Heart Rate Detection Research Articles

Assessing the efficacy of electrocardiogram for heart rate evaluation during newborn resuscitation at birth: a prospective observational study.

This study aimed to assess the efficacy of electrocardiogram (ECG) compared to pulse oximetry (PO) in detecting heart rate (HR) during high-risk newborn resuscitation. A prospective observational study was performed with high-risk delivery cases to measure the time required for HR detection. A conventional PO and a standard ECG monitor were used for HR assessment. Forty-one infants were analyzed in the study, and 11 among them needed resuscitation. Nine were <32 gestational weeks (GA), 28 were 33-35 GA, and 4 were >36 GA at birth. ECG time for placement to HR detection was significantly faster than PO detection [30 (20-43.5) vs. 125 (100-175) seconds, P<0.001]. ECG time for placement to HR detection was the fastest in the infants below 32 GA at birth [19 (11.5-30) vs. 34.5 (25-44.25) vs. 39.5 (30-64.75) seconds, P=0.039). ECG effectively evaluated HR during neonatal resuscitation compared to PO. Low gestational age infants who need resuscitation often may benefit in HR evaluation with nearby standard ECG.

Modification of SMWT for remote heart’s functional capacity determination from oxygen consumption rate

Cardiovascular disease (CVD) is series of diseases affects blood circulation and heart which are fatal to human being. CVD patients recorded that every each of them must have at least one among the following behavioural risks: smoking, diabetes, obesity, hypertension, high cholesterol, and sedentary lifestyle. The diagnosis for CVD is commonly through cardiac stress test. It is a test where electrocardiogram will be used to monitor heart activity under induced stress such as walking on treadmill. However, the facilities for the cardiac stress test are very limited and increasing number of patients with potential CVD caused a long queue for an appointment. This situation makes it hard for healthy people living with behavioral risk wanting for their heart to be checked. Therefore, in this paper, we proposed to determine heart functional capacity which can be done anywhere and sufficient to access heart condition. For this proposal, despite using treadmill-test, Six-Minutes Walking Test (SMWT) is utilized. SMWT induces stress while the heart’s endurance parameters such as heart rate is extracted. This study developed an integrated sensor device which comprises of a sensor to detect step count calculate distance, and a pulse sensor that detect heart rate. Then, oxygen consumption rate during the SMWT, is computed. Metabolic equivalent of task (MET) is equal to the oxygen consumption rate during activity over resting oxygen consumption rate. Heart functional capacity classification is about exercise intensity and the MET of SMWT can be compared to the classification which allows this paper to determine heart functional capacity remotely using SMWT. Based on the results, the heart’s functional capacity classification after performing SMWT using sensor device is Class 1, the same when using manual calculation for distance and heart rate. The device sensor somehow produce error at 20.63% when calculating oxygen uptake. Nevertheless, this paper is identified that the SMWT is able to classify the heart’s functional capacity.

Tyrosine hydroxylase-positive neurons in the rostral ventrolateral medulla mediate sympathetic activation in sepsis

AimSepsis results in high mortality and is associated with organ dysfunction caused by infection. The present study aimed to elucidate whether early-stage sympathetic activation is associated with the prognosis of sepsis and its possible mechanisms. MethodsPatients with sepsis and healthy controls were included. Sepsis in rats was induced by lipopolysaccharide. Dexmedetomidine, a α2-adrenergic receptor agonist, was used in patients and rats with sepsis to evaluate the role of the sympathetic nervous system in sepsis. Holter monitoring was used to detect heart rate variability, while plasma samples were obtained to measure levels of norepinephrine and inflammatory markers. Mean arterial pressure, heart rate, and renal sympathetic nerve activity were recorded. Immunofluorescence was used to detect the activation of neurons in the rostral ventrolateral medulla (RVLM). ResultsIn patients with sepsis, plasma levels of norepinephrine and interleukin-1β were higher compared with those in controls and positively correlated with acute physiology and chronic health evaluation (APACHEII). SDNN and SDANN were significantly reduced as well as negatively correlated with APACHEII. Meanwhile, rats with sepsis showed increased of sympathetic outflow and plasma levels of norepinephrine, with increased c-fos levels in the RVLM. Treatment with dexmedetomidine could improve prognosis. Lesion of tyrosine hydroxylase-positive neurons in the RVLM attenuated sympathetic activation and target organs damage in septic rats as well as improved survival. ConclusionThe results suggest that tyrosine hydroxylase-positive neurons in the RVLM might contribute to the prognosis of sepsis via activation of the sympathetic nervous system, while dexmedetomidine could ameliorate sepsis via inhibiting sympathetic activation.

Arduino-Powered Remote Monitoring System for Real-Time Heart Rate Detection via Bluetooth

With the increasing need for continuous health monitoring, this study presents the development of an Arduinopowered remote monitoring system for real-time heart rate detection via Bluetooth. The system uses an Arduino microcontroller connected to a pulse sensor to measure heart rate, with data wirelessly transmitted via Bluetooth to a remote device, enabling continuous monitoring without direct patient interaction. Initial self-monitoring trials showed consistent heart rate detection within the normal range of 62-70 beats per minute (BPM), suggesting proper functionality for preliminary use. The system also maintained a stable Bluetooth connection with no noticeable data loss. This cost-effective and efficient system is a valuable tool for telemedicine and remote patient care. Future enhancements may include integrating advanced microcontrollers and developing a more sophisticated mobile app with expanded health monitoring features.

Design of an AI-based egg fertility detection system for incubators

The conventional approach to determine the fertility of chicken eggs, called candling, is subjective, time-consuming, and ineffective and thereby results in low hatching rates and financial losses. As a result, a more reliable, accurate, and sustainable system that uses artificial intelligence and automated systems to improve the process of assessing egg fertility for incubation. The system proposes capturing of images of eggs at the early stages of incubation by a camera and sent to a cloud server, where a Convolutional Neural Network (CNN) analyses and classifies the data to determine the fertility status of the eggs. The system aims to increase the quality and hatching rate of chicken eggs, lower labour expenses and human error, and boost the poultry industry's sustainability and profitability. The framework of the system is based on the current poultry egg fertility assessment methods in Zimbabwe, which include candling, infrared thermography, ultrasound, heart rate detection, oxygen flux detection, visible or near-infrared transmittance spectroscopy, and thermal imaging. The system is expected to perform better than existing systems in terms of accuracy, speed, and cost-effectiveness. The experimental results show that, the method's average identification time is 0.210 seconds, and its identification accuracy is 98.40 percent. These results demonstrate the viability of the suggested AI-based method for automatically determining an egg's fertility without human intervention, greatly increasing hatcheries' and related businesses' productivity.

Validity of Wrist-Worn Activity Tracker Heart Rate Detection in Fontan Patients During Exercise.

Physical activity and a healthy lifestyle play an essential role in optimizing long-term health in patients with Fontan physiology. Wrist-worn activity trackers may be useful in medically directed exercise programs for patients with Fontan physiology. The objective of this study was to measure the validity of Garmin and Fitbit activity tracker heart rate detection in patients with Fontan circulation when compared to electrocardiogram (ECG) during cardiopulmonary exercise testing (CPET). 47 Fontan patients undergoing CPET for clinical indications were included and wore activity trackers during CPET. Heart rate via the activity tracker was collected at baseline, maximal exercise, and recovery. Patient heart rates, peak VO2, and peak respiratory exchange ratio (RER) were collected using standard CPET protocols and equipment. Heart rate at each time point was compared between the activity trackers and CPET ECG. Median age of participants was 17.1 years, 15.1 years since Fontan completion. Mean percent of predicted peak VO2 was 56.8%, z-score -3.2 with 61.7% of participants completing a maximal CPET (RER ≥ 1.09). Baseline oxygen saturation mean was 92.9%, 90.0% at maximal exercise. Activity trackers demonstrated mean absolute percentage error < 10% at most time points, comparable with other studies. Demographics, Fontan-associated comorbidities, and echocardiogram findings did not impact the accuracy. Consumer-oriented wrist-worn activity trackers show promising accuracy for heart rate monitoring in medically directed exercise programs for adolescents and young adults with Fontan physiology. Further validation across different exercise modalities is needed.

Wearable broadband MoS2 photodetector for dual heart rate and UV detection powered by PDMS-MXene TENG

Wearable electronic devices capable of monitoring health parameters and environmental factors are tremendously desirable. Environmental energy harvesting as a power source is crucial to replace traditional batteries. This study aims to contribute to the development of sustainable and efficient wearable devices for health monitoring and environmental sensing. In this study, a self-powered wearable MoS2-based photodetector, integrated with a PDMS-MXene-based triboelectric nanogenerator (TENG) is presented. The TENG generates isometric AC voltage using a diode, and it is used to charge a capacitor for DC voltage output. The photodetector, with a responsivity of 1.25 × 105A/W, specific detectivity of 2×1013 Jones, millisecond response time, enhanced responsivity of 1.1×1010 V/W and specific detectivity of 9.5×1016 Jones while coupled with TENG, successfully measured heart rate using a 660 nm LED and compared well with a commercial pulse meter. Additionally, UV illumination was detected using a 395 nm LED in the AC mode of the TENG. The study’s results support the advancement of flexible, integrated, and sustainable wearable devices, offering solutions to challenges posed by current battery-powered devices.

Low foetal heart rate, a potentially ominous finding: case report

Abstract Background Congenital long QT syndrome (LQTS) type 1 is characterized by abnormally prolonged ventricular repolarization caused by inherited defects in cardiac potassium channels. Patients are predisposed to ventricular arrhythmias and even sudden cardiac death. In some cases, foetal sinus bradycardia is the only sign, making prenatal diagnosis challenging. Physicians should be aware of this subtle presentation of LQTS. Early diagnosis and proactive treatment are crucial for preventing unexpected cardiac events. Case summary A healthy and asymptomatic 25-year-old pregnant woman was referred to our institute for cardiac evaluation after persistent foetal sinus bradycardia was detected during repeated ultrasounds, despite the absence of any foetal morphological or functional cardiac anomalies. After a thorough assessment, the mother was diagnosed with LQTS type 1, as confirmed by molecular genetic testing. Appropriate management, including maternal medication and increased surveillance, was initiated. The infant was delivered safely, and his electrocardiogram revealed a significantly prolonged QTc interval. Genetic testing confirmed the maternally inherited variant in KCNQ1 gene, and beta-blocker therapy was started. No arrhythmic events were noted. Discussion Detection and careful stratification of foetal heart rate (FHR) is crucial in every pregnancy. Foetal bradycardia can be caused by both maternal and foetal factors. Persistent low FHR should raise a high suspicion for LQTS. The condition may also present with atrioventricular blocks, torsades de pointes, or sudden intrauterine foetal demise. Accurate and early diagnosis of LQTS is essential for implementing appropriate management strategies, which include vigilant monitoring, effective medical treatment, careful planning of delivery, and post-natal care.

PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring—A Comprehensive Review

The monitoring of hemodynamic parameters, such as heart rate and blood pressure, provides valuable indications of overall cardiovascular health. It is preferable that such monitoring is non-invasive and in real time via an affordable, compact and small-scale device for maximum convenience. Numerous literature sources have exploited derivations of these parameters from photoplethysmogram (PPG) and electrical bioimpedance (EBI) signal measurements through the use of calculation algorithms of varying complexity. Compared to electrocardiogram (ECG), these measurement techniques have a merit of well-established practices of designing a wearable device that could conveniently be put on a wrist. The current paper provides a comprehensive review on the use of PPG and EBI measurement techniques in the context of hemodynamic parameter monitoring using a wearable device. A special emphasis is placed on the most basic hemodynamic parameter—heart rate—describing different algorithms of heart rate detection and monitoring. The last section provides an overview of commercially available and in-home wearable device technologies based on PPG and EBI measurements, their design challenges, and future prospects.

Enhancing Road Safety: Fast and Accurate Noncontact Driver HRV Detection Based on Huber-Kalman and Autocorrelation Algorithms.

Enhancing road safety by monitoring a driver's physical condition is critical in both conventional and autonomous driving contexts. Our research focuses on a wireless intelligent sensor system that utilizes millimeter-wave (mmWave) radar to monitor heart rate variability (HRV) in drivers. By assessing HRV, the system can detect early signs of drowsiness and sudden medical emergencies, such as heart attacks, thereby preventing accidents. This is particularly vital for fully self-driving (FSD) systems, as it ensures control is not transferred to an impaired driver. The proposed system employs a 60 GHz frequency-modulated continuous wave (FMCW) radar placed behind the driver's seat. This article mainly describes how advanced signal processing methods, including the Huber-Kalman filtering algorithm, are applied to mitigate the impact of respiration on heart rate detection. Additionally, the autocorrelation algorithm enables fast detection of vital signs. Intensive experiments demonstrate the system's effectiveness in accurately monitoring HRV, highlighting its potential to enhance safety and reliability in both traditional and autonomous driving environments.

Predicting the Outcomes of In Vitro Fertilization Using Baseline Maternal Serum Inflammatory Markers: A Retrospective Cohort Study.

Achieving pregnancy through in vitro fertilization (IVF) remains a challenge, with less than one-third of women succeeding. There is a pressing need for reliable predictive tools to assess the likelihood of post-IVF pregnancy. While some serum inflammatory biomarkers have been investigated for their predictive potential, substantial knowledge gaps persist. This study examined the utility of different inflammatory markers in predicting IVF outcomes. Inflammatory markers including the white blood cell count, neutrophil-to-lymphocyte ratio (NLR), platelet count, mean platelet volume, platelet distribution width, platelet-to-lymphocyte ratio (PLR), C-reactive protein (CRP), erythrocyte sedimentation rate, and vitamin D3 were assessed. Study outcomes were chemical pregnancy (positive serum beta-human chorionic gonadotropin 2 weeks post-embryo transfer), clinical pregnancy (detection of pregnancy sac via transvaginal ultrasonography), and viable pregnancy (detection of fetal heart rate). Univariate and multivariate logistic regression analyses were conducted, with multivariate analysis incorporating age, body mass index, infertility duration, type, and etiology, as well as all studied serum inflammatory markers, embryo count, stage, quality, and endometrial thickness. Lower NLR (p<0.001, odds ratio [OR]=0.372 [0.247-0.559]) and CRP (p=0.035, odds ratio=0.956 [0.916-0.997]) predicted chemical pregnancy in univariate analysis, with NLR maintaining significance in multivariate analysis (p=0.022, OR=0.319 [0.120-0.848]). Lower NLR (p<0.001, OR=0.309 [0.198-0.482]) and PLR (p=0.013, OR =0.994 [0.990-0.999]) predicted clinical pregnancy, with NLR surviving multivariate analysis (p=0.005, OR = 0.217 [0.075-0.626]). Lower NLR (p<0.001, OR = 0.320 [0.198-0.516]) also predicted viable pregnancy, maintaining statistical significance in multivariate analysis (p = 0.002, OR = 0.177 [0.058-0.541]). Other studied inflammatory markers did not predict IVF outcomes. NLR emerged as a robust independent predictor of pregnancy attainment after IVF.

High-accuracy heart rate detection using multispectral IPPG technology combined with a deep learning algorithm.

Image Photoplethysmography (IPPG) technology is a noncontact physiological parameter detection technology, which has been widely used in heart rate (HR) detection. However, traditional imaging devices still have issues such as narrower receiving spectral range and inferior motion detection performance. In this paper, we propose a HR detection method based on multi-spectral video. Our method combining multispectral imaging with IPPG technology provides more accurate physiological information. To realize real-time evaluation of HR directly from facial multispectral videos, we propose a new end-to-end neural network, namely IPPGResNet18. The IPPGResNet18 model was trained on the multispectral video dataset from which better results were achieved: MAE = 2.793, RMSE = 3.695, SD = 3.707, p = 0.304. The experimental results demonstrate a high accuracy of HR detection under motion state using this detection method. In respect of real-time monitoring of HR during movement, our method is obviously superior to the conventional technical solutions.

Optimal signal quality index for remote photoplethysmogram sensing

Remote photoplethysmography (rPPG) enables non-invasive monitoring of circulatory signals using mobile devices, a crucial advancement in biosensing. Despite its potential, ensuring signal quality amidst noise and artifacts remains a significant challenge, particularly in healthcare applications. Addressing this, our study focuses on a singular signal quality index (SQI) for rPPG, aimed at simplifying high-quality video capture for heart rate detection and cardiac assessment. We introduce a practical threshold for this SQI, specifically the signal-to-noise ratio index (NSQI), optimized for straightforward implementation on portable devices for real-time video analysis. Employing (NSQI < 0.293) as our threshold, our methodology successfully identifies high-quality cardiac information in video frames, effectively mitigating the influence of noise and artifacts. Validated on publicly available datasets with advanced machine learning algorithms and leave-one-out cross-validation, our approach significantly reduces computational complexity. This innovation not only enhances efficiency in health monitoring applications but also offers a pragmatic solution for remote biosensing. Our findings constitute a notable advancement in rPPG signal quality assessment, marking a critical step forward in the development of remote cardiac monitoring technologies with extensive healthcare implications.

ETL-FEXIC Model For Secured Heart Rate Abnormality Healthcare Framework

In traditional methods, it is critical for an effective continuous pulse monitor for humans prone to heart rate abnormalities. This paper proposes a secured heartrate abnormality detector which continuously monitors human pulse rate and SpO2 level. The current studies proposes that machine learning (ML) models performs well in classification; also, TinyML model shows better performance for data from resource constrained IoT devices. Hence, the research first analyses abnormal heart rate detection and spam data identification using standard ML algorithms such as SVM, Random Forest, Decision Tree, and TinyML. Though ML models are superior in classification, deep learning approaches outperforms them in feature learning. Hence, our proposed framework combines the merits of both ML and DL models. In our approach, the generated healthcare dataset is fed to DL models such as ANN, and autoencoder and also to SHAP XAI (eXplainable Artificial Intelligence) for feature extraction and learning. These learnt features are fed to ML models for classification. In this experiment, the proposed ETL-FEXIC (Enhanced Tiny Machine Learning with Automated Feature Extraction) outperforms the other ML models where the extracted features from XAI is fed to optimized TinyML classification model.

Research and Design of A Multifunctional Heart Rate Detection System

Addressing the limitations of conventional heart rate detectors, which solely monitor heart rate without providing alerts during critical incidents, a multifunctional heart rate detection system has been developed. This system incorporates a GD32 as the main controller and utilizes a pulse sensor for heart rate monitoring, an MLX90614 infrared temperature sensor for body temperature measurements, an MPU6050 acceleration sensor for motion posture detection, and a SIM868 module for GPS positioning and server communication. This comprehensive setup allows for real-time monitoring of the wearer's heart rate and body temperature, assessment of fall incidents based on motion posture data, and regular uploading of health data to a server. Additionally, the system is equipped with a one-touch emergency call feature. In the event of abnormal heart rate readings or a fall, it triggers an alarm, acquires GPS coordinates, and automatically dials an emergency contact number. After thorough testing and verification, the system has demonstrated full functionality with heart rate data accuracy within the expected error range, offering a reliable safety mechanism for patients while on the move.

Wearable self-powered ballistic signal detectors with ferroelectric composites arrays for cardiovascular and diabetic complication

Abstract Background Flexible and wearable electronic devices with lightweight are needed to monitor and guide treatment in patients with cardiovascular disease and diabetic complication. We aimed to develop self-powered wearable bio-signal sensing arrays for cardiovascular and diabetic complication diagnosis and continuous monitoring. Methods Ferroelectric composites were designed to have piezoelectric property and high flexibility. Detection of arterial pulse as ballistogram (BCG) was simultaneously compared with electrocardiogram (ECG) in 20 controls. Heart rate and components of ECG was compared with those of BCG. Then, 9 ferroelectric BCG sensors as a square type array with self-power generation were fabricated to attachable thin film. Pressure distribution with BCG sensor array attached to socks were compared between 20 controls and 20 patients with diabetic neuropathy or vasculopathy during standing and ambulation for gait pressure distribution. Pressure distribution pattern was compared to neuropathy and vasculopathy parameters. Results Heart rate between BCG and ECG was significantly correlated (interclass correlation coefficient, ICC 0.99, 95% CI 0.98-0.99, p&amp;lt;0.001). QRS and T wave in ECG were correlated to second and third wave in BCG. Radial artery augmentation index by blood pressure monitoring was significantly correlated to BCG pressure (ICC 0.76, 95% CI 0.38-0.90, p=0.002). Analysis of foot pressure distribution demonstrated the highest pressure point at the anterolateral plantar area below a little tow. However, pressure gradient between anterolateral and anteromedial plantar area in diabetic patients was greater than that in controls (42.3±10.2 mV vs. 31.2±8.5 mV, p=0.024). Pressure gradient in diabetic patients was correlated to ankle-brachial index (ICC 0.62, 95% CI 0.32-0.90, p=0.036), but not to neuropathy parameters. Conclusion Wearable BCG sensor could accurately detect heart rate, blood pressure index and significantly correlated to ABI. Pressure distribution pattern and gradient analysis in diabetic patients by wearable BCG sensor arrays might predict diabetic complications. Gait remedial training guided by wearable BCG sensor arrays might aid to prevent diabetic complications.

PSIII-3 Evaluation of remote detection of heart rate, respiratory rate, and body temperature for improved detection of bovine respiratory disease in high-risk receiving calves

Abstract The objective of this study was to evaluate the effectiveness of the VetGuardian remote monitor to create data-driven detection of bovine respiratory disease (BRD) by identifying vital sign abnormalities for early disease detection. Male calves (n = 20, initial body weight = 213 ± 3 kg) were purchased from regional auction markets and shipped to the University of Arkansas Stocker Receiving Unit. Upon arrival, calves were weighed, branded, received an identification tag, vaccinated, dewormed, and bulls were castrated. On d 1, the remote sensor was used to monitor all calves while restrained in the chute system detecting their body temperatures, and heart and respiratory rates. The remote sensor was placed in 1 of 2 locations for recordings: 1) in front of chute to capture the head, or 2) on the side of chute capturing the torso. Each calf was assigned to a scan location (10 calves/scan location). For all calves, rectal temperatures were also recorded manually, and heart and respiratory rates were determined by thoracic auscultation. Before processing and for the following 28 d, cattle were observed daily for clinical BRD. If presenting symptoms of BRD and if rectal temperature was ≥ 40°C, cattle were deemed morbid and treated with an antibiotic according to a standard preplanned protocol. For any cattle that were examined for BRD the remote sensor was again used for a minimum of 5 min to compare with manual recordings. Statistical analyses were performed using the CORR, MIXED, and MEANS procedures of SAS 9.4 with sex as a random effect and individual calf specified as the subject. There was a positive correlation between manual recordings of rectal temperatures and remote recordings from the head (0.67; P = 0.04) and the side (0.88; P = 0.0008). Remote recordings did not correlate (P ≥ 0.21) with manual recordings for heart rates. However, there was a positive correlation (0.79; P = 0.01) between manual respiratory rates and the side scans, but no relationship (P = 0.16) between manually collected respiratory rates with head scans. LSMeans for temperature data collected manually or by the remote sensor at either location did not differ (P ≥ 0.34). LSMeans for heart rate differed between manual and remote measurements from either location (P ≤ 0.0008). Manual measurements for respiratory rate did not differ (P = 0.26) from remote recordings from the side; however, differed (P &amp;lt; 0.0001) from recordings from the head. In conclusion, the VetGuardian remote sensor varied from manual recordings for heart rates, regardless of sensor location. Scan location had an influence on accuracy of respiratory rate recordings, and body temperature recordings from either location did not differ from manual measurements. Preliminary data should be used for further investigation of this remote sensor.

Non-Invasive Fetal Well-Being Monitoring Approaches: A Mini Review on Fetal Signal Separation Techniques

Fetal signal separation is vital in producing an accurate interpretation of the health condition of a fetal. In the context of a non-invasive fetal monitoring approach, the signals are acquired from the abdomen of pregnant women. As a result, a mix of maternal and fetal signals is obtained. These maternal and fetal signals are vague, as both signals are interchanged during the signal acquisition stage. Since the signals are overlapped, a signal separation technique must be employed to process the fetal signal for further analysis. This paper presents published studies on applying signal processing techniques involving fetal signal separation. These papers are obtained through a strategy known as the PRISMA technique. The online databases include ACM, Emerald Publishing, IEEE Explore Digital Library, Science Direct, Scopus, and Springer, with published years spanning from 2018 until 2022. Numerous separation techniques were found, such as adaptive filtering, blind source separation (BSS), and alternative approaches. Issues on the existing methods for fetal signal separation are discussed. In addition, the limitations and drawbacks of the research work involving existing fetal signal separation are reviewed in the paper. The potential direction of future research in this field is addressed as well. Based on this mini-review, it can be concluded that noise and ambiguity can still occur in the extracted fetal signals, even when signal processing techniques are applied. In the future, deep learning would be accommodating in improving the efficiency of extracting fetal signals obtained from the non-invasive fetal well-being monitoring technique. Meanwhile, apart from fetal heart rate (fHR) detection, fetal hypoxia can also be another important focus of study for improving fetal well-being monitoring.

A PRECISE RESPIRATORY AND HEART RATE DETECTION METHOD FOR MILLIMETER-WAVE RADAR

Millimeter wave radar as a type of noncontact sensor can more conveniently and insensibly obtain breathing and heartbeat signals. However, due to the weak radar echo signal and the influence of noise, there may be phase ambiguity and separation distortion of breathing and heartbeat signals during the processing, which affects the accuracy of detection. In this paper, an enhanced extended differential and cross multiplication (EDACM) algorithm is presented to obtain chest position signals without phase ambiguity. A specific order finite impulse response (FIR) filter with a Blackman window is applied to extract breath signals without waveform distortion from phase signals, and the heartbeat signal without rate distortion is extracted from the phase signal by means of the designed Chebyshev II filter. The measured results show that the proposed method achieves 96.78% accuracy for heart rate detection and 94.44% accuracy for respiratory rate detection, and improves the accuracy of heart rate detection by 2.22% and respiratory rate detection by 4.06% compared to the band-pass filter method under the same parameter conditions.

Development of mobile heart rate monitoring tools using artificial neural networks

Currently, there is a great need for mobile monitoring systems used to detect heart rate abnormalities in citizens working or living in hard-to-reach areas. Due to problems with transmitting data from such areas, it is desirable to carry out the entire information processing process at the study site. But, despite the limited hardware costs for the implementation of such systems, it is necessary to ensure high reliability of monitoring results. Purpose is to develop a structural diagram of a mobile heart rate analysis device, select the architecture of the neural network of the conversion and segmentation subsystem of the mobile device, determine the structure of the neural network of the conversion and segmentation subsystem, train the neural network of the conversion and segmentation subsystem, conduct software modeling of the neural network of the conversion and segmentation subsystem, implementation of a neural network conversion and segmentation subsystem based on field programmable gate arrays (FPGAs). A structural diagram of a mobile heart rate analysis device has been developed. The choice of the neural network architecture of the mobile device conversion and segmentation subsystem is justified. The structure of the neural network of the transformation and segmentation subsystem is determined. The neural network of the transformation and segmentation subsystem was trained. A software simulation of the neural network of the transformation and segmentation subsystem was carried out, confirming the high reliability of the proposed neural network structure. A neural network conversion and segmentation subsystem has been implemented in the hardware description language VHDL based on an FPGA. The developed structural diagram of the mobile device and the neural network structure of the conversion and segmentation subsystem can be used when implementing mobile heart rate monitoring means.