This paper is in the following e-collection/theme issue:

Machine Learning (1965) Cardiology (214)

Published on in Vol 8, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18715, first published .
Machine Learning Model Based on Transthoracic Bioimpedance and Heart Rate Variability for Lung Fluid Accumulation Detection: Prospective Clinical Study

Machine Learning Model Based on Transthoracic Bioimpedance and Heart Rate Variability for Lung Fluid Accumulation Detection: Prospective Clinical Study

Machine Learning Model Based on Transthoracic Bioimpedance and Heart Rate Variability for Lung Fluid Accumulation Detection: Prospective Clinical Study

Authors of this article:

Natasa Reljin1 Author Orcid Image ;   Hugo F. Posada-Quintero1 Author Orcid Image ;   Caitlin Eaton-Robb1 Author Orcid Image ;   Sophia Binici2 Author Orcid Image ;   Emily Ensom3 Author Orcid Image ;   Eric Ding3 Author Orcid Image ;   Anna Hayes3 Author Orcid Image ;   Jarno Riistama4 Author Orcid Image ;   Chad Darling5 Author Orcid Image ;   David McManus3 Author Orcid Image ;   Ki H. Chon1 Author Orcid Image
Article Authors Cited by (11) Tweetations (3) Metrics

Journals

  1. Connaire J, Sundermann M, Perumal R, Herzog C. <p>A Novel Radiofrequency Device to Monitor Changes in Pulmonary Fluid in Dialysis Patients</p>. Medical Devices: Evidence and Research 2020;Volume 13:377 View
  2. Faust O, Hong W, Loh H, Xu S, Tan R, Chakraborty S, Barua P, Molinari F, Acharya U. Heart rate variability for medical decision support systems: A review. Computers in Biology and Medicine 2022;145:105407 View
  3. Sandys V, Sexton D, O'Seaghdha C. Artificial intelligence and digital health for volume maintenance in hemodialysis patients. Hemodialysis International 2022;26(4):480 View
  4. Sandys V, Bhat L, O'Hare E, Ninan A, Doyle K, Kelly S, Conlon P, Sexton D, Edwards C, McAleese P, O’Seaghdha C. Pilot Study of a Wearable Hydration Monitor in Haemodialysis Patients: Haemodialysis Outcomes & Patient Empowerment Study 02. Digital Biomarkers 2023:18 View
  5. Simić M, Freeborn T, Stojanović G. Smartphone-Based Parameter Estimation of the Cole-Impedance Model for Assessment of Agricultural Goods. IEEE Access 2024;12:103192 View
  6. Giménez-Miranda L, Scagliusi S, Pérez-García P, Olmo-Fernández A, Huertas G, Yúfera A, Medrano F. Wearable Devices Based on Bioimpedance Test in Heart Failure: Clinical Relevance: Systematic Review. Reviews in Cardiovascular Medicine 2024;25(9) View
  7. Andrijauskas A, Andrijauskas P, Jovaišienė I, Valaika A, Jovaisa T, Urbonas K, Činčikas D, Svediene S, Scupakova N, Puodziukaite L, Budra M, Kalinauskas G, Stankevičius E. Ask a Doctor a Question: A Clinician’s Message to the Industry. Medicina 2025;61(3):368 View
  8. Mondal A, Dutta D, Sen S, Chanda N, Mandal S. AI-IoT assisted wearable bio-impedance sensor for classification of smoking habits on Fagerstrom scale. Measurement 2025;256:118171 View

Conference Proceedings

  1. Felix Jimenez A, Belmonte I, Sanchez Lee V, Avila U, Parra Gonzalez E. 2022 11th International Conference On Software Process Improvement (CIMPS). Development of a deep learning system for preventive detection of heart failure due to pulmonary disease View
  2. Nouri H, Bouchaala D, Gargouri R, Kanoun O. 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA). Stability Analysis for Howland Current Source for Bioimpedance Measurement View
  3. Li M, Zhao S, Peng Y, Shi Y, Kang Y, Yuan T, He S, Chang W, Zhang Q, Liu Z, Zhang Z. 2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). Multi-Scale Entropy of Nocturnal Breathing from Wearable Devices to Assess Post-Discharge Outcome Differences in ADHF Patients View