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`International application number:
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`International filing date:
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`12 December 2014 (12.12.2014)
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`Certified copy of priority document
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`US
`61/969,019
`21 March 2014 (21.03.2014)
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`L551 1TH. D Slflh‘v DIE I-‘AR’EEV‘! E N'I' U P C()i\~1i\"II‘ZRCI{
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`United States Patent and "l‘rudcmnrk Office
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`January 04, 2015
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`APPLICATION THAT MET THE REQUIREMENTS TO BE GRANTED A
`FILING DATE.
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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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`PTO/SB/16 10343)
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`LONGITUDINAL PATIENT ECG MONITORING
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`DATE March 21, 2014
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`

`UNITED STATES PROVISIONAL PATENT APPLICATION
`
`LONGITUDINAL PATIENT ECG MONITORING
`
`Inventor:
`
`Euan THOMPSON
`
` Shay
`Shay Glenn LLP
`2755 Campus Drive, Suite 210
`San Mateo, CA 94403
`(650) 212-1700 Telephone
`(650) 212-7562 Facsimile
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`I hereby certify that this correspondence is being transmitted to the USPTO via
`EFS-Web on March 21, 2014.
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`Mary Buggic
`Date
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`MGM/L1 Wig?)
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`E/ZLIM
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`LONGITUDINAL PATIENT ECG MONITORING
`
`INCORPORATION BY REFERENCE
`
`[0001]
`
`All publications and patent applications mentioned in this specification are herein
`
`incorporated by reference to the same extent as if each individual publication or patent
`
`application was specifically and individually indicated to be incorporated by reference.
`
`FIELD
`
`[0002]
`
`Embodiments of the invention relate generally to ECG monitoring systems and
`
`methods, and more specifically, relate to longitudinal ECG monitoring systems and methods for
`
`determining a patient’ s health status.
`
`BACKGROUND
`
`[0003]
`
`Electrocardiograms (ECGs) have long been used detect and/or confirm various
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`cardiovascular conditions, such as myocardial infarction and cardiac arrhythmias. Initial ECG
`
`recording devices were large, hospital based devices utilizing many leads. A patient suspected of
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`suffering a cardiovascular event or abnormality can have an ECG taken using the device at the
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`hospital. A health care professional, such as a cardiologist or electrophysiologist, could then
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`analyze and interpret the ECG to determine Whether the cardiovascular event or abnormality
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`occurred.
`
`[0004]
`
`One problem with using a hospital based device is that certain adverse cardiovascular
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`events result in only temporary or transitory changes in the ECG. By the time the patient has an
`
`ECG taken at the hospital, the ECG may appear normal.
`
`[0005]
`
`Various, home-based or portable, ambulatory ECG recording devices have been
`
`developed to address some of these shortcomings. For example, Holter monitors have been
`
`developed for home use. The Holter monitor can continuously record ECGs and transmit the
`
`ECGs to a center for analysis by a trained health care professional. Portable event recorders
`
`have been developed to record ECGs on-demand when the patient 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 systems and devices.
`
`[0006]
`
`For example, US. Patent No. 6,564,090 to Taha et a1. discloses a method and
`
`apparatus for performing serial comparison of ECGs acquired from a patient. The device
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`- 2 of 15 -
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`acquires an index ECG and subsequent ECG and performs an analysis and comparison to
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`determine various changes in the ECG waveform. In addition, the index ECG and subsequent
`
`ECG can be compared with a baseline ECG taken using a standard twelve lead recording device
`
`and stored on a separate ECG management system.
`
`[0007]
`
`U.S. Publication No. 2013/0096447 to Dhawan et 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 ECG data, and
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`computing and comparing 3—D based vectorial markers to improve diagnosis.
`
`[0008]
`
`US. Publication No. 2011/0190650 to McNair teaches a portable, wearable device
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`equipped with signal-processing software and statistical predictive algorithms that calculate
`stability-theoretic measures derived from the digital electrocardiogram timeseries acquired by the
`device.
`
`[0009]
`
`US. Patent No. 6,389,308 to Shusterman discloses a system and method that includes
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`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
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`can be done on a separate computer system to detect smaller changes in the ECG waveform that
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`may not be detectable using the basic analysis.
`
`[00010] US. Patent No. 6,246,903 to Kletskin discloses a system and method for evaluating a
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`plurality of predetermined ECG features and comparing the evaluated features to reference
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`values in order to determine the patient’s condition.
`
`[00011] US. Patent No. 2012/0311092 to Musiol et al. discloses system for recording, storing
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`and analyzing ECG data that includes an ECG recording device that can transmit ECG data to a
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`mobile phone. The mobile phone can then transmit the ECG data to a remote server for storage
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`and analysis.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`[00012]
`
`The novel features of the invention are set forth with particularity in the claims that
`
`follow. A better understanding of the features and advantages of the present invention will be
`
`obtained by reference to the following detailed description that sets forth illustrative
`
`embodiments, in which the principles of the invention are utilized, and the accompanying
`
`drawings of which:
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`7
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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 “patien ”) health using various ECG monitoring devices is described herein.
`
`The ECG monitoring devices generate ECG signal data which can be stored in a database for
`
`further analysis. The ECG data, which can be stored in a database along with other patient
`
`information, can be analyzed by a processing device, such as a computer or server, using various
`
`algorithms.
`
`[00015]
`
`Various ECG monitoring or recording devices, hereinafter referred to as ECG
`
`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 US.
`
`Patent No. 8,301,232, which is 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 server directly through the intemet.
`
`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 phone line 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,
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`where each lead generally terminates with an electrode. Some embodiments may even be
`
`leadless and have electrodes that are integrated with the body or housing of the device, and
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`therefore have a predetermined relationship with each other, such as a fixed spacing apart from
`
`each other. The orientation and positioning of the single lead in a single lead device or of each
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`lead of the multiple lead device or of the electrodes of the leadless device can be transmitted with
`
`the ECG data. The lead and/or electrode placement may be predetermined and specified to the
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`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 predetermined lead and/or electrode placement can be
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`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 on the patient’s skin for imaging and for assisting subsequent placement of the leads
`
`and electrodes. The electrodes can be attached to the skin using conventional methods which
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`may include adhesives and conducting gels, or the electrodes may simply be pressed into contact
`
`with the patient’s skin. The lead and electrode placement may be changed after taking one
`
`recording or after recording for a predetermined or variable amount of 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 ECG signal data can be continuously recorded over a predetermined or variable
`
`length of time. Continuous ECG recording devices can record for up to l, 2, 3, 4, 5, 6, 7, 8, 9,
`
`10, ll, 12, 13, or 14 days. Alternatively or additionally, the ECG data can be recorded on
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`demand by the patient at various discrete times, such as when the patient feels chest pains or
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`experiences other unusual or abnormal feelings. The on demand ECG recorder can have a
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`memory buffer that can record a predetermined amount of ECG data on a rolling basis, and when
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`activated by the patient to record a potential event, a predetermined amount of ECG data can be
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`saved and/or transmitted. The predetermined amount of ECG data can include a predetermined
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`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
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`period between ECG recordings may be regular or irregular. For example, the time period may
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`be once a day, once a week, once a month, or at some other predetermined interval. The ECG
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`recordings may be taken at the same or different times of days, under similar or different
`
`circumstances, as described herein. One or more baseline ECGs can be recorded while the
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`patient is free of symptoms. The baseline ECGs can be periodically recorded and predetermined
`
`intervals and/or on—demand. The same ECG recording device or different ECG recording
`
`devices may be used to record the various ECG of a particular patient. All this information may
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`be tagged to or associated with the ECG data by, for example, including it in the metadata for
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`indexing and searching purposes.
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`- 5 of 15 -
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`[00018]
`
`The ECG data can be time stamped and can be annotated by the patient or health care
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`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
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`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 ECG signal can all
`
`be inputted into the device, smart phone, computer or other computing device to 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,
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`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
`
`circumstance so 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
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`server and database for storage and analysis. The transmission can be real-time, at regular
`
`intervals such as hourly, daily, weekly and any interval in between, or can be on demand. The
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`metadata facilitates the searching, organizing, analyzing and retrieving of ECG data.
`
`Comparison and analysis of a single patient’s ECG data can be performed, and/or comparison of
`
`ECG data 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 as the taking of
`
`medication, occurred Within a predetermined amount of time to the ECG data. The components
`
`of the ECG signal data, such as the P wave, T wave, and QRS complex and the like, the
`amplitudes of the 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 identify atrial fibrillation, where the absence of P waves and/or an irregular heart rate
`
`may indicate atrial fibrillation. The extracted ECG features can also be included in the metadata
`
`for indexing and searching.
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`- 6 of 15 —
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`[00020]
`
`The changes in the ECG signal over time in View of the activities and circumstances
`
`can be compared with changes over time and circumstances observed within a database of
`
`ECG’s. Comparisons may include any comparison of data derived from any other ECG signal or
`
`any database of ECG’s or any subset of ECG data, or with data derived from any database of
`
`ECG’s. Changes in any feature of the ECG signal over time may be used for a 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 changes or deviations from baseline values. In addition,
`
`both the baseline ECG and the ECG data recorded from the patient can be compared to 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 comparisons and 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 measurement or at future times. Conclusions and
`
`determinations may be predictive of future health conditions or diagnostic of conditions that the
`
`patient already has. The conclusions and determinations may also include insights into the
`
`effectiveness or risks associated with drugs or medications that the patient may be taking, have
`
`taken or may be contemplating taking in the future. In addition, the comparisons and analysis
`
`can be used to 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 of risk 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 may require 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 other health 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 other tests or
`
`examination procedures. Professional diagnosis and determinations can 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 determinations of the
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`system can be compared with actual diagnosis and determinations from medical professions to
`
`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 included in
`
`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 of time preceding or following the abnormality may be
`
`predetermined and be up to l to 30 days, or greater than 1 to 12 months. Analysis of the time
`
`before the abnormality, adverse event or 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 event or 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 whether the patient’s
`
`condition in improving, worsening or staying the same. The diagnosis and determination can
`
`also be used for indexing by, for example, including it 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 may include the patient’s age, gender, weight, blood pressure, medications,
`
`behaviors, habits, activities, food consumption, drink consumption, drugs, medical history and
`
`other factors that may influence 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/or insights into the patient’s health generated by
`
`the system may be communicated to 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 secure site 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 sent to the patient’s caregiver.
`
`The notification may also be provided via an application on a smartphone, tablet, laptop, desktop
`
`or other computing device.
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`- 8 of 15 -
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`SG Docket No.: 12212-713.100
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`[00025]
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`As described herein, the system can identify behaviors, habits, activities, foods,
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`drinks, medications, drugs, and the like which are associated With the patient’s abnormal ECG
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`readings. In addition to informing the patient of these associations, the system can provide
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`instructions or recommendations to the patient to avoid these behaviors, habits, activities, foods,
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`drinks, medications, drugs, and the like which are associated with the patient’s abnormal ECG
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`readings. Similarly, the system can identify behaviors, habits, activities, foods, drinks,
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`medications, drugs, and the like which are associated With normal or improving ECG readings,
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`and can instruct or recommend that the patient perform these behaviors, habits, and activities
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`and/or consume these foods, drinks, medications, and drugs. The patient may avoid a future
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`healthcare issue, as instructed or recommended by the system, by modifying their behavior,
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`habits or by taking any course of action, including but not limited to taking a medication, drug or
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`adhering to a diet or exercise program, which may be a predetermined course of action
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`recommended by the system independent of any analysis of the ECG data, and/or may also result
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`from insights learned through this system and method as described herein. In addition, the
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`insights of the system may relate to general fitness and or mental wellbeing.
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`[00026]
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`The ECG data and the associated metadata and other related data as described herein
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`can be stored in a central database, a cloud database, or a combination of the two. The data can
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`be indexed, searched, and/or sorted according to any of the features, parameters, or criteria
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`described herein. The system can analyze the ECG data of a single patient, and it can also
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`analyze the ECG data of a group of patients, Which can be selected according to any of the
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`features, parameters or criteria described herein. When analyzing data from a single patient, it
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`may be desirable to reduce and/or correct for the intra-individual variability of the ECG data, so
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`that comparison of one set of ECG data taken at one particular time with another set of ECG data
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`taken at another time reveals differences resulting from changes in health status and not from
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`changes in the type of ECG recording device used, changes in lead and electrode placement,
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`changes in the condition of the skin (i.e. dry, sweaty, conductive gel applied or not applied), and
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`the like. As described above, consistent lead and electrode placement can help reduce variability
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`in the ECG readings. The system can also retrieve the patient’s ECG data that were taken under
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`similar circumstances and can analyze this subset of ECG data.
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`[00027]
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`FIG. 1 illustrates an embodiment of the system and method of ECG monitoring
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`described herein. The system can be implemented on a 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,
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`ECG data can be recorded using any of the devices described herein for one or more patients. In
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`step 102, the ECG data is transmitted along with associated metadata to a server and database
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`that stores the ECG data. In step 104, a subset of the ECG data can be selected based on criteria
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`in the metadata, such as user identity, time, device used to record the ECG data, and the like. In
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`step 106, the subset of ECG data can be analyzed using a machine learning algorithm, which can
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`assign a risk level to the ECG data in step 108. The system can then determine whether the risk
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`level is high, as shown in step 110. If the risk level is low, the user can be notified that the ECG
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`is normal or low risk, as shown in step 112. If the risk level is high, a high risk level alert can be
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`sent to the patient with the option of sending the ECG t0 the medical professional for
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`interpretation, as shown in step 114. The system then waits for the user’s response to determine
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`whether the patient elects to send the ECG to the medical professional for interpretation, as
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`shown in step 116. If the patient does not wish to send the ECG to the medical professional for
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`interpretation, the system can end the routine at this point, as shown in FIG. 118. If the patient
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`does elect to send the ECG to the medical professional for interpretation, the request can be
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`transmitted to the medical professional in step 120. The request to the medical professional can
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`be sent to a workflow auction system as described in US. Provisional Application No.
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`61/800,879, filed March 15, 2013, which is herein incorporated by reference in its entirety for all
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`purposes. Once the medical professional has interpreted the ECG, the system can receive and
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`store the ECG interpretation from the medical professional in the database, as shown in step 122.
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`The system can then notify the user of the professional ECG interpretation, which can be sent to
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`or accessed by the user, as shown in step 124. Additionally, the system can compare the
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`assigned risk level with the medical diagnosis in step 126 and can determine whether the risk
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`level determined by the system agrees with the medical diagnosis in step 128. If the risk level
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`does not agree with the medical diagnosis, the machine learning algorithm can be adjusted until
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`the risk level matches the medical diagnosis, as shown in step 130. If the risk level does agree
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`with the medical diagnosis, the routine can be ended as shown in step 132.
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`[00028]
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`It is understood that this disclosure, in many respects, is only illustrative of the
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`numerous alternative device embodiments of the present invention. Changes may be made in the
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`details, particularly in matters of shape, size> material and arrangement of various device
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`components without exceeding the scope of the various embodiments of the invention. Those
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`skilled in the art will appreciate that the exemplary embodiments and descriptions thereof are
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`merely illustrative of the invention as a whole. While several principles of the invention are
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`made clear in the exemplary embodiments described above, those skilled in the art will
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`appreciate that modifications of the structure, arrangement, proportions, elements, materials and
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`methods of use, may be utilized in the practice of the invention, and otherwise, which are
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`particularly adapted to specific environments and operative requirements without departing from
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`the scope of the invention. In additio