Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13737, first published .
Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Journals

  1. Prinable J, Jones P, Boland D, McEwan A, Thamrin C. Derivation of Respiratory Metrics in Health and Asthma. Sensors 2020;20(24):7134 View
  2. Hejjel L, Béres S. Comment on ‘Pulse rate variability in cardiovascular health: a review on its applications and relationship with heart rate variability’. Physiological Measurement 2021;42(1):018001 View
  3. Tsang K, Pinnock H, Wilson A, Shah S. Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review. Journal of Asthma and Allergy 2022;Volume 15:855 View
  4. Reali P, Lolatto R, Coelli S, Tartaglia G, Bianchi A. Information Retrieval from Photoplethysmographic Sensors: A Comprehensive Comparison of Practical Interpolation and Breath-Extraction Techniques at Different Sampling Rates. Sensors 2022;22(4):1428 View
  5. Natarajan A, Su H, Heneghan C, Blunt L, O’Connor C, Niehaus L. Measurement of respiratory rate using wearable devices and applications to COVID-19 detection. npj Digital Medicine 2021;4(1) View
  6. Kumar A, Ritam M, Han L, Guo S, Chandra R. Deep learning for predicting respiratory rate from biosignals. Computers in Biology and Medicine 2022;144:105338 View
  7. Kavitha K, Monisha M, Nischitha M, Nisha M, Raksitha A. Asthma prediction and monitoring. Indian Journal of Allergy, Asthma and Immunology 2023;37(2):33 View