Modified thoracic impedance plethysmography to monitor sleep apnea syndromes.

BACKGROUND AND PURPOSE In order to identify sleep disorders by thoracic impedance plethysmography (TIP), we propose several new techniques: the application of an adaptive filter, a scaled Fourier linear combiner (SFLC) to eliminate cardiac-derived fluctuation in the impedance waveform, and the use of heart rate variability (HRV) to ascertain whether the airflow is obstructed. PATIENTS AND METHODS Laboratory simulation experiments on four healthy individuals and actual overnight measurements on five patients with sleep disorders were carried out. Amplified thoracic impedance change (DeltaZ), ECG, a phonocardiograph, a pneumotachograph and a standard polysomnograph were recorded. The SFLC was applied to DeltaZ to selectively extract the cardiac-synchronous component (DeltaZ(CSC)), and the remainder of the waveform (DeltaZ(REM)) was low-pass filtered to estimate the waveform driven by respiration. The HRV was divided into respiratory synchronous (HRV(R)) and low frequency (HRV(L)) components. RESULTS The SFLC could drastically extract DeltaZ(CSC) from DeltaZ and thereby demonstrate a DeltaZ(REM) pattern quite similar to the flow-volume curve of the pneumotachograph. Central sleep apnea could be identified as the cessation of DeltaZ(REM) and concomitant attenuation of HRV(R). Obstructive sleep apnea could be identified as the maintenance of rhythmic but attenuated variations of DeltaZ(REM) accompanied by asynchronous fluctuation of HRV(R) against DeltaZ(REM). Central hypopnea could be identified as a normal but attenuated waveform in both DeltaZ(REM) and HRV(R). A large fluctuation in HRV(L) was observed during repetitive appearances of apnea/hypopnea in the nocturnal experiments. CONCLUSION The modified TIP together with HRV provides a superior tool for accurate and convenient definition of sleep apnea syndromes.

[1]  E S Schelegle,et al.  An overview of the anatomy and physiology of slowly adapting pulmonary stretch receptors. , 2001, Respiration physiology.

[2]  A. Murray,et al.  Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings , 2002, Medical and Biological Engineering and Computing.

[3]  M. Hilton,et al.  Evaluation of frequency and time-frequency spectral analysis of heart rate variability as a diagnostic marker of the sleep apnoea syndrome , 1999, Medical & Biological Engineering & Computing.

[4]  W. Flemons,et al.  Obstructive Sleep Apnea , 2002 .

[5]  T. Yambe,et al.  Validity of the adaptive filter for accurate measurement of cardiac output in impedance cardiography. , 2004, The Tohoku journal of experimental medicine.

[6]  F. Plum Handbook of Physiology. , 1960 .

[7]  M. Tobin Sleep-disordered breathing, control of breathing, respiratory muscles, pulmonary function testing in AJRCCM 2003. , 2002, American journal of respiratory and critical care medicine.

[8]  A J Block,et al.  Indications and standards for cardiopulmonary sleep studies. , 1985, Sleep.

[9]  R. Patterson,et al.  Development and evaluation of an impedance cardiac output system. , 1966, Aerospace medicine.

[10]  V. Larsen,et al.  Impedance pneumography for long-term monitoring of respiration during sleep in adult males. , 1984, Clinical physiology.

[11]  T. Young,et al.  The occurrence of sleep-disordered breathing among middle-aged adults. , 1993, The New England journal of medicine.

[12]  L. E. Baker,et al.  THE MEASUREMENT OF RESPIRATORY VOLUMES IN ANIMALS AND MAN WITH USE OF ELECTRICAL IMPEDANCE , 1970 .

[13]  H. Nishino,et al.  Effect of exercise intensity on the spectral properties of skin blood flow. , 1994, The Japanese journal of physiology.

[14]  S. Quan,et al.  Sleep disordered breathing. , 2002, The Nursing clinics of North America.

[15]  Atul Malhotra,et al.  Automated detection of obstructive sleep-disordered breathing events using peripheral arterial tonometry and oximetry , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[16]  C. Hunt,et al.  Comparison of respiratory inductive plethysmography and thoracic impedance for apnea monitoring. , 1987, The Journal of pediatrics.

[17]  E. Phillipson,et al.  Respiratory-related heart rate variability persists during central apnea in dogs: mechanisms and implications. , 1995, Journal of applied physiology.

[18]  T. Welte,et al.  Central sleep apnoea syndrome in patients with chronic heart disease: a critical review of the current literature , 2002, Thorax.

[19]  K. Murphy,et al.  Respiratory modulation of left ventricular stroke volume in man measured using pulsed Doppler ultrasound. , 1987, The Journal of physiology.

[20]  M. Turiel,et al.  Power Spectral Analysis of Heart Rate and Arterial Pressure Variabilities as a Marker of Sympatho‐Vagal Interaction in Man and Conscious Dog , 1986, Circulation research.

[21]  I. L. Freeston,et al.  Methods of filtering the heart-beat artefact from the breathing waveform of infants obtained by impedance pneumography , 2006, Medical and Biological Engineering and Computing.

[22]  Daniel J Buysse,et al.  Sleep–Related Breathing Disorders in Adults: Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research , 2000 .

[23]  Y. Yasuda,et al.  Filtering noncorrelated noise in impedance cardiography , 1995, IEEE Transactions on Biomedical Engineering.

[24]  J. Fleiss,et al.  Frequency Domain Measures of Heart Period Variability and Mortality After Myocardial Infarction , 1992, Circulation.

[25]  J. Hirsch,et al.  Respiratory sinus arrhythmia in humans: how breathing pattern modulates heart rate. , 1981, The American journal of physiology.