Biomedizinische Technik. Biomedical engineering
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We evaluated the role played by the autonomic nervous system in producing non-linear dynamics in short heart period variability (HPV) series recorded in healthy young humans. Non-linear dynamics are detected using an index of predictability based on a local non-linear predictor and a surrogate data approach. Different types of surrogates are utilized: (i) phase-randomized Fourier-transform based (FT) data; (ii) amplitude-adjusted FT (AAFT) data; and (iii) iteratively refined AAFT (IAAFT) data of two types (IAAFT-1 and IAAFT-2). ⋯ Experimental protocols activating the sympathetic or parasympathetic nervous system did not produce non-linear dynamics. In contrast, paced respiration, especially at slow breathing rates, elicited significantly non-linear dynamics. Therefore, in short-term HPV ( approximately 300 beats) the use of non-linear models is not supported by the data, except under conditions whereby the subject is constrained to a slow respiratory rate.
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We assessed the clinical correlates of a comprehensive set of non-linear heart rate variability (HRV) indices computed from 24-h Holter recordings for 200 stable chronic heart failure (CHF) patients [median age (lower quartile, upper quartile) 54 (47, 58) years, LVEF 23% (19%, 28%)]. A total of 19 non-linear indices belonging to six major families, namely symbolic dynamics, entropy, empirical mode decomposition, fractality-multifractality, unpredictability and Poincaré plots, were considered. ⋯ Our results demonstrate the existence of selective links between non-linear indexes of HRV and the clinical status and functional impairment of CHF patients. This indicates that further studies should be designed to investigate the physiopathological mechanisms involved in such links.
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Current alarm systems in intensive care units create a very high rate of false positive alarms because most of them simply compare physiological measurements to fixed thresholds. An improvement can be expected when the actual measurements are replaced by smoothed estimates of the underlying signal. ⋯ Alternative approaches are needed to extract the relevant information from the data, i.e., the underlying signal of the monitored variables and the relevant patterns of change, such as abrupt shifts and trends. This article reviews recent research on filter-based online signal extraction methods designed for application in intensive care.
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EEG parameters for assessment of depth of anaesthesia are typically based on different signal processing methods, such as spectral and complexity analysis. In the present study, the parameters investigated (WSMF, qWSMF, approximate entropy and Lempel-Ziv complexity) do not correlate monotonically to depth of anaesthesia. To obtain this correlation, parameters are combined based on fuzzy inference, whereby each parameter only operates in a specific range. Fuzzy inference seems to be a suitable approach, as the indicator designed separates wakefulness from unconsciousness as well as the best single parameter does and correlates to the depth of anaesthesia.
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In recent years the analysis of heart rate variability (HRV) has become a suitable method for characterizing autonomous cardiovascular regulation. The aim of this study was to investigate the differences in HRV estimated from continuous blood pressure (BP) measurement by different methods in comparison to electrocardiogram (ECG) signals. The beat-to-beat intervals (BBI) were simultaneously extracted from the ECG and blood pressure of 9 cardiac patients (10 min, Colin system, 1000-Hz sampling frequency). ⋯ Besides measurement noise, respiratory modulation and pulse transit time play an important role in determining BBI. The slope detection method applied to ECG should be preferred, because it is more robust as regards morphological changes in the signals, as well as physiological properties. As the ECG is not recorded in most animal studies, distal pulse wave measurement in combination with correlation or slope detection may be considered an acceptable alternative.