Continuous Heart Rate Monitoring Research Articles

Source separation from single-channel abdominal phonocardiographic signals based on independent component analysis.

Purpose: Continuous monitoring of fetal heart rate (FHR) is essential to diagnose heart abnormalities. Therefore, FHR measurement is considered as the most important parameter to evaluate heart function. One method of FHR extraction is done by using fetal phonocardiogram (fPCG) signal, which is obtained directly from the mother abdominal surface with a medical stethoscope. A variety of high-amplitude interference such as maternal heart sound and environmental noise cause a low SNR fPCG signal. In addition, the signal is nonstationary because of changes in features that are highly dependent on pregnancy age, fetal position, maternal obesity, bandwidth of the recording system and nonlinear transmission environment. Methods: In this paper, a sources separation process from the recorded fPCG signal is proposed. Independent component analysis (ICA) has always been one of the most efficient methods for extracting background noise from multichannel data. In order to extract the source signals from the single-channel fPCG data using ICA algorithm, it is necessary to first decompose the signal into multivariate data using a proper decomposition technique. In this paper, we implemented three combined methods of SSA-ICA, Wavelet-ICA and EEMD-ICA. Results: In order to validate the performance of the methods, we used simulated and real fPCG signals. The results indicated that SSA-ICA recovers sources of single-channel signals with different SNRs. Conclusion: The performance criteria such as power spectral density (PSD) peak and cross correlation value show that the SSA-ICA method has been more successful in extracting independent sources.

Age and extraversion differences in heart rate reactivity during working memory tasks.

Research and theory have shown a link between heart rate reactivity during cognitive testing and extraversion in younger adults; however, similar work has not been conducted with older adults. This study was designed to explore age and extraversion-related differences in within-person heart rate (HR) reactivity during two working memory tasks of varying difficulty using a multi-level modeling approach. Across 570 total within-person assessments of continuous HR monitoring, 28 younger adults (M = 19.76, SD = 1.15) and 29 older adults (M = 71.19, SD = 6.63) were administered two working memory tasks (backward digit span and n-back). There were no age differences in reactivity during the backward digit span. However, similar to previous findings, on the more difficult n-back task, younger adults low in extraversion showed a trend toward higher HR reactivity than young adults high in extraversion. Interestingly, the older adults showed the opposite pattern in that lower extraversion older adults were less reactive than the higher extraversion older adults who showed the steepest increase in HR. The HR increase of the older adults high in extraversion may be an indication of higher engagement in this more difficult task. Individual differences in extraversion need to be taken into account when administering working memory tasks in older adults.

ОСОБЕННОСТИ ГЕМОДИНАМИКИ У ЗДОРОВЫХ МУЖЧИН В ГИПОМАГНИТНЫХ УСЛОВИЯХ

Hemodynamics was studied in essentially healthy resting 8 male subjects exposed to an experimental hypomagnetic environment (HME). The cardio-respiratory system functioning was evaluated by 8-hour continuous monitoring of heart rate (HR), blood pressure (BP) fluctuations during every cardiac cycle, Kerdo autonomic index (KAI) and blood oxygenation (SpO2) in the Earth's natural magnetic field and a field attenuated in approximately 1,000 times. Comparative analysis of the measurements made in the control and HME tests elicited different trends. Thus, the HR average reduction amounted to 4 bpm; BP dropped significantly, i.e. diastolic BP by 11 mmHg and systolic BP, 16 mmHg on average. In HME, KAI rose commonly 20 %. Hemoglobin saturation did not change. The paper contains graphic interpretations of the KAI dependence on duration of the HME exposure. Mathematical modeling suggests a physiological interpretation of these results. More often than not sitting at rest in HME reduced substantially the modulating effect of parasympathetic regulation on the cardiovascular system in comparison with the control test in the normal geomagnetic field.

Mudança de decúbito na UTI: uma análise sobre as repercussões hemodinâmicas

A study that aimed to analyze and compare hemodynamic parameters before and after changing position. This is an observational, analytical, cross-sectional study with a quantitative approach, carried out in an intensive care unit. After performing a sample calculation, 27 patients were included in the research who were under continuous hemodynamic monitoring of heart rate, respiratory rate, systolic, diastolic, and mean blood pressure, as well as peripheral oxygen saturation and axillary temperature. There were no statistically significant differences (p> 0.05) in hemodynamic parameters when comparing the pre- and post-change positions. It was concluded that the patients analyzed in the sample did not present hemodynamic repercussions after the change in decubitus, praising this as a safe technique from the hemodynamic point of view and beneficial for the treatment and prevention of health problems of critical patients.

Continuous Glucose and Heart Rate Monitoring in Young People with Type 1 Diabetes: An Exploratory Study about Perspectives in Nocturnal Hypoglycemia Detection.

A combination of information from blood glucose (BG) and heart rate (HR) measurements has been proposed to investigate the HR changes related to nocturnal hypoglycemia (NH) episodes in pediatric subjects with type 1 diabetes (T1D), examining whether they could improve hypoglycemia prediction. We enrolled seventeen children and adolescents with T1D, monitored on average for 194 days. BG was detected by flash glucose monitoring devices, and HR was measured by wrist-worn fitness trackers. For each subject, we compared HR values recorded in the hour before NH episodes (before-hypoglycemia) with HR values recorded during sleep intervals without hypoglycemia (no-hypoglycemia). Furthermore, we investigated the behavior after the end of NH. Nine participants (53%) experienced at least three NH. Among these nine subjects, six (67%) showed a statistically significant difference between the before-hypoglycemia HR distribution and the no-hypoglycemia HR distribution. In all these six cases, the before-hypoglycemia HR median value was higher than the no-hypoglycemia HR median value. In almost all cases, HR values after the end of hypoglycemia remained higher compared to no-hypoglycemia sleep intervals. This exploratory study support that HR modifications occur during NH in T1D subjects. The identification of specific HR patterns can be helpful to improve NH detection and prevent fatal events.

WITHDRAWN: Computational efficient wearable sensor network health monitoring system for sports athletics using IoT

Abstract The rapid development and growth in the Internet of Things (IoT) area offer great potential in the healthcare sector. Currently, wireless technology, fitness trackers, and body sensors in the sports field have a major impact on living efficiency and health system reliability. Wearable devices have become increasingly interested in measuring physiological factors, promoting health, and improving adherence to the practice in different populations from elite athletes to patients. In this paper, fog assisted Computational efficient Wearable sensor networks (FCE-WSN) has been proposed in health monitoring systems for sports athletic using IoT. The wearable device for the continuous real-time monitoring of heart rate, respiratory frequency, and movement cadence during physical activity has been analyzed. Besides, the sensor collected data is uploaded to the IoT-connection system Ethernet module, and the Authorized Individual accesses the data via the internet to track the athletics health. Moreover, the wearable device's analytical model and its use illustrate how computing resources' costs can be minimized while preserving health requests for access to medical information stored in a fog and cloud distribution setting. Our assessment system depends on a queue and can estimate the minimum computing resources needed to achieve the Service Level Agreement (SLA) (both fog and cloud nodes). The experimental results show that the proposed method is user-friendly, reliable, and economical to use regularly.

An effective digitization method for CTG paper report with binary background grids taken by smartphone

Background and ObjectiveCardiotocography (CTG) is the most popular prenatal diagnostic examination, which includes continuous monitoring of foetal heart rate (FHR, bpm) and uterine contraction (UC, mmHg) signals. Compared with CTG paper reports, digitized reports have better storage, transmission and retrieval capabilities, in addition to being able to assess foetal health. However, most of the existing digitization methods extract signals from paper reports with colour background grids, and they cannot extract signals completely from paper reports with binary background grids, which are widely used in clinical CTG monitoring. Moreover, the existing digitization algorithms often neglect the image distortion caused by the imaging equipment. MethodsTo overcome the above drawbacks, a digitization method for CTG paper reports with binary background grids taken by smartphones is proposed in this paper. In the stage of removing the grid background, a region merger based on super-pixels and an improved binary line mask removal are designed. Then, signal extraction is performed separately according to the different states of the image column. Through a projection map used to synchronize the signal, the distortion effect of the mobile phone is removed. ResultsThe experimental results show that the average correlation coefficient (ρ) between the recovery signal obtained by the proposed method and the reference signal is 0.9855±0.0108 for FHR and 0.9866 ± 0.1020 for UC, and the root mean square errors (RMSE) of FHR and UC processed by the proposed method are 1.0366 ± 0.4953 and 2.0355 ± 1.0246, and the mean absolute errors (MAE) of FHR and UC processed by the proposed method are 0.8735 ± 0.0684 and 1.4991 ± 0.2837, which are higher than those of the existing digitization methods. Compared with clinical signals, no significant difference is found in the feature of digitization CTG. ConclusionThe proposed digitization method is a promising useful tool to realize the electronization of CTG signal.

Single-Channel Bioimpedance Measurement for Wearable Continuous Blood Pressure Monitoring

Pulse transit time (PTT) sensing technology is rapidly evolving for cuffless continuous blood pressure (BP) monitoring. However, PTT-based BP methods must implement multidevices that could impede wearable applications. This work presents the study and development of a single-channel wrist-worn impedance plethysmography (IPG) system for wearable continuous BP measurement. IPG-based BP mathematical model with two-step calibration was derived for single physiological signal acquisition. The high-resolution IPG measurement was performed by optimized injection current frequency and electrode arrangement in response to obvious impedance difference between systolic BP (SBP) and diastolic BP (DBP) feature points in the IPG waveform, resulting in an impedance difference of 594 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}\Omega $ </tex-math></inline-formula> . The BP accuracy results within 30 subjects indicated that SBP estimation error was 2.01 ± 1.40 mmHg and 1.83 ± 1.29% in terms of mean absolute error (MAE) and mean absolute percentage error (MAPE), and 2.26 ± 1.43 mmHg and 4.96 ± 3.02% for DBP estimation. The novel system could potentially be implemented as a wearable cardiovascular monitoring device with two-in-one functions (continuous BP and heart rate monitoring) using a single physiological sensor in the future.

Peat smoke inhalation alters blood pressure, baroreflex sensitivity, and cardiac arrhythmia risk in rats

ABSTRACT Wildland fires (WF) are linked to adverse health impacts related to poor air quality. The cardiovascular impacts of emissions from specific biomass sources are however unknown. The purpose of this study was to assess the cardiovascular impacts of a single exposure to peat smoke, a key regional WF air pollution source, and relate these to baroreceptor sensitivity and inflammation. Three-month-old male Wistar-Kyoto rats, implanted with radiotelemeters for continuous monitoring of heart rate (HR), blood pressure (BP), and spontaneous baroreflex sensitivity (BRS), were exposed once, for 1-hr, to filtered air or low (0.38 mg/m3 PM) or high (4.04 mg/m3) concentrations of peat smoke. Systemic markers of inflammation and sensitivity to aconitine-induced cardiac arrhythmias, a measure of latent myocardial vulnerability, were assessed in separate cohorts of rats 24 hr after exposure. PM size (low peat = 0.4–0.5 microns vs. high peat = 0.8–1.2 microns) and proportion of organic carbon (low peat = 77% vs. high peat = 65%) varied with exposure level. Exposure to high peat and to a lesser extent low peat increased systolic and diastolic BP relative to filtered air. In contrast, only exposure to low peat elevated BRS and aconitine-induced arrhythmogenesis relative to filtered air and increased circulating levels of low-density lipoprotein cholesterol, complement components C3 and C4, angiotensin-converting enzyme (ACE), and white blood cells. Taken together, exposure to peat smoke produced overt and latent cardiovascular consequences that were likely influenced by physicochemical characteristics of the smoke and associated adaptive homeostatic mechanisms.

Early-Onset Neonatal listeriosis –A Case Report

A 25-year-old primigravida in her 30 weeks of gestation presented with fever and some flu-like symptoms. There was minimal vaginal discharge. Keeping in view the possibility of chorioamnionitis a prompt intravenous infusion of antibiotics was instituted with continuous external monitoring of fetal heart rate. An emergency cesarean section was performed 3 hours later for persistent fetal tachycardia suggestive of fetal distress. A preterm baby girl was born with evidence of respiratory distress requiring ventilatory support. Listeria monocytogenes was isolated from newborn’s blood culture, tracheal secretions and the placental swab, confirming neonatal listeriosis. Cerebro-spinal fluid examination did not reveal evidence of meningitis. Prompt laboratory diagnosis and aggressive antibiotic treatment led to complete recovery of the infant from septicemia. This represents the first reported case of early-onset neonatal listeriosis in Malaysia

Non-Invasive PPG-Based System for Continuous Heart Rate Monitoring of Incubated Avian Embryo.

The chicken embryo is a widely used experimental animal model in many studies, including in the field of developmental biology, of the physiological responses and adaptation to altered environments, and for cancer and neurobiology research. The embryonic heart rate is an important physiological variable used as an index reflecting the embryo’s natural activity and is considered one of the most difficult parameters to measure. An acceptable measurement technique of embryonic heart rate should provide a reliable cardiac signal quality while maintaining adequate gas exchange through the eggshell during the incubation and embryonic developmental period. In this paper, we present a detailed design and methodology for a non-invasive photoplethysmography (PPG)-based prototype (Egg-PPG) for real-time and continuous monitoring of embryonic heart rate during incubation. An automatic embryonic cardiac wave detection algorithm, based on normalised spectral entropy, is described. The developed algorithm successfully estimated the embryonic heart rate with 98.7% accuracy. We believe that the system presented in this paper is a promising solution for non-invasive, real-time monitoring of the embryonic cardiac signal. The proposed system can be used in both experimental studies (e.g., developmental embryology and cardiovascular research) and in industrial incubation applications.

Silent hypoxia in patients with SARS CoV-2 infection before hospital discharge

ObjectiveTo assess the degree of hypoxia and subjective dyspnea elicited by a 6-minute walking test (6MWT) in COVID-19 patients prior to discharge. MethodsA 6MWT was performed in 26 discharge-ready COVID-19 patients without chronic pulmonary disease or cardiac failure. Heart rate, oxyhemoglobin saturation (SpO2), respiratory rate, and subjective dyspnea measured on the Borg CR-10 scale were measured before and immediately after the 6MWT, with continuous monitoring of SpO2 and heart rate during the 6MWT. The 6MWT was terminated if SpO2 dropped below 90%. A historical cohort of 204 patients with idiopathic pulmonary fibrosis (IPF) was used for comparison. Results13 (50%) of the COVID-19 patients developed exercise-induced hypoxia (SpO2 < 90%) during the 6MWT, of which one third had pulmonary embolism. COVID-19 patients experienced less hypoxia-related dyspnea during the 6MWT compared with patients with IPF. ConclusionThe 6MWT is a potential tool in the diagnosis of asymptomatic exercise-induced hypoxia in hospitalized COVID-19 patients prior to discharge. Due to important methodological limitations, further studies are needed to confirm our findings and to investigate their clinical consequences.

Robust PPG-based Ambulatory Heart Rate Tracking Algorithm.

Recent advances in wearable devices with optical Photoplethysmography (PPG) and actigraphy have enabled inexpensive, accessible, and convenient Heart Rate (HR) monitoring. Nevertheless, PPG's susceptibility to motion presents challenges in obtaining reliable and accurate HR estimates during ambulatory and intense activity conditions. This study proposes a lightweight HR algorithm, TAPIR: a Time-domain based method involving Adaptive filtering, Peak detection, Interval tracking, and Refinement, using simultaneously acquired PPG and accelerometer signals. The proposed method is applied to four unique, wrist-wearable based, publicly available databases that capture a variety of controlled and uncontrolled daily life activities, stress, and emotion. The results suggest that the current HR prediction is significantly (P<0.01) more accurate during intense activity conditions than the contemporary algorithms involving Wiener filtering, time-frequency analysis, and deep learning. The current HR tracking algorithm is validated to be of clinical-grade and suitable for low-power embedded wearable systems as a powerful tool for continuous HR monitoring in real-world ambulatory conditions.

Internal Load In Football Players: Can The Mind See What The Heart Says?

Rating of perceived exertion (RPE) is a simple, validated, measure of physical effort, largely reflecting heart rate (HR). However, RPE is being replaced by continuous HR monitoring to quantify internal loads which maximize performance and enhance recovery. PURPOSE: Primary aim: assess relationships between RPE (athlete and coach) versus HR during football practice. Secondary aims: quantify internal load via HR assessments of training impulse (TRIMP) and recovery. METHODS: Data represents pre-season testing, in an ongoing study. Twenty-nine collegiate male football players (age=20±2years; body mass index= 34±6kg/m2; weight=118±20kg) and 3 coaches volunteered to participate. Each athlete was pre-assigned a HR monitor for the first 7 days of football camp. All participants underwent a 3-minute quick recovery test (QRT). HR monitors were then worn during practice. Athlete HR, TRIMP, %heart rate reserve (%HRR), %heart rate max (%HRmax) were recorded and visible only to researchers. Immediately following practice, all athletes and coaches were asked to assess RPE for the entire training session (sRPE) using the Borg CR-10 scale. %HRmax and %HRR were transformed to scale from 1-10, to match the Borg CR-10. Significance set at p<0.05. RESULTS: Training sessions over this 7-day period, lasted 145.8±33.7 minutes. When weekly data were combined, significant overall correlations (pairwise;N=187) were noted between %HRR versus: %HRmax (r=0.84;p<0.0001), athlete sRPE (r=0.30;p<0.0001) and head coach RPE (r=0.30;p<0.0001). Athlete sRPE was correlated with one coach sRPE (r=0.30;p<0.0001). Athletes were then subdivided into Big, Combo, and Skill position groups. Two-way ANOVA for sRPE between athletes, coaches and %HRR demonstrated a significant interaction effect (F=15.2;p<0.0001;22.8%), time effect (F=36.4;p<0.0001;13.6%), and position effect between groups (F=26.1;p<0.0001;6.5%). TRIMP decreased over time, with a significant time effect (F=23.7;p<0.0001;33%) and position effect (F=5.4;p=0.005;2.5%). Trend for QRT to dip on Day 2, then increase over time. CONCLUSIONS: Athletes and one coach significantly predicted training effort (HR) during practice. However, the variance was small (9%) and sRPE remained steady despite decreased training load and increased QRT scores (trend) over time.

Monitoring of vital bio-signs by analysis of speckle patterns in a fabric-integrated multimode optical fiber sensor.

Continuous noninvasive measurement of vital bio-signs, such as cardiovascular parameters, is an important tool in evaluation of the patient's physiological condition and health monitoring. Based on new enabling technologies, continuous monitoring of heart and respiration rate, pulse wave velocity and blood pressure have been investigated, advanced and reflected in numerous papers published in recent years. In this paper, we introduce a new technique for noninvasive sensing of vital bio-signs based on a multimode optical fiber sensor that can be integrated into a fabric. The sensor consists of a laser, optical fiber, video camera and computer. Its operation is based on tracking of point-wise intensity variations on speckle patterns caused by interference of the light modes within the fiber subjected to deformation. The paper contains theoretical analysis and experimental validation of the proposed scheme. The main goal is to advance a simple low-cost sensor embedded in a cloth fabric to track changes in the cardiovascular condition of the wearer.

Detection of Artifacts in Ambulatory Electrodermal Activity Data

Recent wearable devices enable continuous and unobtrusive monitoring of human's physiological parameters, like e.g., electrodermal activity and heart rate, over long periods of time in everyday life settings. Continuous monitoring of these parameters enables the creation of systems able to predict affective states and stress with the goal of providing feedback to improve them. Deployment of such systems in everyday life settings is still complex and prone to errors due to the low quality of the collected data impacted by the presence of artifacts. In this paper we present an automatic approach to detect artifacts in electrodermal activity (EDA) signals collected in-the-wild over long periods of time. To this end we first perform a systematic literature review and compile a set of guidelines for human annotators to label artifacts manually and we use these labels as ground-truth to test our automatic approach. To evaluate our approach, we collect physiological data from 13 participants in-the-wild and two human annotators label 107.56 hours of this data set. We make the data set publicly available to other researchers upon request. Our model achieves a recall of 98% for clean and shape artifacts classification on data collected in-the-wild using leave-one-subject-out cross-validation, which is 42 percentage points higher than the baseline. We show that state of the art approaches do not generalize well when tested with completely in-the-wild data and identify only 17% of the artifacts present in our data set, even after manual adaption. We further test the robustness of our approach over time using leave-one-day-out and achieve very similar performance. We then introduce a new metric to evaluate the quality of EDA segments that considers the impact of not only artifacts in the shape of EDA but also artifacts generated by environmental temperature changes or user's high intensity movement. Our results imply that we can eliminate the need for human annotators or significantly reduce the time they need to label data. Also, our approach can be used in an online manner to automatically detect artifacts in EDA signals.

SO052EFFECT OF SEVERE RENAL IMPAIRMENT OR END-STAGE RENAL DISEASE ON THE PHARMACOKINETICS AND PHARMACODYNAMICS OF ROXADUSTAT, AN ORAL HYPOXIA-INDUCIBLE FACTOR PROLYL HYDROXYLASE INHIBITOR

Abstract Background and Aims Roxadustat is an orally active hypoxia-inducible factor prolyl hydroxylase inhibitor used at titrated doses to treat anaemia in CKD patients. This study investigated the pharmacokinetics (PK), metabolic profile, pharmacodynamics, and safety of roxadustat in subjects with different degrees of renal function (RF). Method This was a phase 1, open-label study conducted in Germany and the United Kingdom (EudraCT Number: 2015-002565-28). Subjects were enrolled in four RF groups: normal RF (eGFR ≥90 mL/min/1.73 m2); severely impaired RF (not on dialysis; eGFR &amp;lt;30 mL/min/1.73 m2); end-stage renal disease (ESRD) on continuous ambulatory or automated peritoneal dialysis (CAPD/APD); or ESRD on 4-hour haemodialysis or haemodiafiltration (HD/HDF). Subjects in the normal RF, severely impaired RF, and ESRD on CAPD/APD groups received a single dose of 100-mg oral roxadustat on Day 1. A single-sequence design with two treatment periods (P1/P2) separated by a washout period was used for subjects with ESRD on HD/HDF; a single administration of 100-mg oral roxadustat occurred 2 hours after completion of a HD/HDF session (P1) and 2 hours before initiation of a HD/HDF session, 2 days after the previous session (P2). Plasma and urine PK of roxadustat and its circulating metabolites (O-glucuronide-, O-glucoside-, and sulphate of hydroxy-roxadustat) and plasma erythropoietin (EPO) were evaluated. Continuous heart rate monitoring was conducted. Results Of 73 screened subjects, 34 (46.6%) were enrolled and received roxadustat (normal RF, n=12; severely impaired RF, n=9; ESRD on CAPD, n=1; ESRD on HD/HDF, n=12). The geometric least-square mean ratio of area under the plasma concentration-time curve (AUC) from administration to infinity (AUCinf) relative to subjects with normal RF was 223% (90% CI: 185, 268) and 195% (90% CI: 165, 229) in subjects with severely impaired RF and in those with ESRD on HD/HDF, respectively. Roxadustat’s maximum concentration (Cmax) and terminal elimination half-life (t1/2) were comparable between groups. The PK of roxadustat and its metabolites were not affected by HD/HDF. In subjects with normal RF, the amount of roxadustat excreted unchanged in urine was &amp;lt;1%; urinary excretion and renal clearance of roxadustat and its metabolites decreased with lower baseline RF. Mean AUCinf and t1/2 for roxadustat’s circulating metabolites were higher in subjects with severely impaired RF and with ESRD on HD/HDF than in those with normal RF. Metabolite to parent ratio of AUCinf was &amp;lt;1% for O-glucuronide- and O-glucoside-roxadustat and &amp;lt;10% for sulphate of hydroxy-roxadustat. EPO baseline-corrected AUC from administration to last measurable concentration and EPO maximum effect were higher in subjects with severely impaired RF or with ESRD on HD/HDF than in those with normal RF. Conclusion The AUC for roxadustat and its metabolites was higher in patients with severely impaired RF or with ESRD on HD/HDF than in those with normal RF. Roxadustat’s Cmax and t1/2 were comparable among all groups. Roxadustat and its metabolites were not significantly cleared by HD/HDF.

Is Continuous Heart Rate Monitoring of Livestock a Dream or Is It Realistic? A Review.

For all homoeothermic living organisms, heart rate (HR) is a core variable to control the metabolic energy production in the body, which is crucial to realize essential bodily functions. Consequently, HR monitoring is becoming increasingly important in research of farm animals, not only for production efficiency, but also for animal welfare. Real-time HR monitoring for humans has become feasible though there are still shortcomings for continuously accurate measuring. This paper is an effort to estimate whether it is realistic to get a continuous HR sensor for livestock that can be used for long term monitoring. The review provides the reported techniques to monitor HR of living organisms by emphasizing their principles, advantages, and drawbacks. Various properties and capabilities of these techniques are compared to check the potential to transfer the mostly adequate sensor technology of humans to livestock in term of application. Based upon this review, we conclude that the photoplethysmographic (PPG) technique seems feasible for implementation in livestock. Therefore, we present the contributions to overcome challenges to evolve to better solutions. Our study indicates that it is realistic today to develop a PPG sensor able to be integrated into an ear tag for mid-sized and larger farm animals for continuously and accurately monitoring their HRs.

Ensemble based technique for the assessment of fetal health using cardiotocograph – a case study with standard feature reduction techniques

Intrauterine fetal hypoxia is one of the leading cause of perinatal mortality and morbidity. This can eventually lead to severe neurological damage like cerebral palsy and in extreme cases to fetal demise. It is thus necessary to monitor the fetus during intrapartum and antepartum period. Cardiotocograph (CTG) as a method of assessing the status of the fetus had been in use for last six decades. Nowadays it is the most widely used non-invasive technique for the continuous monitoring of the fetal heart rate (FHR) and the uterine contraction pressure (UCP). Though its introduction limited the birth related problems, the accuracy of interpretation was hindered by quite a few factors. Different guidelines that are provided for the interpretation are based on crisp logic which fails to capture the inherent uncertainty present in the medical diagnosis. Misinterpretations had led to inaccurate diagnosis which resulted in many medico-legal litigations. The vagueness present in the physician’s evaluation is best modeled using soft-computing based techniques. In this paper authors used the CTG dataset from UCI Irvine Machine Learning Data Repository which contains 2126 data and each data-point is represented by 37 features. Dimensionality of the feature set was reduced using different automated methods as well as manually by the physicians. The resulting data sets were classified using various machine learning algorithms. Aim of this study is to establish which set of features is best suited to give good insight into the status of the fetus and also determine the most effective machine learning technique for this purpose. The accuracy of the outcomes were measured using statistical methods such as sensitivity, specificity, precision, F-Measure, confusion matrix and kappa value. We obtained an accuracy of 99.91% and kappa measure of 0.997 when the feature set was reduced using MRMR.

Compliance with Guidelines and Efficacy of Heart Rate Monitoring during Newborn Resuscitation: A Prospective Video Study

Objective: Newborn resuscitation guidelines recommend initial assessment of heart rate (HR) and initiation of positive pressure ventilation (PPV) within 60 s after birth in non-breathing newborns. Pulse oximeter (PO) and electrocardiogram (ECG) are suggested methods for continuous HR monitoring during resuscitation. Our aim was to evaluate compliance with guidelines and the efficacy of PO versus ECG monitoring in real-life newborn resuscitations. Methods: In this prospective observational study, we video recorded resuscitations of newborns ≥34 weeks of gestation receiving PPV at birth. Results: 104 resuscitations were analysed. Median (IQR) time from birth to arrival at the resuscitation bay was 48 (22–68) s (n = 62), to initial HR assessment 70 (47–118) s (n = 61), and to initiation of PPV 78 (42–118) s (n = 62). Initial HR assessment (stethoscope or palpation) and initiation of PPV were achieved within 60 s for 35% of the resuscitated newborns. Time to initial HR assessment and initiating PPV was significantly longer following vaginal deliveries than caesarean sections: 84 (70–139) versus 44 (30–66) s (p < 0.001) and 93 (73–139) versus 38 (30–66) s (p < 0.001). Time from birth and sensor application to provision of a reliable HR signal from PO versus ECG was 348 (217–524) (n = 42) versus 174 (105–277) s (n = 30) (p < 0.001) and 199 (77–352) (n = 65) versus 16 (11–22) s (n = 52) (p < 0.001). Conclusion: Initial HR assessment and initiation of PPV were achieved within 60 s after birth in only 1/3 of newborn resuscitations. When applied for continuous HR monitoring, ECG was superior to PO in time to achieve reliable HR signals in real-life resuscitations.