Comparison of linear–stochastic and nonlinear–deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death
James E Skinner1, Michael Meyer2, Brian A Nester3, Una Geary4, Pamela Taggart4, Antoinette Mangione4, George Ramalanjaona5, Carol Terregino6, William C Dalsey4
1Vicor Technologies, Inc., Boca Raton, FL, USA; 2Max Planck Institute for Experimental Physiology, Goettingen, Germany; 3Lehigh Valley Hospital, Allentown, PA, USA; 4Albert Einstein Medical Center, Philadelphia, PA, USA; 5North Shore University Hospital, Plainview, NY, USA; 6Cooper Medical Center, Camden, NJ, USA
Objective: Comparative algorithmic evaluation of heartbeat series in low-to-high risk cardiac patients for the prospective prediction of risk of arrhythmic death (AD).
Background: Heartbeat variation reflects cardiac autonomic function and risk of AD. Indices based on linear stochastic models are independent risk factors for AD in post-myocardial infarction (post-MI) cohorts. Indices based on nonlinear deterministic models have superior predictability in retrospective data.
Methods: Patients were enrolled (N = 397) in three emergency departments upon presenting with chest pain and were determined to be at low-to-high risk of acute MI (>7%). Brief ECGs were recorded (15 min) and R-R intervals assessed by three nonlinear algorithms (PD2i, DFA, and ApEn) and four conventional linear-stochastic measures (SDNN, MNN, 1/f-Slope, LF/HF). Out-of-hospital AD was determined by modified Hinkle–Thaler criteria.
Results: All-cause mortality at one-year follow-up was 10.3%, with 7.7% adjudicated to be AD. The sensitivity and relative risk for predicting AD was highest at all time-points for the nonlinear PD2i algorithm (p ≤ 0.001). The sensitivity at 30 days was 100%, specificity 58%, and relative risk >100 (p ≤ 0.001); sensitivity at 360 days was 95%, specificity 58%, and relative risk >11.4 (p ≤ 0.001).
Conclusions: Heartbeat analysis by the time-dependent nonlinear PD2i algorithm is comparatively the superior test.
Keywords: autonomic nervous system, regulatory systems, electrophysiology, heart rate variability, sudden cardiac death, ventricular arrhythmias, ECG, HRV, PD2i, nonlinear, nonlinear, chaos
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