`6:20-cv-1112
`
`Exhibit “1”
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 2 of 33
`I 1111111111111111 1111111111 lllll lllll 111111111111111 111111111111111 IIII IIII
`USO 10595731B2
`
`c12) United States Patent
`Gopalakrishnan et al.
`
`(IO) Patent No.: US 10,595,731 B2
`(45) Date of Patent:
`Mar.24,2020
`
`(54) METHODS AND SYSTEMS FOR
`ARRHYTHMIA TRACKING AND SCORING
`
`(71) Applicant: AliveCor, Inc.
`
`(72)
`
`Inventors: Ravi Gopalakrishnan, San Francisco,
`CA (US); Lev Korzinov, San
`Francisco, CA (US); Fei Wang, San
`Francisco, CA (US); Euan Thomson,
`Los Gatos, CA (US); Nupur
`Srivastava, San Francisco, CA (US);
`Omar Dawood, San Francisco, CA
`(US); Iman Abuzeid, San Francisco,
`CA (US); David E. Albert, Oklahoma
`City, OK (US)
`
`(73) Assignee: AliveCor, Inc., Mountain View, CA
`(US)
`
`(52) U.S. Cl.
`CPC ........ A61B 5102055 (2013.01); A61B 510022
`(2013.01); A61B 510245 (2013.01); A61B
`5102405 (2013.01); A61B 5102416 (2013.01);
`A61B 51046 (2013.01); A61B 51681 (2013.01);
`A61B 516898 (2013.01);
`(Continued)
`(58) Field of Classification Search
`USPC .................................................. 600/508-509
`See application file for complete search history.
`
`(56)
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`7,846,106 B2
`9,839,363 B2
`
`12/2010 Andrews et al.
`12/2017 Albert
`(Continued)
`
`( *) Notice:
`
`Subject to any disclaimer, the term ofthis
`patent is extended or adjusted under 35
`U.S.C. 154(b) by O days.
`
`Primary Examiner - Nicole F Lavert
`(74) Attorney, Agent, or Firm - Womble Bond Dickinson
`(US) LLP; Daniel E. Ovanezian
`
`(21) Appl. No.: 16/588,201
`
`(22)
`
`Filed:
`
`Sep. 30, 2019
`
`(65)
`
`(63)
`
`(51)
`
`Prior Publication Data
`
`Jan. 23, 2020
`US 2020/0022594 Al
`Related U.S. Application Data
`
`Continuation of application No. 16/153,446, filed on
`Oct. 5, 2018, now Pat. No. 10,426,359, which is a
`(Continued)
`
`Int. Cl.
`A61B 51024
`A61B 510205
`
`(2006.01)
`(2006.01)
`(Continued)
`
`ABSTRACT
`(57)
`A dashboard centered around arrhythmia or atrial fibrillation
`tracking is provided. The dashboard includes a heart or
`cardiac health score that can be calculated in response to
`data from the user such as their ECG and other personal
`information and cardiac health influencing factors. The
`dashboard also provides to the user recommendations or
`goals, such as daily goals, for the user to meet and thereby
`improve their heart or cardiac health score. These goals and
`recommendations may be set by the user or a medical
`professional and routinely updated as his or her heart or
`cardiac health score improves or otherwise changes. The
`dashboard is generally displayed from an application pro(cid:173)
`vided on a smartphone or tablet computer of the user.
`
`30 Claims, 16 Drawing Sheets
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 3 of 33
`
`US 10,595,731 B2
`Page 2
`
`Related U.S. Application Data
`
`continuation of application No. 15/393,077, filed on
`Dec. 28, 2016, now Pat. No. 10,159,415, which is a
`continuation of application No. 14/730,122, filed on
`Jun. 3, 2015, now Pat. No. 9,572,499, which is a
`continuation of application No. 14/569,513, filed on
`Dec. 12, 2014, now Pat. No. 9,420,956.
`
`(52)
`
`(2006.01)
`(2018.01)
`
`A61B 5111
`Gl6H 50/30
`U.S. Cl.
`CPC .......... A61B 517264 (2013.01); A61B 517275
`(2013.01); A61B 51746 (2013.01); G16H
`20140 (2018.01); G16H 40167 (2018.01); A61B
`5/021 (2013.01); A61B 5/02438 (2013.01);
`A61B 5/0452 (2013.01); A61B 511118
`(2013.01); Gl6H 10/60 (2018.01); Gl6H
`15/00 (2018.01); Gl6H 40/63 (2018.01);
`Gl6H 50/30 (2018.01)
`
`(60)
`
`(51)
`
`Provisional application No. 62/014,516, filed on Jun.
`19, 2014, provisional application No. 61/970,551,
`filed on Mar. 26, 2014, provisional application No.
`61/969,019, filed on Mar. 21, 2014, provisional
`application No. 61/953,616, filed on Mar. 14, 2014,
`provisional application No. 61/915,115, filed on Dec.
`12, 2013.
`
`Int. Cl.
`A61B 510245
`A61B 51046
`A61B 5100
`G16H 20140
`G16H 40167
`Gl6H 40/63
`Gl6H 15/00
`Gl6H 10/60
`A61B 5/021
`A61B 5/0452
`
`(2006.01)
`(2006.01)
`(2006.01)
`(2018.01)
`(2018.01)
`(2018.01)
`(2018.01)
`(2018.01)
`(2006.01)
`(2006.01)
`
`(56)
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`2007 /0213624 Al *
`
`9/2007 Reisfeld .
`
`2012/0109675 Al
`2012/0197148 Al*
`
`5/2012 Ziegler et al.
`8/2012
`Levitan .
`
`2012/0289790 Al
`2014/0125619 Al*
`
`11/2012
`5/2014
`
`2014/0163393 Al
`2014/0276154 Al
`2015/0057512 Al
`2015/0122018 Al
`2015/0305684 Al
`
`6/2014
`9/2014
`2/2015
`5/2015
`10/2015
`
`* cited by examiner
`
`Jain et al.
`Panther ............... G06F 3/04883
`345/173
`
`Mccombie et al.
`Katra et al.
`Kapoor
`Yuen
`Gross
`
`A61B 5/0402
`600/504
`
`A61B 5/02405
`600/515
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 4 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 1 of 16
`
`US 10,595,731 B2
`
`100,
`
`105
`
`115
`
`109
`
`109a
`
`111
`
`Fl0.1
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 5 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 2 of 16
`
`US 10,595,731 B2
`
`200
`
`!- 202
`~~ ............... I,..,.,.,,..,.,,
`
`206
`I
`TradH:ionat HRV Me;1$urnm€nts,
`1, tlme dcimain measures,
`2. freque1v:y dornainineasurns,
`3, ni:in,Hnear HRV
`
`204
`I
`Non Trnd}tional flR-lnti:,:rval tvteasuremer.tt stKh .as
`1, RR(n,i) for B1gemni, Tf:igemni,
`2. pi:,,riodk autore,gress:h1e roo,J'!ng average {MRMA}
`
`I
`
`208 - -! Feature Sefoction
`
`210-
`
`Real Time
`AFIB
`Predictk>n
`
`OffHne training
`~---~· Based on labeled _
`Data using
`,,.. · · ·
`Random Forests
`
`212
`
`1'1(,: 2
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 6 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 3 of 16
`
`US 10,595,731 B2
`
`300
`
`l- 302
`........__------!---........... --""'
`!
`!
`RR Interval Extraction
`~l,,,,,,,,,,,,,,,~~3o=s ~,_~ _ _
`
`$ ....
`
`I
`
`310
`I
`
`304 -
`
`306
`
`r···
`
`T~ad~ti~r5:,-,f }t~v: ~1:S:asurs:~l"i~t'ft5;,
`1. ·um~ d<.1w.:Sjr~ .m~as:SJr~~:•
`:t -fr~tl::.H,~~:cv ~k":!P.~.a.ki Hma~-;.t:,~~;.
`J .. n<>n-liri;z;-sr H!lV
`
`Ncr: ·ff:,kdltlcmii rm-ir:te:va!
`tvl~~~.;Jrem:e.r:>:ts~ :~>;,t:~ .as- R:~{n;,;$}:;
`f>~f kt~fo·~; auto,~J ~~:s $.h:'½-·,s-~::;fS:::~
`,Wel'<iJi:'2 {i'Ai'iMA.}
`
`Feat,i,<35 fmi'i', Rat">' slgr,;i/
`1. Waveletf>:;.atwe,$
`2~ $h9j.~ .. ha~~d- ~~~t>;:pJ:'.$ him>
`Hi!&eit tl',::;r-,;;form
`
`•. _.
`312 -[~·--·-f_ea_t_u_.r_e_s_e_le_c_t_io_n_. _____________ .:~-----~1
`4
`Real Time
`AFlB
`Predictkm
`
`314 ····-
`
`316
`
`O'mine Training
`Based on Labeled
`~"""""""""""""'i Data using Random
`Forests
`
`PJG. 3
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 7 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 4 of 16
`
`US 10,595,731 B2
`
`400
`
`4(12
`
`404
`
`406
`
`408
`
`416
`
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`f
`420
`
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`
`/
`
`422
`
`FlG,4
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 8 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 5 of 16
`
`US 10,595,731 B2
`
`500
`
`508
`
`He:art He:a!th
`5ctire
`
`504 l
`
`1
`
`Step3:
`
`502
`I
`
`Step 1:
`
`506-
`
`tount•Prer:nature.
`Beats/ cafout.:ite HRT
`
`Heart: Health
`........ ·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.,;-..,·1 Scare based
`on Arrhythmia
`
`510
`
`FIG. 5
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 9 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 6 of 16
`
`US 10,595,731 B2
`
`600
`
`pu_:;. 6
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 10 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 7 of 16
`
`US 10,595,731 B2
`
`~,,gr;,Vt-fo!~
`®tmaiJahOOrma!:
`Arff:-i~h~n~i
`·
`t4r:§it•J.>:1fo;s{: t?t;s~g:e~ {Tlif)}
`
`FIG. 7
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 11 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 8 of 16
`
`US 10,595,731 B2
`
`Consumer Application transition to Medical
`Application
`
`--weight
`•SM!
`•Cho!estf.':Wl
`»Sl:t--ep
`•Caffunle
`--Acttvffy
`•Diet
`,.Stfl:.!SS
`--Almho! etc
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`He..irt ~~le f-ai:tws
`That .a Patient Can
`fafluence
`
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`
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`~ats
`
`- Riu~ct:on
`
`Managed and
`Tracked by
`Da$hboard
`
`f,'J(;. 8
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 12 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 9 of 16
`
`US 10,595,731 B2
`
`902
`
`I
`
`;,.!,,
`
`------------------------------------------------------------------,
`Provide User Access to
`Cardiac Disease
`
`:
`
`900
`
`904
`I
`----------------------------------------------------
`Provide User Biometric Sensor(s} For
`Coupling to User Computing Device
`and Cardiac Disease Management
`System
`
`····i
`
`l
`j
`l
`l
`l
`
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`~ System.onto.Urer.Colputing.device-
`
`·
`
`1
`1 Receive User Personal Information Input to i ..
`908 - -! Cardiac Disease Management System
`(:_----
`
`9 l0--1 t::~,:~~:~i~fc~fo:i~:~;;~;;;;~ ;
`, -J -1
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`
`912 --l Generate Heart Health Score
`
`::
`....,tr
`
`1 Generate Recommendation(s) and/or
`914-- j Goal(s) for User
`~ ....................................................................................... ~~-............................................................................................................ ·
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`I
`
`i
`1:~\i~~~; n;~;;;;;;;;~;;;ti;~i;i~~;;;;;~-- ~ 1
`l
`918 -t.W~=-~:~-=UWJ ............................................. ••••
`
`916
`
`FIG. 9
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 13 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 10 of 16
`
`US 10,595,731 B2
`
`1014
`I
`
`-
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`
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`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 14 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 11 of 16
`
`US 10,595,731 B2
`
`AFlB PATIENT DASHBOARD
`
`1100~
`
`HEARTSCOHE
`
`OCSTSCORE
`
`7DAY
`
`HEART RATE
`
`AFIBDASH ·*
`GOALS *
`@
`*
`
`6
`
`0:13secs
`AVG DURA TlON
`0:1 Osecs 1 :OOmln
`7/7
`LONGEST MEtfADHERE
`
`AF!B EP!S.ODES
`
`SHORTEST
`
`SCROLL CONTINUES
`~---------------------------------
`
`!
`
`:}.
`:i
`*
`i
`:f.
`:i
`:i
`
`FIG.11
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 15 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 12 of 16
`
`US 10,595,731 B2
`
`,--1100
`
`1100b
`
`,11ooc:
`
`BLOOD PRESSURE
`
`,JAN .24
`
`(
`
`180
`
`0 i
`
`)
`
`INFLUENCERS
`
`CAFFEINE
`
`STRESS
`
`◄ e ►
`
`ALCOHOL
`
`NUTRITION
`
`c··f~)
`
`BLOOD PRESSURE
`
`I
`I
`L --------------------------------
`
`Fl(,. llA
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 16 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 13 of 16
`
`US 10,595,731 B2
`
`·1200~
`
`t200a
`
`PATIENT GOALS
`
`70AYGOALS@
`
`DAY:1
`
`2
`
`3 4
`
`5 6
`
`7
`
`TODAY'S GOALS:
`
`~
`RECORDED MY ECG
`0
`GET AT LEAST 7 HRS SLEEP
`NIGHTLY (recommended)
`[tJ
`
`VvALKED 20 M!NUTES
`
`$MAE GOALS
`
`NE\NGOALS
`
`FIG.12
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 17 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 14 of 16
`
`US 10,595,731 B2
`
`@ NEWGOALS
`
`1200~
`
`1200b ··· □
`□
`□
`
`TAKE DAILY MEDICATIONS
`
`REDUCE CAFFEINE IN TAKE
`(reenmrnended)
`
`REDUCE ALCOHOL INTAKE
`
`□
`□
`□
`
`MEDITATE FOR 5 MIN DAILY
`
`TAKE BLOOD PRESSURE
`READING DAILY
`
`GET AT LEAST 7 HRS SLEEP
`N!GHTL Y (recommended)
`
`FJ(i. l2A
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 18 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 15 of 16
`
`US 10,595,731 B2
`
`AUVECOR
`
`HAVE YOU MET
`YOUR GOA.LS FOR.
`TODAY?
`
`[~~!~~] I UPDATE I
`
`FlO. 13
`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 19 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 16 of 16
`
`US 10,595,731 B2
`
`y--1400
`
`1412
`
`1402
`
`1404
`
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`
`
`
`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 20 of 33
`
`US 10,595,731 B2
`
`1
`METHODS AND SYSTEMS FOR
`ARRHYTHMIA TRACKING AND SCORING
`
`CROSS-REFERENCE TO RELATED
`APPLICATIONS
`
`5
`
`2
`for subjects to administer without the aid of a medical
`professional. For example, the use of Holter monitors
`requires a patient to wear a bulky device on their chest and
`precisely place a plurality of electrode leads on precise
`locations on their chest. These requirements can impede the
`activities of the subject, including their natural movement,
`bathing, and showering. Once an ECG is generated, the ECG
`is sent to the patient's physician who may analyze the ECG
`and provide a diagnosis and other recommendations. Cur-
`10 rently, this process often must be performed through hospital
`administrators and health management organizations and
`many patients do not receive feedback in an expedient
`manner.
`
`This application is a continuation of U.S. application Ser.
`No. 16/153,446, filed Oct. 5, 2018, now U.S. Pat. No.
`10,426,359, issued Oct. 1, 2019, which is a continuation of
`U.S. application Ser. No. 15/393,077, filed Dec. 28, 2016,
`now U.S. Pat. No. 10,159,415, issued Dec. 25, 2018, which
`is a continuation of U.S. application Ser. No. 14/730,122,
`filed Jun. 3, 2015, now U.S. Pat. No. 9,572,499, issued Feb.
`21, 2017, which is a continuation of U.S. application Ser.
`No. 14/569,513 filed Dec. 12, 2014, now U.S. Pat. No. 15
`9,420,956, issued Aug. 23, 2016, which claims the benefit of
`U.S. Provisional Application No. 61/915,113, filed Dec. 12,
`2013, which application is incorporated herein by reference,
`U.S. Provisional Application No. 61/953,616 filed Mar. 14,
`2014, U.S. Provisional Application No. 61/969,019, filed 20
`Mar. 21, 2014, U.S. Provisional Application No. 61/970,551
`filed Mar. 26, 2014 which application is incorporated herein
`by reference, and U.S. Provisional Application No. 62/014,
`516, filed Jun. 19, 2014, which application is incorporated
`herein by reference.
`
`SUMMARY
`
`Disclosed herein are devices, systems, and methods for
`managing health and disease such as cardiac diseases,
`including arrhythmia and atrial fibrillation. In particular, a
`cardiac disease and/or rhythm management system, accord(cid:173)
`ing to aspects of the present disclosure, allows a user to
`conveniently document their electrocardiograms (ECG) and
`other biometric data and receive recommendation(s) and/or
`goal(s) generated by the system or by a physician in
`25 response to the documented data. The cardiac disease and/or
`rhythm management system can be loaded onto a local
`computing device of the user, where biometric data can be
`conveniently entered onto the system while the user may
`continue to use the local computing device for other pur-
`30 poses. A local computing device may comprise, for example,
`a computing device worn on the body (e.g. a head-worn
`computing device such as a Google Glass, a wrist-worn
`computing device such as a Samsung Galaxy Gear Smart
`Watch, etc.), a tablet computer (e.g. an Apple iPad, an Apple
`35 iPod, a Google Nexus tablet, a Samsung Galaxy Tab, a
`Microsoft Surface, etc.), a smartphone (e.g. an Apple
`iPhone, a Google Nexus phone, a Samsung Galaxy phone,
`etc.)
`A portable computing device or an accessory thereof may
`40 be configured to continuously measure one or more physi(cid:173)
`ological signals of a user. The heart rate of the user may be
`continuously measured. The continuously measurement may
`be made with a wrist or arm band or a patch in communi(cid:173)
`cation with the portable computing device. The portable
`45 computing device may have loaded onto (e.g. onto a non(cid:173)
`transitory computer readable medium of the computing
`device) and executing thereon ( e.g. by a processor of the
`computing device) an application for one or more of receiv(cid:173)
`ing the continuously measured physiological signal(s), ana-
`50 lyzing the physiological signal(s), sending the physiological
`signal(s) to a remote computer for further analysis and
`storage, and displaying to the user analysis of the physi(cid:173)
`ological signal(s). The heart rate may be measured by one or
`more electrodes provided on the computing device or acces-
`55 sory, a motion sensor provided on the computing device or
`accessory, or by imaging and lighting sources provided on
`the computing device or accessory. In response to the
`continuous measurement and recordation of the heart rate of
`the user, parameters such as heart rate (HR), heart rate
`60 variability (R-R variability or HRV), and heart rate turbu(cid:173)
`lence (HRT) may be determined. These parameters and
`further parameters may be analyzed to detect and/or predict
`one or more of atrial fibrillation, tachycardia, bradycardia,
`bigeminy, trigeminy, or other cardiac conditions. A quanti-
`65 tative heart health score may also be generated from the
`determined parameters. One or more of the heart health
`score, detected heart conditions, or recommended user
`
`BACKGROUND
`
`The present disclosure relates to medical devices, sys(cid:173)
`tems, and methods. In particular, the present disclosure
`relates to methods and systems for managing health and
`disease such as cardiac diseases including arrhythmia and
`atrial fibrillation.
`Cardiovascular diseases are the leading cause of death in
`the world. In 2008, 30% of all global death can be attributed
`to cardiovascular diseases. It is also estimated that by 2030,
`over 23 million people will die from cardiovascular diseases
`annually. Cardiovascular diseases are prevalent in the popu(cid:173)
`lations of high-income and low-income countries alike.
`Arrhythmia is a cardiac condition in which the electrical
`activity of the heart is irregular or is faster (tachycardia) or
`slower (bradycardia) than normal. Although many arrhyth(cid:173)
`mias are not life-threatening, some can cause cardiac arrest
`and even sudden cardiac death. Atrial fibrillation is the most
`common cardiac arrhythmia. In atrial fibrillation, electrical
`conduction through the ventricles of heart is irregular and
`disorganized. While atrial fibrillation may cause no symp(cid:173)
`toms, it is often associated with palpitations, shortness of
`breath, fainting, chest, pain or congestive heart failure. Atrial
`fibrillation is also associated with atrial clot formation,
`which is associated with clot migration and stroke.
`Atrial fibrillation is typically diagnosed by taking an
`electrocardiogram (ECG) of a subject, which shows a char(cid:173)
`acteristic atrial fibrillation waveform
`To treat atrial fibrillation, a patient may take medications
`to slow heart rate or modify the rhythm of the heart. Patients
`may also take anticoagulants to prevent atrial clot formation
`and stroke. Patients may even undergo surgical intervention
`including cardiac ablation to treat atrial fibrillation.
`Often, a patient with arrhythmia or atrial fibrillation is
`monitored for extended periods of time to manage the
`disease. For example, a patient may be provided with a
`Holter monitor or other ambulatory electrocardiography
`device to continuously monitor a patient's heart rate and
`rhythm for at least 24 hours.
`Current ambulatory electrocardiography devices such as
`Holter monitors, however, are typically bulky and difficult
`
`
`
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`
`US 10,595,731 B2
`
`3
`action items based on the heart health score may be dis(cid:173)
`played to the user through a display of the portable com(cid:173)
`puting device.
`The biometric data may be uploaded onto a remote server
`where one or more cardiac technicians or cardiac specialists 5
`may analyze the biometric data and provide ECG interpre(cid:173)
`tations, diagnoses, recommendations such as lifestyle rec(cid:173)
`ommendations, and/or goals such as lifestyle goals for
`subject. These interpretations, diagnoses, recommendations,
`and/or goals may be provided to the subject through the 10
`cardiac disease and/or rhythm management system on their
`local computing device. The cardiac disease and/or rhythm
`management system may also include tools for the subject to
`track their biometric data and the associated interpretations,
`diagnoses, recommendations, and/or goals from the cardiac 15
`technicians or specialists.
`An aspect of the present disclosure includes a dashboard
`centered around arrhythmia or atrial fibrillation tracking.
`The dashboard includes a heart score that can be calculated
`in response to data from the user such as their ECG and other
`personal information such as age, gender, height, weight,
`body fat, disease risks, etc. The main driver of this heart
`score will often be the incidence of the user's atrial fibril(cid:173)
`lation. Other drivers and influencing factors include the
`aforementioned personal information. The heart score will
`be frequently related to output from a machine learning
`algorithm that combines and weights many if not all of
`influencing factors.
`The dashboard will often display and track many if not all
`of the influencing factors. Some of these influencing factors 30
`may be entered directly by the user or may be input by the
`use of other mobile health monitoring or sensor devices. The
`user may also use the dashboard as an atrial fibrillation or
`arrhythmia management tool to set goals to improve their
`heart score.
`The dashboard may also be accessed by the user's phy(cid:173)
`sician ( e.g. the physician prescribing the system to the user,
`another regular physician, or other physician) to allow the
`physician to view the ECG and biometric data of the user,
`view the influencing factors of the user, and/or provide 40
`additional ECG interpretations, diagnoses, recommenda(cid:173)
`tions, and/or goals.
`Another aspect of the present disclosure provides a
`method for managing cardiac health. Biometric data of a
`user may be received. A cardiac health score may be 45
`generated in response to the received biometric data. One or
`more recommendations or goals for improving the generated
`cardiac health score may be displayed to the user. The
`biometric data may comprise one or more of an electrocar(cid:173)
`diogram (ECG), dietary information, stress level, activity 50
`level, gender, height, weight, age, body fat percentage, blood
`pressure, results from imaging scans, blood chemistry val(cid:173)
`ues, or genotype data. The recommendations or goals may
`be updated in response to the user meeting the displayed
`recommendations or goals. The user may be alerted if one or 55
`more recommendations or goals have not been completed by
`the user, for example if the user has not completed one or
`more recommendations or goals for the day.
`The analysis applied may be through one or more of the
`generation of a heart health score or the application of one 60
`or more machine learning algorithms. The machine learning
`algorithms may be trained using population data of heart
`rate. The population data may be collected from a plurality
`of the heart rate monitoring enabled portable computing
`devices or accessories provided to a plurality of users. The 65
`training population of users may have been previously
`identified as either having atrial fibrillation or not having
`
`4
`atrial fibrillation prior to the generation of data for continu(cid:173)
`ously measured heart rate. The data may be used to train the
`machine learning algorithm to extract one or more features
`from any continuously measured heart rate data and identify
`atrial fibrillation or other conditions therefrom. After the
`machine learning algorithm has been trained, the machine
`learning algorithm may recognize atrial fibrillation from the
`continuously measured heart rate data of a new user who has
`not yet been identified as having atrial fibrillation or other
`heart conditions. One or more of training population data or
`the trained machine learning algorithm may be provided on
`a central computing device (e.g. be stored on a non-transi(cid:173)
`tory computer readable medium of a server) which is in
`communication with the local computing devices of the
`users and the application executed thereon ( e.g. through an
`Internet or an intranet connection.)
`A set of instructions for managing cardiac health may be
`downloaded from the Internet. These set of instructions may
`be configured to automatically generate the cardiac health
`20 score. The cardiac health score may be generated using a
`machine learning algorithm. The machine learning algo(cid:173)
`rithm may generate the cardiac health score of the user
`and/or the recommendations and/or goals in response to
`biometric data from a plurality of users. The set of instruc-
`25 tions may be configured to allow a medical professional to
`access the received biometric data. The cardiac health score
`and/or the recommendations and/or goals may be generated
`by the medical professional.
`The set of instructions may be stored on a non-transitory
`computer readable storage medium of one or more of a
`body-worn computer, a tablet computer, a smartphone, or
`other computing device. These set of instructions may be
`capable of being executed by the computing device. When
`executed, the set of instructions may cause the computing
`35 device to perform any of the methods described herein,
`including the method for managing cardiac health described
`above.
`Another aspect of the present disclosure provides a sys(cid:173)
`tem for managing cardiac health. The system may comprise
`a sensor for recording biometric data of a user and a local
`computing device receiving the biometric data from the
`sensor. The local computing device may be configured to
`display a cardiac health score and one or more recommen(cid:173)
`dations or goals for the user to improve the cardiac health
`score in response to the received biometric data.
`The system may further comprise a remote server receiv(cid:173)
`ing the biometric data from the local computing device. One
`or more of the local computing device or the remote server
`may comprise a machine learning algorithm which generates
`one or more of the cardiac health score or the one or more
`recommendations or goals for the user. The remote server
`may be configured for access by a medical professional.
`Alternatively, or in combination, one or more of the cardiac
`health score or one or more recommendations or goals may
`be generated by the medical professional and provided to the
`local computing device through the remote server.
`The sensor may comprise one or more of a hand-held
`electrocardiogram (ECG) sensor, a wrist-worn activity sen(cid:173)
`sor, a blood pressure monitor, a personal weighing scale, a
`body fat percentage sensor, a personal thermometer, a pulse
`oximeter sensor, or any mobile health monitor or sensor.
`Often, the sensor is configured to be in wireless communi(cid:173)
`cation with the local computing device. The local computing
`device comprises one or more of a personal computer, a
`laptop computer, a palmtop computer, a tablet computer, a
`smartphone, a body-worn computer, or the like. The bio-
`metric data may comprise one or more of an electrocardio-
`
`
`
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`
`US 10,595,731 B2
`
`5
`
`6
`FIG. 9 shows an exemplary method for cardiac disease
`and rhythm management;
`FIG. 10 shows an exemplary method for monitoring a
`subject to determine when to record an electrocardiogram
`(ECG);
`FIG. 11 shows an exemplary screenshot of a first aspect
`of a dashboard application;
`FIG. llA shows an exemplary screenshot of a second
`aspect of a dashboard application;
`FIG. 12 shows an exemplary screenshot of a first aspect
`of a goals and recommendations page of the cardiac disease
`and rhythm management system interface or mobile app;
`FIG. 12A shows an exemplary screenshot of a second
`15 aspect of a goals and recommendations page of the cardiac
`disease and rhythm management system interface or mobile
`app;
`FIG. 13 shows an exemplary screenshot of a user's local
`computing device notifying the user with a pop-up notice to
`meet their daily recommendations and goals; and
`FIG. 14 shows an embodiment comprising a smart watch
`which includes at least one heart rate monitor and at least
`one activity monitor.
`
`5
`gram (ECG), dietary information, stress level, activity level,
`gender, height, weight, age, body fat percentage, or blood
`pressure.
`Other physiological signals or parameters such as physi(cid:173)
`cal activity, heart sounds, blood pressure, blood oxygen(cid:173)
`ation, blood glucose, temperature, activity, breath composi(cid:173)
`tion, weight, hydration levels, an electroencephalograph
`(EEG), an electromyography (EMG), a mechanomyogram
`(MMG), an electrooculogram (EOG), etc. may also be
`monitored. The user may also input user-related health data 10
`such as age, height, weight, body mass index (BMI), diet,
`sleep levels, rest levels, or stress levels. One or more of these
`physiological signals and/or parameters may be combined
`with the heart rate data to detect atrial fibrillation or other
`conditions. The machine learning algorithm may be config(cid:173)
`ured to identify atrial fibrillation or other conditions in
`response to heart rate data in combination with one or more
`of the other physiological signals and/or parameters for
`instance. Triggers or alerts may be provided to the user in
`response to the measured physiological signals and/or 20
`parameters. Such triggers or alerts may notify the user to
`take corrective steps to improve their health or monitor other
`vital signs or physiological parameters. The application
`loaded onto and executed on the portable computing device
`may provide a health dash board integrating and displaying 25
`heart rate information, heart health parameters determined in
`response to the heart rate information, other physiological
`parameters and trends thereof, and recommended user action
`items or steps to improve health.
`
`INCORPORATION BY REFERENCE
`
`All publications, patents, and patent applications men(cid:173)
`tioned in this specification are herein incorporated by ref(cid:173)
`erence to the same extent as if each individual publication,
`patent, or patent application was specifically and individu(cid:173)
`ally indicated to be incorporated by reference.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`The novel features of the subject matter disclosed herein
`are set forth with particularity in the appended claims. A
`better understanding of the features and advantages of the
`present disclosure will be obtained by reference to the
`following detailed description that sets forth illustrative
`embodiments, in which the principles of the disclosure are
`utilized, and the accompanying drawings of which:
`FIG. 1 shows a system for cardiac disease and rhythm
`management;
`FIG. 2 shows a flow chart of a method 200 for predicting
`and/or detecting atrial fibrillation from R-R interval mea(cid:173)
`surements;
`FIG. 3 shows a flow chart of a method for predicting
`and/or detecting atrial fibrillation from R-R interval mea(cid:173)
`surements and for predicting and/or detecting atrial fibril(cid:173)
`lation from raw heart rate signals;
`FIG. 4 shows an embodiment of the system and method
`of the ECG monitoring described herein;
`FIG. 5 shows a flow chart of an exemplary method to
`generate a heart health score in accordance with many
`embodiments;
`FIG. 6 shows an exemplary method of generating a heart
`score;
`FIG. 7 shows a schematic diagram of the executed appli(cid:173)
`cation described herein;
`FIG. 8 shows exemplary screenshots of the executed
`application;
`
`DETAILED DESCRIPTION
`
`Devices, systems, and methods for managing health and
`disease such as cardiac diseases, including arrhythmia and
`atrial fibrillation, are disclosed. In particular, a cardiac
`30 disease and/or rhythm management system, according to
`aspects of the present disclosure, allows a user to conve(cid:173)
`niently document their electrocardiograms (ECG) and other
`biometric data and receive recommendation(s) and/or
`goal(s) generated by the system or by a physician in
`35 response to the documented data.
`The term "atrial fibrillation," denoting a type of cardiac
`arrhythmia, may also be abbreviated in either the figures or
`description herein as "AFIB."
`FIG. 1 shows a system 100 for cardiac disease and rhythm
`40 management. The system 100 may be prescribed for u