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      Nonlinear analysis of the heartbeats in public patient ECGs using an automated PD2i algorithm for risk stratification of arrhythmic death

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          Abstract

          Heart rate variability (HRV) reflects both cardiac autonomic function and risk of arrhythmic death (AD). Reduced indices of HRV based on linear stochastic models are independent risk factors for AD in post-myocardial infarct cohorts. Indices based on nonlinear deterministic models have a significantly higher sensitivity and specificity for predicting AD in retrospective data. A need exists for nonlinear analytic software easily used by a medical technician. In the current study, an automated nonlinear algorithm, the time-dependent point correlation dimension (PD2i), was evaluated. The electrocardiogram (ECG) data were provided through an National Institutes of Health-sponsored internet archive (PhysioBank) and consisted of all 22 malignant arrhythmia ECG files (VF/VT) and 22 randomly selected arrhythmia files as the controls. The results were blindly calculated by automated software (Vicor 2.0, Vicor Technologies, Inc., Boca Raton, FL) and showed all analyzable VF/VT files had PD2i < 1.4 and all analyzable controls had PD2i > 1.4. Five VF/VT and six controls were excluded because surrogate testing showed the RR-intervals to contain noise, possibly resulting from the low digitization rate of the ECGs. The sensitivity was 100%, specificity 85%, relative risk > 100; p < 0.01, power > 90%. Thus, automated heartbeat analysis by the time-dependent nonlinear PD2i-algorithm can accurately stratify risk of AD in public data made available for competitive testing of algorithms.

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          Most cited references 39

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          Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics.

          Despite much recent interest in quantification of heart rate variability (HRV), the prognostic value of conventional measures of HRV and of newer indices based on nonlinear dynamics is not universally accepted. We have designed algorithms for analyzing ambulatory ECG recordings and measuring HRV without human intervention, using robust methods for obtaining time-domain measures (mean and SD of heart rate), frequency-domain measures (power in the bands of 0.001 to 0.01 Hz [VLF], 0.01 to 0.15 Hz [LF], and 0.15 to 0.5 Hz [HF] and total spectral power [TP] over all three of these bands), and measures based on nonlinear dynamics (approximate entropy [ApEn], a measure of complexity, and detrended fluctuation analysis [DFA], a measure of long-term correlations). The study population consisted of chronic congestive heart failure (CHF) case patients and sex- and age-matched control subjects in the Framingham Heart Study. After exclusion of technically inadequate studies and those with atrial fibrillation, we used these algorithms to study HRV in 2-hour ambulatory ECG recordings of 69 participants (mean age, 71.7+/-8.1 years). By use of separate Cox proportional-hazards models, the conventional measures SD (P .3), were not. In multivariable models, DFA was of borderline predictive significance (P=.06) after adjustment for the diagnosis of CHF and SD. These results demonstrate that HRV analysis of ambulatory ECG recordings based on fully automated methods can have prognostic value in a population-based study and that nonlinear HRV indices may contribute prognostic value to complement traditional HRV measures.
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            Heart rate variability: a measure of cardiac autonomic tone.

            Analysis of HRV based on routine 24-hour Holter recordings provides a sensitive, noninvasive measurement of autonomic input to the heart. HRV can be measured in the time or frequency domain. Each frequency domain variable correlates at least r = 0.85 with a time domain variable. Thus time domain measures can be used as surrogates for frequency domain measures which may simplify future studies. Abnormalities of autonomic input to the heart, which are indicated by decreased indices of HRV, are associated with increased susceptibility to ventricular arrhythmias. Decreased indices of HRV are also associated with CHF, diabetes, and alcoholic cardiomyopathy. Decreased indices of HRV are an independent risk factor for mortality post MI and in patients with advanced CHF. Medications can also affect HRV, and that effect may become an important clinical consideration, especially in high-risk patients.
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              A reduction in the correlation dimension of heartbeat intervals precedes imminent ventricular fibrillation in human subjects.

              Reduced reflexive control of heartbeat intervals occurs with advanced heart disease and is an independent risk factor for mortality. Based on a previous study of experimental myocardial infarction in pigs, we hypothesized that a deterministic measure of heartbeat dynamics, the correlation dimension of R-R intervals (D2), may be a better predictor of risk than a stochastic measure, such as the standard deviation (SD). We determined the point estimates of the heartbeat D2 (i.e., PD2s) in Holter electrocardiographic recordings from 11 high-risk patients who manifested ventricular fibrillation (VF) during the recording and in high-risk controls having only nonsustained ventricular tachycardia (14 patients) or premature ventricular complexes (13 patients). We found that PD2 reduction (i.e., PD2s < 1.2) precedes lethal arrhythmias by hours, but is not reduced in high-risk controls (p < 0.001; sensitivity, 91%; specificity, 85%). Heartbeat SD did not discriminate among the patients. Thus PD2 of heartbeat intervals may provide an important diagnostic test and early warning sign of VF.
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                Author and article information

                Journal
                Ther Clin Risk Manag
                Therapeutics and Clinical Risk Management
                Therapeutics and Clinical Risk Management
                Dove Medical Press
                1176-6336
                1178-203X
                April 2008
                April 2008
                : 4
                : 2
                : 549-557
                Affiliations
                Vicor Technologies, Inc. Bangor, PA, USA
                Author notes
                Correspondence: James E Skinner Vicor Technologies, Inc., 399 Autumn Drive, Bangor, PA 18013, USA Tel +1 570 897 5797 Email jskinner@ 123456vicortech.com
                Article
                2504053
                18728829
                © 2008 Skinner et al, publisher and licensee Dove Medical Press Ltd.
                Categories
                Original Research

                Medicine

                heart rate variability, sudden death, nonlinear, chaos, ventricular arrhythmias

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