Key result
Multifractal HRV algorithm differentiates SCD from CHF with ~84% accuracy minutes before the event.
Why the study?
Autonomic system changes occur in both SCD and CHF patients, leading to feature overlap in cardiac signals that challenges SCD prediction algorithms.
Can a multifractal-based method using HRV signals accurately differentiate subjects with sudden cardiac death from patients with congestive heart failure?
Can a multifractal-based method using HRV signals accurately differentiate subjects with sudden cardiac death from patients with congestive heart failure?
A novel multifractal-based algorithm using HRV signals can predict sudden cardiac death and differentiate it from congestive heart failure with 84% accuracy 26 minutes prior to the event.
May aid imminent SCD identification via HRV; leaves open prospective validation before clinical adoption.
Many of the used features in sudden cardiac death (SCD) classification algorithms are based on features present in the autonomic system. However, changes in the autonomic system occur in both SCD subjects and patients with congestive heart failure (CHF). Therefore, many overlaps are observed in the features extracted from the cardiac signals of these two groups. To solve this challenge, this paper studies the changes in the multifractal dimension in patients with SCD and compares it with the subjects with CHF using the heart rate variability (HRV) signal processing. For this purpose, HRV signals are initially extracted, and their four sub-signals are determined using the empirical mode decomposition (EMD) method. Afterward, the instant amplitude of each sub-signal obtained in the previous step is calculated using the Teager energy method; thus, new signals are generated through the utilization of these instant amplitudes. Subsequently, modifications in each new signal’s fractal dimensions are obtained using the multifractal detrended fluctuation analysis (MF-DFA) method. The appropriate features are selected using the t-test method and are applied to the support vector machine algorithm as input data. The proposed algorithm can differentiate the signal of SCD subjects with an average accuracy of 84.08% in 26[Formula: see text]min prior to the event.
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Dorostghol et al. (2023) studied Sudden cardiac death and congestive heart failure. Multifractal-based prediction method using HRV signals was evaluated on Differentiation of SCD signals from CHF signals (accuracy). A multifractal-based algorithm using heart rate variability signals differentiated sudden cardiac death from congestive heart failure with an average accuracy of 84.08% 26 minutes prior to the event.
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