Patent Publication Number: US-2019175118-A1

Title: Ecg training and skill enhancement

Description:
FIELD OF THE INVENTION 
     The present disclosure relates generally to electrocardiogram (ECG) training and skill enhancement, and more particular to systems, devices and methods for ECG training and skill enhancement by communicating (e.g., displaying, printing, linking, etc.) morphology matching ECGs from a training ECG set. 
     BACKGROUND OF THE INVENTION 
     The skill of reading n-lead ECG (e.g., 12-lead ECG) typically starts with textbook examples and explanation of the ECG phenomena. More skill typically comes with supervised reading of ECG. ECG skill typically is further enhanced with practice and feedback from experts. At some point, there is no ready feedback and the electrocardiographer is on his/her own. Examples of an electrocardiographer include, but is not limited to, physicians, nurse practitioners, physician assistants, nurses, paramedics, medical assistants, trained nursing assistants and emergency medical technicians. 
     More particular, diagnostic ECG interpretation or “reading” an ECG is typically a skill that takes considerable time and practice to truly master. There is a large body of knowledge related to the technical aspects of ECG and most importantly, the reflection of many cardiac disorders in the ECG signal. ECG training typically starts with textbook explanations of where the signal comes from, how it is recorded and how signals from the four chambers appear in the ECG signal. The textbook instruction typically includes example ECGs in the main areas of arrhythmia and signal morphology which relates to conditions such as conduction system problems and infarction and ischemia. Some on-the-job training typically completes the training, which is typically verified with nursing or medical boards. 
     The problem is that training typically does not continue. Moreover, electrocardiographers usually do not get feedback on the quality or correctness of their ECG interpretation. In addition, patients frequently have a long list of comorbidities with a confusing mixture of effects simultaneously present in the ECG. Textbook ECG examples almost never include mixtures of effects because it is confusing for beginner electrocardiographers. 
     Electrocardiographers would benefit from a set of example ECGs to be able to look up similar ECGs to the types they do not see frequently. The problem is that the example ECGs are typically organized by ECG interpretation. Therefore, one must know the interpretation already to find a similar example. 
     ECG currently is the most common cardiac investigation provided in many settings including primary care, in the field or on the ambulance for suspected heart condition patients, etc. Although it is accepted as core medical practice, it is believed that only a low percentage of electrocardiographers receive formalized training and assessment in interpreting ECGs. In recent years, many electrocardiographers rely on computer algorithms to interpret the ECG for them. However, such algorithms are not perfect as they usually do not have access to the clinical context and other needed information to reliably make an accurate diagnostic. This is why, it is often mandatory in the clinical setting that all computer-interpreted ECGs be verified and appropriately corrected by an experienced electrocardiographer. More particular, although many physicians acquire the cognitive skills needed for proper interpretation of the ECG, e.g., during a fellowship or a residency program, completion of a fellowship or residency does not guarantee competence. The present disclosure can help electrocardiographers to continue their training on the job and get help with those ECGs difficult to interpret. As one having ordinary skill in the art shall appreciate in view of teachings herein, the present disclosure can have numerous other benefits too. 
     SUMMARY OF THE INVENTION 
     The present disclosure helps an electrocardiographer (e.g., a physician, a nurse practitioner, a physician assistant, a nurse, a paramedic, a medical assistant, a trained nursing assistants and an emergency medical technicians) continue to improve his/her ECG reading skills by, e.g., offering (and/or providing, displaying, printing or otherwise communicating) a set of similar ECGs for (virtually) every (or most and/or a predefined number or percentage) ECG interpreted or “read”, e.g., in a particular environment or as otherwise may be available to be tracked, stored, processed etc. Generally, it is preferable to have a relatively large number of ECGs in the training set. 
     In accordance with exemplary embodiments of the present disclosure, in the main application of electronic ECG editing, the inventions of the present disclosure provide and electrocardiographer with example ECGs that are similar to the ECG they are currently editing or viewing. The inventions of the present disclosure select similar ECGs by characteristics of the signal, not by correct interpretation. In that way, the electrocardiographer can see many ECGs that have a similar look but potentially different ECG interpretation because many ECG characteristics have a set of possible differential diagnoses. Not only can the electrocardiographer see differential diagnosis possibilities, they can also see the opinions of different electrocardiographers for similar ECGs because the database consists of prior ECGs from their and/or associated institution(s). In addition, the inventions of the present disclosure can provide the probability that the ECG in question is in a particular diagnostic category, such as, for example, left bundle branch block (LBBB), right bundle branch block (RBBB), left ventricular hypertrophy, right ventricular hypertrophy, left anterior fascicular block, acute myocardial infarction, prior myocardial infarction, and many others. Only the higher probabilities may be presented to the user. 
     One form of the inventions of the present disclosure is a diagnostic electrocardiogram system employing an electrode lead system for generating one or more electrode signals indicative of electrical activity of a subject heart. The diagnostic electrocardiogram system further employs a diagnostic electrocardiograph coupled to the electrode lead system for communicating a subject electrocardiogram and one or more diagnostic electrocardiograms determined by the diagnostic electrocardiograph as a morphology match to the subject electrocardiogram (e.g., a linking, displaying, and/or printing the morphology matched subject electrocardiogram and the diagnostic electrocardiogram(s)). The subject electrocardiogram includes one or more interpretations of ECG features derived from the electrical activity of the subject heart as indicated by the electrode signal(s) (e.g., an algorithmic interpretation and/or an electrocardiographer interpretation of the subject electrocardiogram). The diagnostic electrocardiogram(s) includes one or more diagnoses of ECG features derived from recorded electrical activity of the diagnosed heart(s) (e.g., an algorithmic diagnosis and/or an electrocardiographer diagnosis of the diagnostic electrocardiograms(s)). 
     The designation by the diagnostic electrocardiograph may be accomplished by the diagnostic electrocardiograph navigating a cluster tree contrasted from a training set of diagnostic electrocardiograms whereby the dimensional space of the cluster tree is derived from a linear regression modeling of ECG features of the training set of diagnostic electrocardiograms. 
     A second form of the inventions of the present disclosure is the aforementioned electrocardiograph employing a subject ECG controller for controlling a generation of the subject electrocardiogram. The electrocardiograph further employs a diagnostic electrocardiogram controller for controlling a determination of the diagnostic electrocardiogram(s) as a morphology match to the subject electrocardiogram. 
     A third form of the inventions of the present disclosure is a diagnostic electrocardiograph method involving the diagnostic electrocardiograph communicating the subject electrocardiogram informative of one or more interpretations of ECG features derived from the electrical activity of the subject heart as indicated by electrode signal(s) generated by a lead system. The diagnostic electrocardiograph method further involves diagnostic electrocardiograph further communicating the diagnostic electrocardiogram(s) determined by the diagnostic electrocardiograph as a morphology match to the subject electrocardiogram (e.g., a linking, displaying, and/or printing of the subject electrocardiogram and the morphology matched diagnostic electrocardiogram(s)). The diagnostic electrocardiogram(s) include(s) one or more diagnoses of ECG features derived from recorded electrical activity of diagnosed heart(s). 
     For purposes of the present disclosure, the term “electrocardiograph” broadly encompasses all devices, known prior to and subsequent to the present disclosure, for recording the electrical activity of a heart over a period of time, and the term “ECG device” broadly encompasses all stand-alone electrocardiographs and devices/systems incorporating an electrocardiograph including, but not limited to:
         (1) diagnostic ECG devices (e.g., PageWriter TC cardiographs, Efficia series of cardiograph);   (2) exercise ECG devices (e.g., ST80i stress testing system);   (3) ambulatory ECG devices (Holter monitor);   (4) bed-side monitoring ECG device (e.g., IntelliVue monitors, SureSigns monitors, and Goldway monitors);   (5) hemodynamic monitoring (e.g., per Flex Cardio Physiomonitoring system);   (6) telemetry ECG device (e.g., IntelliVue MX40 monitor);   (7) automated external defibrillator and advanced life support products (e.g., HeartStart MRx and HeartStart XL defibrillators, and Efficia DFM100 defibrillator/monitor);   (8) ECG management system (e.g., IntelliSpace ECG management system); and   (9) central monitoring system (e.g., PIIC iX and IntelliVue IL central monitoring systems).       

     Also for purposes of the present disclosure,
         (1) the term “diagnostic electrocardiograph” broadly encompasses all electrocardiographs having a structural configuration incorporating inventive principles of the present disclosure as exemplary described herein, and the term “diagnostic electrocardiograph method” broadly encompasses all methods for training and/or operating a diagnostic electrocardiograph that incorporate the inventive principles of the present disclosure as exemplary described herein;   (2) terms of the art including, but not limited to, “electrocardiographer”, “electrode”, “electrocardiogram”, “ECG features”, “interpretation”, “diagnosis”, “linear regression” and “cluster tree” are to be interpreted as understood in the art of the present disclosure and as exemplary described herein;   (3) more particular to the inventions of the present disclosure, the term “electrocardiogram” broadly encompasses, as understood in the art of the present disclosure and as exemplary described herein, all types of cardiograms for recording an electrical activity of the heart including, but not limited, to a 12-lead electrocardiogram and 3-lead vectorcardiogram;   (4) the descriptive labeling for the term “electrocardiogram” herein as a “subject electrocardiogram” or as a “diagnostic electrocardiogram” facilitates a distinction between electrocardiograms as described and claimed herein without specifying or implying any additional limitation to the term “electrocardiogram”;   (5) more particular to the inventions of the present disclosure, the term “interpretation” broadly encompasses, as understood in the art of the present disclosure and as exemplary described herein, one or more proposed explanation(s) of a normality and/or an abnormality of a morphology of an electrocardiogram as would be understood by those skilled in the art of the present disclosure. Examples of an interpretation of an electrocardigoram include, but are not limited to, an algorithmic interpretation of the electrocardiogram generated by an electrocardiograph and an electrocardiographer interpretation of the electrocardiogram annotated by an electrocardiographer;   (6) more particular to the inventions of the present disclosure, the term “diagnosis” broadly encompasses, as understood in the art of the present disclosure and as exemplary described herein, one or more formalized statement(s) of a normality and/or an abnormality of a morphology of an electrocardiogram as would be understood by those skilled in the art of the present disclosure. Examples of a diagnosis of an electrocardiogram include, but are not limited to, an enactment, a confirmation, an approval, an acceptance, etc. of an algorithmic interpretation of the electrocardiogram generated by an electrocardiograph and of an electrocardiographer interpretation of the electrocardiogram annotated by an electrocardiographer;   (7) more particular to the inventions of the present disclosure, the term “inexpensive ECG features” broadly encompasses, as understood in the art of the present disclosure and as exemplary described herein, global features and per-lead features of an electrocardiogram including, but not limited to, a QRS axis, a QRS duration, a QT interval, a Q/R/S wave amplitude, ST-segment amplitude, T-wave amplitude, and vector loop.   (8) more particular to the inventions of the present disclosure, the term “expensive ECG features” broadly encompasses, as understood in the art of the present disclosure and as exemplary described herein, ECG features derived from a comparable processing of multiple electrocardiograms including, but not limited to, a template matching, a cross correlation and a RMS difference between electrocardiograms.   (9) the term “feature vector” broadly encompasses, as understood in the art of the present disclosure and as exemplary described herein, a m-dimensional vector of ECG feature(s), m≥1 or a vector loop;   (10) the term “morphology match” broadly encompasses, as understood in the art of the present disclosure and as exemplary described herein, similarity of ECG feature(s) between corresponding electrode signal(s) of a pair of electrocardiograms with the ECG feature(s) being characteristic of a shape of the electrocardiograms;   (11) the term “diagnostic category” broadly encompasses, as understood in the art of the present disclosure and as exemplary described herein, a category representative of a particular diagnostic assessment of an electrocardiogram. Examples of a diagnostic category include, but are not limited to, (a) ventricular conduction defect including interpretation left anterior fascicular block, left bundle branch block (LBBB), and right bundle branch block (RBBB), (b) hypertrophy including interpretation left ventricular hypertrophy, right ventricular hypertrophy, (c) ischemia and infarction including interpretation acute myocardial infarction, prior myocardial infarction and subendocardial ischemia.   (12) the term “accurate diagnosis probability” broadly encompasses, as exemplary described herein, a probability a particular diagnostic category represents an accurate diagnostic assessment of an electrocardiogram;   (13) the term “controller” broadly encompasses all structural configurations, as understood in the art of the present disclosure and as exemplary described herein, of an application specific main board or an application specific integrated circuit housed within or linked to an electrocardiograph for controlling an application of various inventive principles of the present disclosure as subsequently described herein. The structural configuration of the controller may include, but is not limited to, processor(s), computer-usable/computer readable storage medium(s), an operating system, application module(s), peripheral device controller(s), slot(s) and port(s). Any descriptive labeling of a controller herein (e.g., a “subject ECG” controller and a “diagnostic ECG” controller) serves to identify a particular controller as described and claimed herein without specifying or implying any additional limitation to the term “controller”;   (14) the term “application module” broadly encompasses a component of the controller including an electronic circuit and/or an executable program (e.g., executable software and/o firmware stored on non-transitory computer readable medium(s) for executing a specific application. Any descriptive labeling of an application module herein (e.g., a “ECG feature extractor” module and a “cluster tree generator” module) serves to identify a particular application module as described and claimed herein without specifying or implying any additional limitation to the term “application module”;   (15) the terms “communicating” broadly encompasses all communication schemes utilized by an electrocardiograph known prior to, concurrently with and subsequently to the present disclosure for conveying an electrocardiogram to a user of the electrocardiograph. Examples of such communication schemes include, but are not limited, providing a link to the electrocardiogram, a display of the electrocardiogram and a printing of the electrocardiogram;   (16) the terms “signal” and “data” broadly encompasses all forms of a detectable physical quantity or impulse (e.g., voltage, current, or magnetic field strength) as understood in the art of the present disclosure and as exemplary described herein for transmitting information in support of applying various inventive principles of the present disclosure as subsequently described herein;   (17) any descriptive labeling for the term “signal” herein facilitates a distinction between signals as described and claimed herein without specifying or implying any additional limitation to the term “signal”; and   (17) any descriptive labeling for the term “data” herein facilitates a distinction between data as described and claimed herein without specifying or implying any additional limitation to the term “data”.       

     The foregoing forms and other forms of the inventions of the present disclosure as well as various features and advantages of the present disclosure will become further apparent from the following detailed description of various embodiments of the present disclosure read in conjunction with the accompanying drawings. The detailed description and drawings are merely illustrative of the present disclosure rather than limiting, the scope of the present disclosure being defined by the appended claims and equivalents thereof. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an exemplary embodiment of a subject electrocardiograms and diagnostic electrocardiograms in accordance with the inventive principles of the present disclosure. 
         FIG. 2  illustrates exemplary embodiments of a subject ECG and a pair of diagnostic ECGs in accordance with the inventive principles of the present disclosure. 
         FIG. 3  illustrates exemplary embodiments of a subject ECG and a pair of diagnostic ECGs in accordance with the inventive principles of the present disclosure. 
         FIG. 4  illustrates an exemplary embodiment of a diagnostic electrocardiograph in accordance with the inventive principles of the present disclosure. 
         FIG. 5  illustrates an exemplary embodiment of a diagnostic ECG controller in accordance with the inventive principles of the present disclosure. 
         FIG. 6  illustrates a flowchart representative of an exemplary embodiment of a diagnostic electrocardiograph training method in accordance with the inventive principles of the present disclosure. 
         FIG. 7  illustrates an exemplary embodiment of a generation of a vector loop version of an ECG feature vector in accordance with the inventive principles of the present disclosure. 
         FIGS. 8A and 8B  illustrate an exemplary embodiment of a construction of a cluster tree in accordance with the inventive principles of the present disclosure. 
         FIG. 9  illustrates a flowchart representative of an exemplary embodiment of a diagnostic electrocardiograph operational method in accordance with the inventive principles of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     To facilitate an understanding of the present disclosure, the following description of  FIG. 1  teaches inventive principles of a diagnostic electrocardiogram of the present disclosure as compared to a subject electrocardiogram as known in the art of the present disclosure. More particular, the present disclosure is premised on a designation by an electrocardiograph of one or more diagnostic electrocardiograms as a morphology match to a subject electrocardiogram with the subject electrocardiogram being generated from a current ECG monitoring and/or testing by the electrocardiograph of a subject heart and with the diagnostic electrocardiogram(s) being generated from previously diagnosed ECG monitoring and/or testing of non-subject heart(s) (i.e., diagnosed heart(s)). From the description of  FIG. 1 , those having ordinary skill in the art of the present disclosure will appreciate how to apply the inventive principles of the present disclosure for making and using numerous and various embodiments of a diagnostic electrocardiogram of the present disclosure. 
     Referring to  FIG. 1 , a subject electrocardiogram  20  is an example of a subject electrocardiogram that may be communicated (e.g., displayed, printed, linked, etc.) by an electrocardiograph during an ECG monitoring and/or testing of a subject heart  10  via any type of lead system as known in the art of the present disclosure (e.g., a 12 lead system, a 3-lead system, etc.). Subject electrocardiogram  20  as communicated by the electrocardiograph includes graphical image(s)  21 , such as, for example, a 12-lead ECG  22  as known in the art of the present disclosure, a ECG waveform  23  generated as known in the art of the present disclosure, and a vectorcardiogram (not shown) as known in the art of the present disclosure. The exemplary subject electrocardiogram  21  further includes a textual interpretation  24  of a normality or an abnormality of an ECG morphology of graphical image(s)  21  generated by an interpretation algorithm executed by the diagnostic electrocardiograph as known in the art of the present disclosure and/or by an annotated interpretation by an electrocardiographer via a graphical user interface of the associated electrocardiograph as known in the art of the present disclosure. More particularly, interpretation  24  is directed to one or more proposed explanation(s) of the normality or the abnormality of the ECG morphology of graphical image(s)  21  as would be understood by those skilled in the art of the present disclosure. 
     Still referring to  FIG. 1 , diagnostic electrocardiograms  30  are examples of an X number of diagnostic electrocardiogram(s), X≥1, that may be communicated (e.g., displayed or printed) by the electrocardiograph during the aforementioned ECG monitoring and/or testing of subject heart  10 . Each diagnostic electrocardiogram  30  was generated from a previous diagnosed ECG monitoring and/or testing of a non-subject heart  11  (i.e., a diagnosed heart) via any type of lead system as known in the art of the present disclosure (e.g., a 12 lead system, a 3-lead system, etc.). 
     Each diagnostic electrocardiogram  30  as communicated by the electrocardiograph includes graphical image(s)  31 , such as, for example, a 12-lead ECG  32  as known in the art of the present disclosure, an ECG waveform  33  generated as known in the art of the present disclosure or a vectorcardiogram as known in the art of the present disclosure. The exemplary diagnostic electrocardiograms  31  further include a textual diagnosis  34  by an electrocardiographer of a normality and/or an abnormality of the ECG morphology of graphical image(s)  31 . Each ECG diagnosis  34  is directed to one or more formalized explanations of the normality and/or the abnormality of the ECG morphology of corresponding graphical image(s)  31  as would be understood by those skilled in the art of the present disclosure (e.g., an enactment, a confirmation, an approval, an acceptance, etc. of an interpretation of the diagnostic electrocardiogram). 
     Still referring to  FIG. 1 , by simultaneously communicating a subject electrocardiogram and one or more diagnostic cardiograms designated as a morphology match to the subject electrocardiogram, the present disclosure improves upon an electrocardiograph&#39;s capability to facilitate an accurate diagnosis by an electrocardiographer of the subject electrocardiogram. 
     For example,  FIG. 2  illustrates an exemplary 12-lead ECG  22   a  of a subject electrocardiogram having an ECG morphology of the QRS in leads V 1  through V 4  that could be interpreted, algorithmically and/or via annotation, as a left bundle branch block, a left ventricular hypertrophy or a prior myocardial infarction. These possible interpretations of 12-lead ECG  22   a  make it difficult for anelectrocardiographer to render a diagnosis of the morphology of 12-lead ECG  22   a,  particularly an inexperienced electrocardiographer. 
       FIG. 2  further illustrates 12-lead ECGs  32 ( 1 ) and  32 ( 2 ) of a pair of diagnostic electrocardiograms designated by the electrocardiograph, from a sample database of roughly 10,000 diagnostic electrocardiograms, as a morphology match in view of ECG morphology of the QRS in leads V 1  through V 4  of 12-lead ECGs  32 ( 1 ) and  32 ( 2 ) essentially being the same as the ECG morphology of the QRS in leads V 1  through V 4  of 12-lead ECG  22   a.  From these morphology matches, a diagnosis of 12-lead ECG  32 ( 1 ) as a left bundle branch block for example and a diagnosis of 12-lead ECG  32 ( 2 ) as a left bundle branch block for example improves upon the electrocardiographer ability to render an accurate diagnosis of the ECG morphology of the QRS in leads V 1  through V 4  of 12-lead ECG  22   a  as a left bundle branch block. 
     By further example,  FIG. 3  illustrates an exemplary 12-lead ECG  22   b  of a subject electrocardiogram having an ECG morphology that could be interpreted, algorithmically and/or via by annotation, as a right bundle branch block with or without ischemia (i.e., ST-segment depression and inverted T-waves). These possible interpretations of 12-lead ECG  22   b  make it difficult for an electrocardiographer to render a diagnosis of the morphology of 12-lead ECG  22   b,  particularly an inexperienced electrocardiographer. 
       FIG. 3  further illustrates 12-lead ECGs  32 ( 3 ) and  32 ( 4 ) of a pair of diagnostic electrocardiograms designated by the electrocardiograph, from a sample database of roughly 10,000 diagnostic electrocardiograms, as a morphology match in view of abnormal shapes of the QRS in leads V 1  through V 4  of 12-lead ECGs  32 ( 1 ) and  32 ( 2 ) essentially being the same as the ECG morphology of the QRS in leads V 1  through V 4  of 12-lead ECG  22   b.  From these morphology matches, a diagnosis of 12-lead ECGs  32 ( 3 ) and  32 ( 4 ) as a right bundle branch block with ischemia for example improves upon the electrocardiographer&#39;s ability to render an accurate diagnosis of the ECG morphology of the QRS in leads V 1  through V 4  of 12-lead ECG  22   b  as a right bundle branch block with ischemia. 
     As one having ordinary skill in the art of the present disclosure shall appreciate in view of the teachings of  FIG. 1 , whatever an interpretation of the subject electrocardiogram, the present disclosure provides confidence in an electrocardiographer in rendering a diagnosis of the subject electrocardiogram when an interpretation of the subject electrocardiogram characteristically match a diagnosis of one or more diagnostic electrocardiograms. 
     To further facilitate an understanding of the present disclosure, the following description of  FIG. 4  teaches inventive principles of a diagnostic electrocardiograph of the present disclosure. From this description, those having ordinary skill in the art will appreciate how to apply the inventive principles of the present disclosure for making and using numerous and various embodiments of a diagnostic electrocardiograph of the present disclosure. 
     Referring to  FIG. 4 , a diagnostic electrocardiograph  50  of the present disclosure employs a control network  60 , a display  70 , user input device(s)  80  (e.g., button(s), dial(s), touchpad, etc.) and a printer  90 . Diagnostic electrocardiograph  50  may further employ one or more additional devices as known in the art of the present disclosure (e.g., a speaker and LED status indicators). 
     Diagnostic electrocardiograph  50  is linked to and/or incorporates any necessary hardware/software interface to a cable connector  40  for receiving on or more electrode signal(s) from an electrode lead system connected to a subject 12 for monitoring and/or testing a subject heart  10  (e.g., a standard 12-lead system like a Mason-Likar lead system as shown or a reduced lead system like the EASI lead system). 
     Control network  60  includes a subject ECG controller  61 , a diagnostic ECG controller  62 , a ECG display controller  63  and a ECG printer controller  64  linked to or housed within diagnostic electrocardiograph  50  as shown. In practice, controllers  61 - 64  may be integrated to a designed degree and/or segregated as shown. Also in practice, control network  60  may include one or more additional controllers as known in the art of the present disclosure (e.g., a canopy controller, an automatic defibrillation controller, etc.). 
     Subject ECG controller  61  is structurally configured as known in the art of the present disclosure for controlling a generation of a subject electrocardiogram from the electrode signal(s) (e.g., a subject ECG controller commercially employed by a Holter monitor, a IntelliVue monitor, a HeartStart MRx defibrillator and a HeartStart XL defibrillator). In practice, the generation of the subject electrocardiogram by subject ECG controller  61  includes a generation of one or more subject ECG graphical image(s) (e.g., subject graphical ECG image(s)  21  of  FIG. 1 ), and may further includes an algorithmic generation and/or electrocardiographer annotation of one or more interpretations of the subject ECG graphical image(s) (e.g., subject interpretation(s)  24  of  FIG. 1 . 
     Diagnostic ECG controller  62  is structurally configured in accordance with the inventive principles of the present disclosure for designating one or more diagnostic electrocardiograms (e.g., diagnostic electrocardiograms  30  of  FIG. 1 ) as an morphology match to the subject electrocardiogram as will be further exemplarily described herein in connection with  FIGS. 5-9 . 
     ECG display controller  63  is structurally configured as known in the art of the present disclosure for displaying electrocardiograms (e.g., an ECG display controller commercially employed by a Holter monitor, a IntelliVue monitor, a HeartStart MRx defibrillator and a HeartStart XL defibrillator) and for displaying a graphical user interface for accessing the diagnostic electrocardiograms in accordance with the inventive principles of the present disclosure. In practice, the display of the electrocardiograms by ECG display controller  63  may include:
         1. a user customization of a view of the subject ECG graphical images via user input device(s)  80  and/or a graphical user interface (not shown);   2. a user annotation of an algorithmic interpretation of a subject electrocardiogram via user input device(s)  80  and/or a graphical user interface (not shown); and/or   3. a user selection of a diagnostic electrocardiogram to be displayed via user input device(s)  80  or a diagnostic graphical user interface  25  having a grid of large thumbnail images of diagnostic electrocardiograms (“ECG grid”), a tabbed organization of diagnostic electrocardiograms (“ECG tabs”) or any other icon suitable for a managed review of diagnostic electrocardiograms.       

     ECG printer controller  64  is structurally configured as known in the art of the present disclosure for printing electrocardiograms via user input device(s)  80  and/or a graphical user interface (not shown) (e.g., an ECG printer controller commercially employed by a Holter monitor, a IntelliVue monitor, a HeartStart MRx defibrillator and a HeartStart XL defibrillator). 
     To facilitate a further understanding of the present disclosure, the following description of  FIG. 5  teaches inventive principles of a diagnostic ECG controller of the present disclosure. From this description, those having ordinary skill in the art will appreciate how to apply the inventive principles of the present disclosure for making and using numerous and various embodiments of a diagnostic ECG controller of the present disclosure. 
     Referring to  FIG. 5 , an embodiment  62   a  of diagnostic ECG controller  62  ( FIG. 4 ) employs an ECG feature extractor  100 , an ECG profile builder  110 , and a cluster tree constructor  120  for purposes of training diagnostic ECG controller  62   a  as will be further described herein in connection with  FIGS. 6-8 . Diagnostic ECG controller  62  further employs a cluster tree navigator  130 , an ECG morphology matcher  140  and a diagnosis category assignor  150  for operating diagnostic ECG controller  62   a  for monitoring/testing purposes as will be further described herein in connection with  FIGS. 8 and 9 . For both training and monitoring/testing purposes, diagnostic ECG controller  62   a  may further employ a database manager  160  and a diagnostic ECG database  170  as shown, or alternatively be in communication with database manager  160  for purposes of accessing diagnostic ECG database  170 . 
     Diagnostic ECG database  170  stores an X number of diagnostic electrocardiograms  30  as shown. As previously described herein, each diagnostic electrocardiogram  30  is generated from a previous diagnosed ECG monitoring and/or testing of a non-subject heart (i.e., a diagnosed heart). Each diagnostic electrocardiogram  30  includes graphical image(s), such as, for example, a 12-lead ECG, an ECG waveform and/or a vectorcardiogram. Each diagnostic electrocardiogram  30  further includes an ECG diagnosis by an electrocardiographer of an ECG morphology of the graphical image(s) with each ECG diagnosis being directed to one or more formalized statements by an electrocardiographer of an ECG morphology of corresponding graphical image(s) as would be understood by those skilled in the art of the present disclosure. 
     Still referring to  FIG. 5 , ECG feature extractor  100  is structurally configured with hardware, software, firmware and/or circuitry for processing an electrocardiogram, subject or diagnostic, to calculate an inexpensive ECG feature vector (“IEFV”)  101  from the electrocardiogram with IEFV  101  including a m number of inexpensive ECG features, m≥1. Examples of an inexpensive ECG feature include, but are not limited to, QRS axis, QRS duration, QT interval, Q/R/S wave amplitudes, ST-segment amplitude, T-wave amplitude and a vector loop. In practice, ECG feature extractor  100  may implement any technique for calculating inexpensive ECG features as known in the art of the present disclosure. 
     ECG feature extractor  100  is further structurally configured with hardware, software, firmware and/or circuitry for processing pairings of electrocardiograms, subject-diagnostic and/or diagnostic-diagnostic, and/or for processing pairings of inexpensive ECG feature vectors  101 , subject-diagnostic or diagnostic-diagnostic, to calculate expensive ECG feature vectors (“EEFV”)  102  between the electrocardiogram pair with EEFV  101  including a q number of expensive ECG features, q≥1. Examples of an expensive ECG feature include, but are not limited to, a template matching, a cross correlation and a RMS difference between the electrocardiogram pair. In practice, ECG feature extractor  100  may implement any technique for calculating expensive ECG features as known in the art of the present disclosure. 
     ECG diagnosis profiler  110  is structurally configured with hardware, software, firmware and/or circuitry for processing an inexpensive ECG feature vector  101  for each diagnostic electrocardiogram  30  and an expensive ECF feature vector  102  of each pairing of electrocardiograms  30  to build a diagnostic ECG profile vector (“DEPV”)  111  including a n number of inexpensive ECG features best representative of the interpretative prowess of expensive ECG features as known in the art of the present disclosure, m≥n≥1 (i.e., diagnostic inexpensive ECG features). In practice, ECG diagnosis profiler  110  may implement any technique for determining which inexpensive ECG features best model the expensive ECG features as known in the art of the present disclosure including, but not limited to, a linear regression of IEFVs  101  and EEFVs  102 . 
     Cluster tree constructor  120  is structurally configured with hardware, software, firmware and/or circuitry for processing diagnostic ECG profile vector  111  to construct a cluster tree (“CT”)  121  of nodes and leafs established by the profiled inexpensive ECG features. Each node will be associated with one of the profiled inexpensive ECG features and corresponding threshold value. Each leaf will be associated with one or more diagnostic electrocardiograms  30 . In practice, cluster tree constructor  120  may implement any technique for constructing clustering tree  121  including, but not limited to, constructing a decision tree from a partitioned data space derived from diagnostic ECG profile vector  111  into cluster (or dense) regions and empty (or sparse) regions formed by a partitioned clustering or a hierarchical clustering. 
     Cluster tree navigator  130  is structurally configured with hardware, software, firmware and/or circuitry for processing inexpensive ECG feature vector  101  of a subject electrocardiogram to navigate the nodes of cluster tree  121  until reaching a leaf whereby cluster tree navigator  130  generates a nearest neighbor listing (“NNL”)  131  of all of the diagnostic electrocardiogram(s)  30  associated with the reached leaf. 
     ECG morphology matcher  140  is structurally configured with hardware, software, firmware and/or circuitry for processing nearest neighboring listing  131  to designate one or more of the nearest neighbor diagnostic electrocardiograms  30  as an morphology match to the subject electrocardiogram whereby ECG morphology matcher  140  generates a morphology match listing (“EMML”)  141  of each designated nearest neighbor diagnostic electrocardiogram  30 . In practice, ECG morphology matcher  140  may implement any known technique for determining any similarity of ECG morphologies between the subject electrocardiogram and each nearest neighbor diagnostic electrocardiogram. 
     Diagnosis category assignor  150  is structurally configured with hardware, software, firmware and/or circuitry for processing morphology match listing  141  to assign each morphology matched diagnostic electrocardiogram to one of numerous diagnostic categories with each diagnostic category being representative of a particular diagnostic assessment of a diagnostic electrocardiogram. Examples of a diagnostic category include, but are not limited to, left bundle branch block (LBBB), right bundle branch block (RBBB), left ventricular hypertrophy, right ventricular hypertrophy, left anterior fascicular block, acute myocardial infarction and prior myocardial infarction. 
     Diagnosis category assignor  150  generates a diagnostic category listing (“DCA”) of each diagnostic category and associated diagnostic electrocardiograms to provide a diagnostic assessment of the subject cardiogram. In practice, diagnosis category assignor  150  may further determine a probability that each listed diagnostic category represents an accurate diagnostic assessment of the subject electrocardiogram. 
     To facilitate a further understanding of the present disclosure, the following description of  FIG. 6  teaches inventive principles of a diagnostic electrocardiograph training method of the present disclosure. From this description, those having ordinary skill in the art will appreciate how to apply the inventive principles of the present disclosure for setting up and using numerous and various embodiments of diagnostic electrocardiograph training methods of the present disclosure. 
     Referring to  FIG. 6 , a flowchart  200  is representative of a diagnostic electrocardiograph training method of the present disclosure executed during a training phase of diagnostic ECG controller  62   a  ( FIG. 5 ). In practice, diagnostic ECG database  170  will typically include thousands, even millions, of a large morphology variation of diagnostic electrocardiograms  30 . Flowchart  200  facilitates a partitioning of inexpensive ECG features of diagnostic electrocardiograms  30  that are best representative of possible interpretations of the morphology of diagnostic electrocardiograms  30 . 
     Still referring to  FIG. 6 , a stage  5202  of flowchart  200  encompasses ECG feature extractor  100  processing a set of diagnostic electrocardiograms  30  to calculate a Y number of inexpensive ECG feature vectors  101 , X≥Y≥1, (e.g., a Y-dimensional vector of inexpensive ECG features or alternatively a vector loop) and a Z number of expensive ECG feature vectors  102 , Z≥1. 
     More particular to 12-lead electrocardiogram, in practice inexpensive ECG feature vector  101  would be made of a processed version of the lead signals instead of a set of measurements (e.g., R-wave amplitude and QRS duration). Since the number of points in the representative beat (or average beat made up of similar shaped beats, excluding noisy and ectopic beats) may be very big for an inexpensive feature vector (e.g., 12 leads×500 points per lead), the number of points should be reduced if possible. This implementation may use (2) methods to reduce the number of points in an inexpensive ECG feature vector while still retaining the unique morphology information. First, the number of points would be reduced by changing from 12-leads which contains a fair amount of redundant information to three (3) orthogonal leads with a 12-lead ECG to Frank lead vectorcardiogram transform. This is a 4:1 reduction in points. Second, the number of points would be further reduced by using the approximation given by a multilevel wavelet decomposition. Using the approximation from the 4h level decomposition, the final number of points in the inexpensive ECG feature vector reduced to roughly 100. 
       FIG. 7  illustrates an exemplary transformation of a 12-lead representative beat  103  into a Frank lead representative beat  102   a  to be utilized as an inexpensive ECG feature vector  101  of an electrocardiogram. The Frank lead X, Y and Z signals are used to generate two dimensional vector loops from pairs of the X, Y and Z signals. In practice, pairs of vectorcardiograms may also be utilized to generate expensive ECG feature vectors  102 . 
     In practice for stage S 202 , the entire database  170  of diagnostic electrocardiograms  30  or a subset thereof may be processed by ECG feature extractor  100  dependent on various factors. 
     For example, the calculation of the expensive ECG feature vectors  102  in practice may involve a sample of a comparison for every diagnostic electrocardiogram  30  to every other electrocardiogram  30 , or a random sample for a subset of diagnostic electrocardiograms  30 , or targeted groups of diagnostic electrocardiograms  30  which are expected to be within the same diagnostic groups. 
     Additionally, if diagnostic ECG database  70  is relatively large relative to the processing power of diagnostic ECG controller  62   a  ( FIG. 5 ), then diagnostic electrocardiograms  30  may be segmented in practice by age groups and/or gender to limit a size of a resulting cluster tree. 
     Furthermore, diagnostic electrocardiograms  30  processed by ECG feature extractor  100  may be based in practice only on select electrocardiographers with many years of experience or proven excellence in ECG reading accuracy. This omits diagnostic electrocardiograms  30  from less experienced electrocardiographers. 
     Even further, those having ordinary skill in the art of the present disclosure will recognize a ECG morphology for a stress test of a subject heart is different from a morphology of a resting diagnostic ECG of the same subject heart. Nonetheless, the present disclosure is equally applicable to a relaxed monitoring and a stress testing of the same subject heart. Consequently, in practice, diagnostic ECG database  70  may be divided into a resting ECG training database resulting in a resting ECG cluster tree and a stress test training database resulting in a stress testing ECG cluster tree. 
     Still referring to  FIG. 6 , a stage S 204  of flowchart  200  encompasses ECG diagnosis profiler  110  processing the Y number of inexpensive ECG feature vectors  101  and the Z number of expensive ECG feature vectors  102  to build diagnostic ECG profile vector (“DEPV”)  111  including a n number of inexpensive ECG features best representative of the interpretative prowess of expensive ECG features. 
     In one embodiment of stage S 204 , ECG diagnosis profiler  110  implements a linear regression or another similar method to determine which inexpensive ECG features best model the expensive ECG features. For this embodiment, the dependent variables are the expensive ECG features and the independent variables are the differences in the inexpensive ECG features. The training set for this linear regression operation is the set of differences in ECG features for each diagnostic electrocardiogram  30  compared to other diagnostic electrocardiogram  30  in the training set. In the simplest case, linear regression is fitting a line to a scatter plot of points in view of having one dependent variable and multiple independent variables. After fitting a line to the data, i.e. training, the dependent variable is a linear function of the independent variables or features. The following is a model equation [1]: 
         Y=b 0+ b 1* x 1 +b 2* x 2 + . . . +bn*xn.    [1]
         where Y is the dependent variable,   where x1, x2, . . . , xn are the independent variables, and   b0, b1, . . . , bn are the coefficients determined in the training operation.       

     In the extreme case, the set of rows (each row is a trial and each column is a feature) is a comparison of every diagnostic electrocardiogram  30  to every other diagnostic electrocardiogram  30 . 
     After the linear regression model is calculated, ECG diagnosis profiler  110  will generate a vector of inexpensive ECG features with a low p-value (i.e., inexpensive ECG feature(s) making a significant contribution to the dependent variable as would be recognized by one skilled in the art of the present disclosure). 
     Still referring to  FIG. 6 , a stage S 206  of flowchart  200  encompasses cluster tree generator  120  processing ECG profile vector  111  to construct cluster tree  121 . 
     In one embodiment of stage S 206 , cluster tree generator  120  implements a nearest neighbor algorithm having a k-d tree, which stands for k-dimensional tree. K dimensions means there are k features used in the clustering operation. This is a binary tree. Each node in the tree has two nodes below, a left node and a right node. Below these nodes are more nodes therefore each split into the left and right results in a left and right sub-tree. The termination of a branch of the tree, a leaf, is a k-dimensional data point. The left and right subtrees represent a splitting of all points below by a plane. Since there are k-dimensions, it is a hyperplane in general. As you move from the root node at the top, down level after level of the tree, the splitting at each level corresponds to splitting based on just one of the k features. Usually, the split happens about the median of that feature. All points for the subtree with a value of the particular feature higher than the median value for the subtree go on one side of the hyperplane, all the other points go to the other side of the hyperplane. Going down the levels of the tree, the splitting rotates through the features meaning that the splitting for the root node is based on the first feature, the splitting at the next level uses the next feature and so forth. 
       FIGS. 8A  shows a clustering example of stage S 206  involving twenty (20) diagnostic electrocardiograms  30  whereby three (3) diagnostic inexpensive ECG features DIEF have been determined to be best representative of the interpretative prowess of expensive ECG features (e.g., QRS axis, QRS duration and QT interval). The diagnostic inexpensive ECG features DIEF of the twenty (20) diagnostic electrocardiograms  30  are clustered within a three-dimensional data space  123  whereby a partitioning clustering is applied by cluster tree generator  120  to yield a partitioned data space  124  via feature partitions FP( 1 ) through FP ( 3 ) (e.g., a partition based on a median or mode of each diagnostic inexpensive ECG feature). 
       FIG. 8B  shows a construction of a clustered decision tree  121   a  from partitioned data space  124 . Clustered decision tree  121   a  include nodes N 1  through N 7 , and leaf L 1  through leaf  8 . Node N 1  is associated with diagnostic inexpensive ECG feature DIEF( 1 ) having a median or mode value r. Nodes N 2  and N 3  associated with diagnostic inexpensive ECG feature DIEF( 2 ) having a median or mode value s. Nodes N 4  through N 7  are associated with diagnostic inexpensive ECG feature DIEF( 3 ) having a median or mode value t. 
     Each leaf is associated with one or more of the twenty (20) diagnostic electrocardiograms  30  ( FIG. 7A ). For a simple example, leaf L 1  may be associated with diagnostic electrocardiograms  30 ( 1 ) through  30 ( 3 ). Leaf L 2  may be associated with diagnostic electrocardiograms  30 ( 4 ) and  30 ( 5 ). Leaf L 3  may be associated with diagnostic electrocardiograms  30 ( 6 ) through  30 ( 9 ). Leaf L 4  may be associated with diagnostic electrocardiograms  30 ( 10 ). Leaf L 5  may be associated with diagnostic electrocardiograms  30 ( 11 ) and  30 ( 12 ). Leaf L 6  may be associated with diagnostic electrocardiograms  30 ( 13 ) through  30 ( 15 ). Leaf L 7  may be associated with diagnostic electrocardiograms  30 ( 16 ) through  30 ( 18 ). Leaf L 8  may be associated with diagnostic electrocardiograms  30 ( 19 ) and  30 ( 20 ). 
     Those skilled in the art of the present disclosure will appreciate flowchart  200  will typically involve a processing of thousands, if not millions, of diagnostic electrocardiograms  30  and  FIGS. 8A and 8B  were provided to demonstrate a simple example to facilitate an understanding of stage S 206 . 
     To facilitate a further understanding of the present disclosure, the following description of  FIG. 8  teaches inventive principles of a diagnostic electrocardiogram operating method of the present disclosure. From this description, those having ordinary skill in the art will appreciate how to apply the inventive principles of the present disclosure for setting up and using numerous and various embodiments of diagnostic electrocardiogram operation methods of the present disclosure. 
     Referring to  FIG. 8 , a flowchart  210  is representative of a diagnostic electrocardiogram assessment method of the present disclosure executed during an activation phase of diagnostic ECG controller  62   a  ( FIG. 5 ). Flowchart  210  facilitates a diagnostic assessment of a subject electrocardiogram 
     Still referring to  FIG. 8 , a stage S 212  of flowchart  210  encompasses ECG feature extractor  100  processing a subject electrocardiogram (e.g., subject electrocardiogram  20  shown in  FIG. 1 ) and diagnostic ECG profile vector  111  to generate an inexpensive ECG feature vector  101   s  corresponding to diagnostic ECG profile vector  111 . For example, in the context of  FIGS. 8A and 8B , ECG feature extractor  100  generates an inexpensive ECG feature vector  101   s  including diagnostic inexpensive ECG features DIEF( 1 ) through DIEF( 3 ). 
     A stage S 214  of flowchart  210  encompasses cluster tree navigator  130  processing inexpensive ECG feature vector  101   s  to navigate the nodes of cluster tree  121  until reaching a leaf whereby cluster tree navigator  130  generates a nearest neighbor listing (“NNL”)  131  of all of the diagnostic electrocardiogram(s)  30  associated with the reached leaf. For example, in the context of  FIGS. 8A and 8B , cluster tree navigator  130  may reach leaf L 1  and generates a nearest neighbor listing  131  including diagnostic electrocardiograms  30 ( 1 ) through  30 ( 3 ). 
     A stage S 216  of flowchart  210  encompasses ECG morphology matcher  140  processing nearest neighboring listing  131  to generate an morphology match listing (“EMML”)  141  of each designated nearest neighbor diagnostic electrocardiogram  30 . 
     In one embodiment of stage S 216 , ECG morphology matcher  140  calculates the expensive ECG features between the subject electrocardiogram and each nearest neighbor diagnostic electrocardiogram  30  (e.g., a template match, cross correlation or RMS error), and determines a cross correlation between the average beat of the subject electrocardiogram and the average beats of the nearest neighbor diagnostic electrocardiograms resulting in a vector of cross correlation numbers. ECG morphology matcher  140  chooses the subset of nearest neighbor diagnostic electrocardiograms by sorting the cross correlation vector from highest to lowest and selecting the subset with the highest cross correlation(s) (i.e., most similar to the subject electrocardiogram). 
     For example, in the context of  FIGS. 8A and 8B , ECG morphology matcher  140  may designate diagnostic electrocardiograms  30 ( 1 ) and  30  ( 2 ) as morphology matches. 
     A stage S 218  of flowchart  210  encompasses diagnosis category assignor processing morphology match listing  141  to assign each matched diagnostic electrocardiogram to a diagnostic category with each diagnostic category being representative of a particular diagnostic assessment of a diagnostic electrocardiogram and to determine a probability that each listed diagnostic category represents an accurate diagnostic assessment of the subject electrocardiogram. 
     In one embodiment of stage S 218 , the probability of diagnostic category is calculated as the frequency of the notation of that diagnostic category for the morphology matched subset of nearest neighbors. Specifically, a diagnosis of each morphology matched nearest neighbor is mapped to a broader diagnostic category. The number of times that each diagnostic category is noted is divided by the number of diagnostic electrocardiograms in the morphology matched set of nearest neighbors. That ratio is an estimate of the probability. 
     For example, in the context of  FIGS. 8A and 8B , diagnostic electrocardiograms  30 ( 1 ) and  30 ( 2 ) may be mapped to a left bundle branch block and diagnostic electrocardiogram  30 ( 2 ) may be further mapped to a left ventricular hypertrophy. As such, the probability of the subject electrocardiogram may be exhibiting left bundle branch block would be 66% and the probability of the subject electrocardiogram may be exhibiting left ventricular hypertrophy would be 33%. 
     Upon completion of flowchart  210 , the morphology matched set of nearest neighbors are presented to the electrocardiographer in a grid of large thumbnail images, or a tabbed organization or some other icon that allows quick change from one diagnostic electrocardiogram to the next for fast review of all diagnostic electrocardiograms for the morphology matched subset of nearest neighbors. 
     Referring to  FIGS. 1-9 , those having ordinary skill in the art will appreciate numerous benefits of the inventions of the present disclosure including, but not limited to, an improvement of electrocardiographs in diagnostically assessing a subject electrocardiogram. 
     The present disclosure disclosed herein has been described with reference to the preferred embodiments. Modifications and alterations may occur to others upon reading and understanding the preceding detailed description. It is intended that the invention be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof 
     Further, as one having ordinary skill in the art shall appreciate in view of the teachings provided herein, features, elements, components, etc. disclosed and described in the present disclosure/specification and/or depicted in the appended Figures may be implemented in various combinations of hardware and software, and provide functions which may be combined in a single element or multiple elements. For example, the functions of the various features, elements, components, etc. shown/illustrated/depicted in the Figures can be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions can be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which can be shared and/or multiplexed. Moreover, explicit use of the term “processor” or “controller” should not be construed to refer exclusively to hardware capable of executing software, and can implicitly include, without limitation, digital signal processor (“DSP”) hardware, memory (e.g., read only memory (“ROM”) for storing software, random access memory (“RAM”), non-volatile storage, etc.) and virtually any means and/or machine (including hardware, software, firmware, combinations thereof, etc.) which is capable of (and/or configurable) to perform and/or control a process. 
     Moreover, all statements herein reciting principles, aspects, and exemplary embodiments of the present disclosure, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (e.g., any elements developed that can perform the same or substantially similar functionality, regardless of structure). Thus, for example, it will be appreciated by one having ordinary skill in the art in view of the teachings provided herein that any block diagrams presented herein can represent conceptual views of illustrative system components and/or circuitry embodying the principles of the invention. Similarly, one having ordinary skill in the art should appreciate in view of the teachings provided herein that any flow charts, flow diagrams and the like can represent various processes which can be substantially represented in computer readable storage media and so executed by a computer, processor or other device with processing capabilities, whether or not such computer or processor is explicitly shown. 
     Having described preferred and exemplary embodiments of diagnostic electrocardiographs and operating methods thereof, (which embodiments are intended to be illustrative and not limiting), it is noted that modifications and variations can be made by persons having ordinary skill in the art in view of the teachings provided herein, including the appended Figures and claims. It is therefore to be understood that changes can be made in/to the preferred and exemplary embodiments of the present disclosure which are within the scope of the present disclosure and exemplary embodiments disclosed and described herein. 
     Moreover, it is contemplated that corresponding and/or related systems incorporating and/or implementing the device or such as may be used/implemented in a device in accordance with the present disclosure are also contemplated and considered to be within the scope of the present disclosure. Further, corresponding and/or related method for manufacturing and/or using a device and/or system in accordance with the present disclosure are also contemplated and considered to be within the scope of the present disclosure.