Bedside Big Data, multiorgan variability and complex systems science

Patents

  1. Townsend D, Herry CL, Bravi A, et al. (2015) Method for Multi-Scale Quality Assessment for Variability Analysis.
  2. Seely AJE (2014) Method and apparatus for monitoring physiological parameter variability over time for one or more organs.
  3. Seely AJE (2010) Method and apparatus for multiple patient parameter variability analysis and display.

Book Chapters

  1. Herry CL, Scales NB, Newman KD, Seely AJE (2018) Transforming Monitoring and Improving Care with Variability-Derived Clinical Decision Support. In : Sturmberg JP, editor(s). Putting Systems and Complexity Sciences Into Practice: Sharing the Experience. pp 73-82.
  2. Seely AJE, Newman KD, Herry C (2016) Monitoring Variability and Complexity at the Bedside. In: Sturmberg JP (ed) The Value of Systems and Complexity Sciences for Healthcare. Springer International Publishing, pp 91-105
  3. Alam MB, Jones G, Seely AJE, Kamath WFSP and MV (2016) Heart Rate Variability in the Intensive Care Unit. Heart Rate Variability (HRV) Signal Analysis: Clinical Applications. doi
  4. Herry CL, Green GC, Bravi A, Seely AJ (2013) Continuous Multiorgan Variability Monitoring in Critically Ill Patients: Complexity Science at the Bedside. In: Handbook of Systems and Complexity in Health. Springer New York, pp 467-481

Journal articles

  1. Brodeur N, Notley SR, Kenny GP, et al. (2023). Continuous Monitoring of Entropy Production and Entropy Flow in Humans Exercising under Heat Stress. Entropy. 25(9):1290. doi
  2. Herry CL, Frasch M, Seely AJ, Wu H (2017) Heart beat classification from single-lead ECG using the synchrosqueezing transform. Physiol Meas 38:171. doi
  3. Sturmberg JP, Bennett JM, Picard M, Seely AJE (2015) The trajectory of life. Decreasing physiological network complexity through changing fractal patterns. Front Physiol 6:169. doi
  4. Herry CL, Townsend D, Green GC, et al (2014) Segmentation and classification of capnograms: application in respiratory variability analysis. Physiol Meas 35:2343-2358. doi
  5. Seely AJE, Newman KD, Herry CL (2014) Fractal Structure and Entropy Production within the Central Nervous System. Entropy 16:4497-4520. doi
  6. Seely AJE (2014) Data intelligence is the future of monitoring. J Clin Monit Comput 28:325-327. doi
  7. Seely AJ (2013) Embracing the certainty of uncertainty: implications for health care and research. Perspect Biol Med 56:65-77. doi
  8. Bravi A, Green G, Herry C, et al (2013) Do physiological and pathological stresses produce different changes in heart rate variability? Front Physiol 4:197. doi
  9. Seely AJE, Macklem P (2012) Fractal variability: an emergent property of complex dissipative systems. Chaos 22:013108. doi
  10. Bravi A, Longtin A, Seely AJE (2011) Review and classification of variability analysis techniques with clinical applications. Biomed Eng Online 10:90. doi
  11. Seely AJE, Kauffman SA, Bates JHT, et al (2011) Proceedings from the Montebello Round Table Discussion. Second annual conference on Complexity and Variability discusses research that brings innovation to the bedside. Journal of Critical Care 26:325-327. doi
  12. Seely AJE (2011) What is the meaning and origin of complex biologic variability? Journal of Critical Care 26:e28-e29. doi
  13. Seely AJE (2011) Complexity at the bedside. Journal of Critical Care 26:323-324. doi
  14. Seely AJE, Macklem PT, Suki B, et al (2010) The Wakefield roundtable discussion on complexity and variability at the bedside. Journal of Critical Care 25:536-537. doi
  15. Macklem PT, Seely A (2010) Towards a definition of life. Perspect Biol Med 53:330-340. doi
  16. Seely AJE (2005) Uncertainty and variability: complexity at the bedside. Journal of Critical Care 20:388-389. doi
  17. Seely AJ, Macklem PT (2004) Complex systems and the technology of variability analysis. Critical Care 8:R367. doi

Conference papers

  1. Bravi A, Herry C, Townsend D, et al (2013) Towards the identification of independent measures of heart rate variability. Journal of Critical Care 28:e16. doi
  2. Bravi A, Herry C, Seely A, Longtin A (2013) What is the meaning of a measure of heart rate variability? Journal of Critical Care 28:e38-e39.
  3. Bravi A, Green G, Longtin A, Seely AJE (2012) The problem of parameter estimation within variability analysis: Generalization through optimization. Journal of Critical Care 27:e6. doi
  4. Bravi A, Green G, Longtin A, Seely AJE (2012) Development of a composite variability metric for clinical application. Journal of Critical Care 27:e2. doi
  5. Familil AF, Liu Z, Bravi A, Seely A (2012) Searching for patterns in clinical data: Choosing the right data mining approach. In: Proceedings of the 1st International Workshop on Artificial Intelligence and NetMedicine p 51
  6. Seely AJE, Green GC, Bravi A (2011) Continuous Multiorgan Variability monitoring in critically ill patients — Complexity science at the bedside. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society pp 5503-5506
  7. Seely AJE (2010) Variability and entropy. Journal of Critical Care 25:e3. doi
  8. Seely AJE (2010) Weaning and variability evaluation. Journal of Critical Care 25:e11. doi

Artificial Intelligence at the bedside: predicting patient outcomes

Predicting Extubation Outcomes

Patents

  1. Bravi A, Herry CL, Seely AJE (2017) System and method for providing multi-organ variability decision support for extubation management.

Journal articles

  1. Zheng Z, Kumar S, Sarti A, et al. (2022) Economic feasibility of a novel tool to assist extubation decision-making: an early health economic modeling. International Journal of Technology Assessment in Health Care. doi
  2. Sarti A, Zheng K, Herry CL, et al. (2021) Feasibility of Implementing Extubation Advisor, a Clinical Decision Support Tool to Improve Extubation Decision-Making in the ICU: a Mixed-Methods Observational Study. BMJ Open. 11 (8), e045674. doi
  3. Godard S, Herry C, Westergaard P, et al (2016) Practice Variation in Spontaneous Breathing Trial Performance and Reporting. Can Respir J 2016:9848942. doi
  4. Seely AJE, Bravi A, Herry C, et al (2014) Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients? Crit Care 18:R65. doi

Predicting deterioration in patients and risk stratification

Journal articles

  1. Fernando SM, Barnaby DP, Herry CL, et al. (2019) Predictive performance of the quick Sepsis-related Organ Failure Assessment in a population of emergency department patients with sepsis. European Journal of Emergency Medicine 26(1):71-73. doi
  2. Barnaby DP, Fernando SM, Herry CL, et al. (2019) Heart Rate Variability, Clinical and Laboratory Measures to Predict Future Deterioration in Patients Presenting with Sepsis. Shock (Augusta, Ga.). 51(4):416-422. doi
  3. Fernando SM, Rochwerg B, Seely AJE (2018) Clinical implications of the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). CMAJ 190:E1058-E1059. doi
  4. Fernando SM, Reardon PM, Rochwerg B, et al (2018) Sepsis-3 Septic Shock Criteria and Associated Mortality Among Infected Hospitalized Patients Assessed by a Rapid Response Team. Chest 154:309-316. doi
  5. Fernando SM, Rochwerg B, Reardon PM, et al (2018) Emergency Department disposition decisions and associated mortality and costs in ICU patients with suspected infection. Critical Care 22:172. doi
  6. Fernando SM, Tran A, Taljaard M, et al (2018) Prognostic Accuracy of the Quick Sequential Organ Failure Assessment for Mortality in Patients With Suspected Infection: A Systematic Review and Meta-analysis. Ann Intern Med 168:266-275. doi
  7. Barnaby DP, Fernando SM, Ferrick KJ, et al (2018) Use of the low-frequency/high-frequency ratio of heart rate variability to predict short-term deterioration in emergency department patients with sepsis. Emerg Med J 35:96-102. doi
  8. Fernando S, Reardon P, Van Katwyk S, et al (2018) Outcomes and costs of patients with sepsis transferred to a tertiary care intensive care unit. Critical Care Medicine 46:690. doi
  9. Fernando SM, Barnaby DP, Herry CL, et al (2018) Helpful Only When Elevated: Initial Serum Lactate in Stable Emergency Department Patients with Sepsis Is Specific, but Not Sensitive for Future Deterioration. J Emerg Med 54:766-773. doi
  10. Seely AJE (2016) Prediction Is Difficult, Especially About Future Unexpected Deterioration. Crit Care Med 44:1781-1783. doi
  11. Arnold R, Green G, Bravi A, et al (2012) Impaired heart rate variability predicts clinical deterioration and progressive organ failure in emergency department sepsis patients. Crit Care 16:P37. doi
  12. Arnold, RC, Green, Geoffrey, Glaspey, L, et al (2010) Continuous heart rate predicts worsening organ failure and mortality in sepsis. Shock 33:14. doi

Conference papers

  1. Fernando SM, Rochwerg B, Reardon PM, et al (2018) LO51: Increased mortality and costs in emergency department sepsis patients with delayed intensive care unit admission. Canadian Journal of Emergency Medicine 20:S24-S24. doi
  2. Fernando SM, Barnaby DP, Herry CL, Seely AJ (2017) PL04: Initial serum lactate predicts deterioration in emergency department patients with sepsis. Canadian Journal of Emergency Medicine 19:S27-S27. doi
  3. Arnold R, Lundy D, Glaspey L, et al (2009) Heart rate variability in the early resuscitation of septic shock. Critical Care 13:P50. doi

Early warning of sepsis in at risk populations

Journal articles

  1. Buchan CA, Oi-Yee Li H, Herry CL, et al. (2022) Early Warning of Infection in Patients Undergoing Hematopoietic Stem Cell Transplantation Using Heart Rate Variability and Serum Biomarkers. Transplantation and Cellular Therapy. 28(3). doi
  2. Buchan CA, Bravi A, Seely AJE (2012) Variability analysis and the diagnosis, management, and treatment of sepsis. Curr Infect Dis Rep 14:512-521. doi
  3. Bravi A, Green G, Longtin A, Seely AJE (2012) Monitoring and identification of sepsis development through a composite measure of heart rate variability. PLoS ONE 7:e45666. doi
  4. Ahmad S, Ramsay T, Huebsch L, et al (2009) Continuous multi-parameter heart rate variability analysis heralds onset of sepsis in adults. PLoS ONE 4:e6642. doi
  5. Ahmad S, Tejuja A, Newman KD, et al (2009) Clinical review: a review and analysis of heart rate variability and the diagnosis and prognosis of infection. Crit Care 13:232. doi
  6. Seely AJE (2006) Heart rate variability and infection: Diagnosis, prognosis, and prediction. Journal of Critical Care 21:286-289. doi

Severity of illness in the ICU

Journal articles

  1. Martin S, Du Pont-Thibodeau G, Seely AJ, et al. (2022) Heart Rate Variability in Children with Moderate and Severe Traumatic Brain Injury: A Prospective Observational Study. Journal of Pediatric Intensive Care. doi
  2. Typpo KV, Wong HR, Finley SD, et al (2017) Monitoring Severity of Multiple Organ Dysfunction Syndrome: New Technologies. Pediatr Crit Care Med 18:S24-S31. doi
  3. Green GC, Bradley B, Bravi A, Seely AJE (2013) Continuous multiorgan variability analysis to track severity of organ failure in critically ill patients. J Crit Care 28:879.e1-11. doi
  4. Bradley BD, Green G, Ramsay T, Seely AJE (2013) Impact of sedation and organ failure on continuous heart and respiratory rate variability monitoring in critically ill patients: a pilot study. Crit Care Med 41:433-444. doi
  5. Bradley B, Green GC, Batkin I, Seely AJE (2012) Feasibility of continuous multiorgan variability analysis in the intensive care unit. J Crit Care 27:218.e9-20. doi
  6. Seely AJE, Christou NV (2000) Multiple organ dysfunction syndrome: Exploring the paradigm of complex nonlinear systems. Critical Care Medicine 28:2193.

Conference papers

  1. Bradley B, Green G, Yadav R, et al (2011) The feasibility of continuous heart and respiratory rate variability analysis in the intensive care unit: A pilot investigation. Journal of Critical Care 26:e5. doi
  2. Bradley, Beverley, Green, Geoffrey, Yadav, Rajeev, et al (2010) Impact of Sedation on Continuous Heart and Respiratory Rate Variability Monitoring in Critically Ill Patients. Critical Care Medicine 38:

Predicting organ donor suitability and physiology at the end of life

Patents

  1. Seely AJE, Dhanani S, Scales NB, et al. (2017) System and Method for Assisting Decisions Associated with Events Relative to Withdrawal of Life-Sustaining Therapy Using Variability Measurements.

Journal articles

  1. Scales NB, Herry CL, van Beinum A, et al. (2022) Predicting Time to Death After Withdrawal of Life-Sustaining Measures Using Vital Sign Variability: Derivation and Validation. Crit Care Explor. 4(4):e0675. doi
  2. Dhanani S, Hornby L, van Beinum A, et al. (2021) Resumption of Cardiac Activity after Withdrawal of Life-Sustaining Measures. New England Journal of Medicine 384(4):345-352. doi

Variability monitoring

Autonomic Nervous System function and ability to dissipate heat during environmental and physical stresses

Journal articles

  1. Carrillo AE, Akerman AP, Notley SR, et al. (2023) Cardiac autonomic modulation in individuals with controlled and uncomplicated hypertension during exercise-heat stress. Appl Physiol Nutr Metab. doi
  2. De Barros JA, Macartney M., Peoples GE, et al. (2022) The impact of age, type 2 diabetes and hypertension on heart rate variability during rest and exercise at increasing levels of heat stress. Eur J Appl Physiol 122:1249–1259. doi
  3. De Barros JA, Macartney M., Peoples GE, et al. (2022) Effects of sex and wet-bulb globe temperature on heart rate variability during prolonged moderate-intensity exercise: A secondary analysis. Applied Physiology, Nutrition, and Metabolism. doi
  4. Macartney MJ, Notley SR, Herry C, et al. (2020) Effect of exercise-heat acclimation on cardiac autonomic modulation in Type 2 Diabetes: a pilot study. Applied Physiology, Nutrition, and Metabolism. doi
  5. Kaltsatou A, Flouris AD, Herry CL, et al. (2020) Heart rate variability in older workers during work under the Threshold Limit Values for heat exposure. American Journal of Industrial Medicine. 63(9):787-795. doi
  6. Macartney MJ, Notley SR, Meade RD, et al. (2020) Heart rate variability in older men on the day following prolonged work in the heat. Journal of Occupational and Environmental Hygiene. 17(9):383-389. doi
  7. Macartney MJ, Notley SR, Herry CL, et al. (2020) Cardiac autonomic modulation in type 1 diabetes during exercise-heat stress. Acta Diabetologica. 1-5. doi
  8. Macartney MJ, Notley SR, Herry CL, et al. (2020) Diminished heart rate variability in type 2 diabetes is exacerbated during exercise-heat stress. Acta Diabetologica. 1-3. doi
  9. Kaltsatou A, Flouris AD, Herry CL, et al. (2020) Age differences in cardiac autonomic regulation during intermittent exercise in the heat. European Journal of Applied Physiology 1-13. doi
  10. Carrillo AE, Flouris AD, Herry CL, et al. (2019) Age-related reductions in heart rate variability do not worsen during exposure to humid compared to dry heat: A secondary analysis. Temperature. doi
  11. Macartney ML, Meade RD, Notley SR, et al. (2019) Fluid Loss during Exercise-Heat Stress Reduces Cardiac Vagal Autonomic Modulation. Medicine and Science in Sports and Exercise. doi
  12. Flouris AD, Friesen BJ, Herry CL, et al. (2019) Heart rate variability dynamics during treatment for exertional heat strain when immediate response is not possible. Experimental physiology. doi
  13. Leicht AS, Flouris AD, Kaltsatou A, et al (2018) Age alters cardiac autonomic modulations during and following exercise-induced heat stress in females. Temperature 5:184-196. doi
  14. Kenny GP, Poirier MP, Metsios GS, et al (2017) Hyperthermia and cardiovascular strain during an extreme heat exposure in young versus older adults. Temperature 4:79-88. doi
  15. Carrillo AE, Flouris AD, Herry CL, et al (2016) Heart rate variability during high heat stress: a comparison between young and older adults with and without Type 2 diabetes. Am J Physiol Regul Integr Comp Physiol 311:R669-R675. doi
  16. Flouris AD, Poirier MP, Bravi A, et al (2014) Changes in heart rate variability during the induction and decay of heat acclimation. Eur J Appl Physiol 114:2119-2128. doi
  17. Flouris AD, Bravi A, Wright-Beatty HE, et al (2014) Heart rate variability during exertional heat stress: effects of heat production and treatment. Eur J Appl Physiol 114:785-792. doi
  18. Barrera-Ramirez J, Bravi A, Green G, et al (2013) Comparison of heart and respiratory rate variability measures using an intermittent incremental submaximal exercise model. Appl Physiol Nutr Metab 38:1128-1136. doi
  19. Armstrong RG, Ahmad S, Seely AJ, Kenny GP (2012) Heart rate variability and baroreceptor sensitivity following exercise-induced hyperthermia in endurance trained men. Eur J Appl Physiol 112:501-511. doi
  20. Armstrong RG, Kenny GP, Green G, Seely AJE (2011) Diurnal variation in heart rate variability before and after maximal exercise testing. Chronobiol Int 28:344-351. doi
  21. Armstrong RG, Seely AJ, Kilby D, et al (2010) Cardiovascular and thermal responses to repeated head-up tilts following exercise-induced heat stress. Aviat Space Environ Med 81:646-653.
  22. Ahmad S, Seely A (2007) Cardiopulmonary variability during staged incremental exercise using a novel continuous individualized multiorgan variability analysis system. Journal of Critical Care 22:338-339. doi

Non-invasive fetal health monitoring

Journal Articles

  1. Castel A, Burns P, Wakefield C, et al. (2024) Neonatal Sepsis Is Diminished by Cervical Vagus Nerve Stimulation and Tracked Noninvasively by ECG: A Pilot Report and Dataset in the Piglet Model. In: Frasch, M.G., Porges, E.C. (eds) Vagus Nerve Stimulation. Neuromethods 205. Humana, New York, NY. doi
  2. Herry CL, Soares HMF, Schuler-Faccini L, et al. (2021) Machine learning model on heart rate variability metrics identifies asymptomatic toddlers exposed to Zika virus during pregnancy. Physiological Measurement. doi
  3. Gold N, Herry CL, Wang X, et al. (2021) Fetal Cardiovascular Decompensation During Labor Predicted From the Individual Heart Rate Tracing: A Machine Learning Approach in Near-Term Fetal Sheep Model. Frontiers in Pediatrics. 9:355. doi
  4. Frasch MG, Walter B, Herry CL, et al. (2021) Multimodal pathophysiological dataset of gradual cerebral ischemia in a cohort of juvenile pigs. Scientific Data 8(1):1-12. doi
  5. Frasch MG, Herry CL, Niu Y, et al. (2020) First evidence that intrinsic fetal heart rate variability exists and is affected by chronic hypoxia. The Journal of Physiology 598(2):249-263 doi
  6. Herry CL, Burns P, Desrochers A, et al. (2019) Vagal contributions to fetal heart rate variability: an omics approach. Physiological Measurement doi
  7. Frasch MG, Lobmaier SM, Stampalija T, et al (2018) Non-invasive biomarkers of fetal brain development reflecting prenatal stress: An integrative multi-scale multi-species perspective on data collection and analysis. Neurosci Biobehav Rev. doi
  8. Shen C, Frasch MG, Wu HT, et al (2018) Non-invasive acquisition of fetal ECG from the maternal xyphoid process: a feasibility study in pregnant sheep and a call for open data sets. Physiol Meas 39:035005. doi
  9. Herry CL, Frasch M, Seely AJ, Wu H (2017) Heart beat classification from single-lead ECG using the synchrosqueezing transform. Physiol Meas 38:171. doi
  10. Liu HL, Garzoni L, Herry C, et al (2016) Can Monitoring Fetal Intestinal Inflammation Using Heart Rate Variability Analysis Signal Incipient Necrotizing Enterocolitis of the Neonate? Pediatr Crit Care Med 17:e165-176. doi
  11. Herry CL, Cortes M, Wu H-T, et al (2016) Temporal Patterns in Sheep Fetal Heart Rate Variability Correlate to Systemic Cytokine Inflammatory Response: A Methodological Exploration of Monitoring Potential Using Complex Signals Bioinformatics. PLoS ONE 11:e0153515. doi
  12. Durosier LD, Herry CL, Cortes M, et al (2015) Does heart rate variability reflect the systemic inflammatory response in a fetal sheep model of lipopolysaccharide-induced sepsis? Physiol Meas 36:2089-2102. doi
  13. Li X, Xu Y, Herry C, et al (2015) Sampling frequency of fetal heart rate impacts the ability to predict pH and BE at birth: a retrospective multi-cohort study. Physiol Meas 36:L1-12. doi
  14. Frasch MG, Xu Y, Stampalija T, et al (2014) Correlating multidimensional fetal heart rate variability analysis with acid-base balance at birth. Physiol Meas 35:L1-12. doi
  15. Durosier LD, Green G, Batkin I, et al (2014) Sampling rate of heart rate variability impacts the ability to detect acidemia in ovine fetuses near-term. Front Pediatr 2:38. doi

Conference papers

  1. Liu H, Garzoni L, Herry C, et al (2016) Low-Dose Endotoxin (LPS) Exposure Induces M1-Specific Macrophage Inflammation and Down Regulates Occludin in Terminal Ileum of Near-Term Ovine Fetuses: Relation to Heart Rate Variability (HRV) as Potential Predictive Marker of Incipient Necrotizing Enterocolitis (NEC). In: Reproductive Sciences. Sage Publications Inc. pp 144A-144A
  2. Li X, Xu Y, Herry C, et al (2015) Sampling Rate of Fetal Heart Rate (FHR) Impacts the Ability To Predict pH and BE at Birth: Retrospective Cohort Study. In: Reproductive Sciences. Sage Publications Inc. pp 270A-271A
  3. Gold N, Wang X, Herry C, Frasch M (2015) Prediction of Fetal Cardiovascular Decompensation During Labour From Heart Rate Variability: Validation in Fetal Sheep Model of Human Labour. In: Reproductive Sciences. Sage Publications Inc. pp 269A-270A
  4. Liu H, Garzoni L, Durosier L, et al (2014) Decoding Fetal Brain-Gut Communication during Systemic Inflammation. In: Reproductive Sciences. Sage Publications Inc. pp 174A-174A
  5. Durosier LD, Xu A, Matushewski B, et al (2013) Neural signature of cerebral activity of the fetal cholinergic anti-inflammatory pathway derived from heart rate variability. The FASEB Journal 27:926-11.
  6. Durosier LD, Cao M, Herry C, et al (2013) A signature of fetal systemic inflammatory response in the pattern of heart rate variability measures matrix: a prospective study in fetal sheep model of lipopolysaccharide (LPS)-induced sepsis. The FASEB Journal 27:926-8.
  7. Durosier LD, Cao M, Batkin I, et al (2013) Continuous multivariate electronic fetal monitoring during labor detects early onset of acidemia: Prospective study in fetalsheep model. Journal of Critical Care 28:e5-e6.
  8. Durosier D, Stampalija T, Herry C, et al (2013) Fetal Heart Rate Variability (fHRV) Analysis in Early Labor May Identify Neonatal Severe Acidemia: Come-Back of Fetal Trans-Abdominal ECG. In: Reproductive Sciences. Sage Publications Inc. pp 218A-219A
  9. Durosier DL, Siontas D, Cao M, et al (2013) The timing and degree of fetal systemic and cerebral inflammatory responses correlate with a subset of heart rate variability measures: Evidence of cholinergic anti-inflammatory pathway activation in sepsis. Journal of critical care 28:e25-e25.
  10. Durosier D, Cao M, Green G, et al (2012) Continuous fetal heart rate variability analysis allows for early detection of hypoxic-acidemia near-term. In: Reproductive Sciences. Sage Publications Inc. 19(S3): p. 253A.

Assessment of Cerebral Autoregulation after Sub-Arachnoid Hemorrhage: Variability in Transcranial Doppler signals

Journal articles

  1. Rodriguez RA, Herry CL, English SW, et al. (2021) Variability Predictors of Vasospasm in Subarachnoid Hemorrhage: A Feasibility Study. Canadian Journal of Neurological Sciences 48(2):226-232.

Other Projects

Journal articles

  1. Brunet J, Wurz A, Hussien J, et al. (2022) Exploring the Effects of Yoga Therapy on Heart Rate Variability and Patient-Reported Outcomes After Cancer Treatment: A Study Protocol. Integrative Cancer Therapies. doi
  2. Vézina-Audette R, Herry C, Burns P, et al (2016) Heart rate variability in relation to stress in the Asian elephant (Elephas maximus). Can Vet J 57:289-292.
  3. Gold N, Frasch MG, Herry CL, et al (2017) A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals. Front Physiol 8:1112. doi
  4. Wu J, Li J, Seely A, et al (2017) Chronotropic Competence Indices Extracted from Wearable Sensors for Cardiovascular Diseases Management. Sensors (Basel). doi
  5. Kellett J, Li M, Rasool S, et al (2011) Comparison of the heart and breathing rate of acutely ill medical patients recorded by nursing staff with those measured over 5 min by a piezoelectric belt and ECG monitor at the time of admission to hospital. Resuscitation 82:1381-1386. doi