Influence of Nicotine from Diverse Delivery Tools on the Autonomic Nervous and Hormonal Systems

Background: Through measurements of the heart rate variability (HRV) accompanied by the pertinent biomarker assays, the effects of nicotine and byproducts derived from alternative nicotine delivery systems (ANDS) on the autonomic nervous system (ANS) and hormonal system have been investigated. Methods: HRV was studied in a group of volunteers (17 people), involving non-smokers, i.e., who never smoked before (11), ex-smokers (4) and active smokers (2). ANDS and smoking simulators, including regular, nicotine-free and electronic cigarettes; tobacco heating systems; chewing gums and nicotine packs of oral fixation (nic-packs), were used. Blood pressure, levels of stress hormones in saliva and catecholamines in the blood were also monitored. Results: HRV analysis showed relatively small changes in HRV and in the other studied parameters with the systemic use of nic-packs with low and moderate nicotine contents (up to 6 mg) compared to other ANDS. Conclusions: The HRV method is proven to be a promising technique for evaluation of the risks associated with smoking, dual use of various ANDS and studying the biomedical aspects of smoking cessation. Nic-packs are shown to be leaders in biological safety among the studied ANDS. A sharp surge in the activity of the sympathetic division of the ANS within the first minutes of the use of nicotine packs implies that nicotine begins to act already at very low doses (before entering the blood physically in any significant amount) through fast signal transmission to the brain from the nicotinic and taste buds located in the mouth area.

Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study

Most cancer patients exhibit autonomic dysfunction with attenuated heart rate variability (HRV) levels compared to healthy controls. This research aimed to create and evaluate a machine learning (ML) model enabling discrimination between cancer patients and healthy controls based on 5-min-ECG recordings. We selected 12 HRV features based on previous research and compared the results between cancer patients and healthy individuals using Wilcoxon sum-rank test. Recursive Feature Elimination (RFE) identified the top five features, averaged over 5 min and employed them as input to three different ML. Next, we created an ensemble model based on a stacking method that aggregated the predictions from all three base classifiers. All HRV features were significantly different between the two groups. SDNN, RMSSD, pNN50%, HRV triangular index, and SD1 were selected by RFE and used as an input to three different ML. All three base-classifiers performed above chance level, RF being the most efficient with a testing accuracy of 83%. The ensemble model showed a classification accuracy of 86% and an AUC of 0.95. The results obtained by ML algorithms suggest HRV parameters could be a reliable input for differentiating between cancer patients and healthy controls. Results should be interpreted in light of some limitations that call for replication studies with larger sample sizes.

Quantitative assessment of autonomic nervous system responses induced by graded training workloads in healthy elderly, through indices obtained from heart rate variability analysis

Background Quantitative assessment of individual body adaptability to physical training performed with the purposes of health maintenance is particularly necessary in the elderly age, to avoid the risk of overstrain induced by inappropriate exercises workload and physical stress. For that purpose, heart rate monitors and heart rate variability (HRV) analysis are nowadays commercially available. However, their reliability to guide individualized fitness training in elderly people needs to be tested, knowing that users might not have medical education. Objective To preliminary quantify autonomic nervous system (ANS) responses to graded physical effort and recovery in healthy elderly basing on the parasympathetic nervous system (PNSi), the sympathetic nervous system (SNSi) and the stress (STRi) indices, derived by short-term and time-varying HRV analysis. Methods ECG of a 75 healthy male subject was monitored, from April to November 2020, during three times/week training sessions with a professional bike–ergometer. Each session consisted of 10 minutes baseline rest, 5 minutes warm-up, 30 minutes work and 10 minutes recovery. According to age, the training workload was graded from low (65–75 watt/min), to moderate (75–85 watt/min), semi-intensive (85–95 watt/min) and intensive (95–110 watt/min). For this pilot study, ECG data of only 40 training sessions (10 sessions for each workload to evaluate reproducibility) were analyzed with Kubios Premium software (version 3.4.1), in the time (TD) and frequency (FD) domains, with nonlinear (NL) methods and with time-varying (TV) algorithms. Short-time HRV was calculated from 2-minutes intervals. The PNSi, SNSi and STRi induced by each workload were averaged and compared. Results Average values of PNSi, SNSi and STRi were significantly different (p<0.05) among training sessions carried out with different workloads (Table 1A) and among measurements obtained at rest, at every 5 minutes step of each 30 minutes training session, and at 1 and 5 minutes of recovery (Table 1B). Interestingly, the correlation between SNSi and STRi was strictly linear (R= 0,98), whereas that between PNSi and STRi was better fitted by a cubic function (R=0,82 with cubic vs 0.68 with linear function), when evaluated either as a function of the sessions' workloads (Figure 1A), or of four time-intervals of each training session (Figure 1B). PNSi and SNSi were inversely correlated, with cross-point at about 15 minutes of training and 75 watt/min workload. Conclusions The calculation of PNSi, SNSi and STRi from HRV analysis is an efficient method for quick and simplified quantitative assessment of dynamic ASN adaptation to effort-induced stress from HRV analysis. If confirmed, the method may be useful for safer and even remote monitoring of training/rehabilitation in elderly. However, more detailed evaluation of spectral and NL parameters may be necessary to interpret more complex patterns of abnormal cases. FUNDunding Acknowledgement Type of funding sources: None. Table 1 Figure 1

An Effective Deep Learning Model for Automated Detection of Myocardial Infarction Based on Ultrashort-Term Heart Rate Variability Analysis

Myocardial infarction (MI), usually termed as heart attack, is one of the main cardiovascular diseases that occur due to the blockage of coronary arteries. This blockage reduces the blood supply to heart muscles, and a prolonged deficiency of blood supply causes the death of heart muscles leading to a heart attack that may cause death. An electrocardiogram (ECG) is used to diagnose MI as it causes variations like ST-T changes in the recorded ECG. Manual inspection of these variations is a tedious task and also requires expertise as the variations produced by MI are often very short in duration with a low amplitude. Hence, these changes may be misinterpreted, leading to delayed diagnosis and appropriate treatment. Therefore, computer-aided analysis of ECG may help to detect MI automatically. In this study, a robust deep learning model is proposed to detect MI based on heart rate variability (HRV) analysis of ECG signals from a single lead. Ultrashort-term HRV analysis is performed to compute HRV analysis features from time-domain and frequency-domain parameters through power spectral density estimations. Nonlinear HRV parameters are also computed using Poincare Plot, Recurrence Analysis, and Detrended Fluctuation Analysis. A finely tuned customized artificial neural network (ANN) algorithm is applied on 23 HRV features for MI detection and classification. The K-fold validation method is used to avoid any biases in results and reported 99.1% accuracy, 100% sensitivity, 98.1% specificity, and 99.0% F1 for MI detection, whereas 98.85% accuracy, 97.40% sensitivity, 99.05% specificity, and 97.70% F1 score is achieved for classification. Furthermore, the ANN algorithm completed its execution in just 59 seconds that indicates the efficiency of the proposed ANN model. The overall performance in terms of computed evaluation matrices and execution time indicates the robustness and cost-effectiveness of the proposed methodology. Thus, the proposed model can be used for high-performance MI detection, even in wearable devices.

Alterations in Heart rate Variability in Blind Patients

Blindness affect the daily life activities and the causes and prevalence are different worldwide. This study aimed to investigate the pattern of the autonomic nervous system modulation on the heart in blind and normal vision subjects submitted acutely to low vision. Normal vision (NV) subjects (N = 32) and blind patients (N = 24) were submitted to HRV analysis during resting, intervention and recovery periods. Intervention consisted of handling objects, short walking, and cognitive activities performed with pedagogic games while using sleeping masks. No difference was observed in indexes in the time and frequency domain, and in the geometric indexes comparing blind and NV subjected to acute low vision during resting and recovery. Nevertheless, during intervention, RMSSD, pNN50, and SD1were found lower in blind than in NV subjects. Therefore, blind patients showed similar HRV at resting or upon possible stressful challenges compared to NV subjects acutely subjected to low vision, indicating absence of differences in the cardiovascular risk between groups. In addition, blind patients show a smaller reduction in parasympathetic modulation on the heart during possible stressful challenges than NV individuals submitted to low vision, which is likely an important physiological adaptation for an adequate function of the cardiovascular system in blindness.

CHRONIC OBSTRUCTIVE PULMONARY DISEASE: GRADING OF SEVERITY AS REFLECTED BY SHORT TERM HEART RATE VARIABILITY.

Chronic obstructive pulmonary disease (COPD) is a broad spectrum respiratory illness where there are structural and functional changesin the lungs. According to WHO, COPD is a leading global burden and by 2030 it will be the third leading cause of death worldwide. The structural and functional changes in the lungs in COPD patients tend to inuence the cardiac autonomic functions and heart rate variability (HRV). Previousstudiesshow that there is decrease in heart rate variability in COPD patients. In many previousstudies, it isfound that results ofshort term HRV analysis of 5minutes is comparable to standard 24hours HRV analysis and is very patient friendly and reproducible procedure to analyse the cardiac autonomic functions. So determination of parameters of cardiac autonomic functions with the help of short term HRV analysis in COPD patients is helpful in determining the pathophysiology and subsequent management of such patients. A Descriptive and observational study was conducted upon 100 previously diagnosed COPD patients at the Autonomic function research Laboratory, Department of Physiology, R G Kar Medical College and Hospital, Kolkata, West Bengal. The study includes short term (5min) HRV analysis in COPD patients between the age group 18years and 60years after fullling appropriate inclusion and exclusion criteria and the results were analyzed using proper statistical software. After analysis of different data it was found that there is decrease in heart rate variability (in both Time domain and Frequency domain analysis) in case of COPD and also the decrease is more in case of increasing severity grading of COPD. Sympathetic activity increases and vagal or parasympathetic activity upon heart rate decreases with the increase in COPD grading. Further studies with more number of subjects will be helpful in assessing pathophysiology and management of COPD patients with the help of HRV analysis.