WO2017171802A1

WO2017171802A1 - Aortic stenosis screening

Info

Publication number: WO2017171802A1
Application number: PCT/US2016/025322
Authority: WO
Inventors: Mark A. Konno; Feras Hatib; Carlyn Solomon
Original assignee: Edwards Lifesciences Corp
Current assignee: Edwards Lifesciences Corp
Priority date: 2016-03-31
Filing date: 2016-03-31
Publication date: 2017-10-05
Anticipated expiration: 2018-09-30

Abstract

There are provided systems and methods for performing aortic stenosis screening. An aortic stenosis screening system includes a hardware processor, a system memory, and an aortic stenosis evaluation module stored in the system memory. The hardware processor is configured to execute the aortic stenosis evaluation module to receive a first signal corresponding to an arterial pressure of a living subject, and to apply a transfer function to transform the first signal to a second signal corresponding to a central arterial pressure of the living subject. The hardware processor is further configured to execute the aortic stenosis evaluation module to derive parameters from the second signal, and to determine a degree of aortic stenosis of the living subject based on those parameters.

Description

AORTIC STENOSIS SCREENING

BACKGROUND

Aortic stenosis (AS) can be a progressive, debilitating, and life threatening condition if left untreated. Subjects with AS are typically free from cardiovascular symptoms, such as angina, syncope, or heart failure, for example, until late in the course of disease progression. However, once symptoms manifest, the prognosis for the subject is often poor. As a result, early detection and diagnosis of AS prior to the manifestation of symptoms is important.

AS has traditionally been screened for through cardiac auscultation, typically using a stethoscope to listen to a subject's heart. Although the detection of heart sounds can enable early identification of a subject suffering from AS, there are disadvantages to relying on this approach for AS screening. One disadvantage flows from changes in the way clinicians are trained. As high technology diagnostic techniques are increasingly taught, the importance of traditional and relatively low technology diagnostic techniques may receive less emphasis, resulting in fewer diagnosticians being skilled in the use of cardiac auscultation. Another disadvantage results from the general aging of the subject population. Especially in older subjects, heart sounds indicative of AS may be present, but may not reliably indicate significant aortic valvular obstruction requiring medical intervention.

SUMMARY

There are provided systems and methods for performing aortic stenosis screening, substantially as shown in and/or described in connection with at least one of the figures, and as set forth more completely in the claims. BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 shows a diagram of an exemplary aortic stenosis (AS) screening system, according to one implementation;

Figure 2 shows another exemplary implementation of an AS screening system; Figure 3 shows an exemplary system and a computer-readable non-transitory medium including instructions enabling performance of AS screening;

Figure 4 is a flowchart presenting an exemplary method for use by a system to perform AS screening;

Figure 5A shows a diagram of an exemplary implementation for detecting arterial pressure non-invasively at an extremity of a living subject;

Figure 5B shows a diagram depicting transformation of an arterial pressure detected at an extremity of a living subject to a central arterial pressure of the subject, according to one implementation;

Figure 6A shows a trace of an exemplary central arterial pressure waveform; and Figure 6B shows an exemplary region of diagnostic interest on the central arterial waveform trace of Figure 6A.

DETAILED DESCRIPTION

The following description contains specific information pertaining to

implementations in the present disclosure. One skilled in the art will recognize that the present disclosure may be implemented in a manner different from that specifically discussed herein. The drawings in the present application and their accompanying detailed description are directed to merely exemplary implementations. Unless noted otherwise, like or corresponding elements among the figures may be indicated by like or corresponding reference numerals. Moreover, the drawings and illustrations in the present application are generally not to scale, and are not intended to correspond to actual relative dimensions.

The present application discloses systems and methods for performing aortic stenosis (AS) screening. By enabling performance of AS screening based on an arterial pressure measurement obtained non-invasively from a living subject, the AS screening methods and systems disclosed in the present application advantageously avoid arterial cannulation of the subject during the screening process, thereby enhancing subject comfort and safety. In addition, by enabling substantially continuous beat-to-beat monitoring of arterial pressure at an extremity of the living subject, such as at the subject' s finger, the present application discloses a compact, portable AS screening solution suitable for deployment to cardiology offices or primary care sites. Moreover, by substantially automating the AS screening process, the solution disclosed by the present application advantageously enables early detection of AS by clinicians having little or no expertise in cardiac auscultation.

The present solution for performing AS screening includes receiving a first signal corresponding to an arterial pressure of a living subject. Such a signal may be obtained as a result of a non-invasive arterial pressure measurement taken at an extremity of the subject, for example, at a finger or wrist of the subject. The present solution also includes applying a transfer function to transform the first signal to a second signal corresponding to a central arterial pressure of the living subject. The present solution further includes deriving parameters that may be indicative of AS from the second signal, and determining the degree of AS present in the subject based on those parameters.

Figure 1 shows a diagram of an exemplary AS screening system, according to one implementation. As shown in Figure 1, AS screening system 102 is situated within AS diagnostic environment 100 including communication network 120, client system 130, living subject 140 (hereinafter "subject 140"), and healthcare worker 160 trained to utilize client system 130 (hereinafter "user 160"). AS screening system 102 includes system processor 104, implemented as a hardware processor, and system memory 106 storing AS evaluation module 110 including analysis unit 112 and indexing database 114 having AS subject population database 116 stored therein.

In addition, Figure 1 illustrates arterial pressure sensors shown in the alternative as non- invasive finger or wrist arterial pressure sensor 150a and non-invasive toe or ankle arterial pressure sensor 150b. Figure 1 further shows signals received by client system 130 and corresponding to an arterial pressure of subject 140 in the alternative as wired signal 124a and wireless signal 124b. It is noted that although Figure 1 depicts signal 124a/124b being received from non-invasive finger or wrist arterial pressure sensor 150a, in other implementations, signal 124a/124b may be received from noninvasive toe or ankle arterial pressure sensor 150. Also shown in Figure 1 are network communication links 122 interactively connecting client system 130 and AS screening system 102 via communication network 120, and diagnostic index 118 generated by AS evaluation module 110.

According to the implementation shown in Figure 1, user 160 may utilize client system 130 to interact with AS screening system 102 over communication network 120, for example to transmit signal 124a/124b to AS evaluation module 110 or to download AS evaluation module 110 to client system 130. In one such implementation, AS screening system 102 may correspond to one or more web servers, accessible over a packet network such as the Internet, for example. Alternatively, AS screening system 102 may correspond to one or more servers supporting a local area network (LAN), or included in another type of limited distribution network.

System processor 104 is configured to execute AS evaluation module 110 to receive signal 124a/124b corresponding to an arterial pressure of subject 140 via client system 130 and communication network 120. System processor 104 is further configured to execute AS evaluation module 110 to use analysis unit 112 to apply a transfer function for transforming signal 124a/124b to a signal corresponding to a central arterial pressure of subject 140. For example, where signal 124a/124b is provided by non-invasive finger or wrist arterial pressure sensor 150a, analysis unit 112 may be used by AS evaluation module 110 to apply a transfer function for transforming signal 124a/124b to an aortic pressure or a brachial pressure of subject 140.

System processor 104 is also configured to execute AS evaluation module 110 to derive parameters that may be indicative of the presence of AS in subject 140 from the signal corresponding to the central arterial pressure of subject 140. Examples of those parameters include the rate of change of the central arterial pressure with respect to time, the second derivative of the central arterial pressure with respect to time, and/or the time duration of the systolic rise or systolic decay of the central arterial pressure, to name a few. In addition, system processor 104 is configured to execute AS evaluation module 110 to use indexing database 114 to determine the degree of AS of subject 140 based on the derived parameters.

In some implementations, for example, system processor 104 is configured to execute AS evaluation module 110 to utilize indexing database to identify, from AS subject population database 116, known parameters corresponding to the parameters derived by analysis unit 112. For instance, known parameters may be identified as corresponding to the derived parameters when one or more of the known parameters are identified as being within a predetermined range of respective one or more of the derived parameters. System processor 104 may be further configured to execute AS evaluation module 110 to generate diagnostic index 118 based on a comparison between the derived parameters and corresponding known parameters.

It is noted that although Figure 1 depicts diagnostic index 118 as residing in system memory 106, in some implementations, diagnostic index 118, when generated, may be copied to non- volatile storage (not shown in Figure 1), or may be transmitted to client system 130 via communication network 120, for display to user 160. It is further noted that client system 130 may be implemented as a computer workstation or personal computer (PC), a dedicated handheld or otherwise portable diagnostic system, or any type of mobile computing device, such as a smartphone or tablet computer, among others.

Referring to Figure 2, Figure 2 shows a more detailed exemplary implementation of client system 230, which may be configured to perform AS screening. AS diagnostic environment 200 in Figure 2 includes client system 230 interactively connected to AS screening system 202 over network communication link 222. As shown in Figure 2, AS screening system 202 includes system processor 204, and system memory 206 storing AS evaluation module 210a including analysis unit 212a and indexing database 214a having AS subject population database 216a stored therein. As further shown in Figure 2, client system 230 includes client processor 234, and client memory 236 storing AS evaluation module 210b including analysis unit 212b and indexing database 214b having AS subject population database 216b stored therein. Also shown in Figure 2 is diagnostic index 218 generated by AS evaluation module 210b on client system 230.

Network communication link 222, and AS screening system 202 including system processor 204 and system memory 206 correspond in general to network communication link 122, and AS screening system 102 including system processor 104 and system memory 106, in Figure 1. In addition, AS evaluation module 210a including analysis unit 212a, indexing database 214a, and AS subject population database 216a, in Figure 2, corresponds to AS evaluation module 110 including analysis unit 112, indexing database 114, and AS subject population database 116, in Figure 1. In other words, AS evaluation module 210a, analysis unit 212a, indexing database 214a, and AS subject population database 216a may share any of the characteristics attributed to corresponding AS evaluation module 110, analysis unit 112, indexing database 114, and AS subject population database 116 shown in Figure 1 and described above.

Client system 230 corresponds in general to client system 130, in Figure 1.

Moreover, AS evaluation module 210b including analysis unit 212b, indexing database 214b, and AS subject population database 216b corresponds to AS evaluation module 110/210b including analysis unit 112/212b, indexing database 114/214b, and AS subject population database 116/216a. As a result, AS evaluation module 210b, analysis unit 212b, indexing database 214b, and AS subject population database 216b may share any of the characteristics attributed to corresponding AS evaluation module 110, analysis unit 112, indexing database 114, and AS subject population database 116 shown in Figure 1 and described above.

According to the exemplary implementation shown in Figure 2, AS evaluation module 210b including analysis unit 212b and indexing database 214b having AS subject population database 216b stored therein is located in client memory 236, having been received from AS screening system 202 via network communication link 222. In one implementation, network communication link 222 corresponds to transfer of AS evaluation module 210b including analysis unit 212b, indexing database 214b, and AS subject population database 216b over a packet network, for example. Once transferred, for instance by being downloaded over network communication link 222, AS evaluation module 210b including analysis unit 212b, indexing database 214b, and AS subject population database 216b may be persistently stored in client memory 236 and may be executed locally on client system 230 by client processor 234.

Client processor 234 may be the hardware central processing unit (CPU) for client system 230, for example, in which role client processor 234 runs the operating system and/or firmware for client system 230 and executes AS evaluation module 210b. In the exemplary implementation of Figure 2, a user, such as user 160, in Figure 1, can utilize AS evaluation module 210b on client system 230 to generate diagnostic index 218, which corresponds in general to diagnostic index 118.

Moving now to Figure 3, Figure 3 shows an exemplary system and a computer- readable non-transitory medium including instructions enabling performance of AS screening, according to one implementation. System 330, in Figure 3, includes computing unit 338 including processor 334 and memory 336, interactively linked to display 332. Display 332 may take the form of a liquid crystal display (LCD), a light- emitting diode (LED) display, an organic light-emitting diode (OLED) display, or another suitable display screen that performs a physical transformation of signals to light. System 330 including processor 334 and memory 336 corresponds in general to any or all of AS screening system 102 and client system 130, in Figure 1, and AS screening system 202 and client system 230, in Figure 2.

Also shown in Figure 3 is computer-readable non-transitory medium 319 having AS evaluation module 310 stored thereon. The expression "computer-readable non- transitory medium," as used in the present application, refers to any medium, excluding a carrier wave or other transitory signal, that provides instructions to processor 334 of computing unit 338. Thus, a computer-readable non-transitory medium may correspond to various types of media, such as volatile media and non-volatile media, for example. Volatile media may include dynamic memory, such as dynamic random access memory (dynamic RAM), while non-volatile memory may include optical, magnetic, or electrostatic storage devices. Common forms of computer-readable non-transitory media include, for example, optical discs, RAM, programmable read-only memory (PROM), erasable PROM (EPROM), and FLASH memory.

According to the implementation shown in Figure 3, computer-readable non- transitory medium 319 provides AS evaluation module 310 for execution by processor 334 of computing unit 338. AS evaluation module 310, when executed by processor 334, instantiates an AS evaluation module corresponding to AS evaluation module 110/2 lOa 210b, in Figure 1/2, and capable of performing all of the operations attributed to those corresponding features by the present disclosure.

Example implementations of the present disclosure will be further described below with reference to Figure 4 and Figures 5 A, 5B, 6A, and 6C. Referring first to Figure 4, Figure 4 presents flowchart 400 outlining an exemplary method for use by a system to perform AS screening. The method outlined in flowchart 400 can be performed using AS evaluation module 110/210a/210b/310 described above.

Flowchart 400 begins with receiving, by AS evaluation module

110/210a/210b/310 executed by respective processor 104/204/234/334, signal 124a/124b corresponding to an arterial pressure of subject 140 (action 472). As noted above, signal 124a/124b may correspond to an arterial pressure of subject 140 measured non- invasively at an extremity of subject 140. It is noted that although subject 140 is depicted as a living human subject, such as a patient receiving diagnostic care from healthcare worker 160, for example, in other implementations, subject 140 may be a living non-human animal subject.

Referring to Figure 5A, Figure 5A shows a diagram of an exemplary

implementation for detecting arterial pressure non-invasively at an extremity of a living subject, once again depicted as a human subject. Diagram 572, in Figure 5A, shows system 502/530 including AS evaluation module 510 receiving signal 524 corresponding to an arterial pressure of subject 540. As further shown by Figure 5A, the arterial pressure of subject 540 is detected non-invasively at finger 542 of subject 540 using finger arterial pressure sensing cuff 550. Subject 540, signal 524, and finger arterial pressure sensing cuff 550 correspond respectively to subject 140, signal 124a/124b, and either of finger or wrist arterial pressure sensor 150a or toe or ankle arterial pressure sensor 150b, in Figure 1, and may share any of the characteristics attributed to those corresponding features, above. Moreover, system 502/530 including AS evaluation module 510, in Figure 5A, may correspond to AS screening system 102/202 including AS evaluation module 110/210a, in respective Figures 1 and 2, and/or to client system 130/230/330 including an AS evaluation module corresponding to AS evaluation module 210b/310, in Figures 1, 2, and 3, and may share any of the characteristics attributed to those corresponding features, above.

According to the implementation shown in Figure 5A, finger arterial pressure sensing cuff 550 is designed to detect an arterial pressure of subject 540 non-invasively. Moreover, as shown in Figure 5A, finger arterial pressure sensing cuff 550 may take the form of a small, lightweight, and comfortable arterial pressure sensor suitable for extended wear by subject 540. It is noted that such extended wear capability may also be attributed to finger or wrist arterial pressure sensor 150a and toe or ankle arterial pressure sensor 150b, in Figure 1. As a result, finger arterial pressure sensing cuff 550 and arterial pressure sensors 150a/150b may be configured to provide substantially continuous beat-to-beat monitoring of the arterial pressure of subject 140/540 over an extended period of time, such as minutes or hours, for example.

Flowchart 400 continues with applying a transfer function to transform signal 124a/124b/524 to a signal corresponding to a central arterial pressure of subject 140/540 (action 474). Referring to Figure 5B, Figure 5B shows diagram 574 depicting transformation of signal 524 corresponding to an arterial pressure of subject 540 detected using finger arterial pressure sensing cuff 550, to signal 544 corresponding to a central arterial pressure of subject 540, according to one implementation. For example, as shown in Figure 5B, signal 524 provided by finger arterial pressure sensing cuff 550 is transformed to signal 544 corresponding to an aortic pressure or a brachial pressure of subject 540. As further shown by Figure 5B, such a transformation is performed by analysis unit 512 of system 502/530 through application of a transfer function to signal 524. That is to say, application of such a transfer function may be performed by AS evaluation module 110/210a/210b/310/510 executed by a processor corresponding to processor 104/204/234/334, and using an analysis unit corresponding to analysis unit 112/212a/212b/512.

The body of subject 540 acts to filter a central arterial pressure signal corresponding to signal 544, so as to cause a modified signal (i.e., an attenuated or otherwise filtered signal) corresponding to signal 524 to be detectable at an extremity of subject 540. As a result, a transfer function for substantially reproducing the central arterial pressure from the arterial pressure measured at the extremity of subject 540 can be identified. Such a transfer function will typically vary depending upon the location on the body of subject 540 at which the arterial pressure corresponding to signal 524 is detected, e.g., at finger 542, as well as upon the specific central arterial pressure being reproduced, e.g., aortic or brachial. Moreover, and as also shown in Figure 5B, in one implementation, signals 524 and 544 may be provided as waveforms corresponding respectively to a finger arterial pressure and a central arterial pressure of subject 540.

Flowchart 400 continues with deriving parameters from signal 544 (action 476). As noted above, the parameters derived from signal 544 may be indicative of the presence of AS in subject 540. As further noted above, examples of those parameters include the rate of change of the central arterial pressure with respect to time, the second derivative of the central arterial pressure with respect to time, and/or the time duration of the systolic rise or systolic decay of the central arterial pressure, to name a few. An exemplary but non-exhaustive table of suitable parameters, as well as exemplary sampling times associated with their derivation, is provided in Appendix A of the present application.

Derivation of such parameters from signal 544 can be performed by AS evaluation module 110/210a/210b/310/510 executed by a processor corresponding to processor 104/204/234/334. Referring to Figure 6A, Figure 6A shows trace 676 of exemplary central arterial pressure waveform 644. As shown in Figure 6A, central arterial pressure waveform 644 is expressed as a function of time, and includes heartbeat parameters 682, 684, 686, and 688. Heartbeat parameters 682, 684, 686, and 688 correspond respectively to the start of a heartbeat, the maximum systolic pressure marking the end of systolic rise, the presence of the dicrotic notch marking the end of systolic decay, and the beginning of the next heartbeat of subject 140/540. It is noted that central arterial pressure waveform 644 corresponds to signal 544 in Figure 5B, while heartbeat parameters 682, 684, 686, and 688 may be included among the parameters derived by AS evaluation module 110/210a/210b/310/510 from signal 544 during action 476 of flowchart 400.

Flowchart 400 can conclude with determining a degree of AS of subject 140/540 based on the parameters derived from signal 544 and corresponding to central arterial pressure waveform 644, e.g., based on some or all of heartbeat parameters 682, 684, 686, and 688 (action 478). Referring to Figure 6B, it is noted that in some implementations, it may be advantageous or desirable to place greater emphasis on parameters derived from region 680 of central arterial pressure waveform 644 when determining the degree of AS of subject 140/540. In other words, in some implementations, it may be advantageous or desirable to focus primarily on parameters derived from the interval shortly after the beginning of the heartbeat and extending to heartbeat parameter 684 corresponding to the maximum systolic pressure and marking the end of systolic rise.

In some implementations, AS evaluation module 110/210a/210b/310/510 may be configured to determine the degree of AS of subject 140/540 using an indexing database corresponding to indexing database 114/214a/214b. For example, indexing database 114/214a/214b may include reference ranges for the parameters derived from signal 544, and determination of the degree of AS of subject 140/540 may include comparison between the derived parameters and those reference ranges.

In other implementations, AS evaluation module 110/210a/210b/310/510 may be configured to determine the degree of AS of subject 140/540 using subject population database 116/216a/216b. Determination of the degree of AS of subject 140/540 may then include identifying, from subject population database 116/216a/216b, parameters corresponding to the parameters derived from signal 544. Such a determination may also include generating diagnostic index 118/218b based on comparison between the parameters identified in subject population database 116/216a/216b and those derived from signal 544. In some implementations, for example, subject population database 116/216a/216b may take the form of a multi-dimensional subject population space having coordinate axes defined by parameters corresponding respectively to the parameters derived from signal 544. AS evaluation module 110/210a/310b/310/510 may be configured to project the parameters derived from signal 544 onto the subject population space, and apply a nearest neighbor algorithm to identify a nearest neighbor subject of the subject population database as indicative of the degree of AS of subject 140/540.

Thus, by enabling performance of AS screening based on an arterial pressure measurement obtained non-invasively from a living subject, the AS screening methods and systems disclosed in the present application advantageously avoid arterial cannulation of the subject during the screening process, thereby enhancing subject comfort and safety. In addition, by enabling substantially continuous beat-to-beat monitoring of arterial pressure at an extremity of the living subject, such as at the subject's finger, the present application discloses a compact, portable AS screening solution suitable for deployment to cardiology offices or primary care sites. Moreover, by substantially automating the AS screening process, the solution disclosed by the present application advantageously enables early detection of AS by clinicians having little or no expertise in cardiac auscultation.

From the above description it is manifest that various techniques can be used for implementing the concepts described in the present application without departing from the scope of those concepts. Moreover, while the concepts have been described with specific reference to certain implementations, a person of ordinary skill in the art would recognize that changes can be made in form and detail without departing from the scope of those concepts. As such, the described implementations are to be considered in all respects as illustrative and not restrictive. It should also be understood that the present application is not limited to the particular implementations described herein, but many rearrangements, modifications, and substitutions are possible without departing from the scope of the present disclosure.

APPENDIX A

Parameter Description Sampling Time Flag showing that the current 20 sec segment signal is bad 20 sec

Flag showing that the current 20 sec and the past 40 sec segment

signals are bad Flag: the signal has been bad in most of the 20 sec iterations in 20 sec the last 5 min Flag: signal drift detected in CO: no drift 0, drift 1 20 sec Flag: the standard deviation of the blood pressure signal is too high 20 sec Flag: the standard deviation of the blood pressure signal is too low 20 sec Flag: the artifacts detected in the respiratory envelope of the signal 20 sec The Windkessel Compliance (Based on the Langewooters paper) 20 sec average Cardiac Output 20 sec average Cardiac Output (CO) - 5 min average 5 min average Heart Rate (HR): 5 min average 5 min average Mean Arterial Pressure (MAP): 5 min average 5 min average Stroke Volume Variation (SVV): 5 min average 5 min average CO: estimated with a multivariate model on normal patients 20 sec average HR 20 sec average Arterial tone estimate: 1 min average 1 min average Arterial tone estimate: 20 sec 20 sec average The kurtosis of the pressure waveform within a beat - (fourth 20 sec median statistical moment) The kurtosis of the 20 sec reconstructed waveform MAP The mean value of the 20 sec reconstructed waveform The standard deviation of the 20 sec reconstructed waveform The skewness of the pressure waveform within a beat - (third statistical moment The skewness of the 20 sec reconstructed waveform The standard deviation of the pressure waveform within a beat The standard deviation of the pressure waveform within a beat adjusted with the kurtosis SVV computed with the detection of the respiratory cycles in the signal FloTrac SVV Systolic Pressure Diastolic Pressure Flag showing variability in HR Flag showing impact of HR variability on SVV Count rejected PVCs Data Quality Index Systolic pressure minus the pressure at the dicrotic notch The pressure at the dicrotic notch minus the diastolic pressure The duration of the systolic phase:

the time from the start of the beat to the dicrotic notch The duration of the diastolic phase: the time from the dicrotic notch to the start of the next beat The bias of the diastolic slope 20 sec median Diastolic slope 20 sec median CO computed with a heavy weighted multivariate model 20 sec average derived from patients in hyperdynamic conditions Vascular tone computed with a heavy weighted multivariate 1 min average model derived from patients in hyperdynamic conditions Vascular tone computed with a heavy weighted multivariate 20 sec average model derived from patients in hyperdynamic conditions Flag indicating hyperdynamic conditions 20 sec Multivariate classification model to detect peripheral 1 min average decoupling during hyperdynamic conditions Multivariate classification model to detect low flow conditions 1 min average Vascular tone computed with a balanced multivariate model 1 min average derived from patients with mild hyperdynamic conditions Vascular tone computed with a weighted multivariate model 1 min average derived from patients with severe hyperdynamic conditions Multivariate classification model to detect peripheral 20 sec average decoupling during hyperdynamic conditions CO computed for patients in normal cardiovascular conditions 20 sec average CO computed for patients in hyperdynamic cardiovascular 20 sec average conditions CO computed for patients in mild hyperdynamic cardiovascular 20 sec average conditions CO computed for patients in severe hyperdynamic cardiovascular 20 sec average conditions Flag for model switching when peripheral decoupling is detected 20 sec Flag indicating peripheral decoupling conditions 20 sec Peripheral decoupling indicator showing the amount of peripheral 20 sec decoupling 57. Peripheral decoupling indicator showing the amount of peripheral decoupling, calculated slightly differently than the one above

58. Area under the pressure waveform greater than the beat mean

59. Time from the first beat sample exceeding the beat mean to the dicrotic notch

60. The time from the start of the beat to the systolic max

61. The time from the systolic max to the dicrotic notch

62. The time from the systolic max to the start of the next beat

63. The area under the whole beat

64. The area under the whole beat normalized by the number of samples

65. The area under the systolic phase of the beat: from start to the dicrotic notch

66. The area under the systolic phase normalized by the number of samples

67. The area from the start of the beat to the systolic max

68. The area from the start of the beat to the systolic max

normalized by the number of samples

69. The area from the systolic max to the dicrotic notch

70. The area from the systolic max to the dicrotic notch

normalized by the number of samples

71. The area from the systolic max to the start of the next beat

72. The area from the systolic max to the start of the next beat

normalized by the number of samples

73. The area under the diastolic portion of the waveform:

from the dicrotic notch to the start of the next beat 74. The area under the diastolic portion of the waveform 20 sec median normalized by the number of samples

75. The area under the whole beat with subtracted diastolic pressure 20 sec median

76. The area under the whole beat with subtracted diastolic pressure 20 sec median and normalized by the number of samples

77. The area under the systolic portion of the waveform with 20 sec median subtracted diastolic pressure

78. The area under the systolic portion of the waveform with subtracted 20 sec median diastolic pressure and normalized by the number of samples

79. The area from the start of the beat to the systolic max with subtracted 20 sec median diastolic pressure

80. The area from the start of the beat to the systolic max with subtracted 20 sec median diastolic pressure and normalized by the number of samples

81. The area from the systolic max to the start of the next beat with 20 sec median subtracted diastolic pressure

82. The area from the systolic max to the start of the next beat with 20 sec median subtracted diastolic pressure and normalized by the number of

samples

83. The area under the diastolic portion of the waveform: from the 20 sec median dicrotic

notch to the start of the next beat with subtracted diastolic pressure

84. The area under the diastolic portion of the waveform: from the 20 sec median dicrotic notch to the start of the next beat with subtracted

diastolic pressure and normalized by the number of samples

85. The standard deviation (representing pulsatility) of the systolic 20 sec median portion of the waveform

86. The standard deviation (representing pulsatility) of the diastolic 20 sec median portion of the waveform

87. The standard deviation (representing pulsatility) of the portion of 20 sec median the waveform extending from the start of the beat to the sys max

88. The standard deviation (representing pulsatility) of the portion of 20 sec median the waveform extending from the sys max to the dicrotic notch ⁾. The standard deviation (representing pulsatility) of the of the 20 sec median portion of the waveform extending from the sys max to the start

of the next beat

90. The mean of the systolic portion of the waveform

91. The mean of the of the diastolic portion of the waveform

92. The mean of the portion of the waveform extending from the start

of the beat to the sys max

93. The mean of the portion of the waveform extending from the sys

max to the dicrotic notch

94. The mean of the portion of the waveform extending from the sys

max to the start of the next beat

95. The mean of the systolic portion of the waveform with subtracted

diastolic pressure

96. The mean of the of the diastolic portion of the waveform with

subtracted diastolic pressure

97. The mean of the portion of the waveform extending from the start

of the beat to the sys max with subtracted diastolic pressure

98. The mean of the portion of the waveform extending from the sys

max to the dicrotic notch with subtracted diastolic pressure

99. The mean of the portion of the waveform extending from the sys

max to the start of the next beat with subtracted diastolic pressure

100. Standard deviation of the entire waveform, using diastolic pressure

instead of the average in the standard deviation equation

101. The standard deviation of the systolic portion of the waveform,

using diastolic pressure instead of the average in the standard

deviation equation

102. The standard deviation of the diastolic portion of the waveform,

using diastolic pressure instead of the average in the standard

deviation equation

103. The standard deviation of the portion of the waveform extending

from the start of the beat to the sys max, using diastolic pressure

instead of the average in the standard deviation equation 104. The standard deviation of the portion of the waveform extending

from the sys max to the dicrotic notch, using diastolic pressure

instead of the average in the standard deviation equation

105. The standard deviation of the portion of the waveform extending

from the sys max to the start of the next beat, using diastolic

pressure instead of the average in the standard deviation equation

106. The slope of the systolic rise

107. The max of the first derivative

108. The max of the second derivative 20 sec median

Claims

CLAIMS What is claimed is:

1. An aortic stenosis screening system comprising:

a hardware processor;

a system memory; and

an aortic stenosis evaluation module stored in the system memory;

wherein the hardware processor is configured to execute the aortic stenosis evaluation module to:

receive a first signal corresponding to an arterial pressure of a living subject;

apply a transfer function to transform the first signal to a second signal corresponding to a central arterial pressure of the living subject; derive a plurality of parameters from the second signal; and determine a degree of aortic stenosis of the living subject based on the plurality of parameters.

2. The aortic stenosis screening system of claim 1 , wherein the first signal corresponds to an arterial pressure of the living subject detected non-invasively at one of a finger and a toe of the living subject.

3. The aortic stenosis screening system of claim 1, wherein the first signal corresponds to an arterial pressure of the living subject detected non-invasively at one of a wrist and an ankle of the living subject.

4. The aortic stenosis screening system of claim 1 , wherein the central arterial pressure of the living subject is one of an aortic pressure and a brachial pressure.

5. The aortic stenosis screening system of claim 1, wherein the plurality of parameters includes at least one of a rate of change of the central arterial pressure with respect to time, and a second derivative of the central arterial pressure with respect to time.

6. The aortic stenosis screening system of claim 1 , wherein the plurality of parameters includes at least one of a time duration of a systolic rise of the central arterial pressure, and a time duration of a systolic decay of the central arterial pressure.

7. The aortic stenosis screening system of claim 1, wherein to determine the degree of aortic stenosis of the living subject, the hardware processor is further configured to:

identify, from an aortic stenosis subject population database, a second plurality of parameters corresponding to the plurality of parameters; and

generate a diagnostic index based on comparison between the plurality of parameters and the second plurality of parameters.

8. A method for use by an aortic stenosis screening system including a hardware processor and an aortic stenosis evaluation module stored in a system memory, the method comprising:

receiving, by the aortic stenosis evaluation module executed by the hardware processor, a first signal corresponding to an arterial pressure of a living subject;

applying a transfer function to transform the first signal to a second signal corresponding to a central arterial pressure of the living subject;

deriving a plurality of parameters from the second signal; and

determining a degree of aortic stenosis of the living subject based on the plurality of parameters.

9. The method of claim 8, wherein the first signal corresponds to an arterial pressure of the living subject detected non-invasively at one of a finger and a toe of the living subject.

10. The method of claim 8, wherein the first signal corresponds to an arterial pressure of the living subject detected non-invasively at one of a wrist and an ankle of the living subject.

11. The method of claim 8, wherein the central arterial pressure of the living subject is one of an aortic pressure and a brachial pressure.

12. The method of claim 8, wherein deriving the plurality of parameters from the second signal includes deriving at least one of a rate of change of the central arterial pressure with respect to time, and a second derivative of the central arterial pressure with respect to time.

13. The method of claim 8, wherein deriving the plurality of parameters from the second signal includes deriving at least one of a time duration of a systolic rise of the central arterial pressure, and a time duration of a systolic decay of the central arterial pressure.

14. The method of claim 8, wherein determining the degree of aortic stenosis of the living subject based on the plurality of parameters comprises:

identifying, from an aortic stenosis subject population database, a second plurality of parameters corresponding to the plurality of parameters; and

generating a diagnostic index based on comparison between the plurality of parameters and the second plurality of parameters.

15. A computer-readable non-transitory medium having stored thereon instructions, which when executed by a hardware processor, instantiate a method comprising:

receiving a first signal corresponding to an arterial pressure of a living subject; applying a transfer function to transform the first signal to a second signal corresponding to a central arterial pressure of the living subject; deriving a plurality of parameters from the second signal; and

16. The computer-readable non-transitory medium of claim 15, wherein the first signal corresponds to an arterial pressure of the living subject detected non- invasively at one of a finger and a wrist of the living subject.

17. The computer-readable non-transitory medium of claim 15, wherein the central arterial pressure of the living subject is one of an aortic pressure and a brachial pressure.

18. The computer-readable non-transitory medium of claim 15, wherein deriving the plurality of parameters from the second signal includes deriving at least one of a rate of change of the central arterial pressure with respect to time, and a second derivative of the central arterial pressure with respect to time.

19. The computer-readable non-transitory medium of claim 15, wherein deriving the plurality of parameters from the second signal includes deriving at least one of a time duration of a systolic rise of the central arterial pressure, and a time duration of a systolic decay of the central arterial pressure.

20. The computer-readable non-transitory medium of claim 15, wherein determining the degree of aortic stenosis of the living subject based on the plurality of parameters comprises: