Derivation of the respiratory rate signal from a single lead ECG

DERIVATION OF THE RESPIRATORY RATE SIGNAL FROM A SINGLE LEAD ECG by Murtaza M. Lakdawala It has been long established that respiration has an influence on heart rate, and this effect is called respiratory sinus arrhythmia. As a result, two inferences can be postulated: first respiration information can be derived from cardiac activity, and second this effect offers the potential of removing the respiration effect that suppresses cardiac information which is of clinical significance. As a result of research performed at NJIT, there is a significant amount of data on exercise and heart rate recovery, but not the associated respiration signal. The motivation of this research was to compare and implement an optimal ECG derived respiration program, develop an adaptive peak detector algorithm to process the complex respiration signal and produce a usable respiration rate waveform. Three methods for deriving respiration from a single lead ECG were identified and implemented in LabVIEW. The three methods were R wave amplitude modulation (RWA), R wave duration (RWD), and the multiplication of RWA and RWD signals. Data analysis was carried out by comparing actual paced breathed respiration signal with lead I ECG derived respiration of ten normal subjects. The data analysis suggests that RWA is the best method with a correlation of 0.95. Then an algorithm to derive a continuous respiration rate signal from actual respiration signal with a high level of accuracy was developed. As a result of this research a program has been developed which provides respiratory information of clinical significance from ordinary single lead ECG for situations in which ECG but respiration is not routinely monitored. DERIVATION OF THE RESPIRATORY RATE SIGNAL FROM A SINGLE LEAD ECG by Murtaza M. Lakdawala A Thesis Submitted to the Faculty of New Jersey Institute of Technology in Partial Fulfillment of the Requirements for the Degree of Master of Science in Biomedical Engineering Department of Biomedical Engineering

[1]  J. H. G..  The Physiological Basis of Medical Practice , 1938, Nature.

[2]  A. Guyton,et al.  Textbook of Medical Physiology , 1961 .

[3]  Harry Elliot Thomas,et al.  Handbook of biomedical instrumentation and measurement , 1974 .

[4]  J. Clayton.  The concept of correlation : Paul Tillich and the possibility of a mediating theology , 1980 .

[5]  J M Bland,et al.  Statistical methods for assessing agreement between two methods of clinical measurement , 1986 .

[6]  T. Findley,et al.  Derivation of respiration from electrocardiogram during heart rate variability studies , 1994, Computers in Cardiology 1994.

[7]  G. Willemsen,et al.  Ambulatory measurement of respiratory sinus arrhythmia and respiration rate , 1995, Biological Psychology.

[8]  B. Clementz,et al.  The construct validity of root-mean-square error for quantifying smooth-pursuit eye tracking abnormalities in schizophrenia , 1996, Biological Psychiatry.

[9]  D. Eckberg.  Sympathovagal balance: a critical appraisal. , 1997, Circulation.

[10]  M. Malik,et al.  Sympathovagal balance: a critical appraisal. , 1998, Circulation.

[11]  P. Sleight,et al.  Sympathovagal balance. , 1998, Circulation.

[12]  Mahendra Varman,et al.  Computer based biomedical equipment design: an EKG recorder, monitor and simulator , 1998, Proceedings. 11th IEEE Symposium on Computer-Based Medical Systems (Cat. No.98CB36237).

[13]  Solange Akselrod,et al.  R wave duration as a measure of body position changes during sleep , 1999, Computers in Cardiology 1999. Vol.26 (Cat. No.99CH37004).

[14]  L. Blatter,et al.  Intracellular Ca2+ release sparks atrial pacemaker activity. , 2001, News in physiological sciences : an international journal of physiology produced jointly by the International Union of Physiological Sciences and the American Physiological Society.

[15]  Luciano Bernardi,et al.  Modulatory effects of respiration , 2001, Autonomic Neuroscience.

[16]  Khosrow Behbehani,et al.  An investigation of the mean electrical axis angle and respiration during sleep , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[17]  D. Menicucci,et al.  Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition , 2003, q-bio/0310002.

[18]  C. Lamberti,et al.  Validation of an ECG-derived respiration monitoring method , 2003, Computers in Cardiology, 2003.

[19]  Conor Heneghan,et al.  Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea , 2003, IEEE Transactions on Biomedical Engineering.

[20]  Raghbir Singh Khandpur.  Biomedical Instrumentation: Technology and Applications , 2003 .

[21]  S. Akselrod,et al.  Electrocardiogram derived respiration during sleep , 2005, Computers in Cardiology, 2005.

[22]  P. Asselin.  Relationship between the autonomic nervous system and the recovering heart post exercise using heart rate variability , 2005 .

[23]  I. L. Freeston,et al.  Algorithms for the detection of breaths from respiratory waveform recordings of infants , 1982, Medical and Biological Engineering and Computing.

[24]  Anne Marie Petrock,et al.  Central and peripheral autonomic influences : analysis of cardio-pulmonary dynamics using novel wavelet statistical methods , 2007 .

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

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