Early Diagnosis of Problems Related to the Self-Organization of the Cardiovascular System Based on the Interplay between RR and JT Cardiac Intervals.

The dynamics of the collapse of complexity observable in the performance of the cardiovascular system during the stress test is investigated in this paper. For this purpose, the interplay between the RR and JT cardiac intervals is measured and assessed for each participant. This case study involves a modest sample size of eight individuals with normal and elevated blood pressure. Although it is anticipated that the interaction between the RR and JT intervals is rather complex during the stress test, the existence of interpretable time delays between those cardiac intervals is demonstrated using the time delayed patterns algorithm. The assessment of the cardiovascular mobilization taking place during the stress test is also an integral part of this study. The velocity of adaptation index Ad and the newly formulated modified adaptation index Ar (computed only for the recovery phase) are used to quantify the healthy mobilization of the cardiovascular system for each participant. The time frequency analysis of the difference signal between the RR and JT intervals is used to quantify the collapse of complexity around the load termination point. Finally, a semi-gauge indication tool is constructed to assess the overall goodness of the self-organization of the cardiovascular system during the stress test.

The Mathematical Characterization of the Complexity Matching during a Healing Circle Meditation.

The aim of the study is to evaluate the complexity matching between the HRVs of the group of Healers and the Healee during the various stages of the meditation protocol by employing a novel mathematical approach based on the H-rank algorithm. The complexity matching of heart rate variability is assessed before and during a heart-focused meditation in a close non-contact healing exercise. The experiment was conducted on a group of individuals (eight Healers and one Healee) throughout the various phases of the protocol over a ~75-minute period. The HRV signal for the cohort of individuals was recorded using high resolution HRV recorders with internal clocks for time synchronization. The Hankel transform (H-rank) approach was employed to reconstruct the real-world complex time series in order to measure the algebraic complexity of the heart rate variability and to assess the complexity matching between the reconstructed H-rank of the Healers and Healee during the different phases of the protocol. The integration of the embedding attractor technique was used to aid in the visualization of reconstructed H-rank in state space across the various phases. The findings demonstrate the changes in the degree of reconstructed H-rank (between the Healers and the Healee) during the heart-focused meditation healing phase by employing mathematically anticipated and validated algorithms. It is natural and thought-provoking to contemplate the mechanisms causing the complexity of the reconstructed H-rank to come closer; it can be explicitly stated that the purpose of the study is to communicate a clear idea that the H-rank algorithm is capable of registering subtle changes in the healing process, and that there was no intention of delving deep to uncover the mechanisms involved in the HRV matching. Therefore, the latter might be a distinct goal of future research.

Statistical analysis of the university sustainability in the higher education institution a case study from the Khyber Pakhtunkhwa province in Pakistan.

Educational institutions can incorporate the idea of sustainability at the grass root level for any society. This study is part of an effort to get insight into the campus sustainability in one of the Higher Education Institution (HEI) in the Khyber Pakhtunkhwa region of Pakistan. Aim is to investigation university students' and faculty members insight regarding sustainability. Thus, questionnaire-based survey followed by statistical inference was conducted for the potential outcomes. The questionnaire is comprised of 24 questions, 05 of which are on demographics and the remaining 19 are about sustainability. The sustainability related questions focused mostly on the respondents' knowledge, understanding, and interest in sustainability. A handful of the other questions in the questionnaire were tailored to the university input to achieve sustainability. The dataset is manipulated with basic statistical and computational approaches, and the results are analyzed using mean values. The mean values are further classified into flag values of 0 and 1. Flag value 1 indicates a good marker of the received response, while flag value 0 indicates the least amount of information included in responses. The results show that respondents' knowledge, awareness, interest, and engagement in sustainability are significantly sufficient, as we obtained a flag value of 1 for all questions about sustainability. The study's findings, on the other hand, indicated that the institution is lagging in terms of supporting, disseminating, and implementing campus-wide sustainability-related activities. This study is one of the first initiatives to provide a baseline dataset and substantial information to go a step further in achieving the bottom-line target of being and acting sustainable in the HEI.

Detection of Atrial Fibrillation Episodes based on 3D Algebraic Relationships between Cardiac Intervals.

In this study, the notion of perfect matrices of Lagrange differences is employed to detect atrial fibrillation episodes based on three ECG parameters (JT interval, QRS interval, RR interval). The case study comprised 8 healthy individuals and 7 unhealthy individuals, and the mean and standard deviation of age was 65.84 ± 1.4 years, height was 1.75 ± 0.12 m, and weight was 79.4 ± 0.9 kg. Initially, it was demonstrated that the sensitivity of algebraic relationships between cardiac intervals increases when the dimension of the perfect matrices of Lagrange differences is extended from two to three. The baseline dataset was established using statistical algorithms for classification by means of the developed decision support system. The classification helps to determine whether the new incoming candidate has indications of atrial fibrillation or not. The application of probability distribution graphs and semi-gauge indicator techniques aided in visualizing the categorization of the new candidates. Though the study's data are limited, this work provides a strong foundation for (1) validating the sensitivity of the perfect matrices of Lagrange differences, (2) establishing a robust baseline dataset for supervised classification, and (3) classifying new incoming candidates within the classification framework. From a clinical standpoint, the developed approach assists in the early detection of atrial fibrillation in an individual.

The Complexity of the Arterial Blood Pressure Regulation during the Stress Test.

In this study, two categories of persons with normal and high ABP are subjected to the bicycle stress test (9 persons with normal ABP and 10 persons with high ABP). All persons are physically active men but not professional sportsmen. The mean and the standard deviation of age is 41.11 ± 10.21 years; height 178.88 ± 0.071 m; weight 80.53 ± 10.01 kg; body mass index 25.10 ± 2.06 kg/m2. Machine learning algorithms are employed to build a set of rules for the classification of the performance during the stress test. The heart rate, the JT interval, and the blood pressure readings are observed during the load and the recovery phases of the exercise. Although it is obvious that the two groups of persons will behave differently throughout the bicycle stress test, with this novel study, we are able to detect subtle variations in the rate at which these changes occur. This paper proves that these differences are measurable and substantial to detect subtle differences in the self-organization of the human cardiovascular system. It is shown that the data collected during the load phase of the stress test plays a more significant role than the data collected during the recovery phase. The data collected from the two groups of persons are approximated by Gaussian distribution. The introduced classification algorithm based on the statistical analysis and the triangle coordinate system helps to determine whether the reaction of the cardiovascular system of a new candidate is more pronounced by an increased heart rate or an increased blood pressure during the stress test. The developed approach produces valuable information about the self-organization of human cardiovascular system during a physical exercise.