Abstract:
An atrial fibrillation classification system collects celectrocardiogram signals and converts them to a frequency, time, or phase domain representation for analysis. An evaluation stage extracts energy density profile over a range of frequencies, time intervals, or phases, which is then summed and normalized to form dispersion metrics. The system then analyzes the dispersion metrics, in their respective domains, to determine whether a patient is experiencing an arrhythmia and then to classify the type of arrhythmia being experienced.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims priority to U.S. Provisional Application No. 61/584,954, entitled “Atrial Fibrillation Classification Using Power Measurement,” filed Jan. 10, 2012, which is hereby incorporated herein in their entirety. 
       FIELD OF TECHNOLOGY 
       [0002]    The present disclosure relates generally to identifying heart rhythm state and, more particularly, to determining whether the heart rhythm is in a cardiac arrhythmia state, such as atrial fibrillation using a measured power value. 
     
    
     BACKGROUND 
       [0003]    The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure. 
         [0004]    Atrial fibrillation is a form of cardiac arrhythmia where there is disorganized electrical conduction in the atria causing rapid uncoordinated depolarizations that result in ineffective pumping of blood into the ventricle and a lack of synchrony. During atrial fibrillation, the atrioventricular node receives electrical impulses from numerous locations throughout the atria instead of only from the sinus node. This overwhelms the atrioventricular node into producing an irregular and often rapid heartbeat. Due to the lack of organized contraction of the atria, blood may pool in the atria increasing the risk for blood clot formation. The major risk factors for atrial fibrillation include age, rheumatic heart disease, valvular heart disease, hypertension, diabetes, coronary artery disease, and thyrotoxicosis. Atrial fibrillation affects about 7% of the population over age 65. At present there are over 6 million patients affected with atrial fibrillation (AF) only in the US. It is estimated that by 2025 over 10 million patients will have atrial fibrillation. AF is the leading cause of strokes and is the most common arrhythmia leading to hospital admissions. 
         [0005]    AF may be intermittent (paroxysmal) or persistent. It may be acute and terminate within a relatively short period of time, or it may be chronic and persist for years. AF may develop in patients with structurally normal hearts as well as in patients with associated comorbidities including structural heart disease. Based on the presence of clinical characteristics and comorbid conditions, patients with AF are at variable risk for stroke and other thromboembolic events. Therefore, based on the degree of risk of stroke, patients are treated either with antiplatelet or anticoagulant agents to reduce the risk of stroke. However these agents, on the other hand, do increase the risk of bleeding complications, including intracranial risk. Therefore, in patients with AF, risk of thromboembolic events, particularly stroke, compete against the risk of bleeding complications due to antiplatelet/anticoagulant agents. To optimize these competing risks, it is desirable to accurately identify when patients with a history of AF go into AF and how long they stay in AF. 
         [0006]    AF treatment options are still evolving. In most patients restoring and maintaining sinus rhythm is preferred, as sinus rhythm is often associated with an improvement in quality of life, improvement in atrial and ventricular remodeling with an improvement in ejection fraction, possibly a reduction in the risk of thromboembolic events, and elimination of the need for anticoagulant therapy in most patients. Cardioversion attempts to restore sinus rhythm; yet even if successful acutely, AF may still recur as cardioversion does not eliminate the underlying mechanisms of AF. In addition, if there is a blood clot in the atria, cardioversion may cause the clot to leave the heart and travel to the brain (leading to stroke) or to some other part of the body. 
         [0007]    One of the more recent procedures for treating cardiac arrhythmias is catheter ablation therapy. Physicians make use of specialized ablation catheters to gain access to interior regions of the body. Catheters with tip electrodes or other ablating devices are used to create ablation lesions that physiologically alter the ablated tissue without removal thereof, and thereby disrupt and/or block electrical pathways through the targeted tissue. In the treatment of cardiac arrhythmias, a specific area of cardiac tissue having aberrant electrically conductive pathways, such as atrial rotors, emitting or conducting erratic electrical impulses, is initially localized. A user (e.g., a physician) directs a catheter through a main vein or artery into the interior region of the heart that is to be treated. The ablating element or elements are next placed near the targeted cardiac tissue that is to be ablated, such as a pulmonary vein ostium or atrum. 
         [0008]    While the available treatment methods provide various degrees of success, it is nonetheless difficult, at times, to determine the effectiveness of the treatment. Part of the difficulty arises because care professionals will typically rely, even partially, on the patient&#39;s perceived state of health to gauge effectiveness. Patients naturally have varying levels of perception; and moreover a patient may experience atrial fibrillation and not know it. This problem is in fact heightened after a medical procedure, when the patient has returned to normal life, and may begin experiencing atrial fibrillation while feeling as those their heart function is normal. Up to one-third of patients with AF do not appreciate or know that they are experiencing AF; and therefore, they do not know they are at heightened risk for stroke. It is, therefore, desirable to have an effective mechanism to measure atrial fibrillation that is cost effective and consistent in operation. 
       SUMMARY 
       [0009]    In an embodiment, an apparatus for detecting arrhythmias within a body, the apparatus includes: an input stage to receive an electrocardiogram signal from a body connected thereto through a detection electrode input, wherein the input stage is a real-time dynamically adjustable signal transformation stage configured to condition the electrocardiogram signal; an analysis stage coupled to the input stage to receive the conditioned electrocardiogram signal from the input stage, wherein the analysis stage comprises, a transformation stage wherein a frequency and/or phase domain representation of the conditioned electrocardiogram signal is formed from the conditioned time-domain electrocardiogram signal, and an evaluation stage, wherein time, frequency and/or phase domain features related to the arrhythmias are extracted from the time, frequency and/or phase domain representation. Wherein one possible instantiation of the evaluation stage comprises a Spectral Dispersion Metric (SDM) evaluation stage configured to determine, for example, the dispersion of frequency energy over a predetermined range of frequencies relative to the frequency with the highest energy Spectral Frequency Dispersion Metric (SFDM), and/or Spectral Time Domain Dispersion metric (STDM) and/or Spectral Phase Domain Dispersion Metric (SPDM) representations and configured to normalize the energy to form a dispersion metric over the predetermined range of frequencies, time intervals and/or phases; and a classification stage coupled to the evaluation stage to receive the total normalized power and determine whether the body is experiencing the arrhythmias. 
         [0010]    In accordance with another example an apparatus for detecting arrhythmias within a body, the apparatus includes: an input stage to receive an electrocardiogram signal from a body connected thereto through a detection electrode input, wherein the input stage is a real-time dynamically adjustable signal transformation stage configured to condition the electrocardiogram signal; an analysis stage coupled to the input stage to receive the conditioned electrocardiogram signal from the input stage, wherein the analysis stage comprises, a transformation stage wherein a frequency, time, or phase domain representation of the conditioned electrocardiogram signal is formed from the conditioned electrocardiogram signal being in a time-domain signal, and an evaluation stage, wherein frequency, time or phase domain features related to the arrhythmias are extracted from the frequency, time, or phase domain representation, wherein the evaluation stage is configured to determine a summed energy over a predetermined range of frequencies, time intervals or phases for the frequency, time, or phase domain representation and configured to normalize the summed energy to form a spectral frequency dispersion metric (SFDM), spectral time domain dispersion metric (STDM), and/or spectral phase domain dispersion metric (SPDM) over the predetermined range of frequencies, time intervals, or phases; and a classification stage coupled to the evaluation stage to receive the SFDM, STDM, and/or SPDM and to determine whether the body is experiencing the arrhythmias. 
         [0011]    In accordance with another example, an apparatus for detecting arrhythmias within a body, the apparatus includes: an input stage to receive an electrocardiogram signal from a body connected thereto through a detection electrode input, wherein the input stage is a real-time dynamically adjustable signal transformation stage configured to condition the electrocardiogram signal; an analysis stage coupled to the input stage to receive the conditioned electrocardiogram signal from the input stage, wherein the analysis stage comprises, a transformation stage wherein a time domain profile of the conditioned electrocardiogram signal is formed, and an evaluation stage configured to determine, over the time domain profile, at a plurality of predetermined time intervals and to determine the number of times a spectral time domain dispersion metric (STDM) is in one or more count ranges over the plurality of predetermined time intervals; and a classification stage coupled to the evaluation stage to receive the STDM and determine whether the body is experiencing the arrhythmias. 
         [0012]    In accordance with another embodiment, a method of analyzing patient data, the patient data including electrocardiogram data, the method comprises: receiving, at an analysis machine, the electrocardiogram data from a database; identifying, at the analysis machine, target patient data from the database; processing, at the analysis machine, the target patient data and the electrocardiogram data; identifying, at the analysis machine, from the processed target patient data and the electrocardiogram data, diagnostic features using an adapting algorithm, wherein the diagnostic features represent correlations or patterns within the processed target patient data and the electrocardiogram data and wherein the diagnostic features correspond to a diagnosable condition of a patient; and assessing, at the analysis machine, the diagnostic features to determine the presence of the diagnosable condition for the patient. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0013]      FIG. 1  is a diagram of a system for atrial fibrillation classification. 
           [0014]      FIG. 2A  is a plot of time domain electrocardiogram signals, taken over a sampling period, for a patient under normal sinus rhythm, while  FIG. 2B  is a plot of the frequency domain representation of these electrocardiogram signals;  FIG. 2C  is a plot of time domain electrocardiogram signals, taken over a sampling period, for a patient experiencing atrial fibrillation, while  FIG. 2D  is a plot of the frequency domain representation of these electrocardiogram signals; and  FIG. 2E  is a plot of SFDM resulting from each of the frequency domain representations in  FIGS. 2B and 2D , for normal patient and a patient experiencing atrial fibrillation. 
           [0015]      FIG. 3  is a block diagram an implementation of the ECG-based arrhythmia detection system of  FIG. 1 . 
           [0016]      FIG. 4  is a functional block diagram of the implementation of a SDM ECG-based arrhythmia detection system of  FIG. 3 . 
           [0017]      FIG. 5  is a flow diagram of an example arrhythmia detection process using the SDM method. 
           [0018]      FIG. 6  is a block diagram of an example optimization and customization of an ECG-based arrhythmia detection system. 
           [0019]      FIGS. 7A-7C  are plots of scattergrams used in optimizing and customizing the arrhythmia detection system of  FIG. 6  according to an example. 
           [0020]      FIG. 8  is a flow diagram of an example process to perform data mining and diagnoses on patient data, such as ECG-based data, according to an example. 
       
    
    
     DETAILED DESCRIPTION 
       [0021]    Generally, techniques herein describe methods and devices for the automated classification of a heart rhythm, across a spectrum from normal rhythm, called sinus rhythm, to cardiac arrhythmia, such as atrial fibrillation (AF). The techniques encompass measuring a metric of heart performance that is chosen to assess heart rhythm performance over a functional operation range. That metric is correlated to heart rhythm conditions in that different values are predetermined to indicate different rhythm conditions, from sinus rhythm to AF. More specifically, in various embodiments discussed below, the techniques involve determining a metric called the Spectral Frequency Dispersion Metric (SFDM), which is the sum of the spectrally distributed energy relative to the maximal energy over a set of specified frequencies and bands obtained from electrical recordings from the heart. The SFDM is, therefore, a metric related to the distribution of a heart performance metric (in this case a measured energy) across a specified functional operation range (in this case discrete or continuous frequency bands). 
         [0022]    The performance metric(s) once determined (i.e. SFDM), is correlated to heart rhythm state for use in diagnostic classification. For example, a baselining procedure is performed to determine the optimal segmentation of clusters generated by the SFDM or transformations of the SFDM (i.e. mean, standard deviations, etc), each corresponding to a different heart rhythm state. In an example implementation, SFDM values within a first region are correlated as identifying sinus rhythm. SFDM values in a second region, exceeding predetermined critical values (i.e., the energy is widely distributed), are correlated to AF. While SFDM values in a third region, between the two, are correlated to an atrial flutter (AFL) condition. Multiple additional SFDM value ranges may be used to further parse the heart rhythm spectrum to identify cardiac arrhythmia states. 
         [0023]    At the point of care or wherever patient assessment may be desired, the techniques may be implemented in a device having a form factor that is simple enough for patient use, allowing for user friendly operation, within or outside of a care facility. The techniques may be implemented in a single device or across multiple devices in a distributed fashion, and either way performing both SFDM measurement and heart rhythm classification, for easy identification by a patient or heath care professional. In some implementations, the device is a portable, stand-alone device that records electrical activity from the heart and then determines the presence of AF in patients, such as those that have already been diagnosed as being at a risk of developing intermittent episodes of arrhythmia. The device may be compact in size, e.g., hand-held, and with easy-to-apply electrodes to establish contact with the patient. Through these electrodes, electrical activity (and its vector) may be recorded from the patient, filtered, amplified and processed using unique embedded algorithms to detect the rhythm status. 
         [0024]    The device may be designed to display both the measured electrical activity in real-time similar to an ECG monitor and the rhythm status, either to a separate monitor or to a display on the device itself. The simplest realization of the device may only employ indicator lights, or an audio/vibratory alert. The rhythm status may be displayable in both a common language manner (e.g., using phrases like “NORMAL”, “ALARM”, “ABNORMAL”, “ATRIAL FIBRILLATION”, “AF”), color-coded to alert the patient of the rhythm status or with the use of intuitive icons, or a combination thereof. In some examples, the techniques are implemented in a networked environment by which data measured from the device, whether displayed at the device or not, may be displayed at a remote system, such as at a healthcare facility (hospital, nurses station, doctors terminal, prescription system, hospital administration system). The device receiving real-time data may store that data in a structured, indexed, database for later retrieval by the device and for transmission to a central monitoring facility or other station, as mentioned herein, for example through a remote wireless network connection. The device may further include voice/sound feedback and/or tactile/vibratory feedback. 
         [0025]    The present techniques may also allow for controlling operation of AF determination and assessment. For example, the device or other computer device networked therewith may provide a medical practitioner with an user input interface through which the practitioner may adjust operation of the device, including the sensitivity and specificity of AF assessments. In yet other examples, the present techniques may be used to store historic patient AF assessment data (e.g., ECG data) and perform data mining on that data, e.g., in an informatics-based manner to identify hard-to-notice, hidden, correlations of different time, frequency of phase patterns, for AF assessment and treatment protocol determination. 
         [0026]    The techniques are able to offer numerous advantages and may be vitally important to determine the rhythm status in patients diagnosed with AF. The techniques will allow patients to know if they are in a normal sinus rhythm condition or in AF condition particularly during or after specific therapy applications, which may include pharmacological therapy with drugs or catheter or surgical based ablation therapies. During some therapy applications, patients are most at risk for stroke when they are experiencing AF. In such examples, an anticoagulant may be greatly helpful in reducing the risk of strokes due to blood clots. However, in other conditions, anticoagulants may increase the risk of bleeding particularly intracranial bleeding, which can cause serious consequences. Therefore, the present techniques provide a measured way for a patient to know if they are experiencing sinus rhythm or AF, so that the patient can know, on their own, whether or not to take an anticoagulant. Even outside of the therapeutic context, and more generally, patients may be experiencing AF and not be aware of it. If AF is untreated and is associated with rapid heart rates, this subsequently may impair the contractile function of the heart and may lead to a cardiomyopathy. With the present techniques, patients have a simplified method of identifying an AF condition. 
         [0027]      FIG. 1  provides a schematic illustration of an atrial fibrillation classification system  100  having an electrode assembly  102  forming at least part of an input stage for the system  100 . In the illustrated configuration, the electrode assembly  102  includes two sensing electrodes  104  that are to be mounted near a patient&#39;s heart, for example using a medical mounting tape. The electrodes  104  may be standard ECG electrodes, for example, capable of positioning at any desired location and reusable. The two electrode configuration, as shown, allows for using two input pre-amplification, amplification, and noise shaping circuitry, as desired. In another configuration, additional numbers of electrodes may be used for example as in a normal ECG measurement. 
         [0028]    The electrode assembly  102  is coupled directly to the electrodes  104  in the illustrated embodiment and may include a signal amplifier, low pass filter, buffer, and/or other front end circuitry. In a wireless communication mode the electrode assembly  102  further includes an antenna and wireless transceiver stage coupled thereto. In an example, that wireless transceiver may be a Bluetooth transceiver stage for wireless communication with a handheld atrial fibrillation classification unit  106 , also having an antenna and a Bluetooth transceiver stage. In some examples, the electrode assembly  102  is connected to the atrial fibrillation classification unit  106  through a direct, wired connection. 
         [0029]    The atrial fibrillation classification unit  106 , discussed further and illustrated in an example in  FIG. 3 , receive electrical signals from the unit  102  and performs an analysis on those electrical singles, including determining a spectral frequency dispersion metric (SFDM) value over a predetermined range of frequencies and then performing a heart rhythm classification based on that total normalized power values. The classification data may be stored at the unit  106  and/or transmitted to a remote computer  108  through a wired or wireless connection (WLAN)  110 . Examples of wired interfaces include serial, universal serial bus (USB), or Ethernet. A WLAN compatible transceiver stage (not shown) within the device  106  may be a transceiver compliant with any one of the various IEEE 802.xx wireless standards, such as 802.15 (Bluetooth) or 802.11a, b, g, and/or n wireless LAN standards. Additionally, longer range wireless communications, such as cellular modems, WiMAX (802.16) or Mobile Broadband Wireless Access (802.20) may be used for longer ranges or direct communication with the hospital or any monitoring station/service. 
         [0030]    The remote computer  108  may be any of a hospital, nurse&#39;s station, doctor&#39;s terminal, prescription system, or hospital administration system. And while a single such remote computer  108  is shown, a plurality of remote computes may be connected to the device. The remote computer  108  is coupled to medical records database  112  for storing historical atrial classification data for the patient and/or other atrial classification data, such as baseline data used for baselining. A display  114  is coupled to the remote computer  108 , as well as output device  116  such as a peripheral printer, such as a bar code printer, line printer, image printer, etc. Other output devices may include an external database or other computer. 
         [0031]      FIG. 2A  illustrates electrocardiogram signals, taken over a sampling period, for a patient under normal sinus rhythm. The signals are in the time domain, while  FIG. 2B  is a plot of the frequency domain representation of these electrocardiogram signals.  FIG. 2C  illustrates electrocardiogram signals, taken over a sampling period, for a patient experiencing atrial fibrillation. The signals in  FIG. 2C  are in the time domain, while  FIG. 2D  is a plot of the frequency domain representation of these electrocardiogram signals. As shown in  FIG. 2E , the determined spectral frequency dispersion metric (SFDM) from each of these two different frequency domain signals varies dramatically for each of 5 different patients examined under trial, with each of the upper SFDM values corresponding to the patient when the patient is experiencing atrial fibrillation and each lower SFDM value corresponding to when the patient is not experiencing atrial fibrillation. From the illustration it is apparent that atrial fibrillation correlates to a higher SFDM compared to sinus rhythm. Also it is apparent that the difference between atrial fibrillation SFDM values and sinus rhythm SFDM values can vary greatly from patient to patient. It can also be seen that in many cases the SFDM variability is greater for atrial fibrillation than sinus rhythm states. 
         [0032]      FIG. 3  is a block diagram of an example atrial fibrillation classification system  300  in accordance with an example and that may be used at a point of care. A battery  302 , including conditioning circuitry, powers the device  300 , which includes a microcontroller  304 , a non-volatile storage memory  306 , such as a flash memory, SD card, universal serial bus (USB) drive, etc. and a local display  308 , such as an light emitting diode (LED) or liquid crystal display (LCD) device. A wireless interface  310  is also provided and includes a wireless transceiver for connecting to remote display, remote computer, and/or remote database for transmitting and receiving data and/or programming instructions. The wireless interface  310  may be compatible with any of the IEEE 802.11a, b, g, and/or n, 802.15, or other wireless standards. In the illustrated example, a wired interface  312  is also provided for connecting to a remote display, remote computer, and/or remote database, using a wired connection such as USB connection, Ethernet connection, or serial connection. 
         [0033]    The microcontroller  304  is coupled to patient electrodes through an interface  314 , which is a wired interface in the illustrated example. In the illustrated example, therefore, no separate electrode assembly  102  is used. Instead, electrodes are coupled directly to the atrial fibrillation classification device  300 . In a configuration like that of  FIG. 1 , no wired interface  314  would be used, but instead the wireless interface  310  would be used to receive electrical signals from the patient electrodes via a wireless electrode assembly. 
         [0034]    Received signals from the interface  314  are coupled to signal conditioning circuit  316 , with integrated switch capacitor network. Example conditioning circuits include ECG signal conditioning circuits, although any conditioning circuit may be used to amplify the received electrical signal, pass the signal through a low pass filter for noise reduction, and perform any shaping on the signal to reduce flutter, jitter, and/or other noise. The conditioned electrocardiogram signal from the interface  316  is coupled to a 12 bit analog-to-digital converter  318  coupled to the microcontroller  304 . 
         [0035]    In this configuration, the blocks  314 ,  316 , and  318  form an input stage  319  receiving an electrocardiogram signal from patient electrodes. This input stage  319  is coupled to the microcontroller  304  through a dynamic reconfiguration interface  320  to allow dynamic adjustment of the signal processing in the input stage. This dynamic adjustment includes signal amplification, DC offset removal, bandwidth adjustment of notch and bandpass filters to optimize the acquired signal. 
         [0036]    The microcontroller  304  includes an analysis stage that receives the amplified electrocardiogram signal from the input stage, and performs various functions such as buffering the received data, performing a Fourier transform on the received data to convert the data into a Fourier domain signal. The microcontroller  304  then determines a SFDM for the Fourier domain signal, where the energy is determined over a predetermined set of frequencies and is divided by the maximum energy level. The set of frequencies may be contiguous, but need not be. Specifically selected frequencies may be used in some examples. The device used sufficient samples to perform an n point FFT, after which the additional data could be used to calculate other statistical metrics, such as means and standard deviations. From the SFDM, the microcontroller  304  performs a heart rhythm classification, identifying whether the SFDM indicates that the patient is experiencing sinus rhythm, atrial fibrillation, atrial flutter or ventricular fibrillation, which indication may be stored on the storage  306 , displayed at the display  308 , and transmitted continuously, periodically, or in response to poling, to a remote system through either the interface  310  or  312 . 
         [0037]    The Fourier domain signal is a frequency domain signal. In other examples, the microcontroller operates in the time domain or phase domain, from which STDM and SPDM values are determined. 
         [0038]      FIG. 4  illustrates a more detailed functional block diagram  400  illustrating various operations as performed by the microcontroller  304 , in an example. The conditioning circuit  316  coupled to the interface  314  has an input impedance input of R in =10 MΩ, and may be impedance matched to the interface  314 . Initially, an input stage includes a high pass filter stage  402  (operating at 0.2 Hz) is used to remove a DC component from the received patient electrode signal. A first gain stage  404  (32 dB) is applied, then a second gain stage  406  (28 dB) is applied (each at or below 100× gain, in some examples) before the amplified signal is applied to an anti-alias low-pass filter stage  408 . The filtered signal is passed to an ADC stage  410  that is coupled to the microcontroller  304 , as shown. 
         [0039]    The received signal is passed to a 60 Hz finite impulse response (FIR) filter  412  having a 6 Hz bandwidth that is part of an analysis stage of the controller  304 . A buffering stage includes an averaging filter  414  that down samples the input signal to 200 Hz. The ADC stage  410  may sample at about 10 kHz, which is then averaged over 50 samples to reduce noise, resulting in a 200 Hz down-sampled signal. A functional block  416  removes a DC offset value from the averaged signal before the signal is passed through a windowing function  418 , which applies a symmetrical function, increasing from zero to a peak and decaying back to zero to optimize the spectral estimate of the signal. In the illustrated example, a Blackman-Harris window is used, but any number of window functions may be used. 
         [0040]    A domain transform is performed on the averaged signal, in the illustrated example by a fast Fourier transform (FFT) function  420 , in this case a 1024 point FFT, as part of a transformation stage. The frequency set used for analysis is selected through a logical conjunction of the frequency domain signal and frequency bin selector mask function  422 . For example, the selector function  422  may identify a frequency range of between 0-100 Hz, and more particularly between, 0-40 Hz, and even more particularly between 3-20 Hz. The bin selector  422  is coupled to a spectrum summation function  424  that determines energy values across the frequency domain signal and sums the energy values to produce a total energy value. The frequency bin selector may focus on a weighted group of frequencies in the frequency domain, weighted group of phase-delays in the phase domain, or weighted group of times in the time domain. The frequency bin selector  422 , or any of the blocks of the evaluation stage  429 , may employ a search algorithm to automatically determine from the electrocardiogram signal optimal sets of frequencies to use in distinguishing between sinus rhythm, atrial fibrillation, atrial flutter and/or ventricular fibrillation. The frequency bin selector function  422  is also coupled to a max spectrum determination function  426  that determines the maximum energy across the frequency domain signal. The SFDM (or STDM or SPDM) is determined at a block  428 , in the illustrated example, by dividing the summed energy from the block  424  by the maximum energy from the block  426 . The blocks  422 ,  424 ,  426 , and  428 , therefore may form part of an evaluation stage  429 , along with blocks  432 ,  434 , and  436  discussed below, in the illustrated example. It will be appreciated that these stage indicators are provided by way of example, and that any number of the blocks may be combined in different combinations into different stages and that the descriptions herein are for convenience and explanation purposes only. 
         [0041]    In some examples, the SFDM from the block  428  is coupled directly to a classification stage  430 , while in other examples, the SFDM from the block  428  is coupled to an averaging stage  432  that determines the mean SFDM over a number of measurement cycles. The classification stage  430  may include a predetermined set of ranges for SFDM values corresponding to normal sinus rhythm, atrial flutter, atrial fibrillation, and ventricular fibrillation. For example, ventricular fibrillation may be characterized by a very sharp decrease in power at around 1-2 Hz relative to sinus rhythm. Atrial fibrillation and atrial flutter may be characterized by different alterations in the mean, standard deviation, skewness and/or kurtosis of the energy profile (power spectrum) of each cycle relative to sinus rhythm. The classification stage thus compares the received TNP value to these ranges and creates a heart rhythm classification, which signal may be displayed on the display  308 , stored on the memory  306 , and/or transmitted to a remote station through interfaces  310  and/or  312 . The classification block  430  may deploy any suitable algorithm, of which a genetic algorithm is an example. This classification occurs automatically as described, and may be part of a learning system in which classification assessment is optimizable, either manually through operator adjustment, in a semi-automated manner, or a fully-automated manner. 
         [0042]    In some examples, additional analysis of the SFDM data is used to strengthen the classification determination, in particular where more than three different classifications are used. For example, additional statistical metrics may be used to extract time-domain statistics of the SFDM metric. The data can be used to provide a more robust prediction by improving accuracy though the evaluation of other features of the signal. For example, the SFDM data from block  428  may be provided to a SFDM standard deviation function  434  which provides the SFDM data to the classification stage  430 . In another example, the SFDM block  428  is coupled to another statistical metric block  436  that performs a skew, kurtosis, or other statistical operation on the data designed to further reduce extraneous signal components before the SFDM data are sent to the classification stage. While the illustrated example uses a FFT, other implementations may be achieved. For example, one could use an field programmable gate array (FPGA) with a parallel arrangement of digital band pass filters to allow real-time parallel computation of the classification result. Another example would be to design an integrated circuit with a parallel array of analog band pass filters, feeding into an analog summing stage and comparator, to provide a completely analog implementation thereof. 
         [0043]    These are examples of how multiple feature extractions may be performed over time to produce statistics metrics for the SFDM, STDM and/or SPDM prior to classification at a classification stage. In some examples, those multiple feature extractions may be performed on frequency, time, or phase domain representations (i.e., on converted spectral dispersion of the frequency, time, or phase, respectively) to produce statistics metrics prior to determination of the SFDM, STDM, and/or SPDM, and thus prior to classification at the classification stage. 
         [0044]    In some examples, the classification stage  430  receives a single feature or vector such as the SFDM. In the case of a single feature (e.g., SFDM) the classification may be achieved by a threshold function. However, in other examples, more features may be supplied to the classification stage  430 . In such examples, the received vector may be applied to a system of equations implemented using a suitable means (i.e. regression, neural network, linguistic expression, etc.) to produce either/or both a binary classification output and continuous measure of the severity or probability the patient is in AF. An example of this may be if both the SFDM and the kurtosis of the SFDM over time are used as inputs to the classification stage  430 . If these features were not combined (i.e., using a regression function) into a single metric again, a 2D classification surface would result. If SFDM values for distinct cardiac states were displayed as a scatter plot, ideally, discrete clusters would be seen. A classification function (i.e. linear, spline, circle, etc.) could be used to describe these regions allowing the device to report the current cardiac state a patient was in, given their ECG. Therefore, the classification stage  430  may comprise an additional 2 sub-stages, 1) additional transformation, for example through a regression function, and 2) multi-dimensional segmentation of the clusters describing various cardiac states. 
         [0045]      FIG. 5  illustrates a process  500  as may be implemented by the classification device  300 . Initially, at a block  502 , input signals, such as electrocardiogram signals, are received from electrodes, as may be received by the electrodes interface  314 , and any signal conditioning is performed, where such signal conditioning may be performed in a separate dedicated circuit device, within a single or multi-processor device or utilizing some form of logic device, either in analog or digital form. Because the electrocardiogram signals are collected and analyzed in real time, detection and analysis may be performed in real time. For implementations into lower-power devices, the data can be captured and the analysis and classification performed at sub-real time speed. To facilitate signal analysis at cycle times needed for energy summation and classification, the collected real-time data is buffered at a block  504  and then re-sampled at a lower sampling rate. The re-sampled signals are transformed to a power conversation domain by a block  506 . For example, at the block  506 , the classification device  300  may perform a Fourier transform on the re-sampled electrical signals, collected in a time domain, into frequency domain signals. In  FIG. 3 , a fast Fourier transform (FFT) is performed by the element  420 , for example. Transformation is performed to isolate the electrocardiogram signals into signal components for energy measurement. As such, the block  506  may perform a transformation into any suitable domain, which may include the phase domain using the FFT. 
         [0046]    Other example transformations include the continuous wavelet transform, which is able to construct a time-frequency representation of a signal with improvements in time and frequency localization compared to standard FFTs. Another technique is time-frequency reassignment, which can be used to overcome the tradeoff between the time and frequency resolution associated with the short-time FFT. This technique refocuses the time-frequency data by mapping the data to time-frequency coordinates that are nearer to the true region of support of the analyzed signal. 
         [0047]    In the illustrated example, the spectral conversion domain signals are passed to a block  508 , which determines the bandwidth of the domain signals and whether a desired sub-band range exists. The block  508 , for example, may be executed by the frequency bin selector  422  containing a predetermined frequency range over which power values are to be totaled. 
         [0048]    At a block  510 , the device  300 , e.g., the elements  426 ,  424 , and  428 , determines the power spectrum over the domain signals and more specifically summed energy across the predetermined domain range. This range may be normalized by the highest energy level, for example as discussed above in regards to element  428 , or classification may be performed over un-normalized data. It has been determined by the present inventors that summed energy over a particular range of frequency components correlates with different states of heart rhythm, in particular with different cardiac arrhythmia conditions. Instead of conventional techniques that determine a dominant frequency among the domain signals, the present technique may avoid determining dominant frequencies and instead determine total energy over a range of frequencies. That summed energy may be absolute summed energy. While in other examples, the domain signals are passed through a threshold process, e.g., at block  510 , that identifies only those frequencies having a summed energy above a threshold, which are then summed to determine total energy. 
         [0049]    Because summed energy is used, problems with low signal-to-noise in conventional systems are avoided, allowing for more accurate determinations of atrial fibrillation. Issues with the drive circuitry pulsing altering sensed electrocardiogram signals is reduced or eliminated, as is operator subjectivity, which can affect measurements in conventional systems. Also, because energy (power) is determined over a minimal set of data (e.g. 0-20 Hz) the ultimate classification is more robust because it focuses on a known, previously analyzed classification spectrum region. The summed energy technique also does not require altering the electrocardiogram signals, by the subtraction of the ventricular activity (i.e., the QRS and T complexes of the ECG); although, such subtraction may still be performed. Further still, the sampling of summed energy is typically performed at a lower sampling interval, e.g., every 3-5 seconds, reducing the computational demand, compared to more real-time systems. This also allows for the implementation entirely in hardware and/or software. Furthermore, the present techniques have low latency times between summed energy measurements. Latency times below 1 s have been shown in some examples. Furthermore, electrical lead placement is not critical for the summed energy determination, as electrocardiogram signals may be recorded by any lead configuration whether leads are placed precordially or on the limbs. 
         [0050]    Optionally, a block  512  may buffer summed energy data over a period of time and perform additional analysis on that data, including determining standard deviation of the summed energy values (with or without normalization) and then performing skew or kurtosis shaping to isolate the buffered data further. 
         [0051]    At a block  514 , the SFDM data is analyzed to determine which classification of heart rhythm the data corresponds, thereby indicating whether the patient is experiencing sinus rhythm, atrial flutter, atrial fibrillation, and ventricular fibrillation. 
         [0052]    The SFDM may be determined from electrocardiogram signals collected from a single location, as discussed above, or from multiple locations. For example, the multiple electrode pairs may be placed simultaneously in the precordial region and on one or more limbs, from which a classification device is able to determine SFDM over a range of regions. In such examples, scaling of summed energy values in different regions may be used to account for inherently different energy levels. Determinations based on STDM and SPDM would follow accordingly. 
         [0053]    In other examples, classification devices are able to isolate summed energy measurements at different locations that are each, individually calibrated to heart rhythm classification. In such examples, certain summed energy values measured at the precordial regions may indicate a first heart rhythm condition, while those same normative summed energy values at a limb may not indicate the first heart rhythm condition, but another. 
         [0054]      FIG. 4  illustrates an AF classification system capable of detecting an arrhythmia condition in a patient and assessing the type of corresponding arrhythmia, e.g., atrial fibrillation, ventricular fibrillation, atrial flutter, or another arrhythmia. In various examples, the AF classification devices herein provide patient-specific customization, health condition specific customization, and/or health care provide specific customization. In some examples, the devices provide for optimization in AF assessment, either through a fully manual adjustment by an operator or healthcare professional or through semi-automated or fully-automated adjustments. In these ways, the devices herein may be tailored to individualized operation, for example, as part of a learning system. 
         [0055]      FIG. 6  illustrates an example learning atrial fibrillation system  600  composed of an AF engine  602 , for example, operating in accordance with the AF system  400  in  FIG. 4 , that receives ECG signals data for the heart. The ECG data is provided to the AF engine  602 , like ECG data would be provided to the R in  input line of the signal conditioning circuit of  FIG. 4 , from which an AF determination and classification is made in a similar manner to that of the classification engine  430 . In the system  600 , AF classification data is provided to a display  604  having an input device (touch screen, stylus, keyboard, mouse, keypad, etc.) allowing for user interaction. In some examples, the display  604  may provide a visual indication of the classified AF condition, ECG data, SFDM, STDM or SPDM data, and/or total normalized energy values plotted in real time or over time. 
         [0056]    The display  604  presents the data to the user, who is able to respond by manually optimizing operation of the AF engine  620 , through a user input  606 . For example, the display  604  may present the user with the AF data and an interface, in response to which the user, at the block  606 , determines a suggested change in the specificity and/or sensitivity of the AF engine  602 . If the user determines that a change in specificity and/or sensitivity is desired then a control signal is sent to a sensitivity and specificity engine  608  that sends a control signal to the AF engine  602  modifying operation thereof. 
         [0057]    In this way, the system  600  may analyze the characteristics of diagnostic metrics utilized to discriminate the arrhythmias for each patient. Based on the distribution of measured metrics from the ECG, for example, the thresholds or other metric criteria used to classify different rhythms as normal, sinus rhythm, atrial fibrillation, ventricular fibrillation, flutter may be adjusted to optimize the diagnostic accuracy of the system for each patient individually. This may be accomplished with a user input interface, e.g., where the display  604  provides an operator/physician a scattergram of the distribution of the metrics from the engine  602 . 
         [0058]    In manual operation, the operator/physician may manually adjust thresholds or other metric criteria based on the scattergram data. In a semi-automated manner, the sensitivity and specificity engine  608  may determine, from the AF data of AF engine  602 , a suggested threshold or other metric criteria that matches an initial desired sensitivity and specificity range. This suggested value or suggested operating range is then provided back to the AF engine  602  and overlayed (or otherwise displayed) to the user along with the AF data on the display  604 . The user may then, at block  606 , accept the suggested value(s) (thresholds or other metric criteria) or make manual adjustments thereto, to thereby alter the sensitivity/specificity of the AF engine  602 . In a fully automated operation, the engine  608  may, not only, automatically determine the suggested threshold or other metric criteria for making the AF assessments, but then automatically adjust the AF engine  602  to operate in accordance with those conditions, i.e., without user input. Applying these techniques to that of  FIG. 4 , for example, the engine  608  may adjust any of the metrics used by blocks  422 - 430  to optimize or otherwise customize operation of the classification at block  430 . For the semi-automated or fully-automated modes, a learning engine  610  is provided to receive the output AF data from the engine  602  and any other patient related dated, whether real-time data, historical data, or otherwise. That learning engine  610  may automatically provide control signals for adjusting the sensitivity and specificity at the engine  608 , or it may do so under control of the user via input  606 . 
         [0059]    Thus, the system  600  is able to optimize sensitivity and specificity of the determination of any rhythm to account for possible false positives and false negative results according to the clinical condition and requirement set forth by a clinician&#39;s discretion. For example, when a patient has a stroke risk higher than bleeding risk, then the detection of AF and use of anti-coagulants could be determined with a higher priority on sensitivity over specificity (100% sensitivity guaranteed). If on the other hand, a patient&#39;s highest risk is of bleeding, then the priority is specificity over sensitivity (100% specificity guaranteed). Such adjustments can be made through manually, semi-automated, or fully automated adjustments. 
         [0060]      FIGS. 7A-7C  illustrate sensitivity-specificity analyses (Receiver operating characteristic, ROC, curves) for a binary detection of AF based on scattergram plots (an example of a scattergram of frequency domain analysis, SFDM, is presented in  FIG. 2E ) in three different metrics or databases, each having a full range of metric discriminating thresholds for assessing the presence of AF with different accuracy. The ROC curve illustrates the fraction of true positives (sensitivity) versus the fraction of the false positives (1-specificity) for sample ECG events for a group of patients. In  FIGS. 7A and 7B , the data from a miss-classified patient is included in the database, reducing the overall accuracy of either SFDM ( FIG. 7A ) or RR-interval variance ( FIG. 7B ) metrics and requiring adjustment of threshold to maximize sensitivity or specificity alternatively. When the total power threshold is adjusted to exclude this miss-classified person (e.g., using the system  600 ), the ROC curve changes to that of  FIG. 7C , demonstrating the ability of SFDM metric to achieve a 100% sensitivity and specificity simultaneously; the ideal condition for AF detection and classification. The plots of  FIGS. 7A-7C  may be provided to the user on the display  604  along with an user input interface for adjusting thresholds or other metrics that result in changes in the scattergram. It will be understood that the plots are provided by way of example and that while adjusting a single threshold is discussed in the illustrated example, any number of metrics may be adjusted by the user as desired. Similar types of optimization of AF data sets would be performed on single patient data as well. 
         [0061]    The ECG data and AF data collected for patients may be used for individualized patient or group data mining. Patient data, whether collected from an handheld AF classification device, electrocardiographic recording system, or otherwise, is stored in a database. A data mining engine may analyze the database, e.g., in an “ECG-informatics” manner deploying a genetic algorithm, to identify hard-to-notice, hidden, correlations of temporal, spectral and phase patterns, within the patient data. This data mining could include identifying metrics calculated in those domains, such as total energy over specified domain ranges. The patient data would be collected across episodes of cardiac electrical activity of various rhythms and properties. Those episodes could be episodes belonging to the same patient at different times, frequencies and/or phases, or those belonging to different patients as part of a population study. In some examples, the data mining engine may search for patterns across ECG data to identify ‘hidden’ occurrences, e.g., in the converted frequency, time, or phase domain of that ECG data, that correlate to different AF classifications. Such occurrences thus may determine indicators that are more nuanced or more complex than the threshold and other metrics discussed herein, or that are more multivariate than would be the case for initial AF classification algorithms. Such data mining thereby may be used along with the adapting algorithms described herein, e.g., the genetic algorithms that may be implemented by the AF classification block  430 , to provide historical data-based optimization and customization of AF assessment. One of the advantages of using historical data in this manner is that additional patient data, including data external to the AF classification systems described herein and otherwise believed irrelevant to AF classification may also be provided and considered by the system. Examples of such data include patient demographic data (e.g., age, gender, ethnicity, etc.), physiological data (e.g., weight, heart size, blood pressure, etc.), and time course of cardiac function (e.g., ECG time-series, ECG intervals, heart rate, incidences of extrasystoles, AF, heart rate variability (HRV) parameters, heart rate turbulence, etc.)”. 
         [0062]      FIG. 8  illustrates an example process  700  to perform data mining on patient collected data, in accordance with an example and as may be implemented on a computer system. The process  700  may be executed on an analysis machine, such as any classification machine described herein, e.g., that of  FIGS. 3 and 4 . Although, the analysis machine may be used in the diagnosis of any number of conditions, arrhythmias and non-arrhythmia conditions. 
         [0063]    A block  702  receives initial patient data, such as raw data like a target ECG data stored in a database  701 , and/or other patient data, as described herein. A block  704  identifies target patient data  703  within the broad data from block  702 . For example, the block  704  may include instructions to target a specific patient or patient group, based on any number of desired patient traits, including demographic traits, physiological data, historical data, or otherwise. The block  704  would in some examples include predetermined instructions for identifying a target group, while in other examples the block  704  may require input from a physician or other healthcare professional to provide characteristics of target patient data to be examined. A block  706  may perform processing on the targeted ECG data corresponding to the target patient data, producing processed ECG data 
         [0064]    At a block  708 , desired diagnostic features in the processed target ECG data are identified and stored in a diagnostic features data set  707 . The block  708  may execute adapting algorithms to mine the ECG data (and other patient data) for features corresponding to a potential diagnosis. Features for diagnosing arrhythmias are described hereinabove, but the block  708  is not limited to diagnosing arrhythmias. Instead, the block  708  may identify any number of features corresponding to any number of diagnosable conditions. Preferably, the diagnostic features may be frequency, time or phase domain features, summed energy values determined over range of frequencies, time intervals or phases for these domain features, SFDM, STDM, and/or SPDM, for example. The identified diagnostic features set  707  may be multi-variant, meaning that the features may include a plurality of features. For example, the block  708  may identify a feature in the target ECG data for certain patients that corresponds to a physiological feature of those same patients, like weight, heart size, blood pressure, ECG time-series, ECG intervals, heart rate, incidences of extrasystoles, AF, heart rate variability (HRV) parameters, heart rate turbulence, etc. The block  708  may execute an adapting algorithm that recursively mines through the data provided thereto for correlations in the patient, patterns (as shown as  707 ) in the patient data, that are then stored as the diagnostic features data set. 
         [0065]    At a block  710 , a diagnostic assessment is made from the diagnostic features data set from block  708 . When mined for assessing arrhythmia, the block  710  may identify an arrhythmia classification from the data mining, thereby offering the potential to classify arrhythmias based on heretofore un-known patterns across patient data (ECG data, physiological data, demographic data, etc.). The block  710 , also capable of executing an adapting algorithm, may thus provide hard-to-notice, hidden, correlations of temporal, spectral and phase patterns, including metrics calculated in those domains, between episodes of cardiac electrical activity of various rhythms and properties. Such diagnoses can be stored as a derived data set  709 , constructed from targeted ECG data and other patient data. As a data mining system, whether adaptive or not, the blocks  706 - 710  may be executed by the computer system in a recursive manner, as indicated by the dashed lines, allowing for iterative-based identification and diagnoses. 
         [0066]    The present techniques may be used in standalone classification devices, as described, as well as in integrated atrial fibrillation mapping systems. Conventional mapping systems determine dominant frequencies and locations of dominant frequencies for mapping the heart tissue to identify arrhythmia source sites. The dominant frequency data, in such systems, is used as domain data from which, separately frequency selection is performed, to identify frequency ranges of interest, and then summed energy values are determined. These configurations include catheter based mapping systems as well as 3 dimensional electrode arrays which may be used endocardially or epicardially. The present techniques may also be used with atrial fibrillation treatment assemblies, such as ablation devices, serving as a real-time assessment of treatment effectiveness by measuring electrocardiogram signals and determining summed energy and classifications during or after treatment. The techniques may be used before treatment as well, of course. 
         [0067]    The techniques may be implemented in devices external to the patient, using externally mounted ECG leads. While in other examples, the devices may be implantable, for example, adjacent the heart. The devices may be included in an implantable cardiac defibrillator device, for example, for detection of AF conditions (e.g., ventricular fibrillation) and used in the controlled delivery of fibrillation therapy. The devices may be contained within an automated internal or external defibrillator for detection of AF conditions (e.g., ventricular fibrillation) and to control delivery of fibrillation therapy. 
         [0068]    The various blocks, operations, and techniques described above may be implemented in hardware, firmware, software, or any combination of hardware, firmware, and/or software. When implemented in hardware, some or all of the blocks, operations, techniques, etc. may be implemented in, for example, a custom integrated circuit (IC), an application specific integrated circuit (ASIC), a field programmable logic array (FPGA), a programmable logic array (PLA), etc. 
         [0069]    When implemented in software, the software may be stored in any computer readable memory such as on a magnetic disk, an optical disk, or other storage medium, in a RAM or ROM or flash memory of a computer, processor, hard disk drive, optical disk drive, tape drive, etc. Likewise, the software may be delivered to a user or a system via any known or desired delivery method including, for example, on a computer readable disk or other transportable computer storage mechanism or via communication media. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency, infrared and other wireless media. Thus, the software may be delivered to a user or a system via a communication channel such as a telephone line, a DSL line, a cable television line, a wireless communication channel, the Internet, etc. (which are viewed as being the same as or interchangeable with providing such software via a transportable storage medium). 
         [0070]    Moreover, while the present invention has been described with reference to specific examples, which are intended to be illustrative only and not to be limiting of the invention, it will be apparent to those of ordinary skill in the art that changes, additions and/or deletions may be made to the disclosed embodiments without departing from the spirit and scope of the invention.