Photoplethysmography as a noninvasive surrogate for microneurography in measuring stress-induced sympathetic nervous activation - A machine learning approach.

The sympathetic nervous system (SNS) is essential for the body's immediate response to stress, initiating physiological changes that can be measured through sympathetic nerve activity (SNA). While microneurography (MNG) is the gold standard for direct SNA measurement, its invasive nature limits its practical use in clinical settings. This study investigates the use of multi-wavelength photoplethysmography (PPG) as a non-invasive alternative for SNA measurement.

Nonlinear Features from Multi-Modal Signals for Continuous Stress Monitoring.

Continuous monitoring of stress in individuals during their daily activities has become an inevitable need in present times. Unattended stress is a silent killer and may lead to fatal physical and mental disorders if left unidentified. Stress identification based on individual judgement often leads to under-diagnosis and delayed treatment possibilities.

Measurement of stress-induced sympathetic nervous activity using multi-wavelength PPG.

The onset of stress triggers sympathetic arousal (SA), which causes detectable changes to physiological parameters such as heart rate, blood pressure, dilation of the pupils and sweat release. The objective quantification of SA has tremendous potential to prevent and manage psychological disorders. Photoplethysmography (PPG), a non-invasive method to measure skin blood flow changes, has been used to estimate SA indirectly.

Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling.

Fetal arrhythmias are a life-threatening disorder occurring in up to 2% of pregnancies. If identified, many fetal arrhythmias can be effectively treated using anti-arrhythmic therapies. In this paper, we present a novel method of detecting fetal arrhythmias in short length non-invasive fetal electrocardiography (NI-FECG) recordings.

Performance Analysis of Entropy Methods in Detecting Epileptic Seizure from Surface Electroencephalograms.

Physiological signals like Electrocardiography (ECG) and Electroencephalography (EEG) are complex and nonlinear in nature. To retrieve diagnostic information from these, we need the help of nonlinear methods of analysis. Entropy estimation is a very popular approach in the nonlinear category, where entropy estimates are used as features for signal classification and analysis.

Entropy Profiling: A Reduced-Parametric Measure of Kolmogorov-Sinai Entropy from Short-Term HRV Signal.

Entropy profiling is a recently introduced approach that reduces parametric dependence in traditional Kolmogorov-Sinai (KS) entropy measurement algorithms. The choice of the threshold parameter of vector distances in traditional entropy computations is crucial in deciding the accuracy of signal irregularity information retrieved by these methods. In addition to making parametric choices completely data-driven, entropy profiling generates a complete profile of entropy information as against a single entropy estimate (seen in traditional algorithms).

Modified Distribution Entropy as a Complexity Measure of Heart Rate Variability (HRV) Signal.

The complexity of a heart rate variability (HRV) signal is considered an important nonlinear feature to detect cardiac abnormalities. This work aims at explaining the physiological meaning of a recently developed complexity measurement method, namely, distribution entropy (DistEn), in the context of HRV signal analysis. We thereby propose modified distribution entropy (mDistEn) to remove the physiological discrepancy involved in the computation of DistEn.

Entropy Profiling to Detect ST Change in Heart Rate Variability Signals.

Elevation or depression in an electrocardiographic ST segment is an important indication of cardiac Ischemia. Computer-aided algorithms have been proposed in the recent past for the detection of ST change in ECG signals. Such algorithms are accompanied by difficulty in locating a functional ST segment from the ECG.

Cross Entropy Profiling to Test Pattern Synchrony in Short-Term Signals.

Examining nonlinear bi-variate time series for pattern synchrony has been largely carried out by the cross sample entropy measure, X-SampEn, which is highly bound by parametric restrictions. Threshold parameter r is the one that limits X-SampEn estimations most adversely. An inappropriate r choice leads to erroneous synchrony detection, even for the case of X-SampEn analysis on simple synthetically generated signals like the MIX(P) process.

Multiscale entropy profiling to estimate complexity of heart rate dynamics.

In the analysis of signal regularity from a physiological system such as the human heart, Approximate entropy (H_{A}) and Sample entropy (H_{S}) have been the most popular statistical tools used so far. While studying heart rate dynamics, it nevertheless becomes more important to extract information about complexities associated with the heart, rather than the regularity of signal patterns produced by it. A complex physiological system does not necessarily produce irregular signals and vice versa.

Understanding Irregularity Characteristics of Short-Term HRV Signals Using Sample Entropy Profile.

Sample entropy (), a popularly used "regularity analysis" tool, has restrictions in handling short-term segments (largely ) of heart rate variability (HRV) data. For such short signals, the estimate either remains undefined or fails to retrieve "accurate" regularity information. These limitations arise due to the extreme dependence of on its functional parameters, in particular the tolerance .

Secondary measures of regularity from an entropy profile in detecting Arrhythmia.

The most recently introduced concept of a `complete entropy profile' is a non-parametric (with regard to tolerance r) approach of entropy estimation. Given a signal, on generating its complete entropy profile, numerous secondary measures of regularity can be derived from the same. These profile based measures are seen to outperform the traditional ApEn statistic (evaluated at a single r) in estimating signal regularity.

Stability, Consistency and Performance of Distribution Entropy in Analysing Short Length Heart Rate Variability (HRV) Signal.

Distribution entropy () is a recently developed measure of complexity that is used to analyse heart rate variability (HRV) data. Its calculation requires two input parameters-the embedding dimension , and the number of bins which replaces the tolerance parameter that is used by the existing approximation entropy () and sample entropy () measures. The performance of can also be affected by the data length .

Effect of embedding dimension on complexity measures in identifying Arrhythmia.

Entropy measures like Approximate entropy (ApEn) and Sample entropy (SampEn) are well established tools to analyze Heart Rate Variability (HRV) data. Critical parameters involved in these computations namely embedding dimension m and tolerance r are in most cases assumed to be 2 and 0.2*signal SD (standard devaition) respectively.

Influence of embedding dimension on distribution entropy in analyzing heart rate variability.

Distribution entropy (DistEn) is a recent measure of complexity that is used to analyze Heart Rate Variability (HRV) data. DistEn which is a function of data length N, number of bins M and embedding dimension m is known to be stable and consistent with respect to parameters N and M respectively. Also, (N, M) are known to have a combined effect in deciding performance of DistEn as a classification feature.

Effect of data length and bin numbers on distribution entropy (DistEn) measurement in analyzing healthy aging.

Complexity analysis of a given time series is executed using various measures of irregularity, the most commonly used being Approximate entropy (ApEn), Sample entropy (SampEn) and Fuzzy entropy (FuzzyEn). However, the dependence of these measures on the critical parameter of tolerance `r' leads to precarious results, owing to random selections of r. Attempts to eliminate the use of r in entropy calculations introduced a new measure of entropy namely distribution entropy (DistEn) based on the empirical probability distribution function (ePDF).

Distribution Entropy (DistEn): A complexity measure to detect arrhythmia from short length RR interval time series.

Heart rate complexity analysis is a powerful non-invasive means to diagnose several cardiac ailments. Non-linear tools of complexity measurement are indispensable in order to bring out the complete non-linear behavior of Physiological signals. The most popularly used non-linear tools to measure signal complexity are the entropy measures like Approximate entropy (ApEn) and Sample entropy (SampEn).