent with hypotension is associated with 90-day postoperative mortality: a retrospective study. BMC anesthesiology 2020 August. DOI: 10.1186/s12871-020-01122-7
  • Yoon D, Jang JH, Choi BJ, Kim TY, Han CH. Discovering hidden information in biosignals from patients using artificial intelligence. Korean J Anesthesiol. 2020 Aug;73(4):275-284. DOI: 10.4097/kja.19475.
  • Yang et al. Comparison of bispectral index-guided and fixed-gas concentration techniques in desflurane and remifentanil anesthesia: A randomized controlled trial. PLOS one 2020 November
  • Kim et al. Comparison of Pupillometry With Surgical Pleth Index Monitoring on Perioperative Opioid Consumption and Nociception During Propofol–Remifentanil Anesthesia: A Prospective Randomized Controlled Trial. 2020 November. DOI: 10.1371/journal.pone.0241828
  • Afshar et al. A Two-Stage Deep Learning Scheme to Estimate Depth of Anesthesia from EEG Signals. IEEE  2020 November. DOI: 10.1109/ICBME51989.2020.9319416
  • Lee et al. Evaluation of the intratidal compliance profile at different PEEP levels in children with healthy lungs: a prospective, crossover study. British Journal of Anaesthesia, 2020 November. DOI: 10.1016/j.bja.2020.06.046
  • Lee et al. Deep learning models for the prediction of intraoperative hypotension. Br J Anaesth. 2021 Apr;126(4):808-817. DOI: 10.1016/j.bja.2020.12.035
  • Smith et al. Clinical application of a model-based cardiac stroke volume estimation method. IFAC-PapersOnLine 2020. DOI: 10.1016/j.ifacol.2020.12.435 
  • Gambino et al. HCI for biomedical decision-making: From diagnosis to therapy. J Biomed Inform. 2020 Nov;111:103593. doi: 10.1016/j.jbi.2020.103593.
  • Yang et al. A Deep Learning Method for Intraoperative Age-agnostic and Disease-specific Cardiac Output Monitoring from Arterial Blood Pressure. IEEE 2020
  • Smith et al. Comparison of Response Times of Anaesthetists to a Simulated Triple Low Event. University of Sydney 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. DOI: DOI: 10.1038/s41598-019-53760-8
  • 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. DOI: 10.1111/pan.13769
  • Cho et al. Effects of ischaemic conditioning on tissue oxygen saturation and heart rate variability: an observational study. JIMR. 2019. DOI: 10.1177/0300060519851656
  • 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
  • Lee et al. 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
  • Anaclet C, Griffith K, Fuller PM. Activation of the GABAergic Parafacial Zone Maintains Sleep and Counteracts the Wake-Promoting Action of the Psychostimulants Armodafinil and Caffeine. Neuropsychopharmacology. 2018 Jan;43(2):415-425. DOI:10.1038/npp.2017.152
  • LEE H.C. 프로포폴과 레미펜타닐 목표 농도 주입 중 이중분광지수의 예측 - 딥러닝 접근법. 서울대학교 대학원 의학 박사 학위논문. 2018.08