Source: http://www.docstoc.com/docs/97548661/Sleep-Quality-Data-Collection-And-Evaluation---Patent-8002553
Timestamp: 2015-03-07 04:32:15
Document Index: 522873750

Matched Legal Cases: ['application No. 04784602', 'application No. 04784602', 'application No. 04784602', 'art 330', 'art 330', 'art 330', 'art 330']

Sleep Quality Data Collection And Evaluation - Patent 8002553
97548661
The present invention relates generally to collecting and evaluating information related to sleep quality.BACKGROUND OF THE INVENTION Sleep is generally beneficial and restorative to a patient, exerting great influence on the quality of life. The human sleep/wake cycle generally conforms to a circadian rhythm that is regulated by a biological clock. Regular periods of sleepenable the body and mind to rejuvenate and rebuild. The body may perform various tasks during sleep, such as organizing long term memory, integrating new information, and renewing tissue and other body structures. Normal sleep is characterized by a general decrease in metabolic rate, body temperature, blood pressure, breathing rate, heart rate, cardiac output, sympathetic nervous activity, and other physiological functions. However, studies have shownthat the brain's activity does not decrease significantly during sleep. Normally a patient alternates between rapid eye movement (REM) and non-REM (NREM) sleep in approximately 90 minute cycles throughout a sleep period. A typical eight hour sleepperiod may be characterized in terms of a five-step sleep cycle identifiable through EEG brain wave activity. Non-REM sleep includes four sleep states or stages that range from light dozing to deep sleep. Throughout NREM sleep, muscle activity is still functional, breathing is low, and brain activity is minimal. Approximately 85% of the sleep cycle isspent in NREM sleep. Stage 1 NREM sleep may be considered a transition stage between wakefulness and sleep. As sleep progresses to stage 2 NREM sleep, eye movements become less frequent and brain waves increase in amplitude and decrease in frequency. As sleep becomes progressively deeper, the patient becomes more difficult to arouse. Stage 3 sleep is characterized by 20 to 40% slow brain wave (delta) sleep as detected by an electroencephalogram (EEG). Sleep stages 3 and 4 are considered to be themost restful sleep stages. REM sleep is associated with
United States Patent: 8002553
8,002,553
A sleep quality assessment approach involves collecting data based on
detected physiological or non-physiological patient conditions. At least
one of detecting patient conditions and collecting data is performed
using an implantable device. Sleep quality may be evaluated using the
collected data by an implantable or patient-external sleep quality
processor. One approach to sleep quality evaluation involves computing
one or more summary metrics based on occurrences of movement disorders or
Hatlestad; John D. (Maplewood, MN), Ni; Quan (Shoreview, MN), Stahmann; Jeffrey E. (Ramsey, MN), Hartley; Jesse (Lino Lakes, MN), Zhu; Qingsheng (Little Canada, MN), Kenknight; Bruce H. (Maple Grove, MN), Daum; Douglas R. (Oakdale, MN), Lee; Kent (Fridley, MN)
10/642,998
434/262  ; 600/300
340/575-576 600/26,300,587,595,544,28 434/262-275 128/630,670-671,716,731,733 446/83
5740797
6959214
de Chazal et al.
2001/0000346
2002/0082652
2002/0138563
2002/0169384
2003/0055348
2003/0088027
2003/0139780
2004/0039605
2004/0116981
2004/0122488
2004/0176695
2005/0069322
Tegge, Jr. et al.
2005/0137645
2006/0047333
Tockmann et al.
2007/0005114
2007/0142741
2007/0239057
WO8605965
WO9301862
WO0009206
WO0017615
WO0124876
WO0143804
WO0176689
WO0226318
WO0234327
WO02085448
WO03041559
WO03076008
WO03082080
WO03099373
WO03099377
WO2004012814
WO2004084990
WO2004084993
WO2004103455
WO2004105870
WO2004110549
WO2005018739
WO2005042091
WO2005053788
WO2005063332
WO2005065771
WO2006031331
&quot;Aircraft Noise and Sleep Disturbance: final report&quot;; Aug. 1980; Department of Trade; retrieved from
http://www.caa.co.uk/docs/33/ERCD%208008.pdf. cited by examiner
Stephane Garrigue, MD, Philippe Bordier, MD, Meleze Hocini, MD, Pierre Jai{umlaut over ( )}s, MD, Michel Haissaguerre, MD and Jacques Clementy, MD, &quot;Night Atrial Overdrive with DDD Pacing: A New Therapy for Sleep Apnea Syndrome&quot;, Hopital
Cardiologique du Haut-Leveque, University of Bordeaux, Pessac-Bordeaux, France, p. 591. cited by other
Terry Young, Ph.D., Mari Palta, Ph.D., Jerome Dempsey, Ph.D., James Skatrud, MD, Steven Weber, Ph.D., and Safwan Badr, MD. &quot;The Occurrence of Sleep-disordered Breathing Among Middle-aged Adults&quot;, The New England Journal of Medicine, vol. 328, No.
17, pp. 1230-1235. cited by other
T. Douglas Bradley, MD and John S. Floras, MD,DPhIL, &quot;Pathophysiologic and Therapeutic Implications of Sleep Apnea in Congestive Heart Failure&quot;, Toronto, Ontario, Canada, Journal of Cardiac Failure, vol. 2, No. 3, pp. 223-240. cited by other
Stephane Garrigue, Philippe Bordier, Meleze Hocini, Pierre Jais, Michel Haissaguerre and Jacques Clementy, Night Atrial Overdrive with DDD Pacing Results in a Significant Reduction of Sleep Apnea Episodes and QOL Improvement in Heart Failure
Patients, Hosp. Cardiologique du Haut-Leveque, Bordeaux-Pessac, France, Abstract Session 25, p. 145. cited by other
Krzysztof W. Balaban, Yong Cho, Luc Mongeon, Lynn Liu, Nancy Foldvary, Richard Burgess, Christoph Scharf, Reto Candinas and Bruce L. Wilkoff, &quot;Feasibility of Screening for Sleep Apnea using Pacemaker Impedance Sensor&quot;, Cleveland Clin. Fdn,
Cleveland, OH, Medtronic, Inc., Minneapolis, MN, Strong Memorial Hosp., Rochester, NY and Univ. Hosp., Zurich, Switzerland, p. 313. cited by other
S. Javaheri, MD, T.J.Parker, MD, J.D. Liming, MD, W.S. Corbett, BS, H. Nishiyama, MD, L. Wexler, MD and G.A. Roselle, MD, &quot;Sleep Apnea in 81 Ambulatory Male Patients with Stable Heart Failure&quot;, from the Sleep Disorders Laboratory, Department of
Veterans Affairs Medical Center, and the Department of Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, pp. 2154-2159. cited by other
Aircraft Noise and Sleep Disturbance: Final Report&#39;, prepared by the Civil Aviation Authority London on behalf of the Department of Trade, Aug. 1980 (CAA Report). cited by other
Altshule et al., The Effect of Position on Periodic Breathing in Chronic Cardiac Decomposition, New Eng. Journal of Med., vol. 259, No. 22, pp. 1064-1066, Nov. 27, 1958. cited by other
Andersen, Long-term follow-up of patients from a randomized trial of atrial versus ventricular pacing for sick-sinus syndrome, Lancet, 350(9086), Oct. 25, 1997, 1210-6. Abstract only. cited by other
Benchimol, Cardiac hemodynamics during stimulation of the right atrium, right ventricle, and left ventricle in normal and abnormal hearts, Circulation, 33(6), Jun. 1966, 933-44. cited by other
Bevan et al., Postganglionic sympathetic delay in vascular smooth muscle, Journal of Pharmacology &amp; Experimental Therapeutics, 152(2), May 1966, 211-30. cited by other
Bevan et al., Sympathetic nerve-free vascular muscle, Journal of Pharmacology &amp; Experimental Therapeutics, 157(1), Jul. 1967, 117-24. cited by other
Bilgutay et al. A new concept in the treatment of hypertension utilizing an implantable electronical device: Baropacer. Trans. Am. Society Artificial Internal Organs. 1964. vol. 10, pp. 387-395. cited by other
Bilgutay et al., Vagal tuning for the control of supraventricular arrhythmias, Surgical Forum, 16, 1965, 151-3. cited by other
Borst et al., Optimal frequency of carotid sinus nerve stimulation in treatment of angina pectoris, Cardiovascular Research, 8(5), Sep. 1974, 674-80. cited by other
Bradley et al, Cardiac Output Response to Continuous Positive Airway Pressure in Congestive Heart Failure, 145 Am. Rev. Respir. Dis. 377-382, 1992. cited by other
Bradley et al., Sleep Apnea and Heart Failure, Park I: Obstructive Sleep Apnea, 107 Circulation 1671-1678, 2003. cited by other
Braunwald et al., Carotid sinus nerve stimulation in the treatment of angina pectoris and supraventricular trachycardia, Califomia Medicine, 112(3), Mar. 1970, 41-50. cited by other
Braunwald et al., Relief of angina pectoris by electrical stimulation of the carotid-sinus nerves, New England Journal of Medicine, 277(24), Dec. 14, 1967, 1278-83. cited by other
Buda et al., Effect of Intrathoracic Pressure on Left Ventricular Performance, 301 Engl. J. Med. 453-459, 1979, Abstract only. cited by other
Calvin et al., Positive End-Expiratory Pressure (PEEP) Does Not Depress Left Ventricular Function in Patients With Pulmonary Edema, 124 Am. Rev. Respir. Dis. 121-128, 1981, Abstract only. cited by other
Chapleau, Contrasting effects of static and pulsatile pressure on carotid baroreceptor activity in dogs, Circulation, vol. 61, No. 5, Nov. 1987, pp. 648-658. cited by other
Chapleau, Pulsatile activation of baroreceptors causes central facilitation of baroreflex, American Journal Physiol Heart Circ Physiol, Jun. 1989, 256:H1735-1741. cited by other
Coleridge et al. &quot;The distribution, connexions and histology of baroreceptors in the pulmonary artery, with some observations on the sensory innervation of the ductus arteriosus.&quot; Physiology, May 1961, pp. 591-602. cited by other
Cooper et al., Neural effects on sinus rate and atrioventricular conduction produced by electrical stimulation from a transvenous electrode catheter in the canine right pulmonary artery, Circulation Research, 46(1), Jan. 1980, 48-57. cited by other
Dart Jr. et al., Carotid sinus nerve stimulation treatment of angina refractory to other surgical procedures, Annals of Thoracic Surgery, 11(4), Apr. 1971, 348-59. cited by other
De Hoyos et al., Haemodynamic Effects of Continuous Positive Airway Pressure in Humans With Normal and Impaired Left Ventricular Function, 88 Clin. Sci., Lond. 173-8, 1995, Abstract only. cited by other
De Landsheere et al., Effect of spinal cord stimulation on regional myocardial perfusion assessed by positron emission tomography, American Journal of Cardiology, 69(14), May 1, 1992, 1143-9, Abstract only. cited by other
Dunning, Electrostimulation of the Carotid Sinus Nerve in Angina Pectoris, University Department of Medicine, Binnengasthuis, Amsterdam: Printed by Royal VanGorcum, Assen, Netherlands, 1971, 1-92. cited by other
Epstein et al., Treatment of angina pectoris by electrical stimulation of the carotid-sinus nerves, New England Journal of Medicine, 280(18), May 1, 1969, 971-8. cited by other
Farrehi, Stimulation of the carotid sinus nerve in treatment of angina pectoris, American Heart Journal, 80(6), Dec. 1970, 759-65. cited by other
Feliciano et al., Vagal nerve stimulation releases vasoactive intestinal peptide which significantly increases coronary artery blood flow, Cardiovascular Research, 40(1), Oct. 1998, 45-55. cited by other
Fromer et al., Ultrarapid subthreshold stimulation for termination of atrioventricular node reentrant tachycardia, Journal of the American College of Cardiology, 20(4), Oct. 1992, 879-83, Abstract only. cited by other
Giardino et al., Respiratory Sinus Arrhythmia is Associated with the Efficiency of Pulmonary Gas Exchange in Healthy Humans, 284 Am. J. Physiol. H1585-1591, 2003. Abstract only. cited by other
Grassi et al., Baroreflex and non-baroreflex modulation of vagal cardiac control after myocardial infarction, Am J Cardiol, 84(5), Sep. 1, 1999, 525-9, Abstract only. cited by other
Griffith et al., Electrical Stimulation of the Carotid Sinus Nerve in Normotensive and Renal Hypertensive Dogs, Circulation, 28, Jul.-Dec. 1963, 730. cited by other
Hanson et al., Cardiac Gated Ventilation, 2433 SPIE 303-308, 1995. cited by other
Henning, Effects of autonomic nerve stimulation, asynchrony, and load on dP/dtmax and on dP/dtmin, American Journal of Physiology, 260(4PT2), Apr. 1991, H1290-8. cited by other
Henning et al., Vagal nerve stimulation increases right ventricular contraction and relaxation and heart rate, Cardiovascular Research, 32(5), Nov. 1996, 846-53. cited by other
Hilton et al., Evaluation of Frequency and Time-frequency Spectral Analysis of Heart Rate Variability as a Diagnostic Marker of the Sleep Apnea Syndrome. Med Biol Eng Comput Nov. 1999, 37(6), 760-9. Abstract only. cited by other
Hood Jr. et al., Asynchronous contraction due to late systolic bulging at left ventricular pacing sites, American Journal of Physiology, 217(1), Jul. 1969, 215-21. cited by other
Ishise, Time course of sympathovagal imbalance and left ventricular dysfunction in conscious dogs with heart failure, Journal of Applied Physiology 84(4), Apr. 1998, 1234-41. cited by other
Janes, &quot;Anatomy of human extrinsic cardiac nerves and ganglia&quot;, Am J Cardiol., 57(4), Feb. 1, 1986, 299-309. cited by other
Cincinnati, OH, pp. 2154-2159. cited by other
Jessurun et al., Coronary blood flow dynamics during transcutaneous electrical nerve stimulation for stable angina pectoris associated with severe narrowing of one major coronary artery, American Journal of Cardiology, 82(8), erratum appears in Am J
Cardiol, Feb. 1999, 15;83(4):642, Oct. 15, 1998, 921-6. cited by other
Junyu et al., Posture Detection Algorithm Using Multi Axis DC-Accelerometer, Pace vol. 22, Apr. 1999. cited by other
Kandel et al., Part VII: Arousal, Emotion, and Behavioral Homeostasis, In: Principles of neural science, New YorK:McGraw-Hill, Health Professions Division, 2000, 966-969. cited by other
Karpawich et al., Altered cardiac histology following apical right ventricular pacing in patients with congenital atrioventricular block, Pacing Clin Electrophysiol, 22(9), Sep. 1999, 1372-7, Abstract only. cited by other
Kaye et al., Acute Effects of Continuous Positive Airway Pressure on Cardiac Sympathetic Tone in Congestive Heart Failure, 103 Circulation 2336-24338, 2001. cited by other
Laude et al., Effects of Breathing Pattern on Blood Pressure and Heart Rate Oscillations in Humans, 20 Clin. Exp. Pharmol. Phisiol 619, 625, 1993. Abstract only. cited by other
Leclercq et al., Hemodynamic importance of preserving the normal sequence of ventricular activation in permanent cardiac pacing, Am Heart J., 129(6), Jun. 1995, 1133-41, Abstract only. cited by other
Lenique et al., Ventilatory and Hemodynamic Effects of Continuous Positive Airway Pressure in Left Heart Failure, 155 Am. J. Respir. Crit. Care Med. 500-505, 1997. Abstract only. cited by other
Li, &quot;Vagal nerve stimulation markedly improves long-term survival after chronic heart failure in rats&quot;, Circulation, 109(1), Epub Dec. 8, 2003, Jan. 6, 2004, 1-5. cited by other
Lugaresi et al., Snoring, 39 Electroencephalogr. Clin. Neurophysiol. 59-64, 1975. cited by other
Mannheimer et al., Epidural spinal electrical stimulation in severe angina pectoris, British Heart Journal, 59(1), Jan. 1988, 56-61, Abstract only. cited by other
Mannheimer et al., Transcutaneous electrical nerve stimulation in severe angina pectoris, European Heart Journal, 3(4), Aug. 1982, 297-302, Abstract only. cited by other
Mannheimer et al., Transcutaneous electrical nerve stimulation (TENS) in angina pectoris, Pain, 26(3), Sep. 1986, 291-300, Abstract only. cited by other
Mansfield, D. et al., Effects of Continuous Positive Airway Pressure on Lung Function in Patients with Chronic Obstructive Pulmonary Disease and Sleep Disordered Breathing, Respirology 365-70, 1999. Abstract only. cited by other
Mazgalev et al., Autonomic modification of the atrioventricular node during atrial fibrillation: role in the slowing of ventricular rate, Circulation 99(21), Jun. 1, 1999, 2806-14. cited by other
Mehta et al., Effects of Continuous Positive Airway Pressure on Cardiac Volumes In Patients With Ischemic and Dilated Cardiomyopathy, 161 Am. J. Respir. Crit. Care Med. 128-134, 2000. cited by other
Millar-Craig et al., Circadian variation of blood-pressure, Lancet, 1(8068), Apr. 15, 1978, 795-7, Abstract only. cited by other
Minisi et al., Regional left ventricular deafferentation increases baroreflex sensitivity following myocardial infarction, Cardiovasc Res., 58(1), Apr. 1, 2003, 136-41, Abstract only. cited by other
Murphy et al., Intractable angina pectoris: management with dorsal column stimulation, Medical Journal of Australia, 146(5), Mar. 2, 1987, 260, Abstract only. cited by other
Naughton et al., Effects of Continuous Positive Airway Pressure on Intrathoracic and Left Ventricular Transmural Pressure in Congestive Heart Failure, 91 Circulation 1725-1731, 1995, pp. 1-25. cited by other
Neil et al. &quot;Effects of electrical stimulation of the aortic nerve on blood pressure and respiration in cats and rabbits under chloralose and nembutal anaesthesia.&quot; Journal of Physiology. Sep. 1949. vol. 109 (3-4), pp. 392-401. cited by other
Neistadt et al., Effects of electrical stimulation of the carotid sinus nerve in reversal of experimentally induced hypertension, Surgery, 61(6), Jun. 1967, 923-31. cited by other
Peters et al. &quot;Tempral and spatial summation caused by aortic nerve stimulation in rabbits. Effects of stimulation frequencies and amplitudes.&quot; Journal of the Autonomic Nervous System. 1989. vol. 27, pp. 193-205, Abstract only. cited by other
Peters et al., The principle of electrical carotid sinus nerve stimulation: a nerve pacemaker system for angina pectoris and hypertension therapy, Annals of Biomedical Engineering, 8(4-6), 1980, 445-58, Abstract only. cited by other
Philbin et al., Inappropriate shocks delivered by an ICD as a result of sensed potentials from a transcutaneous electronic nerve stimulation unit, Pacing &amp; Clinical Electrophysiology, 21(10), Oct. 1998, 2010-1, Abstract only. cited by other
Pinsky et al., Hemodynamic Effect of Cardiac Cycle-Specific Increases in Intrathoracic Pressure, 6 J. Appl. Physiol. 604-612 ,1986. Abstract only. cited by other
Potkin et al., Effect of positive end-expiratory pressure on right and left ventricular function in patients with the adult respiratory distress syndrome, 135 Am. Rev. Respir. Dis. 307-311, 1987. Abstract only. cited by other
Prakash et al. Asymmetrical distribution of aortic nerve fibers in the pig, Anat Rec., 158(1), May 1967, 51-7, Abstract only. cited by other
Reddel et al., Analysis of Adherence to Peak Flow Monitoring When Recording of Data is Electronic, BMJ 146-147, 2002. cited by other
Rees et al., Paroxysmal Nocturnal Dyspnoea and Periodic Respiration, The Lancet, Dec. 22-29, 1979, pp. 1315-1317, Abstract only. cited by other
Rosenqvist, The effect of ventricular activation sequence on cardiac performance during pacing, Pacing and Electrophysiology, 19(9), 1996, 1279-1286. cited by other
Rushmer, Chapter 5--Systemic Arterial Pressure, In: Cardiovascular dynamics, Philadelphia: Saunders, 1976, 176-216. cited by other
Sato et al. &quot;Novel Therapeutic Strategy against Central Baroreflex Failure: A Bionic Baroreflex System.&quot; Circulation. Jul. 1999, vol. 100, pp. 299-304. cited by other
Satoh et al., &quot;Role of Hypoxic Drive in Regulation of Postapneic Ventilation During Sleep in Patients with Obstructive Sleep Apnea&quot;, Am Rev Respir Dis, Mar. 1991 143 (3): 481-485. cited by other
Scharf, Effects of Continuous Positive Airway Pressure on Cardiac Output in Experimental Heart Failure, 19 Sleep S240-2, 1996. Abstract only. cited by other
Schauerte et al., Catheter stimulation of cardiac parasympathetic nerves in humans: a novel approach to the cardiac autonomic nervous system, Circulation, 104(20), Nov. 13, 2001, 2430-5. cited by other
Schauerte et al., Transvenous parasympathetic cardiac nerve stimulation: an approach for stable sinus rate control, Journal of Cardiovascular Electrophysiology, 10(11), Nov. 1999, 1517-24. cited by other
Schauerte, Transvenous Parasympathetic Nerve Stimulation in the Inferior Vena Cava and Atrioventricular Conduction, Journal of Cardiovascular Electrophysiology, 11(1), Jan. 2000, 64-69. cited by other
Schauerte et al., Ventricular rate control during atrial fibrillation by cardiac parasympathetic nerve stimulation: a transvenous approach, Journal of the American College of Cariology, 34(7), Dec. 1999, 2043-50. cited by other
Scherlag, Endovascular Neural Stimulation via a Novel Basket Electrode Catheter: Comparison of Electrode Configurations, Journal of Interventional Cardiac Electrophysiology, 4(1), Apr. 2000, 219-224, Abstract only. cited by other
Schuder et al., Ventricular Defibrillation in the Dog Using Implanted and Partially Implanted Electrode Systems, Am. J. of Cardiology, vol. 33, pp. 243-247, Feb. 1974. cited by other
Smits et al., Defibrillation Threshold (DFT) Model of a Fully Subcutaneous ICD System, Europace Supplements, vol. 2, Jun. 2001 at col. 778, p. B83. cited by other
Stirbis et al., Optmizing the Shape of Implanted Artificial Pacemakers, Kaunas Medical Institute. Translated from Meditsinskaya Tekhnika, No. 6, pp. 25-27, 1986. cited by other
Takahashi, Vagal modulation of ventricular tachyarrhythmias induced by left ansae subclaviae stimulation in rabbits, Japanese Heart Journal, 39(4), Jul. 1998, 503-11, Abstract only. cited by other
Thrasher et al., Unloading arterial baroreceptors causes neurogenic hypertension, American Journal Physiol..sub.--Regulatory Integrative Comp. Physiol. 2002. vol. 282, R1044-R1053. cited by other
Tse et al., Long-term effect of right ventricular pacing on myocardial perfusion and function, J Am Coll Cardiol., 29(4), Mar. 15, 1997, 744-9. cited by other
Vanoli, Vagal stimulation and prevention of sudden death in conscious dogs with a healed myocardial infarction, Circulation Research, 68(5), May 1991, 1471-81, Abstract only. cited by other
Veerman et al., Circadian profile of systemic hemodynamics, Hypertension, 26(1), Jul. 1995, 55-9. cited by other
Verity et al., Pulmonary artery innvervation: a morphopharmacologic correlation, Proceedings of the Western Pharmacology Society, 8, 1965, 57-9. cited by other
Verity et al., Plurivesicular nerve endings in the pulmonary artery, Nature, 211(48), Jul. 30, 1966, 537-8, Abstract only. cited by other
Wallick, Selective AV nodal vagal stimulation improves hemodynamics during acute atrial fibrillation in dogs, American Journal of Physiology--Heart &amp; Circulatory Physiology, 281(4), Oct. 2001, H1490-7. cited by other
Waninger et al., Electrophysiological control of ventricular rate during atrial fibrillation, Pacing &amp; Clinical Electrophysiology, 23(8), Aug. 2000, 1239-44, Abstract only. cited by other
Weber et al., Effects of CPAP and BIPAP on stroke volume in patients with obstructive sleep apnea syndrome. Pneumolgie Mar. 1995; 49(3):233-5. Translated Abstract only. cited by other
Wiggers et al., The muscular reactions of the mammalian ventricles to artificial surface stimuli, American Journal of Physiology, 1925, 346-378. cited by other
Young et al., The Occurrence of Sleep-disordered Breathing Among Middle-aged Adults, The New England Journal of Medicine, vol. 328, No. 17, pp. 1230-1235. cited by other
Zhang et al., Optimal ventricular rate slowing during atrial fibrillation by feedback AV nodal-selective vagal stimulation, American Journal of Physiology--Heart &amp; Circulatory Physiology, 282(3), Mar. 2002, H1102-10. cited by other
Zhou et al., Prevention of high incidence of neurally mediated ventricular arrhythmias by afferent nerve stimulation in dogs, Circulation, 101(7), Feb. 22, 2000, 819-24. cited by other
File History for U.S. Appl. No. 10/939,834 as retrieved from U.S. Patent and Trademark Office on Feb. 13, 2011, 405 pages. cited by other
File History for U.S. Appl. No. 10/939,711 as retrieved from U.S. Patent and Trademark Office on Feb. 13, 2011, 367 pages. cited by other
Baratz et al., Effect of Nasal Continuous Positive Airway Pressure on Cardiac Output and Oxygen Delivery in Patients With Congestive Heart Failure, 102 Chest, 1992, 397-401. cited by other
Garrigue et al., Benefit of Atrial Pacing in Sleep Apnea Syndrome, 346 N. Engl. J. Med. 404-412, 2002. cited by other
Hoffman et al., Cheyne-Stokes Respiration in Patients Recovering from Acute Cardiogenic Pulmonary Edema, Chest 1990, 97:410-12. cited by other
Javaheri, A Mechanism of Central Sleep Apnea in Patients With Heart Failure, 341 N. Engl. J. Med. 949-954, 1999. cited by other
Javaheri et al., Sleep Apnea in 81 Ambulatory Male Patients with Stable Heart Failure: Types and Their Prevalences, Consequences, and Presentations, 97 Circulation 2154-2159, 1998. cited by other
Leng et al., Lead Configuration for Defibrillator Implantation in a Patient with Congenital Heart Disease and a Mechanical Prosthetic Tricuspid Valve, PACE, vol. 24, pp. 1291-1292, Aug. 2001. cited by other
Pinsky et al., Augmentation of Cardiac Function by Elevation of Intrathoracic Pressure, 54 J. Appl. Physiol., 1983, 950-955. cited by other
Rasanen et al., Acute Myocardial Infarction Complicated by Left Ventricular Dysfunction and Respiratory Failure. The Effects of Continuous Positive Airway Pressure, 87 Chest, 1985, 158-62. cited by other
Roche et al., Screening of Obstructive Sleep Apnea Syndrome by Heart Rate Variability Analysis, 100 Circulation 1411-1455, 1999. cited by other
Steltner et al., Diagnosis of Sleep Apnea by Automatic Analysis of Nasal Pressure and Forced Oscillation Impedance. Am. Journal Respiratory Critical Care Medicine, vol. 165, pp. 940-944, 2002. cited by other
Vanninen et al., Cardiac Sympathovagal Balance During Sleep Apnea Episodes, 16 Clin. Physiol. 209-216, 1996. cited by other
Waldemark et al., Detection of Apnea using Short Window FFT Technique and Artificial Neural Network, 3390 SPIE International Society for Optical Engineering 122-133, 1998. cited by other
Office Action from U.S. Appl. No. 10/643,203 dated Mar. 31, 2008, 6 pages. cited by other
Office Action Response dated Jun. 19, 2008 to office action dated Mar. 31, 2008 from U.S. Appl. No. 10/643,203, 7 pages. cited by other
Office Action from U.S. Appl. No. 10/643,203 dated Sep. 22, 2008, 17 pages. cited by other
Office Action Response dated Dec. 22, 2008 to office action dated Sep. 22, 2008 from U.S. Appl. No. 10/643,203, 12 pages. cited by other
Office Action from U.S. Appl. No. 10/643,203 dated Apr. 29, 2009, 8 pages. cited by other
Office Action Response dated Jul. 23, 2009 to office action dated Apr. 29, 2009 from U.S. Appl. No. 10/643,203, 10 pages. cited by other
Notice of allowance for U.S. Appl. No. 10/643,203 dated Dec. 17, 2009, 4 pages. cited by other
Office Action from U.S. Appl. No. 10/939,639 dated Oct. 16, 2006, 17 pages. cited by other
Office Action Response submitted Feb. 16, 2007 to office action dated Oct. 16, 2006 for U.S. Appl. No. 10/939,639, 15 pages. cited by other
Office Action from U.S. Appl. No. 10/939,639 dated Jun. 5, 2007, 13 pages. cited by other
Office Action Response submitted Aug. 6, 2007 to office action dated Jun. 5, 2007 for U.S. Appl. No. 10/939,639, 8 pages. cited by other
Office Action from U.S. Appl. No. 10/939,639 dated Oct. 2, 2007, 7 pages. cited by other
Office Action Response submitted Feb. 4, 2008 to office action dated Oct. 2, 2007 for U.S. Appl. No. 10/939,639, 9 pages. cited by other
Office Action from U.S. Appl. No. 10/939,639 dated May 13, 2008, 8 pages. cited by other
Office Action Response submitted Aug. 8, 2008 to office action dated May 13, 2008 for U.S. Appl. No. 10/939,639, 9 pages. cited by other
Office Action from U.S. Appl. No. 10/939,639 dated Nov. 18, 2008, 10 pages. cited by other
Office Action Response submitted Feb. 10, 2009 to office action dated Nov. 18, 2008 for U.S. Appl. No. 10/939,639, 10 pages. cited by other
Office Action Response submitted Apr. 20, 2009 to office action dated Nov. 18, 2008 for U.S. Appl. No. 10/939,639, 9 pages. cited by other
Notice of Allowance for U.S. Appl. No. 10/939,639 dated Jun. 30, 2009, 4 pages. cited by other
Office Action from European patent application No. 04784602.7 dated May 9, 2007, 3 pages. cited by other
Office Action Response to European patent application No. 04784602.7 dated Jan. 10, 2008, 3 pages. cited by other
Office Action Response to European patent application No. 04784602.7, dated Apr. 17, 2008, 9 pages. cited by other
PCT/US2004/030787 International Search Report dated Sep. 29, 2005, 12 pages. cited by other.
1.  A method for collecting sleep quality data, comprising: sensing physiological information regarding at least one physiological condition related to sleep quality of a
patient and indicative of patient sleep state;  sensing non-physiological information regarding at least one non-physiological condition that affects the sleep quality of the patient, the at least one non-physiological condition comprising an ambient
condition external to the patient, the physiological information and the non-physiological information sensed contemporaneously by respective sensors;  storing the physiological information and the non-physiological information in an implantable device;
and evaluating the patient&#39;s sleep quality using both the physiological and non-physiological information by calculating a composite sleep quality metric as a function of the physiological and non-physiological information stored in the implantable
2.  The method of claim 1, wherein the composite sleep quality metric comprises a sleep disturbance index.
3.  The method of claim 1, wherein the at least one physiological condition comprises a respiratory system condition.
4.  The method of claim 1, wherein the at least one physiological condition comprises a muscle system condition.
5.  The method of claim 1, further comprising trending the composite sleep quality metric over time.
6.  The method of claim 1, wherein the non-physiological information comprises ambient temperature.
7.  The method of claim 1, wherein the at least one non-physiological condition comprises an environmental condition.
8.  The method of claim 1, wherein the at least one non-physiological condition comprises a contextual condition.
9.  The method of claim 1, wherein collecting the physiological information comprises collecting data associated with sleep stages.
10.  The method of claim 1, wherein collecting the physiological information comprises collecting data associated with sleep disruption.
11.  The method of claim 1, wherein collecting the physiological information comprises collecting data associated with disordered breathing.
12.  The method of claim 1, wherein collecting the physiological information comprises collecting data associated with a movement disorder.
13.  The method of claim 1, wherein collecting the non-physiological information comprises sensing the at least one non-physiological condition external to the patient and transmitting the sensed non-physiological condition to the implantable
14.  A method for assessing sleep quality of a patient, comprising: sensing physiological information regarding at least one physiological condition related to sleep quality of a patient using an implantable device;  sensing non-physiological
information regarding at least one non-physiological condition that affects the sleep quality of the patient, the at least one non-physiological condition comprising an ambient condition external to the patient, the physiological information and the
non-physiological information sensed contemporaneously by respective sensors;  and evaluating the sleep quality of the patient using the physiological and non-physiological information by calculating a composite sleep quality metric as a function of the
physiological and non-physiological information, wherein the evaluating the sleep quality is performed at least in part by the implantable device.
15.  The method of claim 14, wherein the non-physiological information comprises patient location information.
16.  The method of claim 14, wherein evaluating the sleep quality further comprises determining one or more sleep stages.
17.  The method of claim 14, wherein the physiological information is indicative of sleep disruption.
18.  The method of claim 14, wherein the composite sleep quality metric comprises a sleep disturbance index.
19.  The method of claim 14, wherein the non-physiological information comprises ambient temperature.
20.  The method of claim 14, wherein the non-physiological information is indicative of external air quality.
21.  The method of claim 14, wherein evaluating the sleep quality further comprises trending the composite sleep quality metric over time.
22.  The method of claim 14, wherein the physiological information is indicative of disordered breathing.
23.  The method of claim 14, wherein the physiological information is indicative of one or more movement disorders.
24.  The method of claim 14, wherein the non-physiological information is indicative of events that disrupt sleep.
25.  The method of claim 14, wherein sensing the non-physiological information comprises sensing the at least one non-physiological condition external to the patient and transmitting the sensed non-physiological condition to the implantable
Normal sleep is characterized by a general decrease in metabolic rate, body temperature, blood pressure, breathing rate, heart rate, cardiac output, sympathetic nervous activity, and other physiological functions.  However, studies have shown
that the brain&#39;s activity does not decrease significantly during sleep.  Normally a patient alternates between rapid eye movement (REM) and non-REM (NREM) sleep in approximately 90 minute cycles throughout a sleep period.  A typical eight hour sleep
period may be characterized in terms of a five-step sleep cycle identifiable through EEG brain wave activity.
Non-REM sleep includes four sleep states or stages that range from light dozing to deep sleep.  Throughout NREM sleep, muscle activity is still functional, breathing is low, and brain activity is minimal.  Approximately 85% of the sleep cycle is
spent in NREM sleep.  Stage 1 NREM sleep may be considered a transition stage between wakefulness and sleep.  As sleep progresses to stage 2 NREM sleep, eye movements become less frequent and brain waves increase in amplitude and decrease in frequency.
As sleep becomes progressively deeper, the patient becomes more difficult to arouse.  Stage 3 sleep is characterized by 20 to 40% slow brain wave (delta) sleep as detected by an electroencephalogram (EEG).  Sleep stages 3 and 4 are considered to be the
most restful sleep stages.
REM sleep is associated with more prevalent dreaming, rapid eye movements, muscle paralysis, and irregular breathing, body temperature, heart rate and blood pressure.  Brain wave activity during REM sleep is similar to brain wave activity during
a state of wakefulness.  There are typically 4-6 REM periods per night, with increasing duration and intensity toward morning.  While dreams can occur during either REM or NREM sleep, the nature of the dreams varies depending on the type of sleep.  REM
sleep dreams tend to be more vivid and emotionally intense than NREM sleep dreams.  Furthermore, autonomic nervous system activity is dramatically altered when REM sleep is initiated.
Lack of sleep and/or decreased sleep quality may be have a number of causal factors including, e.g., nerve or muscle disorders, respiratory disturbances, and emotional conditions, such as depression and anxiety.  Chronic, long-term sleep-related
disorders e.g., chronic insomnia, sleep-disordered breathing, and sleep movement disorders, including restless leg syndrome (RLS), periodic limb movement disorder (PLMD) and bruxism, may significantly affect a patient&#39;s sleep quality and quality of life.
Movement disorders such as restless leg syndrome (RLS), and a related condition, denoted periodic limb movement disorder (PLMD), are emerging as one of the more common sleep disorders, especially among older patients.  Restless leg syndrome is a
disorder causing unpleasant crawling, prickling, or tingling sensations in the legs and feet and an urge to move them for relief.  RLS leads to constant leg movement during the day and insomnia or fragmented sleep at night.  Severe RLS is most common in
elderly people, although symptoms may develop at any age.  In some cases, it may be linked to other conditions such as anemia, pregnancy, or diabetes.
Many RLS patients also have periodic limb movement disorder (PLMD), a disorder that causes repetitive jerking movements of the limbs, especially the legs.  These movements occur approximately every 20 to 40 seconds and cause repeated arousals
and severely fragmented sleep.
A significant percentage of patients between 30 and 60 years experience some symptoms of disordered breathing, primarily during periods of sleep.  Sleep disordered breathing is associated with excessive daytime sleepiness, systemic hypertension,
increased risk of stroke, angina and myocardial infarction.  Disturbed respiration can be particularly serious for patients concurrently suffering from cardiovascular deficiencies.  Disordered breathing is particularly prevalent among congestive heart
failure patients, and may contribute to the progression of heart failure.
Sleep apnea is a fairly common breathing disorder characterized by periods of interrupted breathing experienced during sleep.  Sleep apnea is typically classified based on its etiology.  One type of sleep apnea, denoted obstructive sleep apnea,
occurs when the patient&#39;s airway is obstructed by the collapse of soft tissue in the rear of the throat.  Central sleep apnea is caused by a derangement of the central nervous system control of respiration.  The patient ceases to breathe when control
signals from the brain to the respiratory muscles are absent or interrupted.  Mixed apnea is a combination of the central and obstructive apnea types.  Regardless of the type of apnea, people experiencing an apnea event stop breathing for a period of
time.  The cessation of breathing may occur repeatedly during sleep, sometimes hundreds of times a night and occasionally for a minute or longer.
Combinations of the disordered respiratory events described above have also been observed.  For example, Cheyne-Stokes respiration (CSR) is associated with rhythmic increases and decreases in tidal volume caused by alternating periods of
hyperpnea followed by apnea and/or hypopnea.  The breathing interruptions of CSR may be associated with central apnea, or may be obstructive in nature.  CSR is frequently observed in patients with congestive heart failure (CHF) and is associated with an
increased risk of accelerated CHF progression.
An adequate duration and quality of sleep is required to maintain physiological homeostasis.  Untreated, sleep disturbances may have a number of adverse health and quality of life consequences ranging from high blood pressure and other
Various modifications and additions can be made to the preferred embodiments discussed hereinabove without departing from the scope of the present invention.  Accordingly, the scope of the present invention should not be limited by the
An embodiment of the invention involves a method for collecting sleep quality data.  The method includes detecting physiological and non-physiological conditions associated with the sleep quality of a patient and collecting sleep quality data
based on the detected conditions.  Collecting the sleep quality data is performed at least in part implantably.
Another embodiment of the invention involves a method for evaluating sleep quality.  In accordance with this method, one or more metrics associated with sleep are determined.  One or more metrics associated with events that disrupt sleep are
determined.  A composite sleep quality metric is determined using the one or more metrics associated with sleep and the one or more metrics associated with events that disrupt sleep.
In yet another embodiment of the invention, a method for evaluating sleep quality includes detecting physiological and non-physiological conditions associated with the sleep quality of a patient and collecting sleep quality data based on the
detected conditions.  The sleep quality of the patient is evaluated using the collected data.  At least one of collecting the sleep quality data and evaluating the sleep quality of the patient is performed at least in part implantably.
Another embodiment of the invention involves a method for evaluating sleep quality.  One or more conditions associated with sleep quality of a patient are detected during a period of wakefulness.  Sleep quality data is collected based on the
detected conditions.  The patient&#39;s sleep quality is evaluated using the collected sleep quality data.  At least one of collecting the data and evaluating the sleep quality is performed at least in part implantably.
A further embodiment of the invention involves a medical device including a detector system configured to detect physiological and non-physiological conditions associated with sleep quality and a data collection system for collecting sleep
quality data based on the detected conditions.  The data collection system includes an implantable component.
Yet another embodiment of the invention relates to a medical device configured to evaluate sleep quality.  The medical device includes a detector system configured to detect physiological and non-physiological conditions associated with the
sleep quality of a patient.  A sleep quality processor, coupled to the detection system, is configured to determine metrics based on the detected conditions.  The metrics include one or more metrics associated with sleep, one or more metrics associated
with events that disrupt sleep, and at least one composite sleep quality metric based on the one or more metrics associated with sleep and the one or more metrics associated with events that disrupt sleep.
In another embodiment of the invention, a medical device for assessing sleep quality includes a detector unit configured to detect physiological and non-physiological conditions associated with sleep quality and a sleep quality data collection
unit configured to collect sleep quality data based on the detected conditions.  A data analysis unit coupled to the data collection unit evaluates sleep quality based on the collected sleep quality data.  At least one of the data collection unit and the
data analysis unit includes an implantable component.
A further embodiment of the invention involves a system for collecting sleep quality data.  The system includes means for detecting physiological and non-physiological conditions associated with sleep quality and means for collecting sleep
quality data based on the detected conditions.  The means for collecting the sleep quality data includes an implantable component.
In yet another embodiment of the invention, a system for assessing sleep quality includes means for determining one or more metrics associated with sleep and means for determining one or more metrics associated with events that disrupt sleep.
The system further includes means for determining a composite sleep quality metric as a function of the metrics associated with sleep and the metrics associated with events that disrupt sleep.
Another embodiment of the invention involves a system for evaluating sleep quality.  The system includes means for detecting physiological and non-physiological conditions associated with sleep quality and means for collecting sleep quality data
based on the detected conditions.  The system includes means for evaluating the sleep quality of the patient based on the collected sleep quality data.  At least one of the means for collecting the sleep quality data and the means for evaluating the
sleep quality comprise an implantable component.
A further embodiment involves a system for evaluating the sleep quality of a patient.  The system includes means for detecting one or more patient conditions associated with sleep quality during a period of wakefulness and means for collecting
sleep quality data based on the detected conditions.  The system further includes means for evaluating the sleep quality of the patient using the collected sleep quality data.  At least one of the means for collecting the sleep quality data and means for
evaluating the sleep quality include an implantable component.
FIG. 7 is a graph of a patient&#39;s activity condition as indicated by an accelerometer signal;
FIG. 8 is a graph of a patient&#39;s heart rate;
FIG. 9 is a graph of a patient&#39;s minute ventilation (MV) condition;
In the following description of the illustrated embodiments, references are made to the accompanying drawings which form a part hereof, and in which are shown, by way of illustration, various embodiments by which the invention may be practiced.
It is to be understood that other embodiments may be utilized.  Structural and functional changes may be made without departing from the scope of the present invention.
Sleep quality assessments depend upon acquiring sleep-related data, including the patient&#39;s typical sleep patterns and the physiological, environmental, contextual, emotional, and other conditions affecting the patient during sleep.  Diagnosis
of sleep disorders and assessment of sleep quality often involves the use of a polysomnographic sleep study at a dedicated sleep facility.  However, such studies are costly, inconvenient to the patient, and may not accurately represent the patient&#39;s
typical sleep behavior.  In a polysomnographic sleep study, the patient is instrumented for data acquisition and observed by trained personnel.  Sleep assessment in a laboratory setting presents a number of obstacles in acquiring an accurate picture of a
patient&#39;s typical sleep patterns.  For example, spending a night in a sleep laboratory typically causes a patient to experience a condition known as &quot;first night syndrome,&quot; involving disrupted sleep during the first few nights in an unfamiliar location.
In addition, sleeping while instrumented and observed may not result in a realistic perspective of the patient&#39;s normal sleep patterns.
Further, polysomnographic sleep studies provide an incomplete data set for the analysis of some sleep disorders, including, for example, sleep disordered breathing.  A number of physiological conditions associated with sleep disordered breathing
are detectable during periods of wakefulness, e.g., decreased heart rate variability, elevated sympathetic nerve activity, norepinephrine concentration, and increased blood pressure variability.  Collection of data during periods of sleep and/or during
periods of wakefulness may provide a more complete picture of the patient&#39;s sleep quality.
Various aspects of sleep quality, including number and severity of arousals, sleep disordered breathing episodes, nocturnal limb movements, and cardiac, respiratory, muscle, and nervous system functioning may provide important information for
diagnosis and/or therapy delivery.  An initial step to sleep quality evaluation is an accurate and reliable method for discriminating between periods of sleep and periods of wakefulness.  Further, acquiring data regarding the patient&#39;s sleep states or
stages, including sleep onset, termination, REM, and NREM sleep states may be used in connection sleep quality assessment.  For example, the most restful sleep occurs during stages 3 and 4 NREM sleep.  One indicator of sleep quality is the percentage of
time a patient spends in these sleep stages.  Knowledge of the patient&#39;s sleep patterns may be used to diagnose sleep disorders and/or adjust patient therapy, including, e.g., cardiac or respiratory therapy.  Trending disordered breathing episodes,
arousal episodes, and other sleep quality aspects may be helpful in determining and maintaining appropriate therapies for patients suffering from disorders ranging from snoring to chronic heart failure.
The present invention involves methods and systems for acquiring sleep quality data using one or more implantable components.  As illustrated in FIG. 1, methods of the invention involve detecting conditions associated with the sleep quality of
the patient 110, including physiological and non-physiological conditions.  Data related to the patient&#39;s sleep quality is collected based on the detected conditions 120.  Detection of patient conditions related to sleep quality may occur during periods
of wakefulness and/or during periods of sleep.  Either detecting the conditions associated with sleep quality, or collecting the sleep quality data, or both, is performed using a device having a component that is at least in part implantable.
A representative set of the conditions associated with sleep quality is listed in Table 1.  Patient conditions used to evaluate sleep quality may include, for example, both physiological and non-physiological (i.e., contextual) conditions.
Physiological conditions associated with sleep quality may be further organized, for example, into conditions of the cardiovascular, respiratory, muscle, and nervous systems, and conditions relating to the patient&#39;s blood chemistry.
Contextual conditions may be further subdivided into environmental conditions, body-related conditions and historical/background conditions.  Environmental conditions may be broadly defined to include the environmental surroundings affecting the
patient, such as ambient light, temperature, humidity, air pollution, noise, and barometric pressure.  Body-related conditions may include, for example, patient location, posture, and altitude.  Contextual conditions relevant to sleep quality may also
include historical or background conditions.  For example, a patient&#39;s medical/psychological history, gender, age, weight, body mass index, neck size, drug use, and emotional state may be detected and used in connection with sleep quality evaluation and
sleep disorder diagnosis.  Methods and systems for detecting contextual conditions are described in commonly owned U.S.  Pat.  No. 7,400,928, which is incorporated herein by reference.
TABLE-US-00001 TABLE 1 Condition Type Condition Sensor type or Detection method Physiological Cardiovascular Heart rate EGM, ECG System Heart rate variability QT interval Ventricular filling pressure Intracardiac pressure sensor Blood pressure
Blood pressure sensor Respiratory System Snoring Accelerometer Microphone Respiration pattern Transthoracic impedance sensor (Tidal volume Minute (AC) ventilation Respiratory rate) Patency of upper airway Intrathoracic impedance sensor Pulmonary
congestion Transthoracic impedance sensor (DC) Nervous System Sympathetic nerve activity Muscle sympathetic nerve Activity sensor Brain activity EEG Blood Chemistry CO2 saturation Blood analysis O2 saturation Blood alcohol content Adrenalin Brain
Natriuretic Peptide (BNP) C-Reactive Protein Drug/Medication/Tobacco use Muscle System Muscle atonia EMG Eye movement EOG Patient activity Accelerometer, MV, etc. Limb movements Accelerometer Jaw movements Non- Environmental Ambient temperature
Thermometer physiological Humidity Hygrometer Pollution Air quality website Time Clock Barometric pressure Barometer Ambient noise Microphone Ambient light Photodetector Body-related Posture Posture sensor Altitude Altimeter Location GPS, proximity
sensor Proximity to bed Proximity to bed sensor Historical/ Historical sleep time Patient input, previously Background detected sleep onset times Medical history Patient input device Age Recent exercise Weight Gender Body mass index Neck size Emotional
state Psychological history Daytime sleepiness Patient perception of sleep quality Drug, alcohol, nicotine use
Each of the conditions listed in Table 1 may serve a variety of purposes in evaluating sleep quality.  For example, a subset of the conditions may be used to detect whether the patient is asleep and to track the various stages of sleep and
arousal incidents.  Another subset of the conditions may be used to detect disordered breathing episodes.  Yet another subset may be used to detect abnormal limb movements.  In one implementation, some or all of the listed conditions may be collected
over a relatively long period of time and used to analyze long term sleep quality trends.  Trending may be used in connection with an overall assessment of sleep quality and diagnosis and treatment of sleep-disordered breathing, movement disorders,
In one implementation, sleep quality analysis may be used within the structure of an advanced patient management system.  In this implementation, an advanced patient management system having sleep quality analysis capability allows a physician
to remotely and automatically monitor cardiac and respiratory functions, as well as other patient conditions, including information related to sleep quality.  In one example, an implantable cardiac rhythm management system, such as a cardiac monitor,
pacemaker, defibrillator, or resynchronization device, may be equipped with various telecommunications and information technologies to enable real-time data collection, diagnosis, and treatment of the patient.  Systems and methods involving advanced
patient management techniques are described in U.S.  Pat.  Nos.  6,336,903, 6,312,378, 6,270,457, and 6,398,728 which are incorporated herein by reference in their respective entireties.
TABLE-US-00002 TABLE 2 Examples of how condition is Condition Type Condition used in sleep quality assessment Physiological Heart rate Decrease in heart rate may indicate disordered breathing episode.  Decrease in heart rate may indicate the
patient is asleep.  Heart rate variability May be used to determine sleep state.  Changes in heart rate variability, detected during periods of sleep or wakefulness, may indicate that the patient suffers from sleep disordered breathing.  QT interval May
be used to detect sleep apnea.  Ventricular filling pressure May be used to identify/predict pulmonary congestion associated with respiratory disturbance.  Blood pressure Variation in blood pressure is associated with apnea.  Snoring Associated with a
higher incidence of obstructive sleep apnea and may be used to detect disordered breathing.  Respiration pattern May be used to detect disordered breathing episodes.  May be used to determine the type of disordered breathing.  May be used to detect
sleep.  Patency of upper airway Related to obstructive sleep apnea and may be used to detect episodes of obstructive sleep apnea.  Pulmonary congestion Associated with respiratory disturbances.  Sympathetic nerve activity Apnea termination is associated
with a spike in SNA.  (SNA) SNA activity may be elevated during periods of wakefulness if the patient experiences sleep disordered breathing.  Electroencephalogram May be used to determine sleep stages, including REM and (EEG) NREM sleep stages CO2
saturation Low CO2 levels may indicate initiation of central apnea.  O2 saturation O2 desaturation occurs during severe apnea/hypopnea episodes.  Blood alcohol content Alcohol tends to increase the incidence of snoring &amp; obstructive apnea.  Adrenalin End
of apnea associated with a spike in blood adrenaline.  Brain Natriuretic Peptide A marker of heart failure status, which is associated with (BNP) Cheyne-Stokes Respiration.  C-Reactive Protein A measure of inflammation that may be related to apnea.
Drug/Medication/Tobacco These substances may affect incidence of both central &amp; use obstructive apnea.  Muscle atonia Muscle atonia may be used in connection with detection of REM and non-REM sleep.  Eye movement Eye movement may be used in connection
with detection of REM and non-REM sleep.  Activity May be used to detect sleep and patient well being.  Limb movements May be used to detect abnormal limb movements during sleep.  Non-physiological Ambient Temperature Ambient temperature may predispose
the patient to episodes of disordered breathing during sleep.  Humidity Humidity may predispose the patient to episodes of disordered breathing during sleep.  Pollution Pollution may predispose the patient to episodes of disordered breathing during
sleep.  Posture Posture may be used to determine if the patient is asleep.  Posture may predispose the patient to disordered breathing.  Time Used to establish historical sleep time.  Ambient noise level Noise level may affect sleep quality.  Location
Patient location may used to determine if the patient is in bed as a part of sleep detection.  Altitude Altitude may predispose the patient to episodes of disordered breathing and may affect sleep quality.  Barometric Pressure Barometric pressure may
predispose the patient to episodes of disordered breathing.  Proximity to bed May be used to determine if patient is in bed.  Historical sleep time May be used in connection with sleep detection.  Medical history History of medical disorders, e.g., CHF,
that are associated with disordered breathing such as Cheyne-Stokes respiration.  Age Age is associated with increased risk of disordered breathing, RLS and other sleep disruptive disorders.  Weight Associated with sleep disordered breathing, e.g.,
obstructive Gender sleep apnea.  Obesity Neck size Patient reported drug, Patient drug, alcohol and nicotine use may affect sleep alcohol, nicotine use quality.  Psychological history Psychological factors, e.g., clinical depression may be associated
with insomnia.  Emotional state Emotional state, e.g., stress, anxiety, euphoria, may affect sleep quality.  Daytime sleepiness May be used to evaluate sleep quality.  Patient perceptions of sleep quality
FIG. 2 illustrates a block diagram of a sleep quality data system 200 configured in accordance with embodiments of the invention.  In this implementation, the sleep quality data system 200 may use signals acquired from a variety of sources to
collect data relevant to sleep quality.  The collected data may be stored in the memory 280 of a data collection unit 250 and/or transmitted to a sleep quality analysis unit 290 for further processing.  It is contemplated that at least one, some, or all
components of the system 200 are implanted in the patient.
The sleep quality data system 200 may use patient-internal sensors 210 implanted within the body of the patient to detect physiological conditions relevant to sleep quality.  The conditions detected using patient-internal sensors 210 may
include, for example, heart rate, respiratory pattern, and patient activity.
The system 200 may also use patient-external sensors 220 to detect physiological or non-physiological patient conditions.  In one example configuration, whether the patient is snoring may be useful in evaluating sleep quality.  Snoring data may
be detected using an external microphone and transferred to the sleep quality data collection unit 250.  In another configuration, ambient temperature and humidity may be factors related to the patient&#39;s sleep quality.  The ambient temperature and
humidity of the patient&#39;s room may be sensed using sensors located near patient.  Signals from the temperature and humidity sensors may be transmitted to the data collection unit 250.  Limb and/or jaw movements may be sensed using patient-external
accelerometers and/or other sensors placed in appropriate locations on or near the patient and transmitted to the data collection unit 250.
Information relevant to sleep quality may also be reported 240 by the patient.  According to embodiments of the invention, the patient&#39;s self-described conditions, including medication use, tobacco use, perceptions of sleep quality, and/or
psychological or emotional state, for example, may be relevant to sleep quality assessment.  The patient may enter information about these conditions through an appropriate interface device, such as a medical device programmer, coupled to the sleep
quality data collection unit 250.
Some information related to sleep quality may be accessible through information systems 230, including network-based systems.  For example, information about the patient&#39;s present cardiac, respiratory, or other therapy may be downloaded from an
external device via a wireless or wired network.  In another example, information about conditions affecting the patient, such as local air quality data, may be accessed through an internet-connected website.
The patient-internal sensors 210, patient-external sensors 220, patient-reported input devices 240, and information systems 230, may be coupled to the data collection unit 250 in a variety of ways.  In one example, one or more of the sensors
210, 220, patient-reported input devices 240, and information systems 230 have wireless communication capabilities, such as a wireless Bluetooth communications link, or other proprietary communications protocol.  In this implementation, the devices
having wireless communication capabilities may remotely transmit signals to the data collection unit 250.  In this application, the data collection unit 250 may be configured as an implantable or patient-external device.  In other implementations, one or
more of the patient-internal sensors 210, patient-external sensors 220, patient-reported input devices 240, and information systems 230 may be coupled to the data collection unit 250 through leads or other wired connections.
The implantable or patient-external data collection unit 250 includes detection circuitry 260 for processing signals from the sensors 210, 220, patient-reported input devices 240, and information systems 230.  The detection circuitry 260 may
include, for example, amplifiers, filters, A/D converters, signal processors and/or sensor driver circuitry configured to provide sensor signals used in the evaluation of sleep quality.  The data collection unit 250 may further include wireless
communication circuitry 270 for receiving and transmitting signals to wirelessly connected components.
In one embodiment, the sleep quality data system 200 collects data from the sensors 210, 220, input devices 240, and information systems 230, and stores the collected data in memory 280 prior to further processing or transmission.  In another
embodiment, the sleep quality data system 200 may transmit the collected data to a separate device (not shown) for storage, analysis, or display.
In a further embodiment, the sleep quality data system 200 may evaluate or further process the collected sleep quality data.  For this purpose, the sleep quality data system 200 may optionally include a sleep quality analysis unit 290.  In one
configuration, the data collection unit 250 and the sleep quality analysis unit 290 are arranged in separate devices.  In such a configuration, the data collection unit 250 transfers the collected sleep quality data to the sleep quality analysis unit 290
through a wireless or wired connection.  In another configuration, the sleep quality analysis unit 290 and the data collection unit 250 are arranged within the same device which may be a patient-external device or a fully or partially implantable device.
The sleep quality analysis unit 290 may include one or more subsystems useful in sleep quality assessment.  The subsystems may include, for example a sleep detector 292 used to detect sleep onset, sleep offset, and arousal, for example.  The
sleep detector may also detect sleep stages, including the various stages of NREM and REM sleep.  The sleep quality analysis unit 290 may include circuitry to detect various sleep-related disorders.  For example, the sleep quality analysis unit 290 may
include circuitry 294 for detecting disordered breathing and circuitry 295 for detecting abnormal nocturnal movements.  Further, the sleep quality analysis unit 290 may include a processor for evaluating sleep quality 296, for example, by calculating one
or more metrics quantifying the patient&#39;s sleep quality.
FIG. 3 illustrates a sleep quality data system 305 incorporated within a cardiac rhythm management system (CRM) 300.  The CRM 300 may include, for example, a cardiac therapy module 320 including a pacemaker 322 and an arrhythmia detector/therapy
unit 324.  The cardiac therapy module 320 is coupled to a lead system having electrodes 331 implantable within the patient&#39;s body.  The implanted electrodes 331 are arranged to receive signals from the heart 330 and deliver stimulation therapy to the
heart 330.  Cardiac signals sensed using the implanted electrodes 331 are received by a cardiac signal detector 350 coupled to the cardiac therapy module 320.
The cardiac therapy module 320 analyzes the cardiac signals to determine an appropriate therapy to treat arrhythmia conditions affecting the heart 330.  The cardiac therapy may include pacing therapy controlled by the pacemaker 322 to treat
cardiac rhythms that are too slow.  The pacemaker 322 controls the delivery of periodic low energy pacing pulses to ensure that the periodic contractions of the heart are maintained at a hemodynamically sufficient rate.
The cardiac therapy may also include therapy to terminate tachyarrhythmia, wherein the heart rate is too fast.  The arrhythmia detector/therapy unit 324 analyzes cardiac signals received from the cardiac signal detector 350 to recognize
tachyarrhythmias including atrial or ventricular tachycardia or fibrillation.  The arrhythmia detector/therapy unit 324 recognizes cardiac signals indicative of tachyarrhythmia and delivers high energy stimulations to the heart 330 through the implanted
electrodes 331 to terminate the arrhythmias.
Various input devices, including implantable sensors 381, patient-external sensors 382, patient input devices 384, and information systems 383 may be coupled to the CRM 300.  These devices 381, 382, 383, 384 may be used to provide information
about physiological and/or non-physiological conditions affecting the patient relevant to sleep quality, such as the representative set of patient conditions listed in Table 1 above.
The CRM 300 includes signal detection circuitry 360 for receiving and processing signals from the various sensors and input devices 381, 382, 384, 383.  As previously discussed, the signal detection circuitry 360 may include signal processing
circuitry configured to amplify, digitize, or otherwise process signals representing the sensed sleep quality conditions.  In the illustrated implementation, the patient input devices 384, patient-external sensors 382, and information systems 383 are
wirelessly coupled to the CRM 300.  The patient-internal sensors 381 may be coupled to the CRM 300 through leads, through a wireless link, or integrated within or on the housing of the CRM 300 (e.g., integral accelerometer).
In one embodiment, the sleep quality data system 305 incorporated within the CRM 300 collects data from cardiac electrodes 331, patient-internal sensors 381, patient-external sensors 382, patient input devices 384, and information systems 383
and stores the collected data in memory.  The sleep quality data system may transmit the collected data to a separate device, such as the CRM programmer 315 or other device, periodically as required or desired.
In another embodiment, the CRM sleep quality data system 305 may perform further processing and/or evaluation of the sleep quality data.  For this purpose, the CRM sleep quality data system 305 may include a sleep quality analysis unit 340
coupled to the signal detector 360.  The sleep quality analysis unit 340 may include one or more components for evaluating the patient&#39;s sleep quality.  For example, the sleep quality analysis unit 340 may include sleep detection circuitry 341,
disordered breathing detection circuitry 342, abnormal nocturnal movement detection circuitry 344, and a sleep quality processor 343, as previously described in connection with FIG. 2.
The cardiac therapy module 320, signal detector 360, and sleep quality analysis unit 340 operate in cooperation with a memory unit 370.  The memory unit 370 may store parameters associated with cardiac therapy in addition to diagnostic or other
data related to cardiac functioning and sleep quality.  A communication unit 310 located within the CRM 300 may be used to transmit programming information and collected data from the CRM 300 to an external device such as a programmer 315.
Sleep quality assessment involves a reliable method for discriminating between a state of sleep and a state of wakefulness.  One method of detecting sleep involves comparing one or more detected physiological conditions to thresholds indicative
of sleep.  When the detected conditions are consistent with thresholds indicating sleep, then sleep onset is declared.  For example, decreased patient activity is a condition associated with sleep.  When the patient&#39;s activity falls below a predetermined
threshold, the system declares the onset of sleep.  When the patient&#39;s activity rises above the threshold, the system declares the end of sleep.  In a similar manner, a number of patient conditions, such as heart rate, respiration rate, brain wave
activity, etc., may be compared individually or collectively compared to thresholds or other indices to detect sleep.
An enhanced method of sleep detection is described in commonly owned U.S.  Pat.  No. 7,189,204, which is incorporated herein by reference.  The method involves adjusting a sleep threshold associated with a first patient condition using a second
patient condition.  The first patient condition is compared to the adjusted threshold to determine if the patient is asleep or awake.
FIG. 4 is a block diagram of a sleep detection unit 400 that may be used as part of a sleep quality data system according to embodiments of the invention.  The sleep detection unit 400 uses a number of sensors 401, 402, 403 to sense
sleep-related patient conditions.  A representative set of sleep-related conditions include, for example, patient activity, patient location, posture, heart rate, QT interval, eye movement, respiration rate, transthoracic impedance, tidal volume, minute
ventilation, brain activity, muscle tone, body temperature, time of day, and blood oxygen level.
According to embodiments of the invention, a first sleep-related condition detected using a sleep detection sensor 401 is compared to a sleep threshold for detecting the onset and termination of sleep.  A second sleep-related condition, detected
using a threshold adjustment sensor 402, is used to adjust the sleep threshold.  Although the example described herein involves one sleep detection sensor 401 and one threshold adjustment sensor 402, any number of thresholds or other indices
corresponding to a number of sleep detection sensors may be used.  Furthermore, conditions detected using any number of adjustment sensors may be used to adjust the thresholds or indices of a plurality of sleep detection signals.  Additional
sleep-related signals derived from one or more confirmation sensors 403 may optionally be used to confirm the onset or termination of the sleep condition.
Signals derived from the sensors 401, 402, 403 are received by a sensor driver/detection circuitry 410 that may include, for example, amplifiers, signal processing circuitry, and/or A/D conversion circuitry for processing each sensor signal.
The sensor driver/detection system 410 may further include sensor drive circuitry required to activate the sensors 401, 402, 403.
The sensor signal detection system 410 is coupled to a sleep detector 430.  The sleep detector 430 is configured to compare the level of a first sleep-related condition detected using the sleep detection sensor 401 to a sleep threshold adjusted
by a second sleep-related condition detected using the threshold adjustment sensor 402.  A determination of sleep onset or sleep termination may be made by the sleep detector 430 based on the comparison.  The onset or termination of sleep may optionally
be confirmed using patient conditions derived using a sleep confirmation sensor 403.
FIG. 5 is a flow chart illustrating a method of detecting sleep used in a sleep quality data system configured according to principles of the invention.  A sleep threshold associated with a first sleep-related patient condition is established
505.  The sleep threshold may be determined from clinical data of a sleep threshold acquired using a group of subjects, for example.  The sleep threshold may also be determined using historical data taken from the particular patient for whom the sleep
condition is to be detected.
First and second sleep-related conditions are detected 510, 520.  The first and the second sleep-related conditions may be detected using sensors implanted in the patient, attached externally to the patient or located remote from the patient,
for example, as previously described in connection with FIG. 3.  The first and the second sleep-related conditions may include any condition associated with sleep and are not limited to the representative sleep-related conditions listed above.
The sleep threshold established for the first sleep-related condition is adjusted using the second sleep-related condition 530.  For example, if the second sleep-related condition indicates a high level of activity that is incompatible with a
sleep state, the sleep threshold of the first sleep-related condition may be adjusted downward to require sensing a decreased level of the first sleep-related condition before a sleep condition is detected.
If the first sleep-related condition is consistent with sleep according to the adjusted sleep threshold 540, sleep is detected 550.  If the first sleep-related condition is not consistent with sleep using the adjusted sleep threshold, the first
and the second sleep-related conditions continue to be detected 510, 520 and the threshold adjusted 530 until sleep is detected 550.
The flow chart of FIG. 6 illustrates a method for detecting sleep using accelerometer and MV signals according to embodiments of the invention.  In the method illustrated in FIG. 6, an accelerometer and a minute ventilation sensor are used to
detect patient activity and patient respiration conditions, respectively.  A preliminary sleep threshold is determined 610 with respect to the patient activity condition sensed by the accelerometer.  The preliminary sleep threshold may be determined from
clinical data derived from a group of subjects or from historical data taken from the patient over a period of time.
The activity condition of the patient is monitored 620 using an accelerometer that may be incorporated in an implantable cardiac rhythm management system as described in connection with FIG. 3.  Alternatively, the accelerometer may be attached
externally to the patient.  The patient&#39;s MV condition is monitored 625, for example, using a transthoracic impedance sensor.  A transthoracic impedance sensor may be implemented as a component of an implantable CRM device.
In this embodiment, the patient&#39;s activity represents the sleep detection condition and is compared to the sleep threshold.  The patient&#39;s MV is used as the threshold adjustment condition to adjust the sleep threshold.  In addition, in this
example, the patient&#39;s heart rate is monitored 630 and used to provide a sleep confirmation condition.
The sleep threshold adjustment is accomplished using the patient&#39;s MV condition to adjust the activity sleep threshold.  If the patient&#39;s MV condition is low relative to an expected MV level associated with sleep, the activity sleep threshold is
increased.  Similarly, if the patient&#39;s MV level is high relative to an expected MV level associated with sleep, the activity sleep threshold is decreased.  Thus, when the patient&#39;s MV level is high, less activity is required to make the determination
that the patient is sleeping.  Conversely when the patient&#39;s MV level is relatively low, a higher activity level may result in detection of sleep.  The use of two sleep-related conditions to determine the patient&#39;s sleep state enhances the accuracy of
sleep detection over previous methods.
Various signal processing techniques may be employed to process the raw sensor signals.  For example, a moving average of a plurality of samples of the sensor signals may be calculated.  Furthermore, the sensor signals may be amplified,
filtered, digitized or otherwise processed.
If the MV level is high 635 relative to an expected MV level associated with sleep, the activity sleep threshold is decreased 640.  If the MV level is low 635 relative to an expected MV level associated with sleep, the activity sleep threshold
is increased 645.
If the patient&#39;s activity level is less than or equal to the adjusted sleep threshold 650, and if the patient is currently in a sleep state 665, then the patient&#39;s heart rate is checked 680 to confirm that the patient is asleep.  If the
patient&#39;s heart rate is compatible with sleep 680, then sleep onset is determined 690.  If the patient&#39;s heart rate is incompatible with sleep, then the patient&#39;s sleep-related conditions continue to be monitored.
If the patient&#39;s activity level is less than or equal to the adjusted sleep threshold 650 and if the patient is currently in a sleep state 665, then a continuing sleep state is determined and the patient&#39;s sleep-related conditions continue to be
monitored for sleep termination to occur.
If the patient&#39;s activity level is greater than the adjusted sleep threshold 650 and the patient is not currently in a sleep state 660, then the patient&#39;s sleep-related conditions continue to be monitored until sleep onset is detected 690.  If
the activity level is greater than the adjusted sleep threshold 650 and the patient is currently in a sleep state 660, then sleep termination is detected 670.
The graphs of FIGS. 7-10 illustrate the adjustment of the activity sleep threshold.  The relationship between patient activity and the accelerometer and MV signals is trended over a period of time to determine relative signal levels associated
with sleep.  The graph of FIG. 7 illustrates the patient&#39;s activity as indicated by an accelerometer.  The patient&#39;s heart rate for the same period is shown in the graph of FIG. 8.  The accelerometer signal indicates a period of sleep associated with a
relatively low level of activity beginning slightly before 23:00 and continuing through 6:00.  The patient&#39;s heart rate appropriately tracks the activity level indicated by the accelerometer indicating a similar period of decreased heart rate
corresponding to sleep.  The signal level of the accelerometer during known periods of sleep may be used to establish a threshold for sleep detection.
FIG. 9 is a graph of the patient&#39;s minute ventilation signal over time.  Historical data of minute ventilation is graphed over an 8 month period.  The minute ventilation data may be used to determine the minute ventilation signal level
associated with sleep.  In this example, a composite minute ventilation graph using the historical data presents a roughly sinusoidal shape with the relatively low minute ventilation levels occurring during the period approximately from hours 21:00
through 8:00.  The decreased minute ventilation level is associated with periods of sleep.  The minute ventilation level associated with sleep is used to implement sleep threshold adjustment.
FIG. 10 illustrates adjustment of the activity sleep threshold using the MV data.  The initial sleep threshold 1010 is established using the baseline activity data acquired as discussed above.  If the patient&#39;s MV level is low relative to an
expected MV level associated with sleep, the activity sleep threshold is increased 1020.  If the patient&#39;s MV level is high relative to an expected MV level associated with sleep, the activity sleep threshold is decreased 1030.  When the patient&#39;s MV
level is high, less activity detected by the accelerometer is required to make the determination that the patient is sleeping.  However, if the patient&#39;s MV level is relatively low, a higher activity level may result in detection of sleep.  The use of
two sleep-related signals to establish and adjust a sleep threshold enhances the accuracy of sleep detection over previous methods.
Additional sleep-related conditions may be sensed and used to improve the sleep detection method described above.  For example, a posture sensor may be used to detect the posture of the patient and used to confirm sleep.  If the posture sensor
signal indicates an upright posture, then the posture sensor signal may be used to override a determination of sleep using the sleep detection and threshold adjustment conditions.  Other conditions may also be used in connection with sleep determination
or confirmation, including the representative set of sleep-related conditions indicated above.  In another example, a proximity to bed sensor may be used alone or in combination with a posture sensor to detect that the patient is in bed and is lying
A sleep detection system may detect sleep onset, termination, arousals as well as the sleep stages, including REM and non-REM sleep.  REM sleep may be discriminated from NREM sleep, for example, by examining one or more signals indicative of
REM, e.g., muscle atonia, rapid eye movements, or EEG signals.  Methods and systems for detecting REM sleep that are particularly useful for patients with implantable devices are discussed in commonly owned U.S.  Publication No. 2005/0043652 and
incorporated herein by reference.  Various conditions indicative of sleep state may be detected using sensors, e.g., electroencephalogram (EEG), electrooculogram (EOG), or electromyogram (EMG) sensors, coupled through wired or wireless connections to the
sleep detection circuitry.  The sleep detection circuitry may analyze the various patient conditions sensed by the sensors to track the patient&#39;s sleep through various sleep states, including REM and NREM stages.
Returning to FIG. 3, the sleep quality data system 300 may also employ disordered breathing detection circuitry 342 to detect episodes of disordered breathing.  Disordered breathing may be detected in numerous ways using one or more of the
patient conditions listed in Table 1.  Methods and systems for detecting disordered breathing are described in commonly owned U.S.  Pat.  No. 7,252,640, which is incorporated herein by reference.  According to this approach, disordered breathing may be
detected by examining characteristics of the patient&#39;s respiration patterns to determine if the respiration patterns are consistent with disordered breathing.
FIG. 11 illustrates a normal respiration pattern as represented by a transthoracic impedance sensor signal.  The transthoracic impedance increases during respiratory inspiration and decreases during respiratory expiration.  During NREM sleep, a
normal respiration pattern includes regular, rhythmic inspiration--expiration cycles without substantial interruptions.
In one embodiment, detection of disordered breathing, including, for example, sleep apnea and hypopnea, involves defining and examining a number of respiratory cycle intervals.  FIG. 12 illustrates respiration intervals used for disordered
breathing detection according to an embodiment of the invention.  A respiration cycle is divided into an inspiration period corresponding to the patient inhaling, an expiration period, corresponding to the patient exhaling, and a non-breathing period
occurring between inhaling and exhaling.  Respiration intervals are established using inspiration 1210 and expiration 1220 thresholds.  The inspiration threshold 1210 marks the beginning of an inspiration period 1230 and is determined by the
transthoracic impedance signal rising above the inspiration threshold 1210.  The inspiration period 1230 ends when the transthoracic impedance signal is maximum 1240.  A maximum transthoracic impedance signal 1240 corresponds to both the end of the
inspiration interval 1230 and the beginning of the expiration interval 1250.  The expiration interval 1250 continues until the transthoracic impedance falls below an expiration threshold 1220.  A non-breathing interval 1260 starts from the end of the
expiration period 1250 and continues until the beginning of the next inspiration period 1270.
Detection of sleep apnea and severe sleep apnea according to embodiments of the invention is illustrated in FIG. 13.  The patient&#39;s respiration signals are monitored and the respiration cycles are defined according to inspiration 1330,
expiration 1350, and non-breathing 1360 intervals as described in connection with FIG. 12.  A condition of sleep apnea is detected when a non-breathing period 1360 exceeds a first predetermined interval 1390, denoted the sleep apnea interval.  A
condition of severe sleep apnea is detected when the non-breathing period 1360 exceeds a second predetermined interval 1395, denoted the severe sleep apnea interval.  For example, sleep apnea may be detected when the non-breathing interval exceeds about
10 seconds, and severe sleep apnea may be detected when the non-breathing interval exceeds about 20 seconds.
Hypopnea is a condition of disordered breathing characterized by abnormally shallow breathing.  FIGS. 14A-B are graphs of respiration patterns derived from transthoracic impedance measurements.  The graphs compare the tidal volume of a normal
breathing cycle to the tidal volume of a hypopnea episode.  FIG. 14A illustrates normal respiration tidal volume and rate.  As shown in FIG. 14B, hypopnea involves a period of abnormally shallow respiration.
According to an embodiment of the invention, hypopnea is detected by comparing a patient&#39;s respiratory tidal volume to a hypopnea tidal volume threshold.  The tidal volume for each respiration cycle may be derived from transthoracic impedance
measurements.  The hypopnea tidal volume threshold may be established using clinical results providing a representative tidal volume and duration for hypopnea events.  In one configuration, hypopnea is detected when an average of the patient&#39;s
respiratory tidal volume taken over a selected time interval falls below the hypopnea tidal volume threshold.
FIG. 15 is a flow chart illustrating a method of apnea and/or hypopnea detection according to embodiments of the invention.  Various parameters are established 1501 before analyzing the patient&#39;s respiration for disordered breathing episodes,
including, for example, inspiration and expiration thresholds, sleep apnea interval, severe sleep apnea interval, and hypopnea tidal volume threshold.
The patient&#39;s transthoracic impedance is detected 1505.  If the transthoracic impedance exceeds 1510 the inspiration threshold, the beginning of an inspiration interval is detected 1515.  If the transthoracic impedance remains below 1510 the
inspiration threshold, then the impedance signal is checked 1505 periodically until inspiration 1515 occurs.
During the inspiration interval, the patient&#39;s transthoracic impedance is monitored until a maximum value of the transthoracic impedance is detected 1520.  Detection of the maximum value signals an end of the inspiration period and a beginning
of an expiration period 1535.
The expiration interval is characterized by decreasing transthoracic impedance.  When the transthoracic impedance falls below 1540 the expiration threshold, a non-breathing interval is detected 1555.
If the transthoracic impedance does not exceed 1560 the inspiration threshold within a first predetermined interval 1565, denoted the sleep apnea interval, then a condition of sleep apnea is detected 1570.  Severe sleep apnea is detected 1580 if
the non-breathing period extends beyond a second predetermined interval 1575, denoted the severe sleep apnea interval.
When the transthoracic impedance exceeds 1560 the inspiration threshold, the tidal volume from the peak-to-peak transthoracic impedance is calculated 1585.  The peak-to-peak transthoracic impedance provides a value proportional to the tidal
volume of the respiration cycle.  This value is compared 1590 to a hypopnea tidal volume threshold.  If the peak-to-peak transthoracic impedance is consistent with 1590 the hypopnea tidal volume threshold for a predetermined time 1592, then a hypopnea
cycle is detected 1595.
Additional sensors, such as motion sensors and/or posture sensors, may be used to confirm or verify the detection of a sleep apnea or hypopnea episode.  The additional sensors may be employed to prevent false or missed detections of sleep apnea
or hypopnea due to posture and/or motion related artifacts.
Another embodiment of the invention involves classifying respiration patterns as disordered breathing episodes based on the breath intervals and/or tidal volumes of one or more respiration cycles within the respiration patterns.  According to
this embodiment, the duration and tidal volumes associated with a respiration pattern are compared to duration and tidal volume thresholds.  The respiration pattern may be determined to represent a disordered breathing episode based on the comparison.
According to this embodiment, a breath interval 1630 is established for each respiration cycle.  A breath interval represents the interval of time between successive breaths, as illustrated in FIG. 16.  A breath interval 1630 may be defined in a
variety of ways, for example, as the interval of time between successive maxima 1610, 1620 of the impedance signal waveform.
Detection of disordered breathing, in accordance with methods of the invention, involves the establishment of a duration threshold and a tidal volume threshold.  If a breath interval exceeds the duration threshold, an apnea event is detected.
Detection of sleep apnea, in accordance with this embodiment, is illustrated in the graph of FIG. 16.  Apnea represents a period of non-breathing.  A breath interval 1630 exceeding a duration threshold 1640 comprises an apnea episode.
Hypopnea may be detected using a duration threshold and a tidal volume threshold.  A hypopnea event represents a period of shallow breathing greater than the duration threshold.  Each respiration cycle in a hypopnea event is characterized by a
tidal volume less than the tidal volume threshold.  Further, the decreased tidal volume cycles persist longer than the duration threshold.
A hypopnea detection approach, in accordance with embodiments of the invention, is illustrated in FIG. 17.  Shallow breathing is detected when the tidal volume of one or more breaths is below a tidal volume threshold 1710.  If the shallow
breathing continues for an interval greater than a duration threshold 1720, then the breathing pattern represented by the sequence of shallow respiration cycles, is classified as a hypopnea event.
FIGS. 18A and 18B provide charts illustrating classification of individual disordered breathing events and combination of periodic breathing events, respectively.  As illustrated in FIG. 18A, individual disordered breathing events may be grouped
into apnea, hypopnea, tachypnea and other disordered breathing events.  Apnea events are characterized by an absence of breathing.  Intervals of reduced respiration are classified as hypopnea events.  Tachypnea events include intervals of rapid
respiration characterized by an elevated respiration rate.
As illustrated in FIG. 18A, apnea and hypopnea events may be further subdivided as either central events, e.g., caused either by central nervous system dysfunction, or obstructive events, e.g., caused by upper airway obstruction.  A tachypnea
event may be further classified as a hyperpnea event, represented by rapid deep breathing (hyperventilation).  A tachypnea event may alternatively be classified as rapid shallow breathing, typically of prolonged duration.
FIG. 18B illustrates classification of periodic disordered breathing events.  Periodic breathing may be classified as obstructive, central or mixed.  Obstructive periodic breathing is characterized by cyclic respiratory patterns with an
obstructive apnea or hypopnea event in each cycle.  In central periodic breathing, the cyclic respiratory patterns include a central apnea or hypopnea event in each cycle.  Periodic breathing may also be of mixed origin.  In this case, cyclic respiratory
patterns have a mixture of obstructive and central apnea events in each cycle.  Cheyne-Stokes respiration is a particular type of periodic breathing characterized by a gradual waxing and waning of tidal volume and having a central apnea and hyperpnea
event in each cycle.  Other manifestations of periodic breathing are also possible.
As illustrated in FIGS. 18C-G, a respiration pattern detected as a disordered breathing episode may include only an apnea respiration cycle 1810 (FIG. 18C), only hypopnea respiration cycles 1850 (FIG. 18F), or a mixture of hypopnea and apnea
respiration cycles 1820 (FIG. 18D), 1830 (FIG. 18E), 1860 (FIG. 18G).  A disordered breathing event 1820 may begin with an apnea respiration cycle and end with one or more hypopnea cycles.  In another pattern, the disordered breathing event 1830 may
begin with hypopnea cycles and end with an apnea cycle.  In yet another pattern, a disordered breathing event 1860 may begin and end with hypopnea cycles with an apnea cycle in between the hypopnea cycles.  Analysis of the characteristic respiration
patterns associated with various types of disordered breathing may be used to detect, classify and evaluate disordered breathing episodes.
FIG. 19 is a flow chart of a method for detecting disordered breathing by classifying breathing patterns using breath intervals in conjunction with tidal volume and duration thresholds as previously described above.  In this example, a duration
threshold and a tidal volume threshold are established for determining both apnea and hypopnea breath intervals.  An apnea episode is detected if the breath interval exceeds the duration threshold.  A hypopnea episode is detected if the tidal volume of
successive breaths remains less than the tidal volume threshold for a period in excess of the duration threshold.  Mixed apnea/hypopnea episodes may also occur.  In these cases, the period of disordered breathing is characterized by shallow breaths or
non-breathing intervals.  During the mixed apnea/hypopnea episodes, the tidal volume of each breath remains less than the tidal volume threshold for a period exceeding the duration threshold.
The patient&#39;s respiration cycles are determined, for example, using transthoracic impedance signals.  Each breath 1910 is characterized by a breath interval, i.e., the interval of time between two impedance signal maxima and a tidal volume (TV). If a breath interval exceeds 1915 the duration threshold, then the respiration pattern is consistent with an apnea event, and an apnea event trigger is turned on 1920.  If the tidal volume of the breath interval exceeds 1925 the tidal volume threshold,
then the breathing pattern is characterized by two respiration cycles of normal volume separated by a non-breathing interval.  This pattern represents a purely apneic disordered breathing event, and apnea is detected 1930.  Because the final breath of
the breath interval was normal, the apnea event trigger is turned off 1932, signaling the end of the disordered breathing episode.  However, if the tidal volume of the breath interval does not exceed 1925 the tidal volume threshold, the disordered
breathing period is continuing and the next breath is checked 1910.
If the breath interval does not exceed 1915 the duration threshold, then the tidal volume of the breath is checked 1935.  If the tidal volume does not exceed 1935 the tidal volume threshold, the breathing pattern is consistent with a hypopnea
cycle and a hypopnea event trigger is set on 1940.  If the tidal volume exceeds the tidal volume threshold, then the breath is normal.
If a period of disordered breathing is in progress, detection of a normal breath signals the end of the disordered breathing.  If disordered breathing was previously detected 1945, and if the disordered breathing event duration has not exceeded
1950 the duration threshold, and the current breath is normal, then no disordered breathing event is detected 1955.  If disordered breathing was previously detected 1945, and if the disordered breathing event duration has extended for a period of time
exceeding 1950 the duration threshold, and the current breath is normal, then the disordered breathing trigger is turned off 1960.  In this situation, the duration of the disordered breathing episode was of sufficient duration to be classified as a
disordered breathing episode.  If an apnea event was previously triggered 1965, then an apnea event is declared 1970.  If a hypopnea was previously triggered 1965, then a hypopnea event is declared 1975.
As previously discussed in connection with FIG. 3, a sleep quality analysis unit 340 may incorporate an abnormal nocturnal movement detector 344 to evaluate the movements of a patient during the night to detect nocturnal movement disorders such
as RLS, PLMD, and/or bruxism.  The patient may be instrumented with accelerometers located on the limbs or jaw, for example, to sense patient movement.  Excessive movement, or movements having a characteristic pattern, e.g., periodic limb or jaw
movements, may be classified as abnormal nocturnal movements.  For example, bruxism is a sleep disorder wherein the patient grinds his teeth during sleep.  An accelerometer attached to the patient&#39;s jaw may be used to sense movement of the jaw.  Signals
from the jaw accelerometer may be transferred to the abnormal movement detector for evaluation to determine if the movements are excessive or unusually periodic, indicating bruxism.  In a similar application, accelerometers attached to the patient&#39;s
limbs may generate signals used by the abnormal movement detector 344 to detect and classify disorders such as RLS and PLMD.
FIG. 20 illustrates a patient 2010 instrumented with a sleep quality data system 2000 according to embodiments of the invention.  The sleep quality data system collects sleep quality data from the patient using a number of sensors 2011-2019.  In
one configuration, the collected data is analyzed by a sleep quality analysis unit that may be an integrated component of an implantable sleep quality data collection and analysis unit 2020.  In another configuration, the collected data may be downloaded
to a patient-external device 2030 for storage, analysis, or display.
In the implementation illustrated in FIG. 20, the sleep quality data system 2000 includes an implantable sleep quality data collection and analysis unit 2020 coupled to a number of sensors 2011-2019.  In this example, the sensors include an EGM
sensor 2016 for detecting heart rate and heart rate variability conditions.  A transthoracic impedance sensor 2017 is used to detect the respiration conditions of the patient, including, for example, minute ventilation, respiration rate, and tidal
volume.  An activity detector, e.g., accelerometer, 2015 may be used to detect patient activity conditions.  The sleep quality data system detects patient conditions including the patient&#39;s posture and location using a posture sensor 2014 and a proximity
to bed sensor 2013, respectively.  The sleep quality data system senses the patient&#39;s brain activity using EEG sensors 2011 and the patient&#39;s eye movements using EOG sensors 2012.  Jaw and limb movements are sensed using accelerometers attached to the
patient&#39;s jaw 2018 and legs 2019.
In this application, the sleep quality data collection and analysis unit 2020 is configured to track the patient&#39;s heart rate, heart rate variability, minute ventilation, respiration rate, tidal volume, posture, proximity to bed, brain activity,
eye movements, jaw movements and leg movements.  At periodic intervals, the system samples signals from the sensors and stores data regarding the detected conditions in memory circuitry within the sleep quality data collection and analysis unit 2020.
The sleep quality data collection and analysis unit 2020 may additionally access an external input unit 2030 to detect patient reported conditions, for example, recent tobacco and medication use by the patient.  Further, the sleep quality data collection
and analysis unit 2020 may monitor conditions using one or more external sensors.  In the illustrated example, a thermometer 2035 is coupled through the external programmer 2030 and a pollution website 2040 is accessible to the sleep quality data
collection and analysis unit 2020 through the internet 2050.
The sleep quality data collection and analysis unit 2020 may operate to acquire data during periods of both sleep and wakefulness.  It may be beneficial, for example, to track changes in particular conditions measured during periods of
wakefulness that are associated with sleep disordered breathing.  For example, some patients who suffer from sleep apnea experience changes in heart rate variability, blood pressure variability, and/or sympathetic nerve activity during periods of
wakefulness.  Detection and analysis of the physiological changes attributable to sleep disorders and measurable during the time the patient is awake provides a more complete picture of sleep quality.
In another example, the patient&#39;s sleep quality may be evaluated by determining the patient&#39;s activity level while the patient is awake.  The activity level of the patient during the day may provide important information regarding the patient&#39;s
sleep quality.  For example, if the patient is very inactive during periods of wakefulness, this may indicate that the patient&#39;s sleep is of inadequate quality or duration.  Such information may also be used in connection with assessing the efficacy of a
particular sleep disorder therapy and/or adjusting the patient&#39;s sleep disorder therapy.  Methods and systems for determining the patient&#39;s activity level and generally assessing the well-being of a patient are described in commonly owned U.S.  Pat.  No.
6,021,351 which is incorporated herein by reference.
The analysis unit 2020 may calculate one or more sleep quality metrics quantifying the patient&#39;s sleep quality.  A representative set of the sleep quality metrics include, for example, sleep efficiency, sleep fragmentation, number of arousals
per hour, denoted the arousal index (AI).
The analysis unit 2020 may also compute one or more metrics quantifying the patient&#39;s disordered breathing, such as the apnea hypopnea index (AHI) providing the number of apneas and hypopneas per hour, and the percent time in periodic breathing
(% PB).
Further, metrics associated with sleep movement disorders may also be determined by the analysis unit 2020.  Such metrics may include, for example, a general sleep movement disorder index (MDI) representing the number of abnormal movements
arising from movement disorders such as restless leg syndrome, periodic limb movement disorder and bruxism per hour.  In addition, specific indices may be calculated for each type of movement disorder, e.g., a bruxism index (BI) characterizing the number
of jaw movements per hour, a RLS index (RLSI) characterizing the number of restless leg syndrome episodes per hour, and a PLM index (PLMI) characterizing the number of periodic limb movements experienced by the patient per hour.
In addition, percentage of sleep time during which the patient experiences movement disorders (% MD) may be calculated.  Specific metrics relating to the percentage of time during which the patient experiences bruxism (% B), restless leg
syndrome (% RLS), and periodic leg movement disorder (% PLMD) may also be determined.
Further, sleep summary metrics may be computed, either directly from the collected patient condition data, or by combining the above-listed sleep quality and sleep disorder metrics.  In one embodiment, a composite sleep disordered respiration
metric (SDRM) may be computed by combining the apnea hypopnea index AHI and the arousal index AI.  The composite sleep disordered respiration metric (SDRM) may be computed as a linear combination of the AHI and AI as follows: SDRM=c.sub.1*AHI+c.sub.2*AI
[1] where c.sub.1 and c.sub.2 are constants chosen to balance the relative contributions of respiratory and arousal effects on sleep disturbance.  The AHI may be monitored by performing disordered breathing detection based on transthoracic impedance
measurements as previously described.  The AI may be estimated, for example, by monitoring the patient activity, minute ventilation, and posture sensors for body motion indicating sleep termination or arousal.  A more sensitive measure of arousal may be
made using EEG signals.  In this implementation, the constant c.sub.2 may be adjusted to reflect the increased sensitivity to arousal.
The URST or URSE metrics may be determined using three parameters: total time in bed (TIB), total time asleep (TA), and combined sleep time duration in disturbed respiration (STDR).  Time in bed may be determined by a combination of posture
sensing and sensing the proximity of the patient to bed.  The posture condition of the patient may determined, for example, using an implantable multiaxis accelerometer sensor.
The patient&#39;s total time in bed (TIB) may be determined using a proximity to bed sensor.  The proximity to bed sensor may use a receiver in the sleep quality data collection and analysis unit 2020 for receiving signals transmitted from a beacon
2070 located at the patient&#39;s bed 2060.  If the proximity to bed receiver detects a signal of sufficient strength from the proximity to bed beacon 2070, then the receiver detects that the patient is in bed 2060.
Total time asleep (TA) may be determined using the sleep detection method described in more detail above.  The total sleep time in disturbed respiration (STDR) may be determined, for example, based on detection of sleep and disordered breathing
using the sleep and disordered breathing detection methods described above.
The patient&#39;s undisturbed respiration sleep time (URST) is calculated as: URST=TA-STDR [2] where TA=total time asleep and STDR=sleep time in disturbed breathing.
The undisturbed respiration sleep efficiency (USE) in percent is calculated URSE=100*URST/TIB [3]
Similar metrics may be calculated for movement disorders generally, or for specific movement disorders, e.g., RLS, PLMD, or bruxism.  For example, the composite RLS, PLMD, and bruxism metrics, RLSM, PLMDM, and BM, respectively, may be calculated
using equations similar in form to equation 1 above: RLSM=c.sub.1*RLSI+c.sub.2*AI [4]
where RLSI=number of restless leg movement syndrome episodes per hour, AI=number of arousals per hour, and c.sub.1 and c.sub.2 are constants chosen to balance the relative contributions of abnormal movement and arousal effects on sleep
disturbance.  PLMDM=c.sub.1*PLMDI+c.sub.2*AI [5]
where PLMDI=number of periodic leg movement syndrome episodes per hour, AI=number of arousals per hour, and c.sub.1 and c.sub.2 are constants chosen to balance the relative contributions of abnormal movement and arousal effects on sleep
disturbance.  BM=c.sub.1*BMI+c.sub.2*AI [6]
where BMI=number of bruxism movement episodes per hour, AI=number of arousals per hour, and c.sub.1 and c.sub.2 are constants chosen to balance the relative contributions of abnormal movement and arousal effects on sleep disturbance.
The patient&#39;s undisturbed movement sleep time (UMST) and undisturbed movement sleep efficiency (UMSE) may be calculated for each movement related disorder separately or in combination using equations similar in form to equations 2 and 3, above.
In addition, a composite sleep disorder index SDI quantifying the combined effect of both respiratory and movement disorders may be computed by combining the apnea hypopnea index (AHI), the movement disorder index (MDI), and the arousal index
A sleep disturbance index (SDI) may be computed as a linear combination of the AHI, and the combined disorder index DI.sub.c.  The combined disorder index may include both abnormal breathing and movement components.  For example, the sleep
disturbance index SDI is characterizable by the equation: SDI=c.sub.4*DI.sub.c+c.sub.3*AI, [7]
where DI.sub.c is a combined disorder index of the form: DI.sub.c=c.sub.41*DI.sub.1+c.sub.42*DI.sub.2 [7a] In equation 7, c.sub.4 and c.sub.3 are constants chosen to balance the relative contributions of the combined disorder and arousal
effects, respectively.  The disorder index, DI.sub.c, may be used to characterize the effects of one or more sleep disorders, including, e.g., disorders associated with disturbed respiration and/or abnormal movements.  The combined disorder index may
represent only one disorder index, or may be a linear combination of two or more sleep disorder indices, e.g., the apnea/hypopnea index (AHI) and the abnormal movement disorder index (MDI).  The constants C.sub.41 and c.sub.42 may be used as weighting
factors associated with particular disorder indices.
The patient&#39;s undisturbed sleep time (UST) may be calculated: UST=TA-STSD [8]
The undisturbed sleep efficiency (USE) in percent may be calculated: USE=100*UST/TIB [9]
Sleep quality metrics, such as those described above, or other metrics, may be acquired and analyzed using the sleep quality data collection and analysis unit 2020.  Sleep quality metrics, in addition to raw or processed data based on
physiological and non-physiological conditions may determined periodically, e.g., daily, and stored or transmitted to another device.  Such data can be presented to the patient&#39;s health care professional on a real-time basis, or as a long-term, e.g.,
month long or year long, trend of daily measurements.
The health care professional may access the data during clinic visits via programmer interrogation of the implanted device, through occasional or periodic trans-telephonic device interrogations, or through an automatic or &quot;on-demand&quot; basis in
the context of an advanced patient management system.  The health care professionals may use the sleep quality indicator trends alone or in conjunction with other device-gathered or clinical data to diagnose disorders and/or adjust the patient&#39;s device
or medical therapy as needed to improve the patient&#39;s quality of sleep.
The present invention provides diagnostic, monitoring, and evaluation capabilities relating to sleep quality and may be particularly valuable in the context of an advanced patient management system.  Undiagnosed sleep disorders can lead to
increased morbidity and mortality, such as those arising from various respiratory and cardiovascular consequences.  Routine monitoring of patient sleep quality may lead to improved diagnosis and treatment of these syndromes and their associated
co-morbidities.  The invention may provide less obtrusive sleep quality monitoring, particularly and is suited for patients having an implanted device.  The present invention serves to improve diagnosis of sleep disorders by reducing the inconveniences,
unnatural sleep environment issues, and expenses associated with sleep clinic polysomnogram studies.
The following commonly owned U.S.  patents applications, some of which have been identified above, are hereby incorporated by reference in their respective entireties: U.S.  patent application Ser.  No. 10/309,770, filed Dec.  4, 2002, U.S.
patent application Ser.  No. 10/309,771, filed Dec.  4, 2002, U.S.  patent application entitled &quot;Prediction of Disordered Breathing,&quot; identified by and concurrently filed with this patent application, U.S.  patent application entitled &quot;Adaptive Therapy
for Disordered Breathing,&quot; identified by and filed concurrently with this patent application, U.S.  patent application entitled &quot;Prediction of Disordered Breathing,&quot; identified by and filed concurrently with this patent application, and U.S.  patent
application entitled &quot;Therapy Triggered by Prediction of Disordered Breathing,&quot; identified by and filed concurrently with this patent application.
"Sleep Quality Data Collection And Evaluation - Patent 8002553"
http://img.docstoccdn.com/thumb/206/132946266.png
Association of sleep hygiene related factors and sleep quality among
VAHTERA_J_retirement_sleep_Sleep
Self-Reported Sleep Quality and Fatigue Correlates With Actigraphy sleep tidur