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Recently we have reported that a previously unidentified V-trough of sympathetic nerve activity (SNA) is a potential precursor of lethal cardiac events by examining 24-hour ambulatory electrocardiograms in which such an event was recorded by chance. The V-trough was marked by three consecutive compartments: a small variation lasting two hours, an abrupt descent lasting 30 min and a sharp ascent for 40 min. We reported that the hemodynamics consisting of heart rate, SNA and blood pressure (BP) is modeled excellently by the modification of a known chaotic electrical circuit, Chua circuit. A V-trough of SNA appears by increasing the resistive element between SNA and BP in the circuit, which corresponds to the impaired regulation of BP by SNA. This finding is consistent with an acknowledged finding that the depressed baroreflex (reflex of BP by SNA) may trigger a lethal arrhythmia.

Sudden cardiac death (SCD) is deemed as one of the main causes of death in humans. Therefore, the prediction of an SCD event will help people to receive timely treatment, allowing saving their life. In this sense, this paper introduces a new methodology based on the adroit fusion of fractal dimension (FD) algorithms and a fuzzy logic system for predicting an SCD event automatically. Five FD implementations are investigated in this work: Katz’s FD, Sevcik’s FD, Box’s FD, Higuchi’s FD, and Petrosian’s FD, in order to evaluate the geometrical complexity in electrocardiogram signals of 20 patients with SCD and 18 patients with a normal cardiac rhythm offered by Boston’s Beth Israel Hospital. The results indicate that the FD-based methodology can predict an SCD event up to 60min before the onset, reaching an accuracy of 91.54%.

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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 26min prior to the event.

Purpose: Combination of ST segment resolution and baseline Troponin I (cTnI) measurement might be a predictor of death at 1 year in patients with acute myocardial infarction.

Methods: Six hundred and fifty eight patients between 23-92 years old (mean 57.3±8.2 years) with ST elevation acute myocardial infarction were enrolled in this study. According to cTnI measurements at admission and ST Res at 60 minutes after percutaneous coronary intervention; patients were divided into 4 groups [Group Al: (-) cTnI and ST Res, Group A2: (-) cTnI and no ST Res, Group B1: (+) cTnI and no STRes, Group B2:(+) cTnI and STRes]. Patients with LBBB, lack of ST monitoring result, and cardiogenic shock prior admission were excluded.

Results: There were no statistically significant differences between two groups at baseline. Non fatal ischemic events and mortality incidence were similar in all groups during index hospitalisation and at 1 month. Patients in Group A1 had significantly less mortality rate than Group B1 at 6 months, and at 1 year (RR 0.55, p<0.001).

Conclusion: Both ST Res at 60 minutes and baseline cTnI measurements are strong and independent predictors of ischemic events at one year. Use of their combination in ST Elevation Acute Myocardial Infarction predicts the most the mortality, and more powerful to identify patients at moderate to high risk.