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`DOCUMENT MADE AVAILABLE UNDER THE
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`International application number:
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`International filing date:
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`21 March 2014 (21.03.2014)
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`APPLE 1028
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`APPLE 1028
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`THIS IS TO CERTIFY THAT ANNEXED HERETOIS A TRUE COPY FROM
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`OFFICE OF THOSE PAPERS OF THE BELOW IDENTIFIED PATENT
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`UNTFED STATES DEPARTMENT OF COMMERCE
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`United States Patent and ‘Praderrark CMfice
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`January 04, 2015
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`APPLICATION NUMBER: 61/969,019
`FILING DATE: March 21, 2014
`RELATED PCT APPLICATION NUMBER: PCT/US14/70170
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`LONGITUDINAL PATIENT ECG MONITORING
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`SIGNATURE
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`TYPED OR PRINTED NAME Richard D. Shoop
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`available.
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`pate March 21, 2014
`REGISTRATION No, 49,763
`{if appropriate)
`DOCKET NUMBER 12212-713.100
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`TELEPHONE (650) 212-1700
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`

`UNITED STATES PROVISIONAL PATENT APPLICATION
`
`LONGITUDINAL PATIENT ECG MONITORING
`
`Inventor:
`
`Euan THOMPSON
`
` Shay
`Shay Glenn LLP
`2755 CampusDrive, Suite 210
`San Mateo, CA 94403
`(650) 212-1700 Telephone
`(650) 212-7562 Facsimile
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`CERTIFICATE OF TRANSMISSION UNDER37 CFR 1.8
`[herebycertify that this correspondence is being transmitted to the USPTO via
`EFS-Webon March 21, 2014.
`
`
`Mary Buggic
`Date
`
`Wary4 Duero)
`
`Bfal yey
`
`
`
`-10f15-
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`SG Docket No.: 12212-713.100
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`LONGITUDINAL PATIENT ECG MONITORING
`
`INCORPORATION BY REFERENCE
`
`All publications and patent applications mentioned in this specification are herein
`[0001]
`incorporated by reference to the sameextentas if each individual publication or patent
`application was specifically and individually indicated to be incorporated by reference.
`
`FIELD
`
`Embodimentsof the invention relate generally to ECG monitoring systems and
`[0002]
`methods, and morespecifically, relate to longitudinal ECG monitoring systems and methodsfor
`determining a patient's health status.
`
`BACKGROUND
`
`Electrocardiograms (ECGs) havelong been used detect and/or confirm various
`(0003)
`cardiovascular conditions, such as myocardial infarction and cardiac arrhythmias. Initial ECG
`recording devices were large, hospital based devicesutilizing many leads. A patient suspected of
`suffering a cardiovascular event or abnormality can have an ECG takenusing the device at the
`hospital. A health care professional, such as a cardiologist or electrophysiologist, could then
`analyze and interpret the ECG to determine whether the cardiovascular event or abnormality
`
`occurred.
`One problem with using a hospital based device is that certain adverse cardiovascular
`[0004]
`events result in only temporary or transitory changes in the ECG. Bythe timethe patient has an
`ECGtaken at the hospital, the ECG may appear normal.
`[0005]
`Various, home-based or portable, ambulatory ECG recording devices have been
`developed to address someof these shortcomings. For example, Holter monitors have been
`developed for home use. The Holter monitor can continuously record ECGs and transmit the
`ECGsto a center for analysis by a trained health care professional. Portable event recorders
`have been developed to record ECGs on-demand when thepatient suspects an occurrence of an
`adverse cardiovascular event. The ECG data generated by these device may be analyzed directly
`by health care professional or by various computer based systemsand devices.
`[0006]
`For example, U.S. Patent No. 6,564,090 to Tahaet al. discloses a method and
`apparatus for performing serial comparison of ECGs acquired from a patient. The device
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`acquires an index ECG and subsequent ECGandperforms an analysis and comparison to
`determine various changes in the ECG waveform. In addition, the index ECG and subsequent
`ECGcan be compared with a baseline ECG takenusing a standard twelve lead recording device
`
`and stored on a separate ECG managementsystem.
`(0007]
`U.S. Publication No. 2013/0096447 to Dhawanet al. also teaches a system and
`method for serial analysis of ECGs. The system and method involves acquiring 12-lead ECG
`data from a patient, constructing a 3-D representation of cardiac activity from the ECGdata, and
`computing and comparing 3-D based vectorial markers to improve diagnosis.
`[0008]
`U.S. Publication No. 2011/0190650 to McNair teaches a portable, wearable device
`equipped with signal-processing software andstatistical predictive algorithmsthat calculate
`stability-theoretic measures derived from the digital electrocardiogram timeseries acquired bythe
`device.
`USS. Patent No. 6,389,308 to Shusterman discloses a system and methodthat includes
`[0009]
`portable ECG recording device that can perform basic analysis of the recorded ECG,such as
`determining the values of various basic ECG features. More advanced processing and analysis
`can be doneona separate computer system to detect smaller changes in the ECG waveform that
`maynot be detectable using the basic analysis.
`[00010]
`U.S. Patent No. 6,246,903 to Kletskin discloses a system and method for evaluating a
`plurality of predetermined ECG features and comparing the evaluated features to reference
`values in order to determine the patient’s condition.
`[00011]
`U.S. Patent No. 2012/0311092 to Musioletal. discloses system for recording, storing
`and analyzing ECG data that includes an ECG recording device that can transmit ECG data to a
`mobile phone. The mobile phone can then transmit the ECG data to a remote server for storage
`
`and analysis.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`The novel features of the invention are set forth with particularity in the claimsthat
`[00012]
`follow. A better understanding of the features and advantagesofthe present invention will be
`obtained by referenceto the following detailed description thatsets forth illustrative
`embodiments, in which the principles of the invention are utilized, and the accompanying
`
`drawings of which:
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`[00013]
`
`FIG.1 is flow chart illustrating an embodiment of an ECG monitoring method.
`
`DETAILED DESCRIPTION
`
`[00014] A method and system for longitudinal monitoring of a patient’s or any consumer’s
`(after referred to as “patient”) health using various ECG monitoring devicesis described herein.
`The ECG monitoring devices generate ECG signal data which can bestored in a database for
`further analysis. The ECG data, which can bestored in a database along with other patient
`information, can be analyzed by a processing device, such as a computeror server, using various
`
`algorithms.
`Various ECG monitoring or recording devices, hereinafter referred to as ECG
`[00015]
`monitoring devices, can be used to record the ECG data. For example, the ECG monitoring
`device can be a handheld,portable, or wearable smartphone based device, as described in U.S.
`Patent No. 8,301,232, whichis herein incorporated by reference in its entirety for all purposes.
`A smartphone based device, or a device having wireless or cellular telecommunication
`capabilities, can transmit the ECG data to a database or serverdirectly through the internet.
`These types of ECG monitoring devices as well as other ECG monitoring devices include
`portable devices, wearable recording devices, event recorders, and Holter monitors. Clinical or
`hospital based ECG recording devices can also be used and integrated into the system. Such
`devices may be able to transmit stored ECG data through a phoneline or wirelessly through the
`internet or cellular network, or may need to be sent to a data collection center for data collection
`and processing. The ECG data can be tagged with the type of ECG monitoring device used to
`record the data by, for example, including it in metadata for indexing and searching purposes.
`[00016]
`|The ECG monitoring devices can be single lead devices or multiple lead devices,
`whereeachlead generally terminates with an electrode. Some embodiments may even be
`leadless and haveelectrodes that are integrated with the body or housing of the device, and
`therefore have a predetermined relationship with each other, such as a fixed spacing apart from
`each other. Theorientation and positioning of the single lead in a single lead device or of each
`lead of the multiple lead device or ofthe electrodes ofthe leadless device can be transmitted with
`the ECG data. The lead and/or electrode placement may be predetermined and specified to the
`patient in instructions for using the device. For example, the patient may be instructed to
`position the leads and/or electrodes with references to one or more anatomical landmarks on the
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`patient’s torso. Any deviation from the predeterminedlead and/or electrode placementcan be
`notated by the patient or user when transmitting the ECG data. The lead and electrode placement
`may be imaged using a digital camera, which may be integrated with a smart phone, and
`transmitted with the ECG data and stored in the database. The lead and electrode placement may
`be marked onthe patient’s skin for imaging and for assisting subsequent placementofthe leads
`and electrodes. The electrodes can be attached to the skin using conventional methods which
`may include adhesives and conducting gels, or the electrodes may simply be pressed into contact
`with the patient’s skin. The lead andelectrode placement may be changedafter taking one
`recordingor after recording for a predetermined orvariable amountof time. The ECG data can
`be tagged with the numbers of leads and/or electrodes and the lead and/or electrode placement,
`including whether adhesives and/or conducting gels were used. Again, this information can be
`including in metadata for indexing and searching purposes.
`[00017]
`|The ECGsignal data can be continuously recorded over a predeterminedorvariable
`length of time. Continuous ECG recording devices can record for up to 1, 2, 3, 4, 5, 6, 7, 8, 9,
`10, 11, 12, 13, or 14 days. Alternatively or additionally, the ECG data can be recorded on
`demand bythe patient at various discrete times, such as when the patient feels chest pains or
`experiences other unusual or abnormalfeelings. The on demand ECG recorder can have a
`memory buffer that can record a predetermined amount of ECG data on a rolling basis, and when
`activated by the patient to record a potential event, a predetermined amount of ECG data can be
`saved and/or transmitted. The predetermined amount of ECG data can include a predetermined
`amount of ECG data before activation and a predetermined amount of ECG data after activation
`such that a window of ECG data is captured that encompasses the potential event. The time
`period between ECG recordings may beregular or irregular. For example,the time period may
`be once a day, once a week, once a month, or at some other predetermined interval. The ECG
`recordings may betaken at the sameordifferent times of days, undersimilar or different
`circumstances, as described herein. One or more baseline ECGscan be recorded while the
`patientis free of symptoms. The baseline ECGscanbeperiodically recorded and predetermined
`intervals and/or on-demand. The same ECGrecording deviceor different ECG recording
`devices may be used to record the various ECGofa particular patient. All this information may
`be taggedto or associated with the ECG data by, for example,includingit in the metadata for
`indexing and searching purposes.
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`[00018]|The ECG data can be time stamped andcan be annotated by the patient or health care
`provider to describe the circumstances during which the ECG was recorded, preceding the ECG
`recording, and/or following the ECG recording. For example, the system and device can have an
`user interface for data entry that allows the patient to enter in notes regarding the conditions and
`circumstances surrounding the ECG recording. This additional data can be also included as
`metadata for indexing and searching purposes. For example, location, food, drink, medication.
`and/or drug consumption,exercise,rest, sleep, feelings of stress, anxiety, pain or other unusual
`or abnormal feelings, or any other circumstance that may affect the patient’s ECGsignal can all
`be inputted into the device, smart phone, computeror other computing deviceto be transmitted
`to the server or database along with the ECG data. The annotated data can also include the
`patient’s identity or unique identifier as well as various patient characteristics including age, sex,
`race, ethnicity, and relevant medical history. The annotated data can also be time stamped or
`tagged so that the ECG data can be matched or correlated with the activity or circumstance of
`interest. This also allows comparison of the ECG before, after and during the activity or
`circumstanceso that the effect on the ECG can be determined.
`[00019]|The ECG data and the associated metadata can be transmitted from the device to a
`server and database for storage and analysis. The transmission can bereal-time, at regular
`intervals such as hourly, daily, weekly and anyinterval in between, or can be on demand. The
`metadata facilitates the searching, organizing, analyzing and retrieving of ECG data.
`Comparison andanalysis of a single patient’s ECG data can be performed, and/or comparison of
`ECGdata between patients can be performed. For example, the metadata can be used to identify
`and select a subset of ECG data where an activity or circumstance, such asthe taking of
`medication, occurred within a predetermined amountof time to the ECG data. The components
`of the ECG signal data, such as the P wave, T wave, and QRS complex andthe like, the
`amplitudes ofthe components, the ratios between the components, the width of the components,
`and the delay or time separation between the components, can be extracted, compared, analyzed,
`and stored as ECG features. For example, the P wave and heart rate can be extracted and
`analyzed to identifyatrial fibrillation, where the absence of P waves and/or an irregular heart rate
`may indicate atrialfibrillation. The extracted ECGfeatures can also be included in the metadata
`for indexing and searching.
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`[00020]|The changes in the ECG signalover time in view ofthe activities and circumstances
`can be compared with changes over time and circumstances observed within a database of
`ECG’s. Comparisons may include any comparison ofdata derived from any other ECG signal or
`any database of ECG’s or any subset of ECGdata, or with data derived from any database of
`ECG’s. Changes in any feature of the ECGsignal over time may beused fora relative
`comparison with similar changes in any ECG database or with data derived from an ECG
`database. The ECG data from the baseline ECG and the ECG data from a potential adverse
`event can be compared to determine the changesor deviations from baseline values. In addition,
`both the baseline ECG and the ECG data recorded from the patient can be comparedto one or
`more predetermined template ECGs which can represent a normal healthy condition as well as
`various diseased conditions, such as myocardial infarction and arrhythmias.
`[00021]
`|The comparisonsand analysis described herein can be used to draw conclusions and
`insights into the patient’s health status, which includes potential health issues that the patient
`may be experiencing at the time of measurementor at future times. Conclusions and
`determinations may bepredictive of future health conditions or diagnostic of conditionsthat the
`patient already has. The conclusions and determinations mayalso include insights into the
`effectiveness or risks associated with drugs or medicationsthat the patient may be taking, have
`taken or may be contemplating taking in the future. In addition, the comparisons and analysis
`can be usedto determine behaviors and activities that may reduce or increase risk of an adverse
`event. Based on the comparisons and analysis described herein, the ECG data can be classified
`according to a level ofrisk of being an adverse event. For example, the ECG data can be
`classified as normal, low risk, moderate risk, high risk, and/or abnormal. The normal and
`abnormal designation mayrequire health care professional evaluation, diagnosis, and/or
`
`confirmation.
`[00022]
`Diagnosis and determination of an abnormality, an adverse event, or a disease state
`by physicians and otherhealth care professionals can be transmitted to the servers and database
`to be tagged with and associated with the corresponding ECG data. The diagnosis and
`determination may be based on analysis of ECG data or may be determined using othertests or
`examination procedures. Professional diagnosis and determinationscan be extracted from the
`patient’s electronic health records, can be entered into the system by the patient, or can be
`entered into the system by the medical professional. The conclusions and determinationsof the
`
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`system can be compared with actual diagnosis and determinations from medical professionsto
`validate and/or refine the machine learning algorithms used by the system. The time of
`occurrence and duration of the abnormality, adverse event or disease state can also be includedin
`the database, such that the ECG data corresponding with the occurrence and/or the ECG data
`preceding and/or following the abnormality, adverse event or disease state can be associated
`together and analyzed. The length oftime preceding or following the abnormality may be
`predetermined and be upto 1 to 30 days, or greater than 1 to 12 months. Analysis of the time
`before the abnormality, adverse eventor disease state may allow the system to identify patterns
`or correlations of various ECG features that precede the occurrence of the abnormality, adverse
`event or disease state, thereby providing advance detection or warning of the abnormality,
`adverse eventor disease state. Analysis of the time following the abnormality, adverse event or
`disease state can provide information regarding the efficacy of treatments and/or provide the
`patient or physician information regarding disease progression, such as whetherthe patient’s
`condition in improving, worsening or staying the same. The diagnosis and determination can
`also be usedfor indexing by, for example, includingit in the metadata associated with the
`
`corresponding ECG data.
`[00023]
`As described above, various parameters may be included in the database along with
`the ECG data. These mayincludethe patient’s age, gender, weight, blood pressure, medications,
`behaviors, habits, activities, food consumption, drink consumption, drugs, medical history and
`other factors that mayinfluence a patient’s ECG signal. The additional parameters may or may
`not be used in the comparison of the changes in ECG signal over time and circumstances.
`[00024]
`The conclusions, determinations, and/orinsights into the patient’s health generated by
`the system may be communicatedto the patient directly or via the patient’s caregiver (doctor or
`other healthcare professional). For example, the patient can be sent an email or text message that
`is automatically generated by the system. The email or text message can be a notification which
`directs the patient to log onto a securesite to retrieve the full conclusion, determination or
`insight, or the email or text message can include the conclusion, determination or insight.
`Alternatively or additionally, the email or text message can be sentto the patient’s caregiver.
`The notification mayalso be provided via an application on a smartphone,tablet, laptop, desktop
`
`or other computing device.
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`As described herein, the system can identify behaviors, habits, activities, foods,
`[00025]
`drinks, medications, drugs, and the like which are associated with the patient’s abnormal ECG
`readings. In addition to informingthepatient of these associations, the system can provide
`instructions or recommendations to the patient to avoid these behaviors, habits, activities, foods,
`drinks, medications, drugs, and the like which are associated with the patient’s abnormal ECG
`readings. Similarly, the system can identify behaviors, habits, activities, foods, drinks,
`medications, drugs, and the like which are associated with normal or improving ECG readings,
`and can instruct or recommendthat the patient perform these behaviors, habits, and activities
`and/or consumethese foods, drinks, medications, and drugs. The patient may avoid a future
`healthcare issue, as instructed or recommendedbythe system, by modifying their behavior,
`habits or by taking any courseofaction, including but not limited to taking a medication, drug or
`adhering to a diet or exercise program, which may be a predetermined course of action
`recommended by the system independentof any analysis of the ECG data, and/or mayalso result
`from insights learned through this system and method as described herein. In addition, the
`insights of the system mayrelate to general fitness and or mental wellbeing.
`[00026]
`|The ECG data andthe associated metadata and other related data as described herein
`can be stored in a central database, a cloud database, or a combination of the two. The data can
`be indexed, searched, and/or sorted according to any of the features, parameters, or criteria
`described herein. The system can analyze the ECG data ofa single patient, andit can also
`analyze the ECG data of a group ofpatients, which can be selected according to any ofthe
`features, parametersorcriteria described herein. When analyzing data from a single patient,it
`maybe desirable to reduce and/or correct for the intra-individual variability of the ECGdata, so
`that comparison of one set of ECG data taken at one particular time with anotherset of ECG data
`taken at another time reveals differences resulting from changesin health status and not from
`changesin the type of ECG recording device used, changesin lead and electrode placement,
`changesin the conditionofthe skin (i.e. dry, sweaty, conductive gel applied or not applied), and
`the like. As described above, consistent lead and electrode placement can help reduce variability
`in the ECG readings. The system can also retrieve the patient’s ECG data that were taken under
`similar circumstances and can analyze this subset of ECG data.
`[00027]
`FIG.1 illustrates an embodiment of the system and method of ECG monitoring
`described herein. The system can be implemented ona server or computer having a processor
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`for executing the instructions described herein, which can be stored in memory. In step 100,
`ECG data can be recorded using any of the devices described herein for one or morepatients. In
`step 102, the ECG data is transmitted along with associated metadata to a server and database
`that stores the ECG data. In step 104, a subset of the ECG data can be selected based oncriteria
`in the metadata, such as useridentity, time, device used to record the ECG data, and the like. In
`step 106, the subset of ECG data can be analyzed using a machinelearning algorithm, which can
`assign a tisk level to the ECG data in step 108. The system can then determine whetherthe risk
`level is high, as shown in step 110. Ifthe risk level is low, the user can be notified that the ECG
`is normal or low risk, as shown in step 112. Ifthe risk level is high, a high risk level alert can be
`sent to the patient with the option of sending the ECG to the medical professional for
`interpretation, as shown in step 114. The system then waits for the user’s response to determine
`whetherthe patient elects to send the ECG to the medical professional for interpretation, as
`shown in step 116. If the patient does not wish to send the ECGto the medical professional for
`interpretation, the system can endthe routine at this point, as shown in FIG.118. If the patient
`doeselect to send the ECG to the medical professionalfor interpretation, the request can be
`transmitted to the medical professional in step 120. The request to the medical professional can
`be sent to a workflowauction system as described in U.S. Provisional Application No.
`61/800,879, filed March 15, 2013, which is herein incorporated by referencein its entirety for all
`purposes. Once the medical professional has interpreted the ECG,the system can receive and
`store the ECG interpretation from the medical professional in the database, as shown in step 122.
`The system can then notify the user of the professional ECG interpretation, which can be sent to
`or accessed by the user, as shown in step 124. Additionally, the system can compare the
`assigned risk level with the medical diagnosis in step 126 and can determine whethertherisk
`level determined bythe system agrees with the medical diagnosis in step 128. If the risk level
`does not agree with the medical diagnosis, the machine learning algorithm can be adjusted until
`the risk level matches the medical diagnosis, as shown in step 130. If the risk level does agree
`with the medical diagnosis, the routine can be ended as shown in step 132.
`[00028]
`It is understoodthatthis disclosure, in many respects, is only illustrative of the
`numerousalternative device embodiments of the present invention. Changes may be made in the
`details, particularly in matters of shape, size, material and arrangementof various device
`components without exceeding the scope ofthe various embodimentsofthe invention. Those
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`skilled in the art will appreciate that the exemplary embodiments anddescriptions thereof are
`merelyillustrative of the invention as a whole. While several principles of the invention are
`made clear in the exemplary embodiments described above, those skilled in the art will
`appreciate that modificationsofthe structure, arrangement, proportions, elements, materials and
`methodsof use, maybe utilized in the practice of the invention, and otherwise, which are
`particularly adapted to specific environments and operative requirements without departing from
`the scope ofthe invention. In addition, while certain features and elements have been described
`in connection with particular embodiments, those skilled in the art will appreciate that those
`features and elements can be combined with the other embodiments disclosed herein.
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`CLAIMS
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`Whatis claimed is:
`
`A method of building an ECG database, the method comprising:
`1.
`recording a first ECG dataat a first time using an ECG recording device having
`
`electrodes placed onafir