Source: https://patents.google.com/patent/US9526429B2/en
Timestamp: 2019-11-19 16:58:41
Document Index: 486159736

Matched Legal Cases: ['Application No. 2007256872', 'Application No. 2010210569', 'Application No. 201080012088', 'Application No. 201080012088', 'Application No. 07784266', 'Application No. 2009', 'Application No. 2009', 'Application No. 2011549255', 'Application No. 200780026740']

US9526429B2 - Apparatus, system and method for chronic disease monitoring - Google Patents
US9526429B2
US9526429B2 US12/367,255 US36725509A US9526429B2 US 9526429 B2 US9526429 B2 US 9526429B2 US 36725509 A US36725509 A US 36725509A US 9526429 B2 US9526429 B2 US 9526429B2
US12/367,255
US20100204550A1 (en
2009-02-06 Application filed by ResMed Sensor Technologies Ltd filed Critical ResMed Sensor Technologies Ltd
2009-02-06 Priority to US12/367,255 priority Critical patent/US9526429B2/en
2009-02-18 Assigned to BIANCAMED LIMITED reassignment BIANCAMED LIMITED ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: DE CHAZAL, PHILIP, SHOULDICE, REDMOND, ZAFFARONI, ALBERTO, HENEGHAN, CONOR
2010-02-04 Priority claimed from CN201410551183.6A external-priority patent/CN104257370B/en
2010-08-12 Publication of US20100204550A1 publication Critical patent/US20100204550A1/en
2015-03-17 Priority claimed from AU2015201380A external-priority patent/AU2015201380B2/en
2016-12-27 Publication of US9526429B2 publication Critical patent/US9526429B2/en
230000001684 chronic Effects 0 abstract description title 29
230000000241 respiratory Effects 0 abstract claims description 111
208000008784 Apnea Diseases 0 abstract claims description 18
230000000422 nocturnal Effects 0 abstract claims description 9
206010007554 Cardiac failure Diseases 0 abstract description 41
206010019280 Heart failures Diseases 0 abstract description 41
206010021079 Hypopnoea Diseases 0 claims description 14
230000036387 respiratory rate Effects 0 abstract description 12
235000019786 weight gain Nutrition 0 claims description 11
108010074051 C-Reactive Protein Proteins 0 claims description 4
102100015724 CRP Human genes 0 claims description 4
239000000692 Natriuretic Peptides Substances 0 claims description 3
108010030519 Natriuretic Peptides Proteins 0 claims description 3
102000005913 Natriuretic Peptides Human genes 0 claims description 3
206010002974 Apnoea Diseases 0 description 13
206010008501 Cheyne-Stokes respiration Diseases 0 description 6
238000009531 respiratory rate measurement Methods 0 description 3
108010062271 Acute-Phase Proteins Proteins 0 description 1
102000011767 Acute-Phase Proteins Human genes 0 description 1
However, despite the widespread use of recommendations on weight gain as a marker of deterioration (e.g., a patient is told that a gain of 2 kg over a 2 to 3 day period should generate a call to their clinic), there is relatively little published data on the sensitivity and specificity of ambulatory monitoring of weight gain in a clinical setting. Groups who have investigated the sensitivity of weight gain in distinguishing clinically stable (CS) Class IV patients from those with clinical deterioration (CD), have found that the performance is quite limited. These researchers found quite modest predictive values for weight gain in isolation. For example, the clinical guideline of 2 kg weight gain over 48-72 h has a specificity of 97% but a sensitivity of only 9%. Reducing the threshold to 2% of body weight, improves the sensitivity to 17% (with specificity only dropping marginally). In general they conclude that weight gain in isolation has relatively poor sensitivity in detecting clinical deterioration (though its specificity is good).
FIGS. 8A-8D show how the respiratory envelope changes over longer periods of time in the presence and absence of periodic breathing, including illustrating the power spectral densities of the respiratory envelopes in the presence of periodic breathing, and its absence. FIG. 8A is the respiratory envelope of a person with heart failure measured over a five minute period. FIG. 8B is the power spectral density of the respiratory envelope shown in FIG. 8A. FIG. 8C is the respiratory envelope of a person without heart failure measured over a five minute period. FIG. 8D is the power spectral density of the respiratory envelope shown in FIG. 8C.
Measurements made from all the sensors mentioned above (respiration, weighing scales and other sensors) may be aggregated together in data aggregation device 106. Aggregation device 106 could be a cell-phone, a personal computer, a tablet computer, or a customized computing device. This aggregation device can also be referred to as a data hub and, at a minimum, it may transfer data from the respiratory sensor 102 to the aggregation device itself. In one aspect of this embodiment, data aggregation device 106 may also have the capability of transmitting the collected data to remote data analyzer 107. Remote data analyzer 107 may itself be a server computer, personal computer, mobile computing device or another customized computing device. Remote data analyzer 107 will typically have storage, processing, memory and computational elements. Remote data analyzer 107 will typically be configured to provide a database capability, and may include further data archiving, processing and analysis means, and would typically have a display capability via display 108 so that a remote user (e.g., a cardiac nurse) can review data.
R(t)=√{square root over (I 2(t)+Q 2(t))},
This respiratory envelope signal can then be processed to recognize apnea and hypopneas. As a specific embodiment, consider the results shown in FIGS. 7 A and 7B. The respiratory envelope signal has been normalized over a period of multiple minutes, and its value is then shown over time. Using pre-established (or adaptive) rules, the amplitude of the respiratory envelope signal is compared to a number of thresholds. For example, in this case, if the amplitude stays above 0.7, breathing is considered normal. If the envelope stays between 0.2 and 0.7 for more than 10 seconds, then a hypopnea event is calculated. If the envelope dips below 0.2 for 10 seconds, then the event is considered an apnea. The person skilled in the art will realize that the exact rules will depend upon clinical definitions of apnea and hypopnea (which may vary from region to region), and the processing methods used for normalization and envelope extraction. In this way, specific events and their start and end times can be established. For example, FIG. 7A shows a hypopnea event which started at time t=18 s, and finished at t=31 s. FIG. 7B shows an apnea event which started at time t=32 s and ended at t=49 s.
An apnea-hypopnea index (AHI) is then calculated by counting the number of average number of apneas and hypopneas per hour of sleep (for example, if a person has 64 apneas, 102 hypopneas, and sleeps for 6.3 hrs, then their AHI is 166/6.3=26.3). This is an important parameter in assessing the overall status of the subject with chronic disease.
Monitoring the respiration rate itself is also an important parameter in chronic disease monitoring. For example, in acute respiratory failure the respiration rate can rise over 30 breaths/minute in adults, from a more typical baseline of 15 or 16 breaths/minute. One technique for tracking the respiratory rate during the night is as follows, as illustrated in FIG. 9A. For the case of a respiratory effort signal obtained from the non-contact sensor discussed earlier, a sliding window is applied to the data (e.g., 30 seconds in length). The power spectral density is then calculated for that epoch (FIG. 9B), using techniques such as the averaged periodogram. The power spectral density will typically contain a peak corresponding to the breathing frequency somewhere between 0.1 and 0.5 Hz. This peak can be identified by using a peak-finding algorithm. In some cases, there may be excessive motion artifact on the data—in such a case a technique such as Lomb's periodogram can be used to estimate the power spectral density (this interpolates through missing data). Alternatively, the respiratory effort signal can be fit with a model using Auto Regressive or Auto Regressive Moving Average techniques. The model parameters can then be used to estimate the respiration frequency. Kalman filtering techniques can also be employed. In this way, an average respiration frequency for the time window can be obtained. The sliding window can then advance by 1 or more seconds. In this way, a time series of the respiration frequency can be build up over the night. A simple average respiration for the night can be obtained by averaging over this time series for the night. Alternatively, more complex measurements of respiratory frequency can be calculated such as median frequency, variance of the respiratory frequency, percentile distributions of the respiratory frequency, and auto-correlation of the respiratory frequency.
FIG. 10 shows an example of the calculated characteristic modulation periods in subjects with sleep apnea, using the signals obtained from a biomotion sensor, as compared to the periods calculated using the full respiratory effort and airflow signals obtained from a polysomnogram. This characteristic modulation period of Cheyne-Stokes respiration may have prognostic significance, as it is related to the circulation time. Circulation time refers to approximately the time it takes for blood to circulate throughout the complete cardiac system. It can be estimated by using the total circulating blood volume (Volume—liters) and cardiac output (CO, Volume/time—typically in liters/minute), so that the circulation time (CT) can be calculated as (blood volume/cardiac output). In normal adults, CT is typically about 20 seconds. Increases in central blood volume and/or reductions in cardiac output lead to a prolongation of circulation time. Increases in the circulation time cause feedback delay between the lungs and carotid chemoreceptors. When the circulation time in prolonged, it will take longer for ventilatory disturbances in the lungs to be sensed by the chemoreceptors. This delay leads to over- and undershooting of ventilation, and a periodic breathing pattern of the central or CheyneStokes type. So in that manner, calculating the modulation period of Cheyne-Stokes respiration provides insight into the overall circulation time.
An alternative embodiment of the decision making process could be to use a more statistically based approach such as a classifier based on linear, logistic or quadratic discriminant as shown in FIG. 13B. In these approaches, the data from the respiration signal 1301 and cardiac signal 1302 is used to generate features (for example, the respiration features could be average nocturnal respiration rate, percentage of periodic breathing, variance of the respiration, etc.). Symptom input can be mapped to 0 or 1 (where 1 is a “yes” and 0 is a “no”). For example, the answer to the question “do you feel breathless” could map to a 0 or 1 and input as element 1303. The answer to the question “do you feel worse than yesterday” could map to element 1304. The answer to the question “did you use more than one pillow” could map to element 1305. Analog measurements such as weight or blood pressure could also be used to generate a “point” feature. Measurements from previous nights' recordings, and demographic features can also be included. The features from the various sources are then combined into a single vector X. The vector is then multiplied by a linear vector a, to produce a discriminant value c. This value is compared to a threshold to make a decision. The distance from the threshold can also be used to generate a posterior probability for a decision.
X = [ AVERAGE ⁢ ⁢ RESPIRATORY ⁢ ⁢ RATE Δ ⁢ ⁢ ( AVERAGE ⁢ ⁢ RESPIRATORY ⁢ ⁢ RATE ) ⁢ ⁢ compared ⁢ ⁢ to ⁢ ⁢ AVG . ⁢ OF ⁢ ⁢ LAST ⁢ ⁢ 5 ⁢ ⁢ NIGHTS 90 ⁢ th ⁢ ⁢ PERCENTILE ⁢ ⁢ VALUE ⁢ ⁢ OF ⁢ ⁢ RESPIRATORY ⁢ ⁢ RATE VARIANCE ⁢ ⁢ OF ⁢ ⁢ RESPIRATORY ⁢ ⁢ RATE AVERAGE ⁢ ⁢ HEART ⁢ ⁢ RATE Δ ⁢ ⁢ ( AVERAGE ⁢ ⁢ HEART ⁢ ⁢ RATE ) ⁢ ⁢ compared ⁢ ⁢ to ⁢ ⁢ AVERAGE ⁢ ⁢ OF ⁢ ⁢ LAST ⁢ ⁢ 5 ⁢ ⁢ NIGHTS 90 ⁢ th ⁢ ⁢ PERCENTILE ⁢ ⁢ VALUE ⁢ ⁢ OF ⁢ ⁢ HEART ⁢ ⁢ RATE Δ ⁢ ⁢ ( WEIGHT ) ⁢ ⁢ compared ⁢ ⁢ to ⁢ ⁢ AVERAGE ⁢ ⁢ OF ⁢ ⁢ LAST ⁢ ⁢ 5 ⁢ ⁢ NIGHTS RESPONSE ⁢ ⁢ TO ⁢ ⁢ “ DO ⁢ ⁢ YOU ⁢ ⁢ FEEL ⁢ ⁢ BREATHLESS ” ⁢ ⁢ ( 0 ⁢ ⁢ or ⁢ ⁢ 1 ) RESPONSE ⁢ ⁢ TO ⁢ ⁢ “ DO ⁢ ⁢ YOU ⁢ ⁢ FEEL ⁢ ⁢ WORSE ⁢ ⁢ THAN ⁢ ⁢ YESTERDAY ” ⁢ ⁢ ( 0 ⁢ ⁢ or ⁢ ⁢ 1 ) RESPONSE ⁢ ⁢ TO ⁢ ⁢ “ DO ⁢ ⁢ YOU ⁢ ⁢ FEEL ⁢ ⁢ BREATHLESS ⁢ ⁢ WHEN ⁢ ⁢ LYING ⁢ ⁢ DOWN ” ⁢ ⁢ ( 0 ⁢ ⁢ or ⁢ ⁢ 1 ) AGE GENDER ⁢ ⁢ ( MALE = 1 , FEMALE = 0 ) ]
a non-contact biomotion sensor configured to:
transmit radio waves,
detect transmitted radio waves reflected by the subject's body, the detected radio waves being modulated due to movement of the subject's body, and
output a first respiratory output signal comprising a measured respiratory parameter of the subject, the respiratory parameter determined by analysis of the detected radio waves; and
an analyzer configured to receive the first respiratory output signal and a second input signal that provides subjective clinical symptom data from the subject, wherein the analyzer is further configured to:
determine a respiratory effort envelope of the first respiratory output signal;
derive parameters related to at least one of the respiratory effort envelope and the second input signal, the parameters including at least an Apnea-Hypopnea Index (AHI);
store the derived parameters in a database comprising previously derived parameters; and
provide an output that provides a health assessment of the subject based on the previously derived parameters and currently derived parameters, the health assessment indicating whether clinical deterioration has happened.
2. The system of claim 1, further comprising a cardiac sensor operatively coupled to the analyzer, wherein the database is further configured to store a plurality of cardiac features derived from a detected cardiac parameter in the database of previously derived parameters,
wherein the analyzer is configured to selectively combine a currently derived cardiac feature, the plurality of cardiac features, a currently derived respiratory feature, and a plurality of respiratory features to determine the health assessment of the subject.
3. The system of claim 1, further comprising a third input signal supplied to the analyzer that provides a measure of a body weight of the subject.
4. The system of claim 1, further comprising a non-respiratory input signal that is distinct from the first respiratory output signal and provides one or more physiological measurements selected from the group consisting of a blood pressure, a blood oxygen level, a measurement of B natriuretic peptides, and a measurement of C-reactive protein.
5. The system of claim 1, further comprising a third input signal supplied to the analyzer that provides a physiological measurement of a blood glucose level.
6. The system of claim 1, further comprising a data hub configured to exchange data at least between the sensor and the analyzer.
7. The system of claim 1, wherein a derived movement signal is calculated through a combination of two quadrature movement signals using a phase demodulation technique.
8. The system of claim 1, wherein the analyzer determines a respiratory pattern by analyzing the respiratory effort envelope.
9. The system of claim 1, wherein the analyzer is configured to determine an occurrence of apneas and hypopneas using the respiratory effort envelope.
10. The system of claim 1, wherein the analyzer is configured to determine an occurrence of periodic breathing using the respiratory effort envelope.
11. The system of claim 1, wherein the analyzer is configured to determine a characteristic time period of periodic breathing using the respiratory effort envelope.
12. The system of claim 1, wherein the first respiratory output signal comprises a respiration rate of the subject.
13. The system of claim 1, wherein the output comprises a proposed clinical intervention step at least based on the respiratory effort envelope.
14. The system of claim 1, wherein the analyzer is further configured to calculate a likelihood of a significant clinical deterioration having occurred based at least on the respiratory effort envelope.
15. The system of claim 1 further comprising a display operatively coupled to the analyzer such that a trend in the first respiratory output signal may be visualized.
16. The system of claim 1, wherein the sensor is configured to output a second output signal that comprises a composite signal that includes a a cardiac feature and a bodily motion feature, and
wherein the analyzer is configured to selectively combine the first respiratory output signal, the cardiac feature, and the bodily motion feature to determine the output that provides the health assessment of the subject.
17. A computer-implemented method for monitoring a subject, the method comprising:
using a non-contact biomotion sensor to transmit radio waves towards the subject;
using the sensor to detect transmitted radio waves reflected by the subject's body, the detected radio waves being modulated due to movement of the subject's body, and to measure a respiratory output signal by analyzing the detected radio waves;
determining, using a processor, a respiratory effort envelope of the respiratory output signal;
generating, with the processor, a plurality of respiratory features derived from the respiratory effort envelope;
receiving, at the processor, subjective clinical symptom data from the subject;
deriving, using the processor, parameters related to at least one of the plurality of respiratory features and the subjective clinical symptom data for a current time period, the parameters including at least an Apnea-Hypopnea Index (AHI) derived from respiratory features from the respiratory effort envelope;
storing, using the processor, the derived parameters in a database of previously derived parameters; and
providing, using the processor, an output that provides a health assessment of the subject based on the previously derived parameters and currently derived parameters, the health assessment indicating whether clinical deterioration has happened.
18. The method of claim 17, further comprising combining one or more of the plurality of respiratory features with a plurality of features based on cardiac measurements to derive the output.
19. The method of claim 17, further comprising combining one or more of the plurality of respiratory features with one or more physiological measurements to derive the output,
wherein the physiological parameters are selected from the group consisting of blood pressure, a blood oxygen level, a measurement of B natriuretic peptides, and a measurement of C-reactive protein.
20. The method of claim 17, further comprising combining one or more of the plurality of respiratory features with one or more physiological measurements selected from the group consisting of body weight and a blood glucose level.
21. A system for monitoring a subject, comprising:
a non-contact biomotion sensor configured to detect radio waves reflected by the subject's body, the detected radio waves being modulated due to movement of the subject's body, and to output a bodily movement signal over the course of a sleeping period,
an input to capture subjective clinical symptom data from the subject;
an analyzer configured to determine a respiratory movement signal through filtering of the bodily movement signal and to determine an Apnea-Hypopnea Index (AHI) based on a respiratory effort envelope of the respiratory movement signal, and
a database configured to store at least a plurality of respiratory features derived from the respiratory movement signal, including the AHI,
wherein the analyzer is configured to implement an automated classifier which combines the subjective clinical symptom data and the plurality of respiratory features from a current sleeping period, together with measurements from previous sleeping periods, to determine an output that provides a health assessment of the subject, the output comprising a numerical value that is compared to a threshold in providing the health assessment, the health assessment indicating whether clinical deterioration has happened.
22. The system of claim 1, wherein the output comprises a numerical value that provides a statistical prediction of the health assessment, and the health assessment is based on a comparison of the numerical value to a threshold.
23. The system of claim 1, wherein the analyzer is further configured to initiate a call or query for additional data in response to the health assessment; and
when more than two symptoms indicated from the subjective clinical symptom data, the output comprises initiation of a nurse call, and
when there is significant weight gain and periodic breathing, the output comprises initiation of a nurse call.
24. The system of claim 1, wherein the subjective clinical symptom data is only related to respiration and heart condition.
25. The system of claim 1, wherein the health assessment of the subject is based on parameters derived for subjects in a same demographic as the subject.
26. The system of claim 1, wherein additional parameters are derived from additional signals, the additional derived parameters a body weight of the subject upon waking and after going to the bathroom and an average nocturnal heart rate; and
wherein the derived parameters and additional derived parameters are stored on a nightly basis.
US12/367,255 2009-02-06 2009-02-06 Apparatus, system and method for chronic disease monitoring Active 2031-04-22 US9526429B2 (en)
CA2751649A CA2751649A1 (en) 2009-02-06 2010-02-04 Apparatus, system and method for chronic disease monitoring
JP2011549255A JP5753795B2 (en) 2009-02-06 2010-02-04 Apparatus, system and method for chronic disease monitoring
CN201410551085.2A CN104257369A (en) 2009-02-06 2010-02-04 Apparatus, System And Method For Chronic Disease Monitoring
EP10705468A EP2393422A1 (en) 2009-02-06 2010-02-04 Apparatus, system and method for chronic disease monitoring
AU2010210569A AU2010210569B2 (en) 2009-02-06 2010-02-04 Apparatus, system and method for chronic disease monitoring
CN201410551183.6A CN104257370B (en) 2009-02-06 2010-02-04 Equipment and system for chronic disease monitoring
AU2015201380A AU2015201380B2 (en) 2009-02-06 2015-03-17 Apparatus, system and method for chronic disease monitoring
JP2015105669A JP6086939B2 (en) 2009-02-06 2015-05-25 Apparatus, system and method for chronic disease monitoring
US15/343,994 US20170231504A1 (en) 2009-02-06 2016-11-04 Apparatus, system and method for chronic disease monitoring
JP2017016132A JP6434548B2 (en) 2009-02-06 2017-01-31 Apparatus, system and method for chronic disease monitoring
US15/343,994 Continuation US20170231504A1 (en) 2009-02-06 2016-11-04 Apparatus, system and method for chronic disease monitoring
US20100204550A1 US20100204550A1 (en) 2010-08-12
US9526429B2 true US9526429B2 (en) 2016-12-27
US12/367,255 Active 2031-04-22 US9526429B2 (en) 2009-02-06 2009-02-06 Apparatus, system and method for chronic disease monitoring
US15/343,994 Pending US20170231504A1 (en) 2009-02-06 2016-11-04 Apparatus, system and method for chronic disease monitoring
KR (2) KR101712974B1 (en)
CN (2) CN102355852B (en)
AU2009231586A1 (en) * 2008-04-03 2009-10-08 Kai Medical, Inc. Non-contact physiologic motion sensors and methods for use
CN104736055A (en) * 2012-05-30 2015-06-24 瑞思迈传感器技术有限公司 Method and apparatus for monitoring cardio-pulmonary health
WO2014189884A1 (en) 2013-05-20 2014-11-27 Cardiac Pacemakers, Inc. Methods and apparatus for detecting heart failure
EP2999396A1 (en) 2013-05-20 2016-03-30 Cardiac Pacemakers, Inc. Apparatus for heart failure risk stratification
EP3077934A1 (en) * 2013-12-06 2016-10-12 Cardiac Pacemakers, Inc. Drug titration and patient monitoring in chronic obstructive pulmonary disease
CN106132286A (en) * 2014-03-07 2016-11-16 心脏起搏器股份公司 Multi-level heart failure event detection
CN104007450B (en) * 2014-06-10 2016-09-14 四川宝英光电有限公司 A positioning method for safe custody
JP6385470B2 (en) * 2014-06-30 2018-09-05 コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． Device, system and computer program for detecting the health condition of a subject
JP2018517448A (en) 2015-04-20 2018-07-05 レスメッド センサー テクノロジーズ リミテッド Human detection and identification from characteristic signals
CN108135534A (en) 2015-08-26 2018-06-08 瑞思迈传感器技术有限公司 Monitoring and the System and method for of management chronic disease
EP3364859A4 (en) * 2015-10-20 2019-07-03 Healthymize Ltd System and method for monitoring and determining a medical condition of a user
US20180353138A1 (en) * 2015-12-08 2018-12-13 Resmed Limited Non-contact diagnosis and monitoring of sleep disorders
WO1997014354A2 (en) 1995-10-16 1997-04-24 MAP Medizintechnik für Arzt und Patient GmbH Method and device for the quantitative analysis of sleep disturbances
JP2000083927A (en) 1998-09-11 2000-03-28 Nippon Avionics Co Ltd Contactless type cardiopulmonary function monitor apparatus
US20040249296A1 (en) 2001-10-27 2004-12-09 Klaus Ellscheid Alarm activated acoustic measuring signals for patient monitoring
US20050119532A1 (en) 2002-08-05 2005-06-02 Christian Cloutier Intelligent system and method for monitoring activity and comfort
JP2005270570A (en) 2004-03-26 2005-10-06 Canon Inc Biological information monitoring apparatus
CN1723842A (en) 2004-07-20 2006-01-25 夏普株式会社 Medical information detection apparatus and health management system using the medical information detection apparatus
US20060187111A1 (en) 2004-02-09 2006-08-24 Masaharu Uchino Radar apparatus
US20060189924A1 (en) 2005-02-24 2006-08-24 Blakley Daniel R Method of making a patient monitor
CN101332329A (en) 2007-06-28 2008-12-31 通用电气公司 Patients with respiratory system
US20090256739A1 (en) 2004-10-14 2009-10-15 Tasuku Teshirogi Short range radar small in size and low in power consumption and controlling method thereof
WO2009127799A1 (en) 2008-04-17 2009-10-22 Oxford Biosignals Limited Method and apparatus for measuring breathing rate
WO2010132850A1 (en) 2009-05-15 2010-11-18 Kai Medical, Inc. Non-contact physiologic motion sensors and methods for use
WO2012073183A1 (en) 2010-12-03 2012-06-07 Koninklijke Philips Electronics N.V. Sleep disturbance monitoring apparatus
US20130135137A1 (en) 2010-08-12 2013-05-30 Koninklijke Philips Electronics N.V. Device, system and method for measuring vital signs
US20130172770A1 (en) 2010-09-22 2013-07-04 Koninklijke Philips Electronics N.V. Method and apparatus for monitoring the respiration activity of a subject
US20080234568A1 (en) 2004-03-26 2008-09-25 Canon Kabushiki Kaisha Biological information monitoring apparatus
"The Fundamentals of FFT-Based Signal Analysis and Measurement in LabVIEW and LabWindows/CVI" National Instruments, Published Jun. 8, 2009. 12 pages. Accessed Jan. 26, 2012. URL: http://zone/ni.com/devzone/cda/tut/p/id/4278#toc0.
Australian Examination Report for Application No. 2007256872 dated Mar. 20, 2012.
Australian Examination Report for Application No. 2010210569 dated Mar. 12, 2014.
Chinese Office Action for Application No. 201080012088.9 dated Jan. 6, 2014.
Chinese Office Action for Application No. 201080012088.9 dated Sep. 3, 2013.
Droitcour et al., "Range Correlation and I/Q Performance Benefits in Single-Chip Silicon Doppler Radars for Noncontact Cardiopulmonary Monitoring", IEEE Transactions on Microwave Theory and Techniques, Mar. 3, 2004, vol. 52, No. 3, pp. 838-848.
European Search Report and Search Opinion for European Patent Application No. 07784266.4, mailed Oct. 7, 2010.
International Preliminary Report on Patentability dated May 26, 2011 of PCT/US2010/023177filed Feb. 4, 2010 (13 pages).
International Preliminary Report on Patentability for PCT International Application No. PCT/US2007/070196, mailed Dec. 3, 2008.
International Search Report and Written Opinion for PCT International Patent Application No. PCT/US2010/023177, mailed on Jul. 23, 2010.
International Search Report and Written Opinion of the International Searching Authority for PCT International Application No. PCT/US2007/070196, mailed Feb. 22, 2008.
International Search Report and Written Opinion of the International Searching Authority for PCT International Application No. PCT/US2007/083155, mailed Mar. 20, 2008.
Invitation to Pay Additional Fees and Partial International Search Report for PCT International Patent Application No. PCT/US2010/023177, mailed on Jun. 7, 2010.
Japanese Office Action dated Oct. 18, 2011 of Japanese Applicatikon No. 2009-513469 filed Jan. 30, 2009 (4 pages).
Japanese Office Action for Application No. 2009-513469 dated May 1, 2012.
Japanese Office Action for Application No. 2009-513469 dated Nov. 27, 2012.
Japanese Office Action for Application No. 2011549255 dated Feb. 28, 2014.
Second Office Action dated Apr. 14, 2011 of Chinese Application No. 200780026740.0 filed Jan. 14, 2009 (16 pages).
KR101850859B1 (en) 2018-04-20
Khandoker et al. 2008 Support vector machines for automated recognition of obstructive sleep apnea syndrome from ECG recordings
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:HENEGHAN, CONOR;ZAFFARONI, ALBERTO;DE CHAZAL, PHILIP;AND OTHERS;SIGNING DATES FROM 20090129 TO 20090130;REEL/FRAME:022278/0569
2019-06-11 CONR Reexamination decision confirms claims