المؤلفون: Iqbal, Talha, Elahi, Adnan, Wijns, William, Amin, Bilal, Shahzad, Atif

المصدر: Applied Sciences (2076-3417); Mar2023, Vol. 13 Issue 5, p2950, 15p

مصطلحات موضوعية: HEART beat, RANK correlation (Statistics), AUTOMATIC classification, TIME series analysis, PRINCIPAL components analysis, FEATURE selection, RANDOM forest algorithms, HEART rate monitors

مصطلحات جغرافية: KENDALL (Fla.)

مستخلص: Time-series features are the characteristics of data periodically collected over time. The calculation of time-series features helps in understanding the underlying patterns and structure of the data, as well as in visualizing the data. The manual calculation and selection of time-series feature from a large temporal dataset are time-consuming. It requires researchers to consider several signal-processing algorithms and time-series analysis methods to identify and extract meaningful features from the given time-series data. These features are the core of a machine learning-based predictive model and are designed to describe the informative characteristics of the time-series signal. For accurate stress monitoring, it is essential that these features are not only informative but also well-distinguishable and interpretable by the classification models. Recently, a lot of work has been carried out on automating the extraction and selection of times-series features. In this paper, a correlation-based time-series feature selection algorithm is proposed and evaluated on the stress-predict dataset. The algorithm calculates a list of 1578 features of heart rate and respiratory rate signals (combined) using the tsfresh library. These features are then shortlisted to the more specific time-series features using Principal Component Analysis (PCA) and Pearson, Kendall, and Spearman correlation ranking techniques. A comparative study of conventional statistical features (like, mean, standard deviation, median, and mean absolute deviation) versus correlation-based selected features is performed using linear (logistic regression), ensemble (random forest), and clustering (k-nearest neighbours) predictive models. The correlation-based selected features achieved higher classification performance with an accuracy of 98.6% as compared to the conventional statistical feature's 67.4%. The outcome of the proposed study suggests that it is vital to have better analytical features rather than conventional statistical features for accurate stress classification. [ABSTRACT FROM AUTHOR]

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المؤلفون: Martinez-Delgado, Gerardo H., Correa-Balan, Alfredo J., May-Chan, José A., Parra-Elizondo, Carlos E., Guzman-Rangel, Luis A., Martinez-Torteya, Antonio

المصدر: Sensors (14248220); Jul2022, Vol. 22 Issue 13, p4690-N.PAG, 11p

مصطلحات موضوعية: HEART beat, FALSE discovery rate, MULTIPLE comparisons (Statistics), PHOTOPLETHYSMOGRAPHY, VIDEO recording, HEART beat measurement

مصطلحات جغرافية: KENDALL (Fla.)

مستخلص: Heart Rate Variability (HRV) has become an important risk assessment tool when diagnosing illnesses related to heart health. HRV is typically measured with an electrocardiogram; however, there are multiple studies that use Photoplethysmography (PPG) instead. Measuring HRV with video is beneficial as a non-invasive, hands-free alternative and represents a more accessible approach. We developed a methodology to extract HRV from video based on face detection algorithms and color augmentation. We applied this methodology to 45 samples. Signals obtained from PPG and video recorded an average mean error of less than 1 bpm when measuring the heart rate of all subjects. Furthermore, utilizing PPG and video, we computed 61 variables related to HRV. We compared each of them with three correlation metrics (i.e., Kendall, Pearson, and Spearman), adjusting them for multiple comparisons with the Benjamini–Hochberg method to control the false discovery rate and to retrieve the q-value when considering statistical significance lower than 0.5. Using these methods, we found significant correlations for 38 variables (e.g., Heart Rate, 0.991; Mean NN Interval, 0.990; and NN Interval Count, 0.955) using time-domain, frequency-domain, and non-linear methods. [ABSTRACT FROM AUTHOR]

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المؤلفون: Parchani, Gaurav, Kumar, Gulshan, Rao, Raghavendra, Udupa, Kaviraja, Saran, Vibhor

المصدر: Annals of Neurosciences; Jan2022, Vol. 29 Issue 1, p16-20, 5p

مصطلحات موضوعية: HEART beat, AUTONOMIC nervous system, CHRONIC kidney failure, ROOT-mean-squares, RANK correlation (Statistics)

مصطلحات جغرافية: KENDALL (Fla.)

مستخلص: Background: Functions of the autonomic nervous system have cardinal importance in day-to-day life. Heart rate variability (HRV) has been shown to estimate the functioning of the autonomic nervous system. Imbalance in the functioning of the autonomic nervous system is seen to be associated with chronic conditions such as chronic kidney disease, cardiovascular diseases, diabetes mellitus, and so on. Purpose: To evaluate the efficacy of a non-contact ballistocardiography (BCG) system to calculate HRV parameters by comparing them to the parameters derived from a standard commercial software that uses an electrocardiogram (ECG). Methods: Current study captured an ECG signal using a three-channel ECG Holter machine, whereas the BCG signal was captured using a BCG sensor sheet consisting of vibroacoustic sensors placed under the mattress of the participants of the study. Results: The study was conducted on 24 subjects for a total of 54 overnight recordings. The proposed method covered 97.92% epochs of the standard deviation of NN intervals (SDNN) and 99.27% epochs of root mean square of successive differences (RMSSD) within 20 ms and 30 ms tolerance, respectively, whereas 98.84% of two-min intervals for low-frequency (LF) to high-frequency (HF) ratio was covered within a tolerance of 1. Kendall's coefficient of concordance was also calculated, giving a P <.001 for all the three parameters and coefficients 0.66, 0.55, and 0.44 for SDNN, RMSSD, and LF/HF, respectively. Conclusion: The results show that HRV parameters captured using unobtrusive and non-invasive BCG sensors are comparable to HRV calculated using ECG. [ABSTRACT FROM AUTHOR]

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المؤلفون: Bonnet, Jean-Francois, Buggy, Eleanor, Cusack, Barbara, Sherwin, Aislinn, Wall, Tom, Fitzgibbon, Maria, Buggy, Donal J.

المصدر: Perioperative Medicine; 3/24/2020, Vol. 9 Issue 1, p1-9, 9p

مصطلحات موضوعية: RANK correlation (Statistics), SYSTOLIC blood pressure, BRAIN natriuretic factor, HEART beat, DISEASES

مصطلحات جغرافية: KENDALL (Fla.)

مستخلص: Background: Postoperative morbidity occurs in 10–15% of patients undergoing major noncardiac surgery. Predicting patients at higher risk of morbidity may help to optimize perioperative prevention. Preoperative haemodynamic parameters, systolic arterial pressure (SAP) < 100 mmHg, pulse pressure (PP) > 62 mmHg or < 53 mmHg, and heart rate (HR) > 87 min^-1 are associated with increased postoperative morbidity. We evaluated the correlation between these and other routine haemodynamic parameters, measured intraoperatively, with postoperative morbidity. Postoperative morbidity was measured using the Comprehensive Complication Index (CCI) and length of stay (LOS). Additionally we correlated CCI with the cardiac risk biomarker, preoperative NT-ProBNP. Methods: This is a retrospective analysis of patients in MET-REPAIR, a European observational study correlating self-reported physical activity with postoperative morbidity. Patients' electronic anaesthetic records (EARs) including perioperative haemodynamic data were correlated with 30-day postoperative morbidity, CCI and LOS parameters. Statistical analysis to assess for correlation was by Kendall's Correlation Coefficient for tied ranks (Tau-B) or Spearman's Correlation Coefficient. Blood for N-terminal prohormone of brain natriuretic peptide (NT-proBNP) measurement was collected < 31 days before surgery. Results: Data from n = 50 patients were analysed. When stratified according to age > 70 years and ASA > 3, the duration of MAP < 100 mmHg, < 75 mmHg or < 55 mmHg were associated with a higher CCI (tau = 0.57, p = 0.001) and duration < 75 mmHg was associated with prolonged LOS (tau = 0.39, p = 0.02). The intraoperative duration of PP > 62 mmHg was associated with LOS (tau = 0.317, p = 0.007). There was no correlation between preoperative NT-proBNP and either CCI or LOS. Conclusions: In older and higher risk patients, duration of intraoperative hypotension by a variety of definitions, or PP > 62 mmHg, are associated with increased postoperative CCI and LOS. These findings warrant confirmation in larger databases with evaluation of whether real-time intraoperative intervention could reduce postoperative morbidity. [ABSTRACT FROM AUTHOR]

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المؤلفون: Harrison, Kaitlyn, Potts, Emily, King, Adam C., Braun-Trocchio, Robyn

المصدر: Sports (2075-4663); Dec2021, Vol. 9 Issue 12, p167-167, 1p

مصطلحات موضوعية: PERFORMANCE anxiety, SOCCER players, ANXIETY, VIRTUAL reality, HEAD-mounted displays, HEART beat

مصطلحات جغرافية: KENDALL (Fla.)

مستخلص: With the increased use of technology, relaxation interventions are finding their way into technology devices like virtual reality head-mounted displays (VR HMDs). However, there is a lack of evidence on the efficacy of VR relaxation interventions to reduce anxiety in athletes and how that is portrayed in their movement patterns. The purpose of the current study was to examine how a VR relaxation intervention affected perceived anxiety levels and penalty kick performance of female soccer players. Thirteen female soccer players took five penalty kicks in baseline, stress-induced, and VR relaxation conditions. Perceived levels of anxiety, self-confidence, mental effort, heart rate (HR), accelerometry of the lumbar spine and thigh, and performance in each condition was obtained. Results indicated that the VR intervention significantly reduced cognitive anxiety and somatic anxiety from baseline (p = 0.002; p = 0.001) and stress (p < 0.001; p < 0.001) with large effect sizes (Kendall's W = 0.72; 0.83). VR significantly increased self-confidence from baseline (p = 0.002) and stress (p = 0.001) with a large effect size (Kendall's W = 0.71). Additionally, all participants felt that VR helped them relax. Mental effort was significantly higher in the stress condition compared to that in baseline (p = 0.007) with moderate effect size (Kendall's W = 0.39). Peak acceleration and performance were not significantly influenced by stress or VR. This study serves as an initial step to evaluate VR relaxation interventions on performance in female soccer players. [ABSTRACT FROM AUTHOR]

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