Office Action 3: Notice of Allowance (2024-11-18)

CLAIMS:
AMENDMENTS TO THE CLAIMS

1\. (Currently Amended) A system, comprising:
a plurality of sensors, each of the plurality of sensors configured to engage with an article on which a first user is positioned, and each of the plurality of sensors configured to measure force;
a communication module in wired communication with each of the plurality of sensors, wherein the communication module receives force data transmitted from each of the plurality of sensors; and
an apparatus comprising:at least one data processor; and
at least one memory storing instructions which, when executed by the at least one data processor, cause the apparatus to at least:
receive, from the communication module, the force data transmitted from each of the plurality of sensors;
transform, in response to a change in the force data indicative of a change in a load on the article , the force data from each of the plurality of sensors into a plurality of physiological signals; and
determine, based on the plurality of physiological signals, a source physiological signal for each of \[\[the\]\] a plurality of physiological sources;
wherein the determination of the source physiological signal for each of the plurality of physiological sources further comprises:

separating into components each of the plurality of physiological signals, wherein the components correspond to each of the plurality of physiological sources at each of the plurality of sensors, and wherein the separating further comprises:pre-processing each of the plurality of physiological signals, specifying a state space of model parameters, each of the model parameters of the state space describing an aspect of the plurality of physiologicalsignals,
determining a posterior distribution of the plurality of physiological signals,
determining a change in at least a first parameter of the model parameters of the state space,
determining if the change in at least the first parameter of the model parameters of the state space is below a predefined threshold, and in response to the change in the at least the first model parameter being less than the predefine threshold, using the posterior distribution to estimate, for each of the plurality of physiological signals, waveforms of the separated components; and compositing the separated components to form the source physiological signal for each of the plurality of physiological sources, and

wherein the transforming the force data further comprises time-domain or frequency-domain filtering the force data to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors.
2\. (Canceled)
3\. (Currently Amended) The system of claim 1, wherein the apparatus is further configured to determine a number of physiological sources on the article, and, based on the determination of the plurality of physiological sources on the article, the physiological signals are based on the force data from at least two of the plurality of sensors.
4\. (Cancelled).
5\. (Cancelled).
6\. (Previously Presented) The system of claim 1, wherein a source respiratory signal is determined, based on the plurality of respiratory signals, for each of the plurality of physiological sources, and wherein the execution of the instructions further cause the apparatus to at least:
identify, from the source respiratory signal for each of the plurality of physiological sources, one or more respiratory parameters.
7\. (Previously Presented) The system of claim 1, wherein transforming the force data comprises time-domain and/or frequency-domain filtering the force data to obtain a plurality of ballistocardiogram signals corresponding to each of the plurality of sensors.

8\. (Original) The system of claim 7, wherein a source ballistocardiogram signal is determined, based on the plurality of ballistocardiogram signals, for each of the plurality of physiological sources, and wherein the execution of the instructions further cause the apparatus to at least:
identify, from the source ballistocardiogram signal for each of the plurality of physiological sources, one or more ballistocardiogram parameters.
9\. (Currently Amended) The system of claim 1, wherein the change in the force data indicative of the change in the load on the article is determined based on identifying the change in the force data greater than \[\[a\]\] the predefined threshold of force for a predefined duration threshold.

10\. (Currently Amended) The system of claim \[\[1\]\]3, wherein the determination of the number of plurality of physiological sources on the article is based on identification of alterations in the physiological signals transformed from the force data.

11-16. (Cancelled)1215

17\. (Currently Amended) A method, comprising:
receiving, by a remote processing device and from a communication module in wired communication with a plurality of sensors, force data transmitted from the plurality of sensors, wherein each of the plurality of sensors is configured to engage with a member of an article on which a first user is positioned, and further wherein each of the plurality of sensors is configured to measure force;

transforming, by the remote processing device and in response to a change in the force data indicative of a change in a load on the article , the force data from each of the plurality of sensors into a plurality of physiological signals; anddetermining, by the remote processing device and based on the plurality of physiological signals, a source physiological signal for each of \[\[the\]\] a plurality of physiological sources;
wherein the determination of the source physiological signal for each of the plurality of physiological sources comprises:
separating into components each of the plurality of physiological signals, wherein the components correspond to each of the plurality of physiological sources at each of the plurality of sensors, and wherein the separating further comprises:pre-processing each of the plurality of physiological signals, specifying a state space of model parameters, each of the model parameters of the state space describing an aspect of the plurality of physiological signals, determining a posterior distribution of the physiological signals, determining a change in at least a first parameter of the model parameters of the state space,
determining if the change in at least the first parameter of the model parameters of the state space is below a predefined threshold, and
in response to the change in at least the first model parameter being less than the predefined threshold, using the posterior distribution to estimate, for each of the plurality of physiological signals, waveforms of the separated components; and

compositing the separated components to form the source physiological signal for each of the plurality of physiological sources, and
wherein the transforming the force data comprises time-domain or frequency-domain filtering the force data to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors.

18\. (Original) The method of claim 17, wherein the determination of the source physiological signal for each of the plurality of physiological sources is based on a model for each of the plurality of physiological sources, wherein each model comprises the components, the components defining a contribution of a respective physiological source to each of the plurality of physiological sources.

19\. (Currently Amended) The method of claim 17 further comprising determining a number of physiological sources on the article, wherein, based on the determination of the plurality of physiological sources on the article, the physiological signals are based on the force data from at least two of the plurality of sensors.

20\. (Cancelled).

21\. (Cancelled).

22\. (Previously Presented) The method of claim 17, wherein a source respiratory signal is determined, based on the plurality of respiratory signals, for each of the plurality of physiological sources, and wherein the method further comprises:

identifying, from the source respiratory signal for each of the plurality of physiological sources, one or more respiratory parameters.

23\. (Previously Presented) The method of claim 17, wherein transforming the force data comprises time-domain and/or frequency-domain filtering the force data to obtain a plurality of ballistocardiogram signals corresponding to each of the plurality of sensors.

24\. (Original) The method of claim 23, wherein a source ballistocardiogram signal is determined, based on the plurality of ballistocardiogram signals, for each of the plurality of physiological sources, and wherein the method further comprises:identifying, from the source ballistocardiogram signal for each of the plurality of physiological sources, one or more ballistocardiogram parameters.

25\. (Currently Amended) The method of claim 17, wherein the change in the force data indicative of the change in the load on the article is determined based on identifying the change in the force data greater than \[\[a\]\] the predefined threshold of force for a predefined duration threshold.

26\. (Currently Amended) The method of claim \[\[17\]\]19, wherein the determination of the number of plurality of physiological sources on the article is based on identification of alterations in the physiological signals transformed from the force data.

27-30. (Cancelled)2829

31\. (Currently Amended) An apparatus, comprising:
at least one data processor; and

at least one memory storing instructions which, when executed by the at least one data processor, cause the apparatus to at least:
receive, from a communication module in wired communication with a plurality of sensors, force data transmitted from the plurality of sensors, wherein each of the plurality of sensors is configured to engage with a member of an article on which a first user is positioned,
and further wherein each of the plurality of sensors is configured to measure force;transform, in response to a change in the force data indicative of a change in a load on the article , the force data from each of the plurality of sensors into a plurality of physiological signals; and
determine, based on the plurality of physiological signals, a source physiological signal for each of \[\[the\]\] a plurality of physiological sources;
wherein the determination of the source physiological signal for each of the plurality of physiological sources comprises:
separating into components each of the plurality of physiological signals, wherein the components correspond to each of the plurality of physiological sources at each of the plurality of sensors, and wherein the separating further comprises:
pre-processing each of the plurality of physiological signals, specifying a state space of model parameters, each of the model parameters of the state space describing an aspect of the plurality of physiological signals.

determining a posterior distribution of the physiological signals, determining a change in at least a first parameter of the model parameters of the state space,
determining if the change in at least the first parameter of the model parameters of the state space is below a predefined threshold, and in response to the change in the at least the first model parameter being less than the predefined threshold, using the posterior distribution to estimate, for each of the plurality of physiological signals, waveforms of the separated components; and
compositing the separated components to form the source physiological signal for each of the plurality of physiological sources, and
wherein the transforming the force data comprises time-domain or frequency- domain filtering the force data to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors.

32-44. (Cancelled)3343

45\. (Currently Amended) A non-transitory computer-readable storage medium including program code, which when executed by at least one data processor, causes operations comprising:
receiving, by a remote processing device and from a communication module in wired communication with a plurality of sensors, force data transmitted from the plurality of sensors, wherein each of the plurality of sensors is configured to engage with a member of an article on which

a first user is positioned, and further wherein each of the plurality of sensors is configured to measure force;transforming, by the remote processing device and in response to a change in the force data indicative of a change in a load on the article , the force data from each of the plurality of sensors into a plurality of physiological signals; anddetermining, by the remote processing device and based on the plurality of physiological signals, a source physiological signal for each of \[\[the\]\] a plurality of physiological sources;
wherein the determination of the source physiological signal for each of the plurality of physiological sources comprises:separating into components each of the plurality of physiological signals, wherein the components correspond to each of the plurality of physiological sources at each of the plurality of sensors, and wherein the separating further comprises:
pre-processing each of the plurality of physiological signals, specifying a state space of model parameters, each of the model parameters of the state space describing an aspect of the plurality of physiological signals, determining a posterior distribution of the physiological signals, determining a change in at least a first parameter of the model parameters of the state space,
determining if the change in at least the first parameter of the model parameters of the state space is below a predefined threshold, and

in response to the change in the at least the first model parameter being less than the predefined threshold, using the posterior distribution to estimate, for each of the plurality of physiological signals, waveforms of the separated components; and
compositing the separated components to form the source physiological signal for each of the plurality of physiological sources, and
wherein the transforming the force data comprises time-domain or frequency-domain filtering the force data to obtain a plurality of respiratory signals corresponding to each of the plurality of sensors.

46-47. (Cancelled).

OFFICE ACTION:
Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Reasons for Allowance
The following is an examiner’s statement of reasons for allowance: The prior art of record fails to disclose or fairly render unpatentable the combination of claim elements. The closest prior art, the combination of US 2020/0110194 A1 to Young et al. and US 2005/0124864 A1 to Mack et al., teaches features of independent claims 1, 17, 31, and 45 (see the 6/25/24 Final Office Action) but fails to teach “specifying a state space of model parameters, each of the model parameters of the state space describing an aspect of the plurality of physiological signals, determining a posterior distribution of the plurality of physiological signals, determining a change in at least a first parameter of the model parameters of the state space, determining if the change in at least the first parameter of the model parameters of the state space is below a predefined threshold, and in response to the change in the at least the first model parameter being less than the predefine threshold, using the posterior distribution to estimate, for each of the plurality of physiological signals, waveforms of the separated components” as claimed in claim 1 and substantially similar limitations in claims 17, 31, and 45.
US 2019/0357854 A1 to Reich & Teixeira teaches the use of a state space model and determining a posterior distribution within biomedical signal processing. However, Reich & Teixeira is not related to sensors configured to measure force and fails to teach “determining a change in at least a first parameter of the model parameters of the state space, determining if the change in at least the first parameter of the model parameters of the state space is below a predefined threshold, and in response to the change in the at least the first model parameter being less than the predefine threshold, using the posterior distribution to estimate, for each of the plurality of physiological signals, waveforms of the separated components.”
US 2017/0188869 A1 to Kale teaches the use of posterior probabilities to aid in biomedical signal separation. However, Kale fails to teach “specifying a state space of model parameters, each of the model parameters of the state space describing an aspect of the plurality of physiological signals” or “determining a change in at least a first parameter of the model parameters of the state space, determining if the change in at least the first parameter of the model parameters of the state space is below a predefined threshold, and in response to the change in the at least the first model parameter being less than the predefine threshold, using the posterior distribution to estimate, for each of the plurality of physiological signals, waveforms of the separated components.”
Thus, no prior art of record teaches or fairly suggests “specifying a state space of model parameters, each of the model parameters of the state space describing an aspect of the plurality of physiological signals, determining a posterior distribution of the plurality of physiological signals, determining a change in at least a first parameter of the model parameters of the state space, determining if the change in at least the first parameter of the model parameters of the state space is below a predefined threshold, and in response to the change in the at least the first model parameter being less than the predefine threshold, using the posterior distribution to estimate, for each of the plurality of physiological signals, waveforms of the separated components” as claimed in claim 1. Claims 17, 31, and 45 recite substantially similar limitations that avoid the prior art for the same reasons discussed above.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion

APPLICANT REBUTTAL: REMARKS
The Examiner has rejected claims 1-3, 6-8, 17-19, 22-24, 31 and 45 under 35 U.S.C. §102(a)(2) as allegedly being anticipated by U.S. Patent Publication No. 2020/0110194 to Young et al. ("Young") in view of U.S. Patent Publication No. 2005/0124864 to Mack et al. ("Mack"); rejected claims 9 and 25 under 35 U.S.C. §103 as allegedly being unpatentably obvious over Young in view of U.S. Patent Publication No. 2020/0146910 to Demirli et al. ("Demirli"); rejected claims 10 and 26 under 35 U.S.C. §103 as allegedly being unpatentably obvious over Young in view of U.S. Patent Publication No. 2014/037163 5 to Shinar et al. ("Shinar").
Summary of Amendments. With this amendment, claims 1, 3, 9, 10, 17, 19, 25, 26, 31, and 45 have been amended. Claim 2 is canceled, without prejudice or disclaimer. The amendments to the claims are fully supported by the application as filed, and no new matter has been added.
Claim Re
j
ections Under 35 U.S.C.
§
Claims 1-3, 6-8, 17-19, 22-24, 31, and 45 stand rejected under 35 U.S.C. § 103 as being allegedly unpatentable over Young in view of Mack. The remaining claims stand rejected under 35 U.S.C. § 103 as being allegedly unpatentable over Young in view of Mack and additional references. Applicant respectfully traverses these rejections.
Claim 1 has recites a system comprising, inter alia, an apparatus configured to separate into components each of a plurality of physiological signals received from a plurality of sensors, wherein
the separating comprises:
pre-processing each of the plurality of physiological signals to generate, for
each of the plurality of physiological signals,
specifying a state space of model parameters, each of the model parameters
of the state space describing an aspect of the plurality physiological signals,
determining a posterior distribution of the physiological signals given the
respective vectors and the state space of model parameters,
determining a change in at least a first parameter of the model parameters of
the state space,
determining if the change in at least the first parameter of the model
parameters of the state space is below a predefined threshold, and in response to the change in the at least the first model parameter being less than the predefined
threshold, and
using the posterior distribution to estimate, for each of the plurality of
physiological signals, waveforms of the separated components ...
Young, taken alone or in combination with Mack, fails to teach or suggest at least the above noted features of claim 1.
The Examiner relies on Young at paragraph [0047] as allegedly teaching "separating into components each of the plurality of physiological signals..." Applicant disagrees. Young at para. [0047] and elsewhere is silent with respect to the features of claim 1 described above. Mack is similarly silent with respect to these features of claim 1. As such, Young fails to disclose or suggest any separating into components each of a plurality of physiological signals received from a plurality of sensors, "wherein the separating comprises pre-processing each of the plurality of physiological signals to generate, for each of the plurality of physiological signals, specifying a state space of model parameters, each of the model parameters of the state space describing an aspect of the plurality physiological signals, determining a posterior distribution of the physiological signals, determining a change in at least a first parameter of the model parameters of the state space, determining if the change in at least the first parameter of the model parameters of the state space is below a predefined threshold, and in response to the change in the at least the first model parameter being below the predefined threshold, using the posterior distribution to estimate, for each of the plurality of physiological signals, waveforms of the separated components ..." as amended claim 1 recites. And Mack fails to cure this deficiency.
In view of the foregoing, claim 1 is thus patentable over Young, taken alone or in combination with Mack. Claims 17, 31, and 45 recite subject matter substantially similar to that of claim 1. At least for the reasons discussed with respect to claim 1, claims 17, 31, and 45 are patentable over Young, taken alone or in combination with Mack.
Claim 2 has been cancelled without prejudice or disclaimer, thus rendering moot its rejection. The remaining claims depend, either directly or indirectly, from one of claims 1, 17, 31, and 45, and are thus patentable over Young, taken alone or in combination with Mack or any of the other cited references, by virtue of their dependencies at least.
In view of the foregoing, the rejections 35 U.S.C. § 103 should be withdrawn.
CONCLUSION


CITED REFERENCE SUMMARIES: Ref US20170188869A1: Claims:
A system for measuring cardiac time intervals, comprising: a non-invasive sensor unit for capturing electrical signals and composite vibration objects; and a processor for separating a plurality of individual heart vibration events from the composite vibration objects by using at least one among bin-wise clustering and permutation alignment, or non-negative matrix factorization, or deep belief networks.
a non-invasive sensor unit for capturing electrical signals and composite vibration objects; and
a processor for separating a plurality of individual heart vibration events from the composite vibration objects by using at least one among bin-wise clustering and permutation alignment, or non-negative matrix factorization, or deep belief networks.
The system for measuring cardiac time intervals of claim 1 , wherein the non-invasive sensor unit includes a first sensor configured for placement at or near a pulmonary auscultation location and a second sensor configured for placement at or near an aortic auscultation location.
The system for measuring cardiac time intervals of claim 1 , wherein the non-invasive sensor unit includes a sensor configured for placement at or near a tricuspid auscultation location and a sensor configured for placement at or near a mitral auscultation location.
The system for measuring cardiac time intervals of claim 1 , wherein the non-invasive sensor unit includes a first sensor configured for placement at a pulmonary auscultation location, a second sensor configured for placement at an aortic auscultation location, and at least a third sensor configured for placement at a tricuspid auscultation location or at a mitral auscultation locations or both.
The system for measuring cardiac time intervals of claim 1 , further comprising a sensor for performing an electrocardiogram and wherein the non-invasive sensor unit comprises at least one sensor for sensing a heart valve opening.
The system of claim 1 , wherein the permutation alignment is a posterior based permutation alignment.
The system of claim 1 , wherein the deep belief networks uses stacked modules of restricted Boltzmann machines to create a context frame having a concatenation of feature vectors from frames that are consecutive in time.
The system for measuring cardiac time intervals of claim 1 , wherein the processor is configured to separate the plurality of individual heart vibration events from the composite vibration objects from multichannel signals by decomposing the multichannel signals into sparse activation patterns and clustering the sparse activation patterns.
The system for measuring cardiac time intervals of claim 1 , wherein the system comprises a wearable device in the form of a patch, band, necklace, belt, or bandage configured to communicate with a wireless node and further configured to capture an electrocardiogram signal synchronized with one or more accelerometer sensors of the non-invasive sensor unit.
The system for measuring cardiac time intervals of claim 1 , wherein the non-invasive sensor unit comprises at least one sensor for sensing vibrations corresponding to a heart valve opening.
The system for measuring cardiac time intervals of claim 1 , wherein a number of vibration sensors in the non-invasive sensor unit is less than the number of the separated vibration sources.
The system for measuring cardiac time intervals of claim 1 , wherein the processor separates a plurality of individual heart vibration events from composite vibration objects from multichannel signals using at least one source separation technique used for extracting the valve heart signals for application in a non-linear time variant system, among Determined Models, Principal Component Analysis (PCA), Independent Component Analysis ICA, Singular Value Decomposition (SVD), Bin-wise Clustering and Permutation posterior probability Alignment, Undetermined Models, Sparseness condition, Dictionary learning, Convolutive models, or K-SVD Matching Pursuit.
The system for measuring cardiac time intervals of claim 1 , wherein the marking of individual valve events comprises at least the marking of one or more among a Mitral valve opening (MO), Aortic valve opening (AO), Tricuspid valve opening (TO), or Pulmonary valve opening (PO).
A sensor array device, comprising: a wearable device configured to capture an electrocardiogram signal synchronized with composite vibration objects from at least one accelerometer and further configured to communicate with a wireless node; wherein the at least one accelerometer is configured for capturing the composite vibration objects; an electrode for sensing the electrocardiogram signal; and a processor for separating a plurality of individual heart vibration events from the composite vibration objects by using at least one among bin-wise clustering and permutation alignment, or non-negative matrix factorization, or deep belief networks.
a wearable device configured to capture an electrocardiogram signal synchronized with composite vibration objects from at least one accelerometer and further configured to communicate with a wireless node;
wherein the at least one accelerometer is configured for capturing the composite vibration objects;
an electrode for sensing the electrocardiogram signal; and
The sensor array device of claim 14 , wherein the at least one accelerometer are configurable for measuring a lower frequency range vibration signal and a higher frequency range vibration signal.
The sensor array device of claim 14 , wherein the processor is operatively coupled to the at least one accelerometer, the processor further being configured for: identifying the plurality of individual heart vibration events from the composite vibration objects; transmitting the composite vibration signals or the plurality of individual heart vibration events to a remote device; and marking and presenting individual valve events from the plurality of individual heart vibration events with respect to the electrocardiogram signal.
identifying the plurality of individual heart vibration events from the composite vibration objects;
transmitting the composite vibration signals or the plurality of individual heart vibration events to a remote device; and
marking and presenting individual valve events from the plurality of individual heart vibration events with respect to the electrocardiogram signal.
The sensor array device of claim 14 , wherein the at least one accelerometer comprises a first sensor configured for placement at or near a pulmonary auscultation location, a second sensor configured for placement at or near an aortic auscultation location, and at least a third sensor configured for placement at or near a tricuspid auscultation location or at or near a mitral auscultation location or both.
The sensor array device of claim 14 , wherein the sensor array device is portable and captures synchronized sensor data to a memory and wherein the processor is configurable in the process of separating the plurality of individual heart vibration events from the composite vibration objects into separate vibration sources and further identifying the individual heart vibration events among at least one of a mitral valve opening, a tricuspid valve opening, an aortic valve opening, or a pulmonary valve opening.
The sensor array device of claim 14 , further comprising a wireless connection for transmission of sensor data or processed sensor data to a remote computing device or a cloud computing device.
A system of measuring cardiac time intervals, comprising: a memory having computer instructions; and one or more processors operatively coupled to the memory, the execution of the computer instructions causing the one or more processors to perform operations comprising: capturing composite vibration objects using a non-invasive sensor unit; separating, by the one or more processors, a plurality of individual heart vibration events from the composite vibration objects using at least one among bin-wise clustering and permutation alignment, or non-negative matrix factorization, or deep belief networks; identifying, by the one or more processors, the individual valve events from the plurality of individual heart vibration events with respect to an electrocardiogram signal, wherein the individual valve events comprises at least one among a mitral valve opening, a tricuspid valve opening, an aortic valve opening, or a pulmonary valve opening; and presenting the individual valve events superimposed with a synchronized electrocardiogram signal.
a memory having computer instructions; and
one or more processors operatively coupled to the memory, the execution of the computer instructions causing the one or more processors to perform operations comprising: capturing composite vibration objects using a non-invasive sensor unit; separating, by the one or more processors, a plurality of individual heart vibration events from the composite vibration objects using at least one among bin-wise clustering and permutation alignment, or non-negative matrix factorization, or deep belief networks; identifying, by the one or more processors, the individual valve events from the plurality of individual heart vibration events with respect to an electrocardiogram signal, wherein the individual valve events comprises at least one among a mitral valve opening, a tricuspid valve opening, an aortic valve opening, or a pulmonary valve opening; and
capturing composite vibration objects using a non-invasive sensor unit;
separating, by the one or more processors, a plurality of individual heart vibration events from the composite vibration objects using at least one among bin-wise clustering and permutation alignment, or non-negative matrix factorization, or deep belief networks;
identifying, by the one or more processors, the individual valve events from the plurality of individual heart vibration events with respect to an electrocardiogram signal, wherein the individual valve events comprises at least one among a mitral valve opening, a tricuspid valve opening, an aortic valve opening, or a pulmonary valve opening; and
presenting the individual valve events superimposed with a synchronized electrocardiogram signal.
Ref US20190357854A1: Claims:
A method for estimating a state of a cardiovascular system of a person having a limb, comprising the steps of: providing a cardiac stroke volume analyzer, comprising: a blood pressure sensor, said blood pressure sensor generating a time-varying pressure state waveform output from a limb of the person; a system processor connected to said blood pressure sensor; and a dynamic state-space model; said system processor receiving cardiovascular input data, from said blood pressure sensor, related to a transient pressure state of the cardiovascular system; at least one probabilistic model, of said dynamic state-space model, operating on the time-varying pressure state waveform output to generate a probability distribution function to a non-pressure state of the cardiovascular system; iteratively updating said probability distribution function using synchronized updated time-varying pressure state waveform output from said blood pressure sensor; and said system processor processing the probability distribution function to generate a non-pressure state output related to at least one of a stroke volume of a heart of the person, central venous pressure, and an arterial compliance of the person, said output displayed to at least one of the person and a medical professional.
providing a cardiac stroke volume analyzer, comprising: a blood pressure sensor, said blood pressure sensor generating a time-varying pressure state waveform output from a limb of the person; a system processor connected to said blood pressure sensor; and a dynamic state-space model;
a blood pressure sensor, said blood pressure sensor generating a time-varying pressure state waveform output from a limb of the person;
a system processor connected to said blood pressure sensor; and
a dynamic state-space model;
said system processor receiving cardiovascular input data, from said blood pressure sensor, related to a transient pressure state of the cardiovascular system;
at least one probabilistic model, of said dynamic state-space model, operating on the time-varying pressure state waveform output to generate a probability distribution function to a non-pressure state of the cardiovascular system;
iteratively updating said probability distribution function using synchronized updated time-varying pressure state waveform output from said blood pressure sensor; and
said system processor processing the probability distribution function to generate a non-pressure state output related to at least one of a stroke volume of a heart of the person, central venous pressure, and an arterial compliance of the person, said output displayed to at least one of the person and a medical professional.
The method of claim 1 , further comprising the steps of: providing an optical input to the limb of the person; and said blood pressure sensor generating a response to the optical input to yield said time-varying pressure state waveform output.
providing an optical input to the limb of the person; and
said blood pressure sensor generating a response to the optical input to yield said time-varying pressure state waveform output.
The method of claim 2 , further comprising the step of: relating the time-varying pressure state waveform output to a blood volume; and relating the blood volume to the arterial compliance.
relating the time-varying pressure state waveform output to a blood volume; and
relating the blood volume to the arterial compliance.
The method of claim 3 , said step of relating further comprising the step of: using an equation with a first term related to a pressure to inflate an artery and a second term related to a second pressure to radially stretch the artery.
using an equation with a first term related to a pressure to inflate an artery and a second term related to a second pressure to radially stretch the artery.
The method of claim 1 , further comprising the step of: a pressure transducer output measuring pressure from an alternating partial inflation of a blood pressure cuff and a partial deflation of said blood pressure cuff to yield said time-varying pressure state waveform output.
a pressure transducer output measuring pressure from an alternating partial inflation of a blood pressure cuff and a partial deflation of said blood pressure cuff to yield said time-varying pressure state waveform output.
The method of claim 1 , further comprising the step of: a pressure transducer, of said blood pressure sensor, reading a passive change of the blood pressure of the person on a blood pressure cuff.
a pressure transducer, of said blood pressure sensor, reading a passive change of the blood pressure of the person on a blood pressure cuff.
The method of claim 1 , said blood pressure sensor generating a time-varying pressure state waveform output further comprising the step of: using an automated blood pressure cuff to alternatingly measure systolic pressure and diastolic pressure at at least one of: a fixed time interval and a non-fixed time interval.
using an automated blood pressure cuff to alternatingly measure systolic pressure and diastolic pressure at at least one of: a fixed time interval and a non-fixed time interval.
The method of claim 1 , further comprising the step of: measuring pressure directly as a function of time to yield said time varying pressure state waveform.
measuring pressure directly as a function of time to yield said time varying pressure state waveform.
The method of claim 1 , further comprising the steps of: using a pulse oximeter to generate said time varying pressure state waveform; and using a blood pressure cuff to calibrate the time varying pressure state waveform.
using a pulse oximeter to generate said time varying pressure state waveform; and
using a blood pressure cuff to calibrate the time varying pressure state waveform.
The method of claim 1 , further comprising the step of: measuring pressure, using a blood pressure cuff alternatingly at a first partially inflated pressure in excess of 100 mm Hg and a second partially deflated pressure of less than 100 mm Hg, directly as a function of time to yield said time varying pressure state waveform.
measuring pressure, using a blood pressure cuff alternatingly at a first partially inflated pressure in excess of 100 mm Hg and a second partially deflated pressure of less than 100 mm Hg, directly as a function of time to yield said time varying pressure state waveform.
An apparatus for estimating state of a cardiovascular system of a person having a limb, comprising: a cardiac stroke volume analyzer, comprising: a blood pressure sensor, said blood pressure sensor configured to generate a time-varying pressure state waveform output from a limb of the person; a system processor connected to said blood pressure sensor; and a dynamic state-space model linked to said system processor; said system processor configured to receive cardiovascular input data, from said blood pressure sensor, related to a transient pressure state of the cardiovascular system; at least one probabilistic model, of said dynamic state-space model, configured to operate on the time-varying pressure state waveform output to generate a probability distribution function to a non-pressure state of the cardiovascular system; said system processor configured to iteratively update said probability distribution function using synchronized updated time-varying pressure state waveform output from said blood pressure sensor; and said system processor configured to process the probability distribution function to generate a non-pressure state output related to at least one of a stroke volume of a heart of the person and arterial compliance of the person, said output displayed to at least one of the person and a doctor.
a cardiac stroke volume analyzer, comprising: a blood pressure sensor, said blood pressure sensor configured to generate a time-varying pressure state waveform output from a limb of the person; a system processor connected to said blood pressure sensor; and a dynamic state-space model linked to said system processor;
a blood pressure sensor, said blood pressure sensor configured to generate a time-varying pressure state waveform output from a limb of the person;
a dynamic state-space model linked to said system processor;
said system processor configured to receive cardiovascular input data, from said blood pressure sensor, related to a transient pressure state of the cardiovascular system;
at least one probabilistic model, of said dynamic state-space model, configured to operate on the time-varying pressure state waveform output to generate a probability distribution function to a non-pressure state of the cardiovascular system;
said system processor configured to iteratively update said probability distribution function using synchronized updated time-varying pressure state waveform output from said blood pressure sensor; and
said system processor configured to process the probability distribution function to generate a non-pressure state output related to at least one of a stroke volume of a heart of the person and arterial compliance of the person, said output displayed to at least one of the person and a doctor.
The apparatus of claim 11 , said blood pressure sensor integrated into said cardiac stroke volume analyzer.
The apparatus of claim 12 , said blood pressure sensor comprising: a blood pressure cuff, said system processor configured to alternatingly partially inflate said blood pressure cuff to a sub-peristaltic pressure and to partially deflate said blood pressure cuff from said sub-peristaltic pressure to generate said time-varying pressure state waveform output.
a blood pressure cuff, said system process
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