fig1 illustrates , according to one embodiment , a system for reporting information related to arrhythmia events , such as atrial fibrillation events . in this embodiment , monitoring system 109 can communicate ( via devices 101 and 102 ) ecg ( electrocardiogram ), cardiac event , and other data to monitoring center 104 . the system 109 can include , for example , an implantable medical device ( imd ), such as an implantable cardiac defibrillator and an associated transceiver or pacemaker and an associated transceiver , or a monitoring device 101 that a patient 110 wears . further , monitoring system 109 can include a monitor processing device 102 that can send standard physiological data ( received from monitoring device 101 ) to monitoring center 104 and that can detect arrhythmia events ( such as atrial fibrillation events ). in one implementation , the devices 101 and 102 are integrated into a single device . moreover , the system 109 can be implemented using , for example , the cardionet mobile cardiac outpatient telemetry ( mcot ) device , which is commercially available and provided by cardionet , inc of san diego , calif . monitor processing device 102 can transmit physiological data ( including data related to arrhythmia events ) through a communication network 103 , which can be a local area network ( lan ), a landline telephone network , a wireless network , a satellite communication network , or other suitable network to facilitate two - way communication with monitoring center 104 . advantageously , monitoring center 104 can be located in the same location ( e . g ., in the same room or building ) as monitoring system 109 or at some remote location . the monitoring center 104 can include a monitoring ( or display ) station 105 and a processing system 106 . in one implementation , a cardiovascular technician ( cvt ) can use the monitoring station 105 to evaluate physiological data received from monitoring system 109 , identifying and reporting , among other things , arrhythmia events ( such as atrial fibrillation events ). the cvt reports these assessments of the physiological data to the processing system 106 , which also receives information related to the arrhythmia events identified by monitoring system 109 . as will be explained further below , processing system 106 analyzes this arrhythmia event data ( both the human - assessed data from the cvt and the data reported by monitoring system 109 ) and determines whether to generate a graph ( or other similar presentation ) related to these events . in certain circumstances , the processing system will send a report related to both arrhythmia and heart rate data to , for example , a physician or other health care provider 108 via transmission path 107 — which may be part of the network 103 . fig3 illustrates , according to one embodiment , a procedure for monitoring , processing , and reporting arrhythmia event data ( such as data associated with atrial fibrillation events ). in this embodiment , the monitoring system 109 ( illustrated in fig1 ) monitors and reports physiological data ( including data related to heart rate ) at 301 . at 302 , various parts of this physiological data can be analyzed ( for example , rr variability and qrs morphology ) and arrhythmia events can be identified based on predefined criteria — the information relating to these events ( among other possible information ) constituting a first group of data . in one implementation , the monitoring system 109 identifies certain of the arrhythmia events that are urgent or representative and reports those events to both a cvt at 303 and to the processing system at 304 . alternatively , the system could simply report the events identified at 302 to the processing system . further , at 303 , a cvt , using station 105 , evaluates various parts of the physiological data received from 302 and / or 301 and also identifies arrhythmia events — the information relating to these human - assessed events ( among other possible information ) constituting a second group of data . here , if needed , the cvt can request additional data from monitoring system 109 . at 304 , the processing system 106 analyzes both the first and second group of data , determining a measure of correlation between these groups . this process can involve , for example , determining whether a correlation measure exceeds and / or equals a predetermined correlation parameter or whether a correlation measure is less than and / or equals that parameter . if , based on the correlation analysis , the information related to the arrhythmia events is determined to be valid , then the system generates a report relating to both heart rate trend and the arrhythmia events at 305 , such as the graph shown in fig2 or the graphs shown in fig4 . if , on the other hand , there is insufficient correlation , then the system does not generate a report and monitoring continues . to illustrate , in one implementation , every ten minutes , the monitoring system 109 transmits a “ flag ” if it has detected an atrial fibrillation ( af ) event in the last ten minutes . in this implementation , the processing system 106 only generates a graph ( or graphs ) related to heart rate trend and atrial fibrillation burden — such as the graph shown in fig2 or the graphs shown in fig4 — if more than 50 % of the ten minute flags ( generated at 302 ) match events identified by a cvt ( at 303 )— a correlation ( with respect to the time period at issue ) indicating a high positive predictivity for the identification of af events . if this 50 % threshold is not met , then the system does not generate a graph ( or graphs ) based on the data at issue and simply continues to process data . the term “ atrial fibrillation burden ” ( or more generally , “ arrhythmia event burden ”) refers generally to the overall amount of time that a patient is in atrial fibrillation ( or arrhythmia ) over a specified time period , taking into account the number and duration of episodes . advantageously , employing pictographic presentations , such as those of fig2 and 4 , a medical practitioner can see whether a patient is more likely to experience an arrhythmia , such as af , at certain times of the day , and this can affect therapeutic approaches in some cases . fig2 represents one example of how to pictographically present both heart rate trend and atrial fibrillation burden on a common time scale ( to “ pictographically present ” such data , however , a graph is not required .). the graph 205 contains information relating to , for example , daily af incidence and time of occurrence 201 , af duration 202 , and heart rate ( 203 and 204 ). a scale 204 ( in this example ) indicates heart rate in average beats - per - minute and the dots and lines shown at 203 ( for example ) indicate values on that scale , standard deviations associated with these values , and heart rates during af . further , graph 205 shows heart rate data at 15 minutes and 45 minutes past the hour . finally , in this graph , the presence of one or more af events in a given 10 - minute period is graphed as a 10 - minute interval . like fig2 , fig4 represents an example of how to pictographically present heart rate trend and atrial fibrillation burden on a common time scale . although fig4 , unlike fig2 , uses two graphs , fig4 presents the same information as fig2 . specifically , graphs 404 and 405 contain information relating to , for example , daily af incidence and time of occurrence 401 , af duration 402 , and heart rate ( 403 and 406 ). a scale 406 ( in this example ) indicates heart rate in average beats - per - minute and the dots and lines shown at 403 ( for example ) indicate values on that scale , standard deviations associated with these values , and heart rates during af . fig5 and 6 are diagrams illustrating another implementation of the invention . specifically , at 501 , the system 111 , employing monitoring system 109 , obtains physiological data , including heart rate data . in turn , at 502 , the system identifies the presence of arrhythmia events ( such as af events ) in this physiological data , examining this data in time intervals . at 503 , the system assigns flags indicating the presence of arrhythmia events and reports those flags — which represent a first group of data — to the processing system . similarly , at 504 , the system identifies and reports physiological data , such as ecg data , for a subset of the events identified at 502 and reported at 503 . notably , the system , in this implementation , need not report physiological data for each flag assigned at 503 , but need only report data associated with the most significant events identified at 502 , thereby minimizing the data sent to a cvt . at 601 , the cvt analyzes this data and reports whether arrhythmia events have occurred , thereby generating a second group of data . the processing system then determines ( at 602 ), based on comparing time stamps associated with each group of data , at least one measure of correlation between the first group of data and the second group of data . to illustrate , if enough of the human - assessed events reported at 601 match the events reported at 503 , then the system determines that the data is valid , that is , that there is a high positive predictivity for the identification of arrhythmia events . if such a determination is made , the data associated with each flag reported at 503 is pictographically presented in a form such as fig2 or fig4 . significantly , in this implementation , while this pictographic representation can contain all such data , the cvt need only review a subset of this data . in short , the system achieves increased accuracy in the presentation of information relating to arrhythmia events while minimizing the data that the cvt reviews . the disclosed system and all of the functional operations described and illustrated in this specification can be implemented in digital electronic circuitry , or in computer hardware , firmware , software , or in combinations of the forgoing . apparatus can be implemented in a software product ( e . g ., a computer program product ) tangibly embodied in a machine - readable storage device for execution by a programmable processor , and processing operations can be performed by a programmable processor executing a program of instructions to perform functions by operating on input data and generating output . further , the system can be implemented advantageously in one or more software programs that are executable on a programmable system . this programmable system can include the following : 1 ) at least one programmable processor coupled to receive data and instructions from , and to transmit data and instructions to , a data storage system ; 2 ) at least one input device ; and 3 ) at least one output device . moreover , each software program can be implemented in a high - level procedural or object - oriented programming language , or in assembly or machine language if desired ; and in any case , the language can be a compiled or an interpreted language . also , suitable processors include , by way of example , both general and special purpose microprocessors . generally , a processor will receive instructions and data from a read - only memory , a random access memory , and / or a machine - readable signal ( e . g ., a digital signal received through a network connection ). generally , a computer will include one or more mass storage devices for storing data files . such devices can include magnetic disks , such as internal hard disks and removable disks , magneto - optical disks , and optical disks . storage devices suitable for tangibly embodying software program instructions and data include all forms of non - volatile memory , including , by way of example , the following : 1 ) semiconductor memory devices , such as eprom ( electrically programmable read - only memory ); eeprom ( electrically erasable programmable read - only memory ) and flash memory devices ; 2 ) magnetic disks such as internal hard disks and removable disks ; 3 ) magneto - optical disks ; and 4 ) cd - rom disks . any of the foregoing can be supplemented by , or incorporated in , asics ( application - specific integrated circuits ). to provide for interaction with a user ( such as the cvt ), the system can be implemented on a computer system having a display device such as a monitor or lcd ( liquid crystal display ) screen for displaying information to the user and a keyboard and a pointing device such as a mouse or a trackball by which the user can provide input to the computer system . the computer system can be programmed to provide a graphical user interface through which computer programs interact with users . finally , while the foregoing system has been described in terms of particular implementations , other embodiments are within the scope of the following claims . for example , the disclosed operations can be performed in a different order and still achieve desirable results . moreover , the system need not employ 10 - minute intervals ; many different time intervals are possible ( as is no interval at all ), including 1 minute , 30 second , and 30 - minute intervals . indeed , because time intervals are not required , the graphs of fig2 and 4 could be modified to show continuous heart rate trend ( accompanied by corresponding af data ) rather than just specific instances of this trend . further , while fig2 and 4 show examples of ( among other things ) pictographically presenting atrial fibrillation burden ( one type of arrhythmia event burden ), one could present the same or similar information for another type of arrhythmia event . in fact , one could employ both the format and procedures associated with generating fig2 or fig4 ( or a similar figure ) to pictographically present information related to a number of different types of arrhythmia event burdens .
a system and method for presenting information relating to heart data can involve operations including identifying arrhythmia events in physiological data obtained for a living being , receiving human assessments of at least a portion of the arrhythmia events , determining a measure of correlation between the human assessments and the identified events , and selectively presenting information regarding the identified events based on the measure of correlation . the operations can also include identifying atrial fibrillation events in physiological data obtained for a living being , obtaining heart rate data for the living being , and presenting information regarding the heart rate data and duration of the atrial fibrillation events together with a common time scale to pictographically represent heart rate trend with atrial fibrillation burden during a defined time period .