Heartbeat Rate Research Articles

HEART ATTACK PREDICTION USING MACHINE LEARNING

Early detection is essential for both the treatment and prevention of heart attacks, which account for the majority of deaths worldwide. A machine learning model for predicting a person's risk of having a heart attack based on various medical factors is the goal of this project. Patients' medical histories, lifestyle, and physical characteristics, such as temperature, humidity, heart rate (in beats per minute), and spo2 (blood oxygen levels) by corresponding sensors. Using machine learning algorithms like the naive Bayes classifier, SVM classifier, and KNN classifier, the data will be cleaned, pre-processed, and analyzed. Using metrics like precision, recall, and the F1-score, the model will be trained and tested on a dataset of patients who are known to be at risk for heart attacks. The final model will be put into use as a web application that lets users enter their medical information to get a customized heart attack risk score. This project aims to provide individuals and healthcare professionals with an easy-to-use, wearable device and accurate tool for identifying heart attack risk factors and taking preventative measures. Key words – Body Temperature, Heart rate, breathing rates, blood oxygen level, smart wearable system

Development of Adaptive Spectrum Noise Cancellation Technique for Enhancing Heartbeat Rate Monitoring

Most of the Heart Rate Monitor (HRM) seems to be a portable monitoring device that can measure and displays real-time heartbeat rate in addition to storing heartbeat rate data for future studies. It is generally used to capture heart rate data during various types of physical exercise. Heart rhythm problems arise whenever the electrical impulses that coordinate the heartbeats aren't working properly. The ineffective signaling causes the heart to beat too fast (tachycardia), overly slowly (bradycardia), or sporadically. In present study, a focused approach on Adaptive Spectrum Noise Cancellation (ASNC) were used for enhancing heartbeat rate monitoring for humans. It finds that simply a photodetector and a source of light are applied to the surface of the skin to monitor the proportional changes in the blood flow. It is concluded that for accurate measurement of heart rate while moving or running, the proposed device ASNC utilizes the application of embedded accelerometer and gyroscope sensors to identify and eradicate the artifacts adaptively. In future, the proposed model may adopted for modification and commercialization of wearable heart-rate sensors based upon photoplethysmography (PPG), it would be better in terms of utility as well as adoptability.

Design and Implementation of Heart Attack Detection and Medication Using Ad a fruit Flora Module

In this project we are trying to decrease the rate of death due to the lack of timely medication during a situation of a heart attack. As we all know the risk of having a heart attack is more now a day’s due to mental stress in daily life. But at the time of a heart attack, if the patient gets the prescribed medicine in time, then the death of the patient can be avoided. So, we have designed a watch which will regularly monitor the heart rate of the person wearing it and then according to the rate of heart beat it will calculate whether the heartbeat of the patient is normal or not. Because in both the case of low heart beat and high heart rate can cause a heart failure. A bradycardia is a condition in which the heart rate is too low, and a tachycardia is a condition in which the heart rate is too high. Thesetwo situations may potentially result in a heart attack or heart failure. The model we created will monitor the heart rate on a regular basis, and if the person's heart rate becomes too high, it will have the prescribed prescription stored in two compartments of the smartwatch. This may aid in the early treatment of the patient and maybe save many lives.

Cocktail effects of emerging contaminants on zebrafish: Nanoplastics and the pharmaceutical diphenhydramine

Nanoplastics (NPLs) became ubiquitous in the environment, from the air we breathe to the food we eat. One of the main concerns about the NPLs risks is their role as carrier of other environmental contaminants, potentially increasing their uptake, bioaccumulation and toxicity to the organisms. Therefore, the main aim of this study was to understand how the presence of polystyrene NPLs (∅ 44 nm) will influence the toxicity (synergism, additivity or antagonism) of the antihistamine diphenhydramine (DPH), towards zebrafish (Danio rerio) embryos, when in dual mixtures. After 96 hours (h) exposure, at the organismal level, NPLs (0.015 or 1.5 mg/L) + DPH (10 mg/L) induced embryo mortality (90%) and malformations (100%) and decreased hatching (80%) and heartbeat rates (60%). After 120 h exposure, NPLs (0.015 or 1.5 mg/L) + DPH (0.01 mg/L) decreased larvae swimming distance (30–40%). At the biochemical level, increased glutathione S-transferases (55–122%) and cholinesterase (182–343%) activities were found after 96 h exposure to NPLs (0.015 or 1.5 mg/L) + DPH (0.01 mg/L). However, catalase (CAT) activity remained similar to the control group in the mixtures, inhibiting the effects detected after the exposure to 1.5 mg/L NPLs alone (increased 230% of CAT activity). In general, the effects of dual combination – NPLs + DPH (even at concentrations as low as 10 μg/L of DPH) – were more harmful than the correspondent individual exposures, showing the synergistic interactions of the dual mixture and answering to the main question of this work. The obtained results, namely the altered toxicity patterns of NPLs + DPH compared with the individual exposures, show the importance of an environmental risk assessment considering NPLs as a co-contaminant due to the potential NPLs role as vector for other contaminants.

Vitality surveillance at distance using thin-film tandem-like narrowband near-infrared photodiodes with light-enhanced responsivity

Remote measurement of vital sign parameters like heartbeat and respiration rate represents a compelling challenge in monitoring an individual's health in a noninvasive way. This could be achieved by large field-of-view, easy-to-integrate unobtrusive sensors, such as large-area thin-film photodiodes. At long distances, however, discriminating weak light signals from background disturbance demands superior near-infrared (NIR) sensitivity and optical noise tolerance. Here, we report an inherently narrowband solution-processed, thin-film photodiode with ultrahigh and controllable NIR responsivity based on a tandem-like perovskite-organic architecture. The device has low dark currents (<10-6 mA cm-2), linear dynamic range >150 dB, and operational stability over time (>8hours). With a narrowband quantum efficiency that can exceed 200% at 850 nm and intrinsic filtering of other wavelengths to limit optical noise, the device exhibits higher tolerance to background light than optically filtered silicon-based sensors. We demonstrate its potential in remote monitoring by measuring the heart rate and respiration rate from distances up to 130 cm in reflection.

Flexible Film Bulk Acoustic Resonator Based on Low-Porosity β-Phase P(VDF-TrFE) Film for Human Vital Signs Monitoring.

P(VDF-TrFE) is a promising material for flexible acoustic devices owing to its good piezoelectric performance and excellent stretchability. However, the high density of internal pores and large surface roughness of the conventional P(VDF-TrFE) results in a high propagation attenuation for acoustic waves, which limits its use in flexible acoustic devices. In this paper, a novel method based on two-step annealing is proposed to effectively remove the pores inside the P(VDF-TrFE) film and reduce its surface roughness. The obtained P(VDF-TrFE) film possesses excellent characteristics, including a high breakdown strength of >300 kV/mm, a high-purity β-phase content of more than 80%, and high piezoelectric coefficients (d33) of 42 pm/V. Based on the low-porosity β-phase P(VDF-TrFE) film, we fabricated flexible film bulk acoustic resonators (FBARs) which exhibit high sharp resonance peaks. The pressure sensor was made by sandwiching the FBARs with two PDMS microneedle patches. Heartbeat and respiration rate monitoring were achieved using the pressure sensor. This work demonstrates the feasibility of high-performance flexible piezoelectric acoustic resonators based on low-porosity P(VDF-TrFE) films, which could see wider applications in the wearable sensors for both physical and chemical sensing.

Toxicity and teratogenicity investigation of methanolic bark extract of Terminalia arjuna (Roxb.) wight and arn in zebrafish (Danio rerio) embryo model

The present research work aims to establish the teratogenic effect of the Terminalia arjuna (T.arjuna) bark extract using fish embryos through zebrafish embryo model. In the present study, selected zebrafish embryos were exposed to a range of methanolic extract of T.arjuna bark and the changes the embryos undergo were identified by comparing them with a control group. The embryo toxicity was assessed by observing morphological abnormalities after 24, 48, 72 and 96 h exposure and end teratogenicity points were assessed after 48 h exposure. All the embryos were imaged and analyzed for various developmental anomalies such as coagulated embryos, lack of somite formation, non-detachment of the tail, and lack of heartbeat, using imageJ software. From the results obtained, it was found that the embryos exposed with 10 mg/L of T.arjuna showed regular developmental patterns similar to control up to 72 h 5 % mortality was observed post 72 h period. 50 and 100 mg/L showed 100 % mortality after 96 h and 72 h respectively. At 48 h, Embryos treated with 50 mg/L of T.arjuna showed 5.58 % decreased heart beat rate, 7.89 % of decrease in eye diameter decreased head trunk angle of 3.56 % and increased yolk sac width of 0.79 %. After 48 h exposure to 50 and 100 mg/L of T.arjuna extract, 45 % and 85 % of the embryos showed the teratogenic endpoint of coagulation respectively with bent tail. Increased yolk sac width was observed in 100 mg/L treated embryos with 1.01 % after 24 h, also at 48 h decreased heart beat rate of 4.02 %,16.8 % of increased eye diameter, with decreased head trunk angle of 2.35 %, decreased yolk sac width of 5.13 % were observed. After 48 h exposure of T.arjuna bark extract, the morphological, physiological changes and teratogenic end points were eminent in 50 and 100 mg/L treated embryos, thus the results signify that methanolic bark extract of T. arjuna possess teratogenic effects at this concentration in zebrafish embryos.

An Approach on the Development of a Smart Monitoring System of a Baby Cradle

Abstract: Baby care has grown increasingly vital and difficult for working women in recent years. Working mothers will not have enough time at home to regularly check their babies. They delegate responsibility for their baby to a baby sitter or send the baby to their grandparents' home. A smart cradle with an automatic baby monitoring system was designed in the proposed work. The necessary parameters of the infant, such as temperature, heartbeat rate, gas molecules, capture the movements, and position of the baby, were measured and monitored in the baby monitoring system. The infant monitoring system is connected to the cradle, creating an incubator-like environment for the baby. The infant monitoring device keeps an eye on the baby 24 hours a day, seven days a week. The mobile application will display the measured parameters concerning the baby's health, such as temperature, heartbeat rate, and wetness on the baby bed. The baby monitoring system prototype aids parents in time management while also making life easier for caregivers. This infant monitoring device has been shown to do the least amount of harm to the baby while providing the highest level of accuracy. Though several models are already available on the market, the primary goal of this research paper would be to propose an approach for recognizing a particular accent of an individual infant among a group of infants using advanced technologies like as Ai, NLP, or speech recognition.

A Model to Predict Heartbeat Rate Using Deep Learning Algorithms.

ECG provides critical information in a waveform about the heart's condition. This information is crucial to physicians as it is the first thing to be performed by cardiologists. When COVID-19 spread globally and became a pandemic, the government of Saudi Arabia placed various restrictions and guidelines to protect and save citizens and residents. One of these restrictions was preventing individuals from touching any surface in public and private places. In addition, the authorities placed a mandatory rule in all public facilities and the private sector to evaluate the temperature of individuals before entering. Thus, the idea of this study stems from the need to have a touchless technique to determine heartbeat rate. This article proposes a viable and dependable method to estimate an average heartbeat rate based on the reflected light on the skin. This model uses various deep learning tools, including AlexNet, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ResNet50V2. Three scenarios have been conducted to evaluate and validate the presented model. In addition, the proposed approach takes its inputs from video streams and converts these streams into frames and images. Numerous trials have been conducted on volunteers to validate the method and assess its outputs in terms of accuracy, mean absolute error (MAE), and mean squared error (MSE). The proposed model achieves an average 99.78% accuracy, MAE is 0.142 when combing LSTMs and ResNet50V2, while MSE is 1.82. Moreover, a comparative measurement between the presented algorithm and some studies from the literature based on utilized methods, MAE, and MSE are performed. The achieved outcomes reveal that the developed technique surpasses other methods. Moreover, the findings show that this algorithm can be applied in healthcare facilities and aid physicians.

Private Backend Server Software-Based Telehealthcare Tracking and Monitoring System

In these recent years, the world has witnessed a kind of social exclusion and the inability to communicate directly due to the Corona Virus Covid 19 (COVID-19) pandemic, and the consequent difficulty of communicating with patients with hospitals led to the need to use modern technology to solve and facilitate the problem of people communicating with each other. healthcare has made many remarkable developments through the Internet of things (IOT) and cloud computing to monitor real-time patients' data, which has enabled many patients' lives to be saved. this paper presents the design and implementation of a Private Backend Server Software based on an IoT health monitoring system concerned emergency medical services utilizing biosensors to detect multivital signs of an individual with an ESP32 microcontroller board and IoT cloud. The device displays the vital data, which is then uploaded to a cloud server for storage and analysis over an IoT network. Vital data is received from the cloud server and shown on the IoT medical client dashboard for remote monitoring. The proposed system allows users to ameliorate healthcare jeopardy and minify its costs by recording, gathering, sharing, and analyzing vast biodata streams such as Intensive Care Units (ICU) (i.e., temperature, heartbeat rate, Oxygen level (CO2), etc.), efficiently in real-time. In this proposal, the data will send from sensors fixed in the patient body to the Web and Mobile App continually in real time for collection and analysis.

SSA-VMD for UWB Radar Sensor Vital Sign Extraction.

The combination of advanced radar sensor technology and smart grid has broad prospects. It is meaningful to monitor the respiration and heartbeat of grid employees under resting state through radar sensors to ensure that they are in a healthy working state. Ultra-wideband (UWB) radar sensor is suitable for this application because of its strong penetration ability, high range resolution and low average power consumption. However, due to weak heartbeat amplitude and measurement noise, the accurate measurement of the target heart rate is a challenge. In this paper, singular spectrum analysis (SSA) is proposed to reconstruct the eigenvalues of noisy vital signs to eliminate noise peaks around the heartbeat rate; combined with the variational modal decomposition (VMD), the target vital signs can be extracted with high accuracy. The experiment confirmed that the target vital sign information can be extracted with high accuracy from ten subjects at different distances, which can play an important role in short distance human detection and vital sign monitoring.

Calculation of Heartbeat Rate and SpO2 Parameters Using a Smartphone Camera: Analysis and Testing.

Mathematical and signal-processing methods were used to obtain reliable measurements of the heartbeat pulse rate and information on oxygen concentration in the blood using short video recordings of the index finger attached to a smartphone built-in camera. Various types of smartphones were used with different operating systems (e.g., iOS, Android) and capabilities. A range of processing algorithms were applied to the red-green-blue (RGB) component signals, including mean intensity calculation, moving average smoothing, and quadratic filtering based on the Savitzky-Golay filter. Two approaches-gradient and local maximum methods-were used to determine the pulse rate, which provided similar results. A fast Fourier transform was applied to the signal to correlate the signal's frequency components with the pulse rate. We resolved the signal into its DC and AC components to calculate the ratio-of-ratios of the AC and DC components of the red and green signals, a method which is often used to estimate the oxygen concentration in blood. A series of measurements were performed on healthy human subjects, producing reliable data that compared favorably to benchmark data obtained by commercial and medically approved oximeters. Furthermore, the effect of the video recording duration on the accuracy of the results was investigated.

ADAPTIVE PHYSIOLOGICAL ACTIVITY OF THE FISH DURING THERMAL-STRESS AND THERMAL ADAPTATION

The Indian major carp Catla catla subjected to slow temperature change from 22°C to 32°C (heat-adaptation) and 32°C to 22°C (cold-adaptation) at the rate of 1°C /60hrs showed a gradual elevation of Rate of heart beat in heat-adaptation and gradual decrease of rate of heart beat in the case of cold-adaptation and in both cases reached the control values within 35days. whereas Catla catla exposed abruptly to a temperature change from 22°C to 32°C and vice-versa at the rate of 1°C/hr. exhibited neither elevation nor decrease in the Rate of heart beat and they could not reach the control values within 35 days. Stress is a physiological load acting upon the fish, whereas adaptation is a slow process of compensation without physiological load.

IOT Based Pregnant Woman Health Monitoring System

The developing countries such as India has more population in the rural areas than urban areas. The people in those areas are not aware of new health issues, and they are not amalgamated for sharing their medical information. In some rural areas the pregnant women are unable to do their normal check-ups during their pregnancy time. This can cause risk in the life of both mother and the child. Due this inefficiency, the number death count of new are more compared to the urban areas. As a remedy for this a situation we are suggesting a system, that contain an accelerometer sensor is designed to measure the kike/force by the fetus and it is transfer to the ARDUINO UNO controller. Movement of the baby and some vital parameter such as Blood pressure, Heartbeat rate, temperature of the mother is measured using various types of sensors. The measured parameters are transmitted by IoT and is displayed. This system is a portable system and can be used in home by the women by their self. Another implementation way in the rural area is by giving the system to the nurse who work in the primary health centre in that particular area. Ultra sound scanning is used now a day, which is expensive which cannot be affordable for the poor in the rural areas. The parameter are recorded and can be used for future medical examination. We use IoT module to calculate the normal and abnormal rate and also enable the gynaecologist to access the date from the cloud

Intelligent system-based stun gloves for women's protection

In recent times, the security of women and girls has become a major concern in urban areas in many countries. To assist with settling this issue, this paper proposes a Global Positioning System (GPS)-based Stun Gloves for women with many safety techniques. The basic operation of this prototype design is that whenever women detect danger, they can simply press the gadget's ON button. When the gadget becomes active, it will track the online position of women using GPS and send an emergency message using the Global System for Mobile Communications (GSM) to the currently enlisted portable number and the police control room. These security Stun Gloves have both an alarm and a shock provider circuit. The heartbeat rate and temperature are also shown on a connected Liquid Crystal Display (LCD). The heartbeat sensor can detect any unusual heart rate patterns and send the woman's current location via GPS to the rescue team or pre-registered mobile number in the form of a Short Message Service (SMS). Likewise, in the case of self-protection, this gadget is incorporated with a shock generator. This safety precaution can be taken by women in case of any emergency crisis, which a female can use against a person. The main benefit of the design of Stun Gloves is that this gadget is compact and easy to use by any woman.

Heartbeat and Respiration Rate Prediction Using Combined Photoplethysmography and Ballisto Cardiography

Owing to the recent trends in remote health monitoring, real-time applications for measuring Heartbeat Rate and Respiration Rate (HARR) from video signals are growing rapidly. Photo Plethysmo Graphy (PPG) is a method that is operated by estimating the infinitesimal change in color of the human face, rigid motion of facial skin and head parts, etc. Ballisto Cardiography (BCG) is a nonsurgical tool for obtaining a graphical depiction of the human body’s heartbeat by inducing repetitive movements found in the heart pulses. The resilience against motion artifacts induced by luminance fluctuation and the patient’s mobility variation is the major difficulty faced while processing the real-time video signals. In this research, a video-based HARR measuring framework is proposed based on combined PPG and BCG. Here, the noise from the input video signals is removed by using an Adaptive Kalman filter (AKF). Three different algorithms are used for estimating the HARR from the noise-free input signals. Initially, the noise-free signals are subjected to Modified Adaptive Fourier Decomposition (MAFD) and then to Enhanced Hilbert vibration Decomposition (EHVD) and finally to Improved Variation mode Decomposition (IVMD) for attaining three various results of HARR. The obtained values are compared with each other and found that the EHVD is showing better results when compared with all the other methods.

Quantum mechanical aspects of cardiac arrhythmias: A mathematical model and pathophysiological implications

&lt;abstract&gt; &lt;p&gt;Cardiac arrhythmias are serious myocardial electrical disturbances that affect the rate and rhythm of heartbeats. Despite the rapidly accumulating data about the pathophysiology and the treatment, new insights are required to improve the overall clinical outcome of patients with cardiac arrhythmias. Three major arrhythmogenic processes can contribute to the pathogenesis of cardiac arrhythmias; 1) enhanced automaticity, 2) afterdepolarization-triggered activity and 3) reentry circuits. The mathematical model of the quantum tunneling of ions is used to investigate these mechanisms from a quantum mechanical perspective. The mathematical model focuses on applying the principle of quantum tunneling to sodium and potassium ions. This implies that these ions have a non-zero probability of passing through the gate, which has an energy that is higher than the kinetic energy of ions. Our mathematical findings indicate that, under pathological conditions, which affect ion channels, the quantum tunneling of sodium and potassium ions is augmented. This augmentation creates a state of hyperexcitability that can explain the enhanced automaticity, after depolarizations that are associated with triggered activity and a reentry circuit. Our mathematical findings stipulate that the augmented and thermally assisted quantum tunneling of sodium and potassium ions can depolarize the membrane potential and trigger spontaneous action potentials, which may explain the automaticity and afterdepolarization. Furthermore, the quantum tunneling of potassium ions during an action potential can provide a new insight regarding the formation of a reentry circuit. Introducing these quantum mechanical aspects may improve our understanding of the pathophysiological mechanisms of cardiac arrhythmias and, thus, contribute to finding more effective anti-arrhythmic drugs.&lt;/p&gt; &lt;/abstract&gt;

Design of WSN Model with NS2 for Animal Tracking and Monitoring

Tracking and monitoring wild life is a significant ecological issue that cannot be overlooked. Especially getting fundamental indications of wild animal like Tigers is critical and evaluating their wellbeing without harming their habitat is another important task and recording their vital signs by sedating causes stress and injuries in animal. This paper proposes a design of wireless based tracking and health monitoring system for Tigers using WSN and Network simulator 2. The important physiological parameters like temperature of heart beat rate, sound signal, respiration rate, accelerometer signal and GPS data is transmitted and monitored through wireless sensor network (WSN).A sensor network is composed of different sensors which remotely communicate with each other and the information of each node are separated. Each node has ability to communicate among each other or to a specific node to communicate with sink node A Network simulator which acts as simulation tool for network of sensor nodes in tracking and monitoring system is employed to simulate the location and health of tigers. This system empowers veterinarians and forest guards to precisely and accurately take an animal's vital signs and analyses the health and location. We analyze the performance of simulation model by using metrics, such as average throughput of all nodes, end to end delay, packet received, packet dropped and packet delivery ratio (PDR), average good put and energy model.

AHMERS: Active Health Monitoring and Emergency Response System

AHMERS, active health monitoring and emergency response system, is a mobile application-based system that wirelessly connects to a smartwatch to constantly monitor the human body and respond to sudden changes in vital data, in case of emergency. This app monitors heartbeat rate, blood oxygen saturation, body temperature, and compares them with pre-set normal values. If the data deviates or the user presses one of the emergency switches, the app immediately asks the person if he/she is ok. If the person fails to respond within a few seconds to the “Are you ok?” message, the app continues to send out distress signals to pre-set phone numbers and server along with the person's altitude, latitude and longitude, and current location on the map so that help can be sent quickly. It can determine whether a person is suffering from a health-related problem such as heart failure, Corona virus infection, hypothermia, etc.

Improving the efficiency of monitoring of old age people at Primary health Centre in “Health Treatment at Door steps” scheme using IoT

Due to the upsurge of non-communicable diseases challenged to communicable diseases, grown-up people are more affected by the disease. so monitoring is unavoidable to diminish the mortality rate. In this paper by using embedded and IoT technology, the monitoring of multi-physiological parameters is probable in a single kit. First, the human interaction with the sensor and second, the data acquisition process, and finally the data communication with the primary health center. The system can collect the data and display multi-physiological parameters like temperature sensors, glucose sensors, and heartbeat rate sensors unified with oxygen detection sensors.