A portable respiratory rate estimation system with a passive single-lead electrocardiogram acquisition module.

BACKGROUND Among vital signs of acutely ill hospital patients, respiratory rate (RR) is a highly accurate predictor of health deterioration. OBJECTIVE This study proposes a system that consists of a passive and non-invasive single-lead electrocardiogram (ECG) acquisition module and an ECG-derived respiratory (EDR) algorithm in the working prototype of a mobile application. METHOD Before estimating RR that produces the EDR rate, ECG signals were evaluated based on the signal quality index (SQI). The SQI algorithm was validated quantitatively using the PhysioNet/Computing in Cardiology Challenge 2011 training data set. The RR extraction algorithm was validated by adopting 40 MIT PhysioNet Multiparameter Intelligent Monitoring in Intensive Care II data set. RESULTS The estimated RR showed a mean absolute error (MAE) of 1.4 compared with the ``gold standard'' RR. The proposed system was used to record 20 ECGs of healthy subjects and obtained the estimated RR with MAE of 0.7 bpm. CONCLUSION Results indicate that the proposed hardware and algorithm could replace the manual counting method, uncomfortable nasal airflow sensor, chest band, and impedance pneumotachography often used in hospitals. The system also takes advantage of the prevalence of smartphone usage and increase the monitoring frequency of the current ECG of patients with critical illnesses.

[1]  J. Stocks,et al.  Validation of respiratory inductive plethysmography using the Qualitative Diagnostic Calibration method in anaesthetized infants. , 1998, The European respiratory journal.

[2]  D. Goldhill,et al.  Physiological values and procedures in the 24 h before ICU admission from the ward , 1999, Anaesthesia.

[3]  Laura. Mason Laura,et al.  Signal processing methods for non-invasive respiration monitoring , 2002 .

[4]  George B Moody,et al.  PhysioNet: an NIH research resource for complex signals. , 2003, Journal of electrocardiology.

[5]  Michael Norris,et al.  Design and development of medical electronic instrumentation : a practical perspective of the design, construction, and test of medical devices , 2004 .

[6]  Patrick E. McSharry,et al.  Advanced Methods And Tools for ECG Data Analysis , 2006 .

[7]  Qinghua Zhang,et al.  An Algorithm for Robust and Efficient Location of T-Wave Ends in Electrocardiograms , 2006, IEEE Transactions on Biomedical Engineering.

[8]  Ciara O'Brien,et al.  A comparison of algorithms for estimation of a respiratory signal from the surface electrocardiogram , 2007, Comput. Biol. Medicine.

[9]  K. Hillman,et al.  The objective medical emergency team activation criteria: a case-control study. , 2007, Resuscitation.

[10]  George B. Moody,et al.  Derivation of Respiratory Signals from Multi-lead ECGs , 2008 .

[11]  T. Rea,et al.  Prediction of critical illness during out-of-hospital emergency care. , 2010, JAMA.

[12]  T. H. Kyaw,et al.  Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database* , 2011, Critical care medicine.

[13]  G. Clifford,et al.  Wireless technology in disease management and medicine. , 2012, Annual review of medicine.

[14]  Lionel Tarassenko,et al.  Data fusion for estimating respiratory rate from a single-lead ECG , 2013, Biomed. Signal Process. Control..

[15]  Qiao Li,et al.  ECG Signal Quality During Arrhythmia and Its Application to False Alarm Reduction , 2013, IEEE Transactions on Biomedical Engineering.

[16]  Ruben Amarasingham,et al.  Predicting out of intensive care unit cardiopulmonary arrest or death using electronic medical record data , 2013, BMC Medical Informatics and Decision Making.

[17]  Eric T. Carlson,et al.  Development of three methods for extracting respiration from the surface ECG: a review. , 2014, Journal of electrocardiology.