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Fulltext The Influence of Virtual Forest Walk on Physiological and Psychological Responses

Alyan E, Combe T, Awang Rambli DR, Sulaiman S, Merienne F, Muhaiyuddin NDM

Int J Environ Res Public Health, 2021 Oct 29;18(21).
PMID: 34769937 DOI: 10.3390/ijerph182111420

The authors of this paper sought to investigate the impact of virtual forest therapy based on realistic versus dreamlike environments on reducing stress levels. Today, people are facing an increase in stress levels in everyday life, which may be due to personal life, work environment, or urban area expansion. Previous studies have reported that urban environments demand more attention and mental workload than natural environments. However, evidence for the effects of natural environments as virtual forest therapy on stress levels has not yet been fully explored. In this study, a total of 20 healthy participants completed a letter-detection test to increase their stress level and were then randomly assigned to two different virtual environments representing realistic and dreamlike graphics. The participants' stress levels were assessed using two physiological methods that measured heart rate and skin conductance levels and one psychological method through the Profile of Mood States (POMS) questionnaire. These indicators were analyzed using a sample t-test and a one-way analysis of variance. The results showed that virtual forest environments could have positive stress-relieving effects. However, realistic graphics were more efficient in reducing stress. These findings contribute to growing forest therapy concepts and provide new directions for future forest therapy research.

Matched MeSH terms: Heart Rate
Heightened interoception in adults with fibromyalgia

Todd J, Plans D, Lee MC, Bird JM, Morelli D, Cunningham A, et al.

Biol Psychol, 2024 Feb;186:108761.
PMID: 38309512 DOI: 10.1016/j.biopsycho.2024.108761

Previous research suggests that the processing of internal body sensations (interoception) affects how we experience pain. Some evidence suggests that people with fibromyalgia syndrome (FMS) - a condition characterised by chronic pain and fatigue - may have altered interoceptive processing. However, extant findings are inconclusive, and some tasks previously used to measure interoception are of questionable validity. Here, we used an alternative measure - the Phase Adjustment Task (PAT) - to examine cardiac interoceptive accuracy in adults with FMS. We examined: (i) the tolerability of the PAT in an FMS sample (N = 154); (ii) if there are differences in facets of interoception (PAT performance, PAT-related confidence, and scores on the Private Body Consciousness Scale) between an FMS sample and an age- and gender-matched pain-free sample (N = 94); and (iii) if subgroups of participants with FMS are identifiable according to interoceptive accuracy levels. We found the PAT was tolerable in the FMS sample, with additional task breaks and a recommended hand posture. The FMS sample were more likely to be classified as 'interoceptive' on the PAT, and had significantly higher self-reported interoception compared to the pain-free sample. Within the FMS sample, we identified a subgroup who demonstrated very strong evidence of being interoceptive, and concurrently had lower fibromyalgia symptom impact (although the effect size was small). Conversely, self-reported interoception was positively correlated with FMS symptom severity and impact. Overall, interoception may be an important factor to consider in understanding and managing FMS symptoms. We recommend future longitudinal work to better understand associations between fluctuating FMS symptoms and interoception.

Matched MeSH terms: Heart Rate
Fulltext Effect of Muslim prayer (Salat) on α electroencephalography and its relationship with autonomic nervous system activity

Doufesh H, Ibrahim F, Ismail NA, Wan Ahmad WA

J Altern Complement Med, 2014 Jul;20(7):558-62.
PMID: 24827587 DOI: 10.1089/acm.2013.0426

OBJECTIVES: This study investigated the effect of Muslim prayer (salat) on the α relative power (RPα) of electroencephalography (EEG) and autonomic nervous activity and the relationship between them by using spectral analysis of EEG and heart rate variability (HRV).
METHODS: Thirty healthy Muslim men participated in the study. Their electrocardiograms and EEGs were continuously recorded before, during, and after salat practice with a computer-based data acquisition system (MP150, BIOPAC Systems Inc., Camino Goleta, California). Power spectral analysis was conducted to extract the RPα and HRV components.
RESULTS: During salat, a significant increase (p

Matched MeSH terms: Heart Rate/physiology*
Fulltext The correlates of body composition with heart rate recovery after step test: an exploratory study of Malaysian adolescents

Abu Hanifah R, Mohamed MN, Jaafar Z, Mohsein NA, Jalaludin MY, Majid HA, et al.

PLoS One, 2013;8(12):e82893.
PMID: 24349388 DOI: 10.1371/journal.pone.0082893

BACKGROUND: In adults, heart rate recovery is a predictor of mortality, while in adolescents it is associated with cardio-metabolic risk factors. The aim of this study was to examine the relationship between body composition measures and heart rate recovery (HRR) after step test in Malaysian secondary school students.
METHODS: In the Malaysian Health and Adolescents Longitudinal Research Team (MyHEART) study, 1071 healthy secondary school students, aged 13 years old, participated in the step test. Parameters for body composition measures were body mass index z-score, body fat percentage, waist circumference, and waist height ratio. The step test was conducted by using a modified Harvard step test. Heart rate recovery of 1 minute (HRR1min) and heart rate recovery of 2 minutes (HRR2min) were calculated by the difference between the peak pulse rate during exercise and the resting pulse rate at 1 and 2 minutes, respectively. Analysis was done separately based on gender. Pearson correlation analysis was used to determine the association between the HRR parameters with body composition measures, while multiple regression analysis was used to determine which body composition measures was the strongest predictor for HRR.
RESULTS: For both gender groups, all body composition measures were inversely correlated with HRR1min. In girls, all body composition measures were inversely correlated with HRR2min, while in boys all body composition measures, except BMI z-score, were associated with HRR2min. In multiple regression, only waist circumference was inversely associated with HRR2min (p=0.024) in boys, while in girls it was body fat percentage for HRR2min (p=0.008).
CONCLUSION: There was an inverse association between body composition measurements and HRR among apparently healthy adolescents. Therefore, it is important to identify cardio-metabolic risk factors in adolescent as an early prevention of consequent adulthood morbidity. This reiterates the importance of healthy living which should start from young.

Matched MeSH terms: Heart Rate/physiology*
Fetal heart rate monitoring based on independent component analysis

Najafabadi FS, Zahedi E, Mohd Ali MA

Comput Biol Med, 2006 Mar;36(3):241-52.
PMID: 16446158

In this paper, an algorithm based on independent component analysis (ICA) for extracting the fetal heart rate (FHR) from maternal abdominal electrodes is presented. Three abdominal ECG channels are used to extract the FHR in three steps: first preprocessing procedures such as DC cancellation and low-pass filtering are applied to remove noise. Then the algorithm for multiple unknown source extraction (AMUSE) algorithm is fed to extract the sources from the observation signals include fetal ECG (FECG). Finally, FHR is extracted from FECG. The method is shown to be capable of completely revealing FECG R-peaks from observation leads even with a SNR=-200dB using semi-synthetic data.

Matched MeSH terms: Heart Rate, Fetal*
On the binaural brain entrainment indicating lower heart rate variability

Palaniappan R, Phon-Amnuaisuk S, Eswaran C

Int J Cardiol, 2015;190:262-3.
PMID: 25932800 DOI: 10.1016/j.ijcard.2015.04.175
Matched MeSH terms: Heart Rate/physiology*
Fetal cerebral Doppler in the recognition of fetal compromise

Chandran R, Serra-Serra V, Sellers SM, Redman CW

Br J Obstet Gynaecol, 1993 Feb;100(2):139-44.
PMID: 8476805

OBJECTIVE: To establish reference ranges for the human fetal middle cerebral artery pulsatility index (MCA PI) for the local obstetric population, and to compare computerised antenatal fetal heart rate (FHR) analysis with the MCA PI as indicators of fetal compromise.
DESIGN: Prospective data collection for selected patients.
SETTING: High risk pregnancy unit of a teaching hospital.
SUBJECTS: Group 1 consisted of 18 healthy women with uncomplicated singleton pregnancies. Group 2 consisted of 27 women admitted to the high risk pregnancy unit over a 9 month period with intrauterine growth retardation and other related problems; all these women were delivered by prelabour caesarean section.
INTERVENTION: Serial Duplex sonography to determine fetal MCA PI in Groups 1 and 2. Serial FHR analysis using computerised numerical techniques in Group 2 only.
MAIN OUTCOME MEASURES: Serial MCA PI values from 24 to 39 completed weeks of gestation in Group 1. Comparison of serial MCA PI values with FHR analysis in relation to fetal outcome in Group 2.
RESULTS: In Group 1 the MCA PI diminished significantly as gestation advanced from 1.73 (SD 0.25) at 24 weeks to 1.38 (SD 0.26) at 39 weeks (P < 0.01). In Group 2 eleven babies were hypoxaemic at delivery: all had low MCA PI values while only nine had an abnormal FHR prior to delivery.
CONCLUSION: In normal pregnancy, there is a fall in the fetal MCA PI with advancing gestation which probably reflects a decreasing vascular resistance to fetal cerebral blood flow. Hypoxaemia at delivery appeared to be better recognised by the fetal MCA flow velocity waveform than the FHR analysis. This increased sensitivity, however, was achieved at the expense of a reduced specificity. Larger studies are needed to confirm the findings of this preliminary investigation.

Matched MeSH terms: Heart Rate, Fetal/physiology*
Acute ingestion of hydrogen-rich water does not improve incremental treadmill running performance in endurance-trained athletes

Ooi CH, Ng SK, Omar EA

Appl Physiol Nutr Metab, 2020 May;45(5):513-519.
PMID: 31675478 DOI: 10.1139/apnm-2019-0553

There is emerging evidence that hydrogen-rich water (H2-water) has beneficial effects on the physiological responses to exercise. However, few studies investigate its ergogenic potential. This randomized controlled trial examined the effects of H2-water ingestion on physiological responses and exercise performance during incremental treadmill running. In a double-blind crossover design, 14 endurance-trained male runners (age, 34 ± 4 years; body mass, 63.1 ± 7.2 kg; height, 1.72 ± 0.05 m) were randomly assigned to ingest 2 doses of 290-mL H2-water or placebo on each occasion. The first bolus was given before six 4-min submaximal running bouts, and the second bolus was consumed before the maximal incremental running test. Expired gas, heart rate (HR), and ratings of perceived exertion (RPE) were recorded; blood samples were collected at the end of each submaximal stage and post maximal running test. Cardiorespiratory responses, RPE, and blood gas indices were not significantly different at each submaximal running intensity (range: 34%-91% maximal oxygen uptake) between H2-water and placebo trials. No statistical difference was observed in running time to exhaustion (618 ± 126 vs. 619 ± 113 s), maximal oxygen uptake (56.9 ± 4.4 vs. 57.1 ± 4.7 mL·kg-1·min-1), maximal HR (184 ± 7 vs. 184 ± 7 beat·min-1), and RPE (19 ± 1 vs. 19 ± 1) in the runners between the trials. The results suggest that the ingestion of 290 mL of H2-water before submaximal treadmill running and an additional dose before the subsequent incremental running to exhaustion were not sufficiently ergogenic in endurance-trained athletes. Novelty Acute ingestion of H2-water does not seem to be ergogenic for endurance performance. A small dose of H2-water does not modulate buffering capacity during intense endurance exercise in athletes.

Matched MeSH terms: Heart Rate/physiology
Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals

Yildirim O, Talo M, Ay B, Baloglu UB, Aydin G, Acharya UR

Comput Biol Med, 2019 10;113:103387.
PMID: 31421276 DOI: 10.1016/j.compbiomed.2019.103387

In this study, a deep-transfer learning approach is proposed for the automated diagnosis of diabetes mellitus (DM), using heart rate (HR) signals obtained from electrocardiogram (ECG) data. Recent progress in deep learning has contributed significantly to improvement in the quality of healthcare. In order for deep learning models to perform well, large datasets are required for training. However, a difficulty in the biomedical field is the lack of clinical data with expert annotation. A recent, commonly implemented technique to train deep learning models using small datasets is to transfer the weighting, developed from a large dataset, to the current model. This deep learning transfer strategy is generally employed for two-dimensional signals. Herein, the weighting of models pre-trained using two-dimensional large image data was applied to one-dimensional HR signals. The one-dimensional HR signals were then converted into frequency spectrum images, which were utilized for application to well-known pre-trained models, specifically: AlexNet, VggNet, ResNet, and DenseNet. The DenseNet pre-trained model yielded the highest classification average accuracy of 97.62%, and sensitivity of 100%, to detect DM subjects via HR signal recordings. In the future, we intend to further test this developed model by utilizing additional data along with cloud-based storage to diagnose DM via heart signal analysis.

Matched MeSH terms: Heart Rate*
Flickering of cardiac state before the onset and termination of atrial fibrillation

Lan BL, Liew YW, Toda M, Kamsani SH

Chaos, 2020 May;30(5):053137.
PMID: 32491883 DOI: 10.1063/1.5130524

Complex dynamical systems can shift abruptly from a stable state to an alternative stable state at a tipping point. Before the critical transition, the system either slows down in its recovery rate or flickers between the basins of attraction of the alternative stable states. Whether the heart critically slows down or flickers before it transitions into and out of paroxysmal atrial fibrillation (PAF) is still an open question. To address this issue, we propose a novel definition of cardiac states based on beat-to-beat (RR) interval fluctuations derived from electrocardiogram data. Our results show the cardiac state flickers before PAF onset and termination. Prior to onset, flickering is due to a "tug-of-war" between the sinus node (the natural pacemaker) and atrial ectopic focus/foci (abnormal pacemakers), or the pacing by the latter interspersed among the pacing by the former. It may also be due to an abnormal autonomic modulation of the sinus node. This abnormal modulation may be the sole cause of flickering prior to termination since atrial ectopic beats are absent. Flickering of the cardiac state could potentially be used as part of an early warning or screening system for PAF and guide the development of new methods to prevent or terminate PAF. The method we have developed to define system states and use them to detect flickering can be adapted to study critical transition in other complex systems.

Matched MeSH terms: Heart Rate/physiology
Automated detection of sleep apnea using sparse residual entropy features with various dictionaries extracted from heart rate and EDR signals

Viswabhargav CSS, Tripathy RK, Acharya UR

Comput Biol Med, 2019 05;108:20-30.
PMID: 31003176 DOI: 10.1016/j.compbiomed.2019.03.016

Sleep is a prominent physiological activity in our daily life. Sleep apnea is the category of sleep disorder during which the breathing of the person diminishes causing the alternation in the upper airway resistance. The electrocardiogram derived respiration (EDR) and heart rate (RR-time-series) signals are normally used for the detection of sleep apnea as these two signals capture cardio-pulmonary activity information. Hence, the analysis of these two signals provides vital information about sleep apnea. In this paper, we propose the novel sparse residual entropy (SRE) features for the automated detection of sleep apnea using EDR and heart rate signals. The features required for the automated detection of sleep apnea are extracted in three steps: (i) atomic decomposition based residual estimation from both EDR and heart rate signals using orthogonal matching pursuit (OMP) with different dictionaries, (ii) estimation of probabilities from each sparse residual, and (iii) calculation of the entropy features. The proposed SRE features are fed to the combination of fuzzy K-means clustering and support vector machine (SVM) to pick the best performing classifier. The experimental results demonstrate that the proposed SRE features with radial basis function (RBF) kernel-based SVM classifier yielded higher performance with accuracy, sensitivity and specificity values of 78.07%, 78.01%, and 78.13%, respectively with Fourier dictionary and 10-fold cross-validation. For subject-specific or leave-one-out validation case, the SVM classifier has sensitivity and specificity of 85.43% and 92.60%, respectively using SRE features with Fourier dictionary (FD).

Matched MeSH terms: Heart Rate*
Fetal heart rate monitoring device using condenser microphone sensor: Validation and comparison to standard devices

Ahmad HAB, El-Badawy IM, Singh OP, Hisham RB, Malarvili MB

Technol Health Care, 2018;26(4):573-579.
PMID: 29758955 DOI: 10.3233/THC-171067

BACKGROUND: Fetal heart rate (FHR) monitoring device is highly demanded to assess the fetus health condition in home environments. Conventional standard devices such as ultrasonography and cardiotocography are expensive, bulky and uncomfortable and consequently not suitable for long-term monitoring. Herein, we report a device that can be used to measure fetal heart rate in clinical and home environments.
METHODS: The proposed device measures and displays the FHR on a screen liquid crystal display (LCD). The device consists of hardware that comprises condenser microphone sensor, signal conditioning, microcontroller and LCD, and software that involves the algorithm used for processing the conditioned fetal heart signal prior to FHR display. The device's performance is validated based on analysis of variance (ANOVA) test.
RESULTS: FHR data was recorded from 22 pregnant women during the 17th to 37th week of gestation using the developed device and two standard devices; AngelSounds and Electronic Stethoscope. The results show that F-value (1.5) is less than F𝑐𝑟𝑖𝑡, (3.1) and p-value (p> 0.05). Accordingly, there is no significant difference between the mean readings of the developed and existing devices. Hence, the developed device can be used for monitoring FHR in clinical and home environments.

Matched MeSH terms: Heart Rate, Fetal/physiology*
Social support as a comfort or an encouragement: A systematic review on the contrasting effects of social support on cardiovascular reactivity

Teoh AN, Hilmert C

Br J Health Psychol, 2018 11;23(4):1040-1065.
PMID: 30084181 DOI: 10.1111/bjhp.12337

PURPOSE: The stress-buffering hypothesis (Cohen & McKay, 1984, Handbook of psychology and health IV: Social psychological aspects of health (pp. 253-256). Hillsdale, NJ: Lawrence Erlbaum) suggests that one way social support enhances health is by attenuating cardiovascular reactivity (CVR) to stress. Research that has tested this hypothesis has reported inconsistent findings. In this review, we systematically reviewed those findings and proposed a dual-effect model of social support and CVR as a potential explanation for the inconsistency in the literature. Specifically, we proposed that when participants are more engaged during a stressor, social support acts primarily as social comfort, attenuating CVR; and when participants are not engaged, social support acts primarily as social encouragement, elevating CVR.
METHODS: We reviewed 22 previous studies that (1) empirically manipulated social support in a stressful situation, (2) measured CVR, and (3) tested a moderator of social support effects on CVR.
RESULTS: Although a majority of studies reported a CVR-mitigating effect of social support resulting in an overall significant combined p-value, we found that there were different effects of social support on CVR when we considered high- and low-engagement contexts. That is, compared to control conditions, social support lowered CVR in more engaging situations but had no significant effect on CVR in less engaging situations.
CONCLUSION: Our results suggest that a dual-effect model of social support effects on CVR may better capture the nature of social support, CVR, and health associations than the buffering hypothesis and emphasize a need to better understand the health implications of physiological reactivity in various contexts. Statement of contribution What is already known on this subject? According to the stress-buffering hypothesis (Cohen & McKay, ), one pathway social support benefits health is through mitigating the physiological arousal caused by stress. However, previous studies that examined the effects of social support on blood pressure and heart rate changes were not consistently supporting the hypothesis. Some studies reported that social support causes elevations in cardiovascular reactivity (CVR) to stress (Anthony & O'Brien, ; Hilmert, Christenfeld, & Kulik, ; Hilmert, Kulik, & Christenfeld, ) and others showed no effect of social support on CVR (Christian & Stoney, ; Craig & Deichert, ; Gallo, Smith, & Kircher, ). What does this study add? When participants were in more engaging conditions, social support decreased CVR relative to no support. When participants were in less engaging conditions, social support did not have a significant effect on CVR. Provide an alternative way to explain the ways social support affects cardiac health.

Matched MeSH terms: Heart Rate/physiology*
A review of automated sleep stage scoring based on physiological signals for the new millennia

Faust O, Razaghi H, Barika R, Ciaccio EJ, Acharya UR

Comput Methods Programs Biomed, 2019 Jul;176:81-91.
PMID: 31200914 DOI: 10.1016/j.cmpb.2019.04.032

BACKGROUND AND OBJECTIVE: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal.
METHODS: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals.
RESULTS: Our review shows that all of these signals contain information for sleep stage scoring.
CONCLUSIONS: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.

Matched MeSH terms: Heart Rate*
The Effect of 5 Minutes of Mindful Breathing to the Perception of Distress and Physiological Responses in Palliative Care Cancer Patients: A Randomized Controlled Study

Ng CG, Lai KT, Tan SB, Sulaiman AH, Zainal NZ

J Palliat Med, 2016 09;19(9):917-24.
PMID: 27110900 DOI: 10.1089/jpm.2016.0046

BACKGROUND: Palliative cancer patients suffer from high levels of distress. There are physiological changes in relation to the level of perceived distress.
OBJECTIVE: To study the efficacy of 5 minutes of mindful breathing (MB) for rapid reduction of distress in a palliative setting. Its effect to the physiological changes of the palliative cancer patients was also examined.
METHODS: This is a randomized controlled trial. Sixty palliative cancer patients were recruited. They were randomly assigned to either 5 minutes of MB or normal listening arms. The changes of perceived distress, blood pressure, pulse rate, breathing rate, galvanic skin response, and skin surface temperature of the patients were measured at baseline, after intervention, and 10 minutes post-intervention.
RESULTS: There was significant reduction of perceived distress, blood pressure, pulse rate, breathing rate, and galvanic skin response; also, significant increment of skin surface temperature in the 5-minute MB group. The changes in the 5-minute breathing group were significantly higher than the normal listening group.
CONCLUSION: Five-minute MB is a quick, easy to administer, and effective therapy for rapid reduction of distress in palliative setting. There is a need for future study to establish the long-term efficacy of the therapy.

Matched MeSH terms: Heart Rate
Real-time signal processing for fetal heart rate monitoring

Ibrahimy MI, Ahmed F, Mohd Ali MA, Zahedi E

IEEE Trans Biomed Eng, 2003 Feb;50(2):258-62.
PMID: 12665042

An algorithm based on digital filtering, adaptive thresholding, statistical properties in the time domain, and differencing of local maxima and minima has been developed for the simultaneous measurement of the fetal and maternal heart rates from the maternal abdominal electrocardiogram during pregnancy and labor for ambulatory monitoring. A microcontroller-based system has been used to implement the algorithm in real-time. A Doppler ultrasound fetal monitor was used for statistical comparison on five volunteers with low risk pregnancies, between 35 and 40 weeks of gestation. Results showed an average percent root mean square difference of 5.32% and linear correlation coefficient from 0.84 to 0.93. The fetal heart rate curves remained inside a +/- 5-beats-per-minute limit relative to the reference ultrasound method for 84.1% of the time.

Matched MeSH terms: Heart Rate/physiology; Heart Rate, Fetal/physiology*
Fulltext Embryonic toxicity of 3,4-dichloroaniline (3,4-DCA) on Javanese medaka (Oryzias javanicus Bleeker, 1854)

Ibrahim MA, Zulkifli SZ, Azmai MNA, Mohamat-Yusuff F, Ismail A

Toxicol Rep, 2020;7:1039-1045.
PMID: 32913717 DOI: 10.1016/j.toxrep.2020.08.011

Early-life exposure to toxic chemicals causes irreversible morphological and physiological abnormalities that may last for a lifetime. The present study aimed to determine the toxicity effect of 3,4-Dichloroaniline (3,4-DCA) on Javanese medaka (Oryzias javanicus) embryos. Healthy embryos were exposed to various 3,4-DCA concentrations for acute toxicity (5, 10, 25, 50, and 100 mg.L-1) and sublethal toxicity (0.10, 0.50, 1.25, 2.50, and 5.00 mg.L-1) for 96 h and 20 days respectively. Acute toxicity test revealed that the median lethal concentration (96h-LC50) was 32.87 mg.L-1 (95 % CI = 27.90-38.74, R2 = 0.95). Sublethal exposure revealed that 1.25 mg.L-1 at 3 days post-exposure (3 dpe) has a significant lower heartrate (120 ± 12.3 beats/min., p heart rate compared to other treatments. Likewise, at 13 dpe, 5.00 mg.L-1 (110.4 ± 17.3 beats/min) and 2.5 mg.L-1 (130.4 ± 8.3 beats/min) were significantly lower (p

Matched MeSH terms: Heart Rate
Fulltext Comparison between conventional human energy measurement and physical human energy measurement methods in wetland rice production

Elsoragaby S, Yahya A, Nawi NM, Mahadi MR, Mairghany M, Muazu A, et al.

Heliyon, 2020 Nov;6(11):e05332.
PMID: 33294651 DOI: 10.1016/j.heliyon.2020.e05332

Measurement of human energy expenditure during crop production helps in the optimization of production operations and costs by identifying steps which that can benefit from the use of appropriate mechanization technologies. This study measures human energy expenditure associated with all 6 major rice (Oryza sativa L.) cultivation operations using two measurement methods-i.e. conventional human energy expenditure method and direct measurement with a Garmin forerunner 35 body media. The aim of this study was to provide a detailed comparison of these two methods and document the human energy costs in a manner that will identify steps to be taken to help optimize agricultural practices. Results (mean + 95%CL) revealed that the total human energy expenditure obtained through the conventional method was 25.5% higher (33.3 ± 1 versus 26.6 ± 1.3) in transplanting and 26.1% higher (30.3 ± 1.9 versus 24.0 ± 2.1) than the human energy expenditure recorded using the Garmin method in broadcast seeding method. Similarly, during the harvesting operation, the conventional measurement and Garmin measurement methods differed significantly, with the conventional method the human energy expenditure was 89.9% higher (3.2 ± 0.4 versus 1.68 ± 0.2) in the fields using the transplanting and 88.7% higher (3.3 ± 0.5 versus 1.8 ± 0.3) in the fields using the broadcast seeding than the human energy expenditure recorded using the Garmin method. When using Garmin method, the human energy expenditure in the case of using the midsize combine harvester was 13.49% lesser (592.4 ± 67.2 versus 522.0 ± 75.1) than the case of using conventional one. Results based on heart rate also indicated that operations such as tillage were less intensive (72 ± 3.3 bpm) compared with operations such as chemicals spraying (135 ± 4 bpm). Although we did not have a criterion measure available to determine which method was the most accurate, the Garmin measurement gives an estimate of actual physical human energy expended in performing a specific task with consider all conditions and thus more information to aid in identifying critical operations that could be optimized and mechanized.

Matched MeSH terms: Heart Rate
Complexity-Based Decoding of the Coupling Among Heart Rate Variability (HRV) and Walking Path

Mujib Kamal S, Babini MH, Krejcar O, Namazi H

Front Physiol, 2020;11:602027.
PMID: 33324242 DOI: 10.3389/fphys.2020.602027

Walking is an everyday activity in our daily life. Because walking affects heart rate variability, in this research, for the first time, we analyzed the coupling among the alterations of the complexity of walking paths and heart rate. We benefited from the fractal theory and sample entropy to evaluate the influence of the complexity of paths on the complexity of heart rate variability (HRV) during walking. We calculated the fractal exponent and sample entropy of the R-R time series for nine participants who walked on four paths with various complexities. The findings showed a strong coupling among the alterations of fractal dimension (an indicator of complexity) of HRV and the walking paths. Besides, the result of the analysis of sample entropy also verified the obtained results from the fractal analysis. In further studies, we can analyze the coupling among the alterations of the complexities of other physiological signals and walking paths.

Matched MeSH terms: Heart Rate
Fulltext A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network

Wu M, Lu Y, Yang W, Wong SY

Front Comput Neurosci, 2020;14:564015.
PMID: 33469423 DOI: 10.3389/fncom.2020.564015

Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice.

Matched MeSH terms: Heart Rate

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