A new algorithm for quadratic sample entropy optimization for very short biomedical signals: application to blood pressure records.

This paper describes a new method to optimize the computation of the quadratic sample entropy (QSE) metric. The objective is to enhance its segmentation capability between pathological and healthy subjects for short and unevenly sampled biomedical records, like those obtained using ambulatory blood pressure monitoring (ABPM). In ABPM, blood pressure is measured every 20-30 min during 24h while patients undergo normal daily activities. ABPM is indicated for a number of applications such as white-coat, suspected, borderline, or masked hypertension. Hypertension is a very important clinical issue that can lead to serious health implications, and therefore its identification and characterization is of paramount importance. Nonlinear processing of signals by means of entropy calculation algorithms has been used in many medical applications to distinguish among signal classes. However, most of these methods do not perform well if the records are not long enough and/or not uniformly sampled. That is the case for ABPM records. These signals are extremely short and scattered with outliers or missing/resampled data. This is why ABPM Blood pressure signal screening using nonlinear methods is a quite unexplored field. We propose an additional stage for the computation of QSE independently of its parameter r and the input signal length. This enabled us to apply a segmentation process to ABPM records successfully. The experimental dataset consisted of 61 blood pressure data records of control and pathological subjects with only 52 samples per time series. The entropy estimation values obtained led to the segmentation of the two groups, while other standard nonlinear methods failed.

Customization of entropy estimation measures for human arterial hypertension records segmentation.

This paper describes a new application of the recently developed Coefficient of Sample Entropy (CosEn) measure. This entropy estimator is specially suited for cases where the length of the time series is extremely short. CosEn has already been used successfully to characterize and detect atrial fibrillation, using as few as 12 heartbeats. We have customized the methodology employed for heartbeat interval series to blood pressure hypertensive (BPHT) human records. Little can be found about BPHT records and its nonlinear regularity analysis. The method described in this paper provides a good segmentation between control and pathologic groups, based on the corresponding labeled BPHT records. The experimental dataset was drawn from the available records at the Hypertension Unit of the University Hospital of Mostoles, in Spain. The hypertension related variables studied were systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean blood pressure (MBP). The hypothesis test yielded the following results in each case: acceptance probability of 0 for SBP, 0.005 for DBP and 0 for MBP. The confidence intervals for the three variables were nonoverlapping.

Characterization of detrended fluctuation analysis in the context of glycemic time series.

There is a growing interest in the analysis of hyperglycemia and its relationship with other pathologies. The level of glucose in blood is regulated by the flux/reflux and controlled by hyperglycemia hormones and hypoglycemic insulin. Glycemic profiles are characterized by a nonlinear and nonstationary behavior but also influenced by circadian rhythms and patient daily routine which introduce quasi-periodic trends into them. This type of signals are commonly analyzed by Detrended Fluctuation Analysis (DFA) which states that the control system in charge of regulating the glucose level usually holds a long-range negative correlation. But there is an inconsistency about the windowing lengths, as no standard or rules are set. This work studies the influence of the windowing length sequence, and shows that there is a need for selecting the optimal values in order to obtain a good differentiation between different groups, and these values are somehow determined by signal characteristics.

Comparative study between Sample Entropy and Detrended Fluctuation Analysis performance on EEG records under data loss.

This study compares two signal entropy measures, Sample Entropy (SampEn) and Detrended Fluctuation Analysis (DFA) over real EEG signals after a randomized sample removal. Both measures have demonstrated their ability to discern between, among others: control and pathologic EEG signals, seizure free or not, control and opened eyes EEG, and side of brain signals. Results show that SampEn behaves better when analyzing control signals, while DFA provides better segmentation results between epileptic signals, in the context of sample loss, particularly when discerning between seizure and seizure free signal intervals.