Differences in the Course of Physiological Functions and in Subjective Evaluations in Connection With Listening to the Sound of a Chainsaw and to the Sounds of a Forest.

We explored differences in the course of physiological functions and in the subjective evaluations in response to listening to a 7-min recording of the sound of a chainsaw and to the sounds of a forest. A Biofeedback 2000x-pert apparatus was used for continual recording of the following physiological functions in 50 examined persons: abdominal and thoracic respiration and their amplitude and frequency, electrodermal activity (skin conductance level), finger skin temperature, heart rate (pulse, blood volume pulse and blood volume pulse amplitude) and heart rate variability (HRV). The group of 25 subjects listening to the sound of a chainsaw exhibited significantly lower values of blood volume pulse amplitude, lower values in peak alpha frequency HRV and higher values in peak high-frequency HRV. In the time interval from 80 s to 209 s, in which the two groups showed the greatest differences, lower values of blood volume pulse were also recorded while listening to the sound of a chainsaw. Listening to the sound of a chainsaw is associated with a greater feeling of fatigue and higher tension, while listening to the sounds of a forest is even considered to elicit feelings of improved learning abilities. The assumption that listening to the sound of a chainsaw results in higher defense arousal was confirmed. The greater variability which is exhibited by a majority of physiological functions while listening to the forest sounds may also be an innovative finding. It seems that there are two types of arousal (sympathetic and parasympathetic) following from correlations between physiological functions and subjective assessment. Low values of blood volume pulse amplitude are especially important from the health perspective. They correspond to the amount of vasoconstriction which occurs in the endothelial dysfunction related to increased mortality, incidence of myocardial infarction, leg atherosclerosis and topically to COVID-19.

Di-4-ANEPPS Modulates Electrical Activity and Progress of Myocardial Ischemia in Rabbit Isolated Heart.

AIMS: Although voltage-sensitive dye di-4-ANEPPS is a common tool for mapping cardiac electrical activity, reported effects on electrophysiological parameters are rather. The main goals of the study were to reveal effects of the dye on rabbit isolated heart and to verify, whether rabbit isolated heart stained with di-4-ANEPPS is a suitable tool for myocardial ischemia investigation. METHODS AND RESULTS: Study involved experiments on stained (n = 9) and non-stained (n = 11) Langendorff perfused rabbit isolated hearts. Electrophysiological effects of the dye were evaluated by analysis of various electrogram (EG) parameters using common paired and unpaired statistical tests. It was shown that staining the hearts with di-4-ANEPPS leads to only short-term sporadic prolongation of impulse conduction through atria and atrioventricular node. On the other hand, significant irreversible slowing of heart rate and ventricular conduction were found in stained hearts as compared to controls. In patch clamp experiments, significant inhibition of sodium current density was observed in differentiated NG108-15 cells stained by the dye. Although no significant differences in mean number of ventricular premature beats were found between the stained and the non-stained hearts in ischemia as well as in reperfusion, all abovementioned results indicate increased arrhythmogenicity. In isolated hearts during ischemia, prominent ischemic patterns appeared in the stained hearts with 3-4 min delay as compared to the non-stained ones. Moreover, the ischemic changes did not achieve the same magnitude as in controls even after 10 min of ischemia. It resulted in poor performance of ischemia detection by proposed EG parameters, as was quantified by receiver operating characteristics analysis. CONCLUSION: Our results demonstrate significant direct irreversible effect of di-4-ANEPPS on spontaneous heart rate and ventricular impulse conduction in rabbit isolated heart model. Particularly, this should be considered when di-4-ANEPPS is used in ischemia studies in rabbit. Delayed attenuated response of such hearts to ischemia might lead to misinterpretation of obtained results.

Testing of features for fatigue detection in EOG.

The article deals with the testing of features for fatigue detection in electrooculography (EOG) records. An optimal methodology for EOG signal acquisition is described; the Biopac data acquisition system was used. EOG signals were being recorded while 10 volunteers were watching prepared scenes. Three scenes were created for this purpose - a rotating ball, a video of driving a car, and a cross. Recorded EOG signals were processed and 20 features were extracted. The features involved blinks, slow eye movement (SEM), rapid eye movement (REM), eye instability, magnitude, and periodicity. These features were statistically tested and discussed in terms of fatigue detection ability. Some of the features were compared with published results. Finally, the best features - fatigue indicators - were selected.

Effect of increased left ventricle mass on ischemia assessment in electrocardiographic signals: rabbit isolated heart study.

BACKGROUND: Detailed quantitative analysis of the effect of left ventricle (LV) hypertrophy on myocardial ischemia manifestation in ECG is still missing. The associations between both phenomena can be studied in animal models. In this study, rabbit isolated hearts with spontaneously increased LV mass were used to evaluate the effect of such LV alteration on ischemia detection criteria and performance. METHODS: Electrophysiological effects of increased LV mass were evaluated on sixteen New Zealand rabbit isolated hearts under non-ischemic and ischemic conditions by analysis of various electrogram (EG) parameters. To reveal hearts with increased LV mass, LV weight/heart weight ratio was proposed. Standard paired and unpaired statistical tests and receiver operating characteristics analysis were used to compare data derived from different groups of animals, monitor EG parameters during global ischemia and evaluate their ability to discriminate between unchanged and increased LV as well as non-ischemic and ischemic state. RESULTS: Successful evaluation of both increased LV mass and ischemia is lead-dependent. Particularly, maximal deviation of QRS and area under QRS associated with anterolateral heart wall respond significantly to even early phase (the 1st-3rd min) of ischemia. Besides ischemia, these parameters reflect increased LV mass as well (with sensitivity reaching approx. 80%). However, the sensitivity of the parameters to both phenomena may lead to misinterpretations, when inappropriate criteria for ischemia detection are selected. Particularly, use of cut-off-based criteria defined from control group for ischemia detection in hearts with increased LV mass may result in dramatic reduction (approx. 15%) of detection specificity due to increased number of false positives. Nevertheless, criteria adjusted to particular experimental group allow achieving ischemia detection sensitivity of 89-100% and specificity of 94-100%, respectively. CONCLUSIONS: It was shown that response of the heart to myocardial ischemia can be successfully evaluated only when taking into account heart-related factors (such as LV mass) and other methodological aspects (such as recording electrodes position, selected EG parameters, cut-off criteria, etc.). Results of this study might be helpful for developing new clinical diagnostic strategies in order to improve myocardial ischemia detection in patients with LV hypertrophy.

Sleep scoring using artificial neural networks.

Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved - next to other classification methods - by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present.