Search results for: heart rate monitoring
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 11112

Search results for: heart rate monitoring

11112 Real-Time Nonintrusive Heart Rate Measurement: Comparative Case Study of LED Sensorics' Accuracy and Benefits in Heart Monitoring

Authors: Goran Begović

Abstract:

In recent years, many researchers are focusing on non-intrusive measuring methods when it comes to human biosignals. These methods provide solutions for everyday use, whether it’s health monitoring or finessing the workout routine. One of the biggest issues with these solutions is that the sensors’ accuracy is highly variable due to many factors, such as ambiental light, skin color diversity, etc. That is why we wanted to explore different outcomes under those kinds of circumstances in order to find the most optimal algorithm(s) for extracting heart rate (HR) information. The optimization of such algorithms can benefit the wider, cheaper, and safer application of home health monitoring, without having to visit medical professionals as often when it comes to observing heart irregularities. In this study, we explored the accuracy of infrared (IR), red, and green LED sensorics in a controlled environment and compared the results with a medically accurate ECG monitoring device.

Keywords: data science, ECG, heart rate, holter monitor, LED sensors

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11111 Development of Sleep Quality Index Using Heart Rate

Authors: Dongjoo Kim, Chang-Sik Son, Won-Seok Kang

Abstract:

Adequate sleep affects various parts of one’s overall physical and mental life. As one of the methods in determining the appropriate amount of sleep, this research presents a heart rate based sleep quality index. In order to evaluate sleep quality using the heart rate, sleep data from 280 subjects taken over one month are used. Their sleep data are categorized by a three-part heart rate range. After categorizing, some features are extracted, and the statistical significances are verified for these features. The results show that some features of this sleep quality index model have statistical significance. Thus, this heart rate based sleep quality index may be a useful discriminator of sleep.

Keywords: sleep, sleep quality, heart rate, statistical analysis

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11110 Development of Soft-Core System for Heart Rate and Oxygen Saturation

Authors: Caje F. Pinto, Jivan S. Parab, Gourish M. Naik

Abstract:

This paper is about the development of non-invasive heart rate and oxygen saturation in human blood using Altera NIOS II soft-core processor system. In today's world, monitoring oxygen saturation and heart rate is very important in hospitals to keep track of low oxygen levels in blood. We have designed an Embedded System On Peripheral Chip (SOPC) reconfigurable system by interfacing two LED’s of different wavelengths (660 nm/940 nm) with a single photo-detector to measure the absorptions of hemoglobin species at different wavelengths. The implementation of the interface with Finger Probe and Liquid Crystal Display (LCD) was carried out using NIOS II soft-core system running on Altera NANO DE0 board having target as Cyclone IVE. This designed system is used to monitor oxygen saturation in blood and heart rate for different test subjects. The designed NIOS II processor based non-invasive heart rate and oxygen saturation was verified with another Operon Pulse oximeter for 50 measurements on 10 different subjects. It was found that the readings taken were very close to the Operon Pulse oximeter.

Keywords: heart rate, NIOS II, oxygen saturation, photoplethysmography, soft-core, SOPC

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11109 Wearable Heart Rate Sensor Based on Wireless System for Heart Health Monitoring

Authors: Murtadha Kareem, Oliver Faust

Abstract:

Wearable biosensor systems can be designed and developed for health monitoring. There is much interest in both scientific and industrial communities established since 2007. Fundamentally, the cost of healthcare has increased dramatically and the world population is aging. That creates the need to harvest technological improvements with small bio-sensing devices, wireless-communication, microelectronics and smart textiles, that leads to non-stop developments of wearable sensor based systems. There has been a significant demand to monitor patient's health status while the patient leaves the hospital in his/her personal environment. To address this need, there are numerous system prototypes which has been launched in the medical market recently, the aim of that is to provide real time information feedback about patient's health status, either to the patient himself/herself or direct to the supervising medical centre station, while being capable to give a notification for the patient in case of possible imminent health threatening conditions. Furthermore, wearable health monitoring systems comprise new techniques to address the problem of managing and monitoring chronic heart diseases for elderly people. Wearable sensor systems for health monitoring include various types of miniature sensors, either wearable or implantable. To be specific, our proposed system able to measure essential physiological parameter, such as heart rate signal which could be transmitted through Bluetooth to the cloud server in order to store, process, analysis and visualise the data acquisition. The acquired measurements are connected through internet of things to a central node, for instance an android smart phone or tablet used for visualising the collected information on application or transmit it to a medical centre.

Keywords: Wearable sensor, Heart rate, Internet of things, Chronic heart disease

Procedia PDF Downloads 126
11108 An Exploratory Research of Human Character Analysis Based on Smart Watch Data: Distinguish the Drinking State from Normal State

Authors: Lu Zhao, Yanrong Kang, Lili Guo, Yuan Long, Guidong Xing

Abstract:

Smart watches, as a handy device with rich functionality, has become one of the most popular wearable devices all over the world. Among the various function, the most basic is health monitoring. The monitoring data can be provided as an effective evidence or a clue for the detection of crime cases. For instance, the step counting data can help to determine whether the watch wearer was quiet or moving during the given time period. There is, however, still quite few research on the analysis of human character based on these data. The purpose of this research is to analyze the health monitoring data to distinguish the drinking state from normal state. The analysis result may play a role in cases involving drinking, such as drunk driving. The experiment mainly focused on finding the figures of smart watch health monitoring data that change with drinking and figuring up the change scope. The chosen subjects are mostly in their 20s, each of whom had been wearing the same smart watch for a week. Each subject drank for several times during the week, and noted down the begin and end time point of the drinking. The researcher, then, extracted and analyzed the health monitoring data from the watch. According to the descriptive statistics analysis, it can be found that the heart rate change when drinking. The average heart rate is about 10% higher than normal, the coefficient of variation is less than about 30% of the normal state. Though more research is needed to be carried out, this experiment and analysis provide a thought of the application of the data from smart watches.

Keywords: character analysis, descriptive statistics analysis, drink state, heart rate, smart watch

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11107 Sportband: An Idea for Workout Monitoring in Amateur and Recreational Sports

Authors: Kamila Mazur-Oleszczuk, Rafal Banasiuk, Dawid Krasnowski, Maciej Pek, Marcin Podgorski, Krzysztof Rykaczewski, Sabina Zoledowska, Dawid Nidzworski

Abstract:

Workout safety is one of the most significant challenges of recreational sports. Loss of water and electrolytes is a consequence of thermoregulatory sweating during exercise. The rate of sweat loss and its chemical composition can fluctuate within and among individuals. That is why we propose our sportband 'Flow' as a device for monitoring these parameters. 'Flow' consists of two parts: an intelligent module and a mobile application. The application allows verifying the training progress and data archiving. The sportband intelligent module includes temperature, heart rate and pulse measurement (non-invasive, continuous methods of workout monitoring). Apart from the standard components, the device will consist of a sweat composition analyzer situated in sportband intelligent module. Sweat is a water solution of numerous compounds such as ions (sodium up to 1609 µg/ml, potassium up to 274 µg/ml), lactic acid (skin pH is between 4.5 - 6) and a small amount of glucose. Awareness of sweat composition allows personalizing electrolyte intake after training. A comprehensive workout monitoring (sweat composition, heart rate, blood oxygen level) will provide improvement in the training routine and time management, which is our goal for the development of the sweat composition analyzer.

Keywords: flow, sportband, sweat, workout monitoring

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11106 Remote Wireless Patient Monitoring System

Authors: Sagar R. Patil, Dinesh R. Gawade, Sudhir N. Divekar

Abstract:

One of the medical devices we found when we visit a hospital care unit such device is ‘patient monitoring system’. This device (patient monitoring system) informs doctors and nurses about the patient’s physiological signals. However, this device (patient monitoring system) does not have a remote monitoring capability, which is necessitates constant onsite attendance by support personnel (doctors and nurses). Thus, we have developed a Remote Wireless Patient Monitoring System using some biomedical sensors and Android OS, which is a portable patient monitoring. This device(Remote Wireless Patient Monitoring System) monitors the biomedical signals of patients in real time and sends them to remote stations (doctors and nurse’s android Smartphone and web) for display and with alerts when necessary. Wireless Patient Monitoring System different from conventional device (Patient Monitoring system) in two aspects: First its wireless communication capability allows physiological signals to be monitored remotely and second, it is portable so patients can move while there biomedical signals are being monitor. Wireless Patient Monitoring is also notable because of its implementation. We are integrated four sensors such as pulse oximeter (SPO2), thermometer, respiration, blood pressure (BP), heart rate and electrocardiogram (ECG) in this device (Wireless Patient Monitoring System) and Monitoring and communication applications are implemented on the Android OS using threads, which facilitate the stable and timely manipulation of signals and the appropriate sharing of resources. The biomedical data will be display on android smart phone as well as on web Using web server and database system we can share these physiological signals with remote place medical personnel’s or with any where in the world medical personnel’s. We verified that the multitasking implementation used in the system was suitable for patient monitoring and for other Healthcare applications.

Keywords: patient monitoring, wireless patient monitoring, bio-medical signals, physiological signals, embedded system, Android OS, healthcare, pulse oximeter (SPO2), thermometer, respiration, blood pressure (BP), heart rate, electrocardiogram (ECG)

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11105 Poincare Plot for Heart Rate Variability

Authors: Mazhar B. Tayel, Eslam I. AlSaba

Abstract:

The heart is the most important part in any body organisms. It effects and affected by any factor in the body. Therefore, it is a good detector of any matter in the body. When the heart signal is non-stationary signal, therefore, it should be study its variability. So, the Heart Rate Variability (HRV) has attracted considerable attention in psychology, medicine and have become important dependent measure in psychophysiology and behavioral medicine. Quantification and interpretation of heart rate variability. However, remain complex issues are fraught with pitfalls. This paper presents one of the non-linear techniques to analyze HRV. It discusses 'What Poincare plot is?', 'How it is work?', 'its usage benefits especially in HRV', 'the limitation of Poincare cause of standard deviation SD1, SD2', and 'How overcome this limitation by using complex correlation measure (CCM)'. The CCM is most sensitive to changes in temporal structure of the Poincaré plot as compared to SD1 and SD2.

Keywords: heart rate variability, chaotic system, poincare, variance, standard deviation, complex correlation measure

Procedia PDF Downloads 361
11104 Remote Patient Monitoring for Covid-19

Authors: Launcelot McGrath

Abstract:

The Coronavirus disease 2019 (COVID-19) has spread rapidly around the world, resulting in high mortality rates and very large numbers of people requiring medical treatment in ICU. Management of patient hospitalisation is a critical aspect to control this disease and reduce chaos in the healthcare systems. Remote monitoring provides a solution to protect vulnerable and elderly high-risk patients. Continuous remote monitoring of oxygen saturation, respiratory rate, heart rate, and temperature, etc., provides medical systems with up-to-the-minute information about their patients' statuses. Remote monitoring also limits the spread of infection by reducing hospital overcrowding. This paper examines the potential of remote monitoring for Covid-19 to assist in the rapid identification of patients at risk, facilitate the detection of patient deterioration, and enable early interventions.

Keywords: remote monitoring, patient care, oxygen saturation, Covid-19, hospital management

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11103 The Effect of Heart Rate and Valence of Emotions on Perceived Intensity of Emotion

Authors: Madeleine Nicole G. Bernardo, Katrina T. Feliciano, Marcelo Nonato A. Nacionales III, Diane Frances M. Peralta, Denise Nicole V. Profeta

Abstract:

This study aims to find out if heart rate variability and valence of emotion have an effect on perceived intensity of emotion. Psychology undergraduates (N = 60) from the University of the Philippines Diliman were shown 10 photographs from the Japanese Female Facial Expression (JAFFE) Database, along with a corresponding questionnaire with a Likert scale on perceived intensity of emotion. In this 3 x 2 mixed subjects factorial design, each group was either made to do a simple exercise prior to answering the questionnaire in order to increase the heart rate, listen to a heart rate of 120 bpm, or colour a drawing to keep the heart rate stable. After doing the activity, the participants then answered the questionnaire, providing a rating of the faces according to the participants’ perceived emotional intensity on the photographs. The photographs presented were either of positive or negative emotional valence. The results of the experiment showed that neither an induced fast heart rate or perceived fast heart rate had any significant effect on the participants’ perceived intensity of emotion. There was also no interaction effect of heart rate variability and valence of emotion. The insignificance of results was explained by the Philippines’ high context culture, accompanied by the prevalence of both intensely valenced positive and negative emotions in Philippine society. Insignificance in the effects were also attributed to the Cannon-Bard theory, Schachter-Singer theory and various methodological limitations.

Keywords: heart rate variability, perceived intensity of emotion, Philippines , valence of emotion

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11102 Capturing the Stress States in Video Conferences by Photoplethysmographic Pulse Detection

Authors: Jarek Krajewski, David Daxberger

Abstract:

We propose a stress detection method based on an RGB camera using heart rate detection, also known as Photoplethysmography Imaging (PPGI). This technique focuses on the measurement of the small changes in skin colour caused by blood perfusion. A stationary lab setting with simulated video conferences is chosen using constant light conditions and a sampling rate of 30 fps. The ground truth measurement of heart rate is conducted with a common PPG system. The proposed approach for pulse peak detection is based on a machine learning-based approach, applying brute force feature extraction for the prediction of heart rate pulses. The statistical analysis showed good agreement (correlation r = .79, p<0.05) between the reference heart rate system and the proposed method. Based on these findings, the proposed method could provide a reliable, low-cost, and contactless way of measuring HR parameters in daily-life environments.

Keywords: heart rate, PPGI, machine learning, brute force feature extraction

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11101 Internet of Things Based Patient Health Monitoring System

Authors: G. Yoga Sairam Teja, K. Harsha Vardhan, A. Vinay Kumar, K. Nithish Kumar, Ch. Shanthi Priyag

Abstract:

The emergence of the Internet of Things (IoT) has facilitated better device control and monitoring in the modern world. The constant monitoring of a patient would be drastically altered by the usage of IoT in healthcare. As we've seen in the case of the COVID-19 pandemic, it's important to keep oneself untouched while continuously checking on the patient's heart rate and temperature. Additionally, patients with paralysis should be closely watched, especially if they are elderly and in need of special care. Our "IoT BASED PATIENT HEALTH MONITORING SYSTEM" project uses IoT to track patient health conditions in an effort to address these issues. In this project, the main board is an 8051 microcontroller that connects a number of sensors, including a heart rate sensor, a temperature sensor (LM-35), and a saline water measuring circuit. These sensors are connected via an ESP832 (WiFi) module, which enables the sending of recorded data directly to the cloud so that the patient's health status can be regularly monitored. An LCD is used to monitor the data in offline mode, and a buzzer will sound if any variation from the regular readings occurs. The data in the cloud may be viewed as a graph, making it simple for a user to spot any unusual conditions.

Keywords: IoT, ESP8266, 8051 microcontrollers, sensors

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11100 Tele-Monitoring and Logging of Patient Health Parameters Using Zigbee

Authors: Kirubasankar, Sanjeevkumar, Aravindh Nagappan

Abstract:

This paper addresses a system for monitoring patients using biomedical sensors and displaying it in a remote place. The main challenges in present health monitoring devices are lack of remote monitoring and logging for future evaluation. Typical instruments used for health parameter measurement provide basic information regarding health status. This paper identifies a set of design principles to address these challenges. This system includes continuous measurement of health parameters such as Heart rate, electrocardiogram, SpO2 level and Body temperature. The accumulated sensor data is relayed to a processing device using a transceiver and viewed by the implementation of cloud services.

Keywords: bio-medical sensors, monitoring, logging, cloud service

Procedia PDF Downloads 475
11099 Video Heart Rate Measurement for the Detection of Trauma-Related Stress States

Authors: Jarek Krajewski, David Daxberger, Luzi Beyer

Abstract:

Finding objective and non-intrusive measurements of emotional and psychopathological states (e.g., post-traumatic stress disorder, PTSD) is an important challenge. Thus, the proposed approach here uses Photoplethysmographic imaging (PPGI) applying facial RGB Cam videos to estimate heart rate levels. A pipeline for the signal processing of the raw image has been proposed containing different preprocessing approaches, e.g., Independent Component Analysis, Non-negative Matrix factorization, and various other artefact correction approaches. Under resting and constant light conditions, we reached a sensitivity of 84% for pulse peak detection. The results indicate that PPGI can be a suitable solution for providing heart rate data derived from these indirectly post-traumatic stress states.

Keywords: heart rate, PTSD, PPGI, stress, preprocessing

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11098 Relationship between Different Heart Rate Control Levels and Risk of Heart Failure Rehospitalization in Patients with Persistent Atrial Fibrillation: A Retrospective Cohort Study

Authors: Yongrong Liu, Xin Tang

Abstract:

Background: Persistent atrial fibrillation is a common arrhythmia closely related to heart failure. Heart rate control is an essential strategy for treating persistent atrial fibrillation. Still, the understanding of the relationship between different heart rate control levels and the risk of heart failure rehospitalization is limited. Objective: The objective of the study is to determine the relationship between different levels of heart rate control in patients with persistent atrial fibrillation and the risk of readmission for heart failure. Methods: We conducted a retrospective dual-centre cohort study, collecting data from patients with persistent atrial fibrillation who received outpatient treatment at two tertiary hospitals in central and western China from March 2019 to March 2020. The collected data included age, gender, body mass index (BMI), medical history, and hospitalization frequency due to heart failure. Patients were divided into three groups based on their heart rate control levels: Group I with a resting heart rate of less than 80 beats per minute, Group II with a resting heart rate between 80 and 100 beats per minute, and Group III with a resting heart rate greater than 100 beats per minute. The readmission rates due to heart failure within one year after discharge were statistically analyzed using propensity score matching in a 1:1 ratio. Differences in readmission rates among the different groups were compared using one-way ANOVA. The impact of varying levels of heart rate control on the risk of readmission for heart failure was assessed using the Cox proportional hazards model. Binary logistic regression analysis was employed to control for potential confounding factors. Results: We enrolled a total of 1136 patients with persistent atrial fibrillation. The results of the one-way ANOVA showed that there were differences in readmission rates among groups exposed to different levels of heart rate control. The readmission rates due to heart failure for each group were as follows: Group I (n=432): 31 (7.17%); Group II (n=387): 11.11%; Group III (n=317): 90 (28.50%) (F=54.3, P<0.001). After performing 1:1 propensity score matching for the different groups, 223 pairs were obtained. Analysis using the Cox proportional hazards model showed that compared to Group I, the risk of readmission for Group II was 1.372 (95% CI: 1.125-1.682, P<0.001), and for Group III was 2.053 (95% CI: 1.006-5.437, P<0.001). Furthermore, binary logistic regression analysis, including variables such as digoxin, hypertension, smoking, coronary heart disease, and chronic obstructive pulmonary disease as independent variables, revealed that coronary heart disease and COPD also had a significant impact on readmission due to heart failure (p<0.001). Conclusion: The correlation between the heart rate control level of patients with persistent atrial fibrillation and the risk of heart failure rehospitalization is positive. Reasonable heart rate control may significantly reduce the risk of heart failure rehospitalization.

Keywords: heart rate control levels, heart failure rehospitalization, persistent atrial fibrillation, retrospective cohort study

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11097 Heart Rate Variability as a Measure of Dairy Calf Welfare

Authors: J. B. Clapp, S. Croarkin, C. Dolphin, S. K. Lyons

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Chronic pain or stress in farm animals impacts both on their welfare and productivity. Measuring chronic pain or stress can be problematic using hormonal or behavioural changes because hormones are modulated by homeostatic mechanisms and observed behaviour can be highly subjective. We propose that heart rate variability (HRV) can quantify chronic pain or stress in farmed animal and represents a more robust and objective measure of their welfare.

Keywords: dairy calf, welfare, heart rate variability, non-invasive, biomonitor

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11096 Remote Vital Signs Monitoring in Neonatal Intensive Care Unit Using a Digital Camera

Authors: Fatema-Tuz-Zohra Khanam, Ali Al-Naji, Asanka G. Perera, Kim Gibson, Javaan Chahl

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Conventional contact-based vital signs monitoring sensors such as pulse oximeters or electrocardiogram (ECG) may cause discomfort, skin damage, and infections, particularly in neonates with fragile, sensitive skin. Therefore, remote monitoring of the vital sign is desired in both clinical and non-clinical settings to overcome these issues. Camera-based vital signs monitoring is a recent technology for these applications with many positive attributes. However, there are still limited camera-based studies on neonates in a clinical setting. In this study, the heart rate (HR) and respiratory rate (RR) of eight infants at the Neonatal Intensive Care Unit (NICU) in Flinders Medical Centre were remotely monitored using a digital camera applying color and motion-based computational methods. The region-of-interest (ROI) was efficiently selected by incorporating an image decomposition method. Furthermore, spatial averaging, spectral analysis, band-pass filtering, and peak detection were also used to extract both HR and RR. The experimental results were validated with the ground truth data obtained from an ECG monitor and showed a strong correlation using the Pearson correlation coefficient (PCC) 0.9794 and 0.9412 for HR and RR, respectively. The RMSE between camera-based data and ECG data for HR and RR were 2.84 beats/min and 2.91 breaths/min, respectively. A Bland Altman analysis of the data also showed a close correlation between both data sets with a mean bias of 0.60 beats/min and 1 breath/min, and the lower and upper limit of agreement -4.9 to + 6.1 beats/min and -4.4 to +6.4 breaths/min for both HR and RR, respectively. Therefore, video camera imaging may replace conventional contact-based monitoring in NICU and has potential applications in other contexts such as home health monitoring.

Keywords: neonates, NICU, digital camera, heart rate, respiratory rate, image decomposition

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11095 Heart-Rate Resistance Electrocardiogram Identification Based on Slope-Oriented Neural Networks

Authors: Tsu-Wang Shen, Shan-Chun Chang, Chih-Hsien Wang, Te-Chao Fang

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For electrocardiogram (ECG) biometrics system, it is a tedious process to pre-install user’s high-intensity heart rate (HR) templates in ECG biometric systems. Based on only resting enrollment templates, it is a challenge to identify human by using ECG with the high-intensity HR caused from exercises and stress. This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. The method has overall system accuracy at 97.73% which includes six levels of HR intensities. A cumulative match characteristic curve is also used to compare with other traditional ECG biometric methods.

Keywords: high-intensity heart rate, heart rate resistant, ECG human identification, decision based artificial neural network

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11094 Implementation of Clinical Monitoring System of Physiological Parameters

Authors: Abdesselam Babouri, Ahcène Lemzadmi, M Rahmane, B. Belhadi, N. Abouchi

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Medical monitoring aims at monitoring and remotely controlling the vital physiological parameters of the patient. The physiological sensors provide repetitive measurements of these parameters in the form of electrical signals that vary continuously over time. Various measures allow informing us about the health of the person's physiological data (weight, blood pressure, heart rate or specific to a disease), environmental conditions (temperature, humidity, light, noise level) and displacement and movements (physical efforts and the completion of major daily living activities). The collected data will allow monitoring the patient’s condition and alerting in case of modification. They are also used in the diagnosis and decision making on medical treatment and the health of the patient. This work presents the implementation of a monitoring system to be used for the control of physiological parameters.

Keywords: clinical monitoring, physiological parameters, biomedical sensors, personal health

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11093 Intelligent System for Diagnosis Heart Attack Using Neural Network

Authors: Oluwaponmile David Alao

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Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field.

Keywords: heart attack, artificial neural network, diagnosis, intelligent system

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11092 Intelligent Prediction System for Diagnosis of Heart Attack

Authors: Oluwaponmile David Alao

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Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

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11091 Heart Attack Prediction Using Several Machine Learning Methods

Authors: Suzan Anwar, Utkarsh Goyal

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Heart rate (HR) is a predictor of cardiovascular, cerebrovascular, and all-cause mortality in the general population, as well as in patients with cardio and cerebrovascular diseases. Machine learning (ML) significantly improves the accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment while avoiding unnecessary treatment of others. This research examines relationship between the individual's various heart health inputs like age, sex, cp, trestbps, thalach, oldpeaketc, and the likelihood of developing heart disease. Machine learning techniques like logistic regression and decision tree, and Python are used. The results of testing and evaluating the model using the Heart Failure Prediction Dataset show the chance of a person having a heart disease with variable accuracy. Logistic regression has yielded an accuracy of 80.48% without data handling. With data handling (normalization, standardscaler), the logistic regression resulted in improved accuracy of 87.80%, decision tree 100%, random forest 100%, and SVM 100%.

Keywords: heart rate, machine learning, SVM, decision tree, logistic regression, random forest

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11090 Design of Demand Pacemaker Using an Embedded Controller

Authors: C. Bala Prashanth Reddy, B. Abhinay, C. Sreekar, D. V. Shobhana Priscilla

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The project aims in designing an emergency pacemaker which is capable of giving shocks to a human heart which has stopped working suddenly. A pacemaker is a machine commonly used by cardiologists. This machine is used in order to shock a human’s heart back into usage. The way the heart works is that there are small cells called pacemakers sending electrical pulses to cardiac muscles that tell the heart when to pump blood. When these electrical pulses stop, the heart stops beating. When this happens, a pacemaker is used to shock the heart muscles and the pacemakers back into action. The way this is achieved is by rubbing the two panels of the pacemaker together to create an adequate electrical current, and then the heart gets back to the normal state. The project aims in designing a system which is capable of continuously displaying the heart beat and blood pressure of a person on LCD. The concerned doctor gets the heart beat and also the blood pressure details continuously through the GSM Modem in the form of SMS alerts. In case of abnormal condition, the doctor sends message format regarding the amount of electric shock needed. Automatically the microcontroller gives the input to the pacemaker which in turn gives the shock to the patient. Heart beat monitor and display system is a portable and a best replacement for the old model stethoscope which is less efficient. The heart beat rate is calculated manually using stethoscope where the probability of error is high because the heart beat rate lies in the range of 70 to 90 per minute whose occurrence is less than 1 sec, so this device can be considered as a very good alternative instead of a stethoscope.

Keywords: missing R wave, PWM, demand pacemaker, heart

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11089 IOT Based Process Model for Heart Monitoring Process

Authors: Dalyah Y. Al-Jamal, Maryam H. Eshtaiwi, Liyakathunisa Syed

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Connecting health services with technology has a huge demand as people health situations are becoming worse day by day. In fact, engaging new technologies such as Internet of Things (IOT) into the medical services can enhance the patient care services. Specifically, patients suffering from chronic diseases such as cardiac patients need a special care and monitoring. In reality, some efforts were previously taken to automate and improve the patient monitoring systems. However, the previous efforts have some limitations and lack the real-time feature needed for chronic kind of diseases. In this paper, an improved process model for patient monitoring system specialized for cardiac patients is presented. A survey was distributed and interviews were conducted to gather the needed requirements to improve the cardiac patient monitoring system. Business Process Model and Notation (BPMN) language was used to model the proposed process. In fact, the proposed system uses the IOT Technology to assist doctors to remotely monitor and follow-up with their heart patients in real-time. In order to validate the effectiveness of the proposed solution, simulation analysis was performed using Bizagi Modeler tool. Analysis results show performance improvements in the heart monitoring process. For the future, authors suggest enhancing the proposed system to cover all the chronic diseases.

Keywords: IoT, process model, remote patient monitoring system, smart watch

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11088 Screening of Congenital Heart Diseases with Fetal Phonocardiography

Authors: F. Kovács, K. Kádár, G. Hosszú, Á. T. Balogh, T. Zsedrovits, N. Kersner, A. Nagy, Gy. Jeney

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The paper presents a novel screening method to indicate congenital heart diseases (CHD), which otherwise could remain undetected because of their low level. Therefore, not belonging to the high-risk population, the pregnancies are not subject to the regular fetal monitoring with ultrasound echocardiography. Based on the fact that CHD is a morphological defect of the heart causing turbulent blood flow, the turbulence appears as a murmur, which can be detected by fetal phonocardiography (fPCG). The proposed method applies measurements on the maternal abdomen and from the recorded sound signal a sophisticated processing determines the fetal heart murmur. The paper describes the problems and the additional advantages of the fPCG method including the possibility of measurements at home and its combination with the prescribed regular cardiotocographic (CTG) monitoring. The proposed screening process implemented on a telemedicine system provides an enhanced safety against hidden cardiac diseases.

Keywords: cardiac murmurs, fetal phonocardiography, screening of CHDs, telemedicine system

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11087 Signal Estimation and Closed Loop System Performance in Atrial Fibrillation Monitoring with Communication Channels

Authors: Mohammad Obeidat, Ayman Mansour

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In this paper a unique issue rising from feedback control of Atrial Fibrillation monitoring system with embedded communication channels has been investigated. One of the important factors to measure the performance of the feedback control closed loop system is disturbance and noise attenuation factor. It is important that the feedback system can attenuate such disturbances on the atrial fibrillation heart rate signals. Communication channels depend on network traffic conditions and deliver different throughput, implying that the sampling intervals may change. Since signal estimation is updated on the arrival of new data, its dynamics actually change with the sampling interval. Consequently, interaction among sampling, signal estimation, and the controller will introduce new issues in remotely controlled Atrial Fibrillation system. This paper treats a remotely controlled atrial fibrillation system with one communication channel which connects between the heart rate and rhythm measurements to the remote controller. Typical and optimal signal estimation schemes is represented by a signal averaging filter with its time constant derived from the step size of the signal estimation algorithm.

Keywords: atrial fibrillation, communication channels, closed loop, estimation

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11086 Contactless Heart Rate Measurement System based on FMCW Radar and LSTM for Automotive Applications

Authors: Asma Omri, Iheb Sifaoui, Sofiane Sayahi, Hichem Besbes

Abstract:

Future vehicle systems demand advanced capabilities, notably in-cabin life detection and driver monitoring systems, with a particular emphasis on drowsiness detection. To meet these requirements, several techniques employ artificial intelligence methods based on real-time vital sign measurements. In parallel, Frequency-Modulated Continuous-Wave (FMCW) radar technology has garnered considerable attention in the domains of healthcare and biomedical engineering for non-invasive vital sign monitoring. FMCW radar offers a multitude of advantages, including its non-intrusive nature, continuous monitoring capacity, and its ability to penetrate through clothing. In this paper, we propose a system utilizing the AWR6843AOP radar from Texas Instruments (TI) to extract precise vital sign information. The radar allows us to estimate Ballistocardiogram (BCG) signals, which capture the mechanical movements of the body, particularly the ballistic forces generated by heartbeats and respiration. These signals are rich sources of information about the cardiac cycle, rendering them suitable for heart rate estimation. The process begins with real-time subject positioning, followed by clutter removal, computation of Doppler phase differences, and the use of various filtering methods to accurately capture subtle physiological movements. To address the challenges associated with FMCW radar-based vital sign monitoring, including motion artifacts due to subjects' movement or radar micro-vibrations, Long Short-Term Memory (LSTM) networks are implemented. LSTM's adaptability to different heart rate patterns and ability to handle real-time data make it suitable for continuous monitoring applications. Several crucial steps were taken, including feature extraction (involving amplitude, time intervals, and signal morphology), sequence modeling, heart rate estimation through the analysis of detected cardiac cycles and their temporal relationships, and performance evaluation using metrics such as Root Mean Square Error (RMSE) and correlation with reference heart rate measurements. For dataset construction and LSTM training, a comprehensive data collection system was established, integrating the AWR6843AOP radar, a Heart Rate Belt, and a smart watch for ground truth measurements. Rigorous synchronization of these devices ensured data accuracy. Twenty participants engaged in various scenarios, encompassing indoor and real-world conditions within a moving vehicle equipped with the radar system. Static and dynamic subject’s conditions were considered. The heart rate estimation through LSTM outperforms traditional signal processing techniques that rely on filtering, Fast Fourier Transform (FFT), and thresholding. It delivers an average accuracy of approximately 91% with an RMSE of 1.01 beat per minute (bpm). In conclusion, this paper underscores the promising potential of FMCW radar technology integrated with artificial intelligence algorithms in the context of automotive applications. This innovation not only enhances road safety but also paves the way for its integration into the automotive ecosystem to improve driver well-being and overall vehicular safety.

Keywords: ballistocardiogram, FMCW Radar, vital sign monitoring, LSTM

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11085 Automated Recognition of Still’s Murmur in Children

Authors: Sukryool Kang, James McConnaughey, Robin Doroshow, Raj Shekhar

Abstract:

Still’s murmur, a vibratory heart murmur, is the most common normal innocent murmur of childhood. Many children with this murmur are unnecessarily referred for cardiology consultation and testing, which exacts a high cost financially and emotionally on the patients and their parents. Pediatricians to date are not successful at distinguishing Still’s murmur from murmurs of true heart disease. In this paper, we present a new algorithmic approach to distinguish Still’s murmur from pathological murmurs in children. We propose two distinct features, spectral width and signal power, which describe the sharpness of the spectrum and the signal intensity of the murmur, respectively. Seventy pediatric heart sound recordings of 41 Still’s and 29 pathological murmurs were used to develop and evaluate our algorithm that achieved a true positive rate of 97% and false positive rate of 0%. This approach would meet clinical standards in recognizing Still’s murmur.

Keywords: AR modeling, auscultation, heart murmurs, Still's murmur

Procedia PDF Downloads 330
11084 Automatic Classification of Periodic Heart Sounds Using Convolutional Neural Network

Authors: Jia Xin Low, Keng Wah Choo

Abstract:

This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

Procedia PDF Downloads 309
11083 Serum Potassium Before, During and After Exercise at 70% Maximal Heart Rate: The Safe Exercise Dosage Across Different Parameters of Health and Fitness Level

Authors: Omar bin Mihat

Abstract:

The number of sudden deaths is increasing over the past years. These deaths occur not during physical activities but upon cessation. Post-mortem confirms these deaths as cardiac arrest non-specifically. Congenital heart disease is a condition undiagnosed whereby only surface upon physical exertion leading to sudden death is unavoidable. Channelopathy, a condition that refers to any disease from the defect in iron-channel function, particularly the sodium-potassium pump, during the cessation of the exercise can be controlled. The derivation of heart rate return (HRrtn) is a procedure of a control cooling down process according to the heart rate (HR). Empirically, potassium rises linearly with intensity and falls sharply upon abrupt cessation of exertion, resulting in fatal arrhythmia due to hypokalaemia. It is vital that the flux of potassium should be maintained within the normal range during physical activities. To achieve this, the dosage of physical exertion (exercise) should be identified. Various percentages of the intensity of maximum heart rate (MHR) will precipitate different adaptations and remodeling of various organs. 70% of MHR will surface physiological adaptations, including enhancement of endurance, fitness level, and general health, and there was no significant rise of serum potassium (K+) during the entire phase of the treadmill brisk walk at a different rate of perceived exertion (RPE) from the subject of various fitness background. There was also no significant rise in blood pressure (BP) during the entire phase of the treadmill brisk walk, substantiating 70% MHR is the safe dosage across different parameters of health and fitness level.

Keywords: potassium, maximal heart rate, exercise dosage, fitness level

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