BACKGROUND

Several common clinical scenarios call for identification of cardiac rhythm in ambulatory out-patients. For example, atrial fibrillation (“AF”) is a common arrhythmia that is often paroxysmal in nature. Decisions about its therapy are best informed by knowledge of the frequency, duration and severity of the arrhythmia. For patients with AF, clinicians must make complex decisions about oral anticoagulation, rate control with drugs or cardiac ablation with pacemaker therapy, and rhythm control with drugs or ablation procedures. The implications for anticoagulation therapy alone are enormously important, as the rate of stroke goes up sharply with even short episodes of AF. These therapies have been shown to lower the risk of clinical thromboembolism in all patients with AF, regardless the different types as paroxysmal, persistent or permanent. However, in the lowest-risk patients, the bleeding risk associated with anticoagulant therapy can exceed the risk of thromboembolism without therapy, thus, decision-making regarding the initiation of antithrombotic treatments requires an accurate estimation of both embolic and bleeding risk.

While implanted devices can record information about duration, frequency and severity of AF with great accuracy, non-invasive diagnostic devices for recording electrocardiographic signals are constrained by the need for skin electrodes. Current solutions for cardiac rhythm detection is limited to high fidelity monitoring that can only be used for short periods, or low-fidelity monitoring for longer periods. They require ECG leads and a digital recording device. While much smaller than in years past, such a recording apparatus may still be obtrusive. Even the high fidelity monitoring can be limited by noisy and distorted waveform data. The most frequently available and reliably accurate data set is the time series of the times between heartbeats or pulses, also referred to as RR interval time series. However, non-invasive devices for determining heart rhythm are not in common use because of reduced confidence in detecting AF based on the heart rate or RR series alone. Specifically, sinus rhythm (SR) with frequent ectopy is expected to share many time series features with AF, and thus be difficult to distinguish. In addition, other transient cardiac arrhythmias cause short-lived symptoms but are difficult to diagnose without skin electrode-based recording devices.

PVCs, alone, lead to poorer prognosis, even in patients with no obvious heart disease. Further evidence lies in the fact that the ejection fraction rises in the great majority of patients with very frequent PVCs and low ejection fraction at the time of PVC ablation.

In ambulatory outpatients, where continuous monitoring currently requires specialized equipment and services, the finding of atrial fibrillation has profound implications for cardiac testing and for therapies. For patients with atrial fibrillation, clinicians must make complex decisions about oral anticoagulation, rate control with drugs or cardiac ablation with pacemaker therapy, and rhythm control with drugs or ablation procedures. The implications for anticoagulation therapy alone are enormously important, as the rate of stroke goes up sharply with even short episodes of atrial fibrillation. The new diagnosis of atrial fibrillation in patients who are monitored because of stroke of unknown origin can lead to life-saving therapy.

In hospital in-patients, the new onset of paroxysmal atrial fibrillation is often interpreted as a sign of clinical deterioration arising from unbalanced activity of the sympathetic and parasympathetic arms of the autonomic nervous system. In addition to the standard decisions about anticoagulation, rate control and rhythm control just noted, clinicians must consider possible causes of deterioration such as sepsis, other infection, hemorrhage, incipient cardiac or respiratory therapy. As a result of the new arrhythmia diagnosis, they may re-examine the patient and order new tests or therapies. New-onset atrial fibrillation in an otherwise apparently stable patient might lead to new decisions about transfers to intensive care units, or decisions about prolonging the hospital stay.

In intensive care unit (“ICU”) patients, where very close monitoring is essential to detect even slight changes for the worse, EKG monitoring is nearly always present. ICU patients can be very unstable, and major changes in diagnosis and therapy can come swiftly. Accurate detection of cardiac rhythm changes in real time can make the difference between life and death.

In the operating room, surgeons and anesthesiologists must make decisions about the status of the patient very quickly indeed. Here, new-onset atrial fibrillation may lead directly to cardioversion, and atrial and ventricular ectopy are very likely to be treated with antiarrhythmic drugs to prevent sustained arrhythmia.

In the Emergency Department, the stability of patients and the severity of their injuries and illness must be accurately assessed at top speed. Cardiac arrhythmias are important clues to guide not only initial therapies, but also differential diagnoses.

Thus detection and quantification of atrial fibrillation, atrial ectopy and ventricular ectopy is an important clinical mandate throughout the spectrum of clinical care.

For ambulatory outpatients, the current art for detection is limited to high-fidelity monitoring that can only be used for short periods, or low-fidelity monitoring for longer periods. The current art requires EKG leads, and a digital recording device. While much smaller than in years past such recording apparatus is obtrusive. Even the high-fidelity monitoring can be limited by noisy and distorted waveform data.

Some hospital in-patients are not continuously monitored by EKG telemetry. Instead, observations are limited to hourly visits by nurses and other health care personnel. Moreover, an increasing number of atrial or ventricular ectopic beats can be an early warning of sustained atrial or ventricular arrhythmia. Intermittent observation alone may mean that these important findings are missed altogether.

Although ICU and operating room patients are continuously monitored, it is the value of the heart rate that is of first concern, and important changes in rhythm such as the appearance of atrial fibrillation or increasing ectopic burden may go undetected.

The most frequently available and reliably accurate data set is the time series of the times between heartbeats. This is derived from ventricular activation leading to the high amplitude QRS complex of the EKG, which is usually discernible even when noise obscures the much smaller P wave that represents atrial activation and is essential in detecting sinus rhythm from all others. This can be referred to as the RR interval time series. Time series measures that can distinguish among sinus rhythm, atrial fibrillation and ectopy of atrial or ventricular origin can be of great use in clinical care.

While algorithms have been developed that distinguish between normal sinus rhythm and atrial fibrillation, there has been no effective solution to distinguish either of these rhythms from the condition of sinus rhythm and degrees of frequency of atrial and ventricular ectopy. Specifically, sinus rhythm with very frequent ectopy is not distinguishable from atrial fibrillation using available algorithms.

RR interval time series measures that can distinguish among normal sinus rhythm, AF and ectopy of atrial or ventricular origin (PACs and PVCs) can be of great use in clinical care. One approach to rhythm classification using only RR interval time series is to examine their dynamics. We can expect, for example, that NSR has a regular and repeating characteristic, that AF is irregular, and that SR with ectopy has a regular baseline with intermittent departures. Numerous algorithms to analyze RR interval time series for clinical purposes exist. These are usually classified as time domain, frequency domain, phase domain, non-linear dynamical domain, although new classifications of statistical, geometric, energetic, informational and invariant domains have been suggested. Known methods have been shown to have modest effect in distinguishing patients at high or low risk, or degrees of illness severity for patients in SR with little or no ectopy. Some have been effective in distinguishing AF from other rhythms, principally SR with little or no ectopy. Examples include coefficient of sample entropy, a histographic method, and a Poincare plot-based method. However, known methods do not distinguish AF from the condition of SR and degrees of frequency of atrial and ventricular ectopy. Specifically, known methods do not distinguish SR with very frequent ectopy from AF.

For example, the entropy of RR interval time series has been studied as a means for detecting AF in short RR interval time series. The results show very clear distinction between AF and NSR that is free of ectopy, but there is an area of overlap where entropy measures are the same for AF compared to SR with frequent ectopy. It may be desirable to characterize the RR interval time series that can elucidate the cardiac rhythm when the entropy measurement is ambiguous.

SUMMARY

A system for classifying cardiac rhythms is disclosed. The system includes one or more processors, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors. The program instructions include first program instructions to obtain data representative of a time series of the times between heartbeats. The program instructions further comprise second program instructions to segment the time series into a plurality of segments. The program instructions further comprise third program instructions to calculate a plurality of parameters corresponding to each of the 30-second segments. The program instructions further comprise fourth program instructions to analyze the obtained data and the calculated parameters using a plurality of multivariable algorithms for rhythm classification. The program instructions further comprise fifth program instructions to synthesize the results of the plurality of multivariable algorithms to formulate a single rhythm classification.

A computer program product for classifying cardiac rhythms is disclosed. The computer program product includes one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices. The program instructions include first program instructions to obtain data representative of a time series of the times between heartbeats. The program instructions further comprise second program instructions to segment the time series into a plurality of segments. The program instructions further comprise third program instructions to calculate a plurality of parameters corresponding to each of the 30-second segments. The program instructions further comprise fourth program instructions to analyze the obtained data and the calculated parameters using a plurality of multivariable algorithms for rhythm classification. The program instructions further comprise fifth program instructions to synthesize the results of the plurality of multivariable algorithms to formulate a single rhythm classification.

