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The feedback regulation of blood pressure and heart rate is an important indicator of human autonomic function usually assessed by baroreflex sensitivity (BRS). We suggest a new method yielding a higher temporal resolution than standard BRS methods. Our approach is based on a regression analysis of the first differences of inter-heartbeat intervals and blood pressure values. Data are recorded from 23 patients with hypertension and sleep apnoea, 22 patients with diabetes mellitus and 23 healthy subjects. Using the proposed method for 3 min data segments, we obtain average regression coefficients of 9.1 and 3.5 ms mmHg(-1) for healthy subjects in supine and orthostatic positions, respectively. In patients with hypertension, we find them to be 3.8 and 2.6 ms mmHg(-1). The diabetes patients with and without autonomic neuropathy are characterized by 3.1 and 6.1 ms mmHg(-1) in the supine position compared with 1.7 and 3.3 ms mmHg(-1) in the orthostatic position. The results are highly correlated with conventional BRS measures; we find r > 0.9 for the dual sequence method. Therefore, we suggest that the new method can quantify BRS. It is superior in distinguishing healthy subjects from patients both in supine and orthostatic positions for short-term recordings. It is suitable for non-stationary data and has good reproducibility. Besides, we cannot exclude that other regulatory mechanisms than BRS may also contribute to the regression coefficients between the first differences.

Despite recent success in advanced signal analysis technologies, simple and universal methods are still of interest in a variety of applications. Wearable devices including biomedical monitoring and diagnostic systems suitable for long-term operation are prominent examples, where simple online signal analysis and early event detection algorithms are required. Here we suggest a simple and universal approach to the online detection of events represented by abrupt bursts in long-term observational data series. We show that simple gradient-based transformations obtained as a product of the signal and its derivative lead to the improved accuracy of the online detection of any significant bursts in the observational data series irrespective of their particular shapes. We provide explicit analytical expressions characterizing the performance of the suggested approach in comparison with the conventional solutions optimized for particular theoretical scenarios and widely utilized in various signal analysis applications. Moreover, we estimate the accuracy of the gradient-based approach in the exact positioning of single ECG cycles, where it outperforms the conventional Pan-Tompkins algorithm in its original formulation, while exhibiting comparable detection efficacy. Finally, we show that our approach is also applicable to the comparative analysis of lanes in electrophoretic gel images widely used in life sciences and molecular diagnostics like restriction fragment length polymorphism (RFLP) and variable number tandem repeats (VNTR) methods.

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