Effectiveness of the heartbeat interval error and compensation method on heart rate variability analysis

A virtual reality experiment to study pedestrian perception of future street scenarios

The current allocation of street space is based on expected vehicular peak-hour flows. Flexible and adaptive use of this space can respond to changing needs. To evaluate the acceptability of flexible street layouts, several urban environments were designed and implemented in virtual reality. Participants explored these designs in immersive virtual reality in a [Formula: see text] mixed factorial experiment, in which we analysed self-reported, behavioural and physiological responses from participants. Distinct communication strategies were varied between subjects. Participants' responses reveal a preference for familiar solutions. Unconventional street layouts are less preferred, perceived as unsafe and cause a measurably greater stress response. Furthermore, information provision focusing on comparisons lead participants to focus primarily on the drawbacks, instead of the advantages of novel scenarios. When being able to freely express thoughts and opinions, participants are focused more on the impact of space design on behaviour rather than the objective physical features themselves. Especially, this last finding suggests that it is vital to develop new street scenarios in an inclusive and democratic way: the success of innovating urban spaces depends on how well the vast diversity of citizens' needs is considered and met.

Machine Learning Techniques for the Performance Enhancement of Multiple Classifiers in the Detection of Cardiovascular Disease from PPG Signals

Photoplethysmography (PPG) signals are widely used in clinical practice as a diagnostic tool since PPG is noninvasive and inexpensive. In this article, machine learning techniques were used to improve the performance of classifiers for the detection of cardiovascular disease (CVD) from PPG signals. PPG signals occupy a large amount of memory and, hence, the signals were dimensionally reduced in the initial stage. A total of 41 subjects from the Capno database were analyzed in this study, including 20 CVD cases and 21 normal subjects. PPG signals are sampled at 200 samples per second. Therefore, 144,000 samples per patient are available. Now, a one-second-long PPG signal is considered a segment. There are 720 PPG segments per patient. For a total of 41 subjects, 29,520 segments of PPG signals are analyzed in this study. Five dimensionality reduction techniques, such as heuristic- (ABC-PSO, cuckoo clusters, and dragonfly clusters) and transformation-based techniques (Hilbert transform and nonlinear regression) were used in this research. Twelve different classifiers, such as PCA, EM, logistic regression, GMM, BLDC, firefly clusters, harmonic search, detrend fluctuation analysis, PAC Bayesian learning, KNN-PAC Bayesian, softmax discriminant classifier, and detrend with SDC were utilized to detect CVD from dimensionally reduced PPG signals. The performance of the classifiers was assessed based on their metrics, such as accuracy, performance index, error rate, and a good detection rate. The Hilbert transform techniques with the harmonic search classifier outperformed all other classifiers, with an accuracy of 98.31% and a good detection rate of 96.55%.

Influences of Intense Physical Effort on the Activity of the Autonomous Nervous System and Stress, as Measured with Photoplethysmography

Background: The autonomic nervous system, which is composed of the sympathetic and parasympathetic nervous system, is closely related to the cardiovascular system. The temporal variation between each of the intervals between the consecutive “R” waves of an electrocardiogram is known as heart rate variability. Depending on the type of activity, both systems can be activated, and also influence the interval between “R” waves. Currently, with advancements in technology and electronic devices, photoplethysmography is used. Photoplethysmography detects changes in the intensity of reflected light that allow differentiation between systole and diastole and, therefore, determines the heart rate, its frequency and its variations. In this way, changes in the autonomic nervous system can be detected by devices such as the Max Pulse®. Objective: To determine whether the information provided by Max Pulse® on autonomic balance and stress is modified after intense physical exercise, thereby determining whether there is a relationship with body composition, and also whether there are differences with respect to gender. Materials and Methods: Fifty-three runners (38.9% female) with a mean age of 31.3 ± 8.1 years participated in the study. Two measurements (before and after intense physical effort) were performed with the Max Pulse® device. The flotoplethysmography measurement lasted 3 min, and was performed in the supine position. The exercise test was performed on a treadmill. It was initiated at a speed of 6 and 7 km/h for women and men, respectively. Subjects indicated the end of the test by making a hand gesture when unable to continue the test. Results: Autonomic nervous system activity and mental stress values decreased significantly (p < 0.05) in men and women, while autonomic nervous system balance decreased only in women. Physical stress increased (p < 0.05) in both sexes. Conclusions: Intense exercise causes changes in variables that assess autonomic nervous system balance and stress, as measured by a device based on photoplethysmography. The changes are evident in both sexes, and are not related to body composition.

Modified photoplethysmography signal processing and analysis procedure for obtaining reliable stiffness index reflecting arteriosclerosis severity

Objective.This study aimed to describe a modified photoplethysmography (PPG) signal processing and analysis procedure to obtain a more reliable arterial stiffness index (SI).Approach.Three parameters were used to assess the PPG signal quality without prominent diastolic waves, which are similar to a sinusoidal waveform shape. The first parameter, sinusoidal ratio (S-value), was based on frequency-domain analysis: a higher S-value indicated the presence of PPG pulse wave with unapparent diastolic peak. The second parameter was the time difference between systolic peak-to-diastolic peak and the systolic peak-to-dicrotic notch. The third parameter was the percentage of sin-like waveform in the PPG signals. The applicability of these parameters was demonstrated in 40 participants, including 11 with apparent diastolic peaks in the PPG signals and 29 with unapparent diastolic peaks.Main results.An S-value of >3.5 indicated apparent diastolic peaks in the PPG signals. In addition, a systolic peak-to-diastolic peak time difference >80% and a sin-like waveform >55% may be associated with severity of vascular aging.Significance.These parameters successfully detected low-quality PPG signals with unapparent diastolic waveform before SI calculation, thereby ensuring the accuracy of subsequent evaluation of cardiovascular-related disease and clinical risk stratification.