Relevant bibliographies by topics / VITAL-ECG

Academic literature on the topic 'VITAL-ECG'

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Published: 14 March 2023

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Journal articles on the topic "VITAL-ECG"

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Bae, Tae Wuk, Kee Koo Kwon, and Kyu Hyung Kim. "Vital Block and Vital Sign Server for ECG and Vital Sign Monitoring in a Portable u-Vital System." Sensors 20, no. 4 (February 17, 2020): 1089. http://dx.doi.org/10.3390/s20041089.

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An important function in the future healthcare system involves measuring a patient’s vital signs, transmitting the measured vital signs to a smart device or a management server, analyzing it in real-time, and informing the patient or medical staff. Internet of Medical Things (IoMT) incorporates information technology (IT) into patient monitoring device (PMD) and is developing traditional measurement devices into healthcare information systems. In the study, a portable ubiquitous-Vital (u-Vital) system is developed and consists of a Vital Block (VB), a small PMD, and Vital Sign Server (VSS), which stores and manages measured vital signs. Specifically, VBs collect a patient’s electrocardiogram (ECG), blood oxygen saturation (SpO2), non-invasive blood pressure (NiBP), body temperature (BT) in real-time, and the collected vital signs are transmitted to a VSS via wireless protocols such as WiFi and Bluetooth. Additionally, an efficient R-point detection algorithm was also proposed for real-time processing and long-term ECG analysis. Experiments demonstrated the effectiveness of measurement, transmission, and analysis of vital signs in the proposed portable u-Vital system.
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Randazzo, Vincenzo, Jacopo Ferretti, and Eros Pasero. "A Wearable Smart Device to Monitor Multiple Vital Parameters—VITAL ECG." Electronics 9, no. 2 (February 9, 2020): 300. http://dx.doi.org/10.3390/electronics9020300.

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Smart devices are more and more present in every aspect of everyday life. From smartphones, which are now like mini-computers, through systems for monitoring sleep or fatigue, to specific sensors for the recording of vital parameters. A particular class of the latter regards health monitoring. Indeed, through the use of such devices, several vital parameters can be acquired and automatically monitored, even remotely. This paper presents the second generation of VITAL-ECG, a smart device designed to monitor the most important vital parameters as a “one touch” device, anywhere, at low cost. It is a wearable device that coupled with a mobile app can track bio-parameters such as: electrocardiogram, SpO2, skin temperature, and physical activity of the patient. Even if it not yet a medical device, a comprehensive comparison with a golden standard electrocardiograph is presented to demonstrate the quality of the recorded signals and the validity of the proposed approach.
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Ko, Li-Wei, Yang Chang, Bo-Kai Lin, and Dar-Shong Lin. "Vital Signs Sensing Gown Employing ECG-Based Intelligent Algorithms." Biosensors 12, no. 11 (November 3, 2022): 964. http://dx.doi.org/10.3390/bios12110964.

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This study presents a long-term vital signs sensing gown consisting of two components: a miniaturized monitoring device and an intelligent computation platform. Vital signs are signs that indicate the functional state of the human body. The general physical health of a person can be assessed by monitoring vital signs, which typically include blood pressure, body temperature, heart rate, and respiration rate. The miniaturized monitoring device is composed of a compact circuit which can acquire two kinds of physiological signals including bioelectrical potentials and skin surface temperature. These two signals were pre-processed in the circuit and transmitted to the intelligent computation platform for further analysis using three algorithms, which incorporate R-wave detection, ECG-derived respiration, and core body temperature estimation. After the processing, the derived vital signs would be displayed on a portable device screen, including ECG signals, heart rate (HR), respiration rate (RR), and core body temperature. An experiment for validating the performance of the intelligent computation platform was conducted in clinical practices. Thirty-one participants were recruited in the study (ten healthy participants and twenty-one clinical patients). The results showed that the relative error of HR is lower than 1.41%, RR is lower than 5.52%, and the bias of core body temperature is lower than 0.04 °C in both healthy participant and clinical patient trials. In this study, a miniaturized monitoring device and three algorithms which derive vital signs including HR, RR, and core body temperature were integrated for developing the vital signs sensing gown. The proposed sensing gown outperformed the commonly used equipment in terms of usability and price in clinical practices. Employing algorithms for estimating vital signs is a continuous and non-invasive approach, and it could be a novel and potential device for home-caring and clinical monitoring, especially during the pandemic.
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Schmidt, Marcus, Johannes W. Krug, Andy Schumann, Karl-Jürgen Bär, and Georg Rose. "Estimation of a respiratory signal from a single-lead ECG using the 4th order central moments." Current Directions in Biomedical Engineering 1, no. 1 (September 1, 2015): 61–64. http://dx.doi.org/10.1515/cdbme-2015-0016.

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AbstractFor a variety of clinical applications like magnetic resonance imaging (MRI) the monitoring of vital signs is a common standard in clinical daily routine. Besides the electrocardiogram (ECG), the respiratory activity is an important vital parameter and might reveal pathological changes. Thoracic movement and the resulting impedance change between ECG electrodes enable the estimation of the respiratory signal from the ECG. This ECG-derived respiration (EDR) can be used to calculate the breathing rate without the need for additional devices or monitoring modules. In this paper a new method is presented to estimate the respiratory signal from a single-lead ECG. The 4th order central moments was used to estimate the EDR signal exploiting the change of the R-wave slopes induced by respiration. This method was compared with two approaches by analyzing the Fantasia database from www.physionet.org. Furthermore, the ECG signals of 24 healthy subjects placed in an 3 T MR-scanner were acquired.
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Gautam, Mayank Kumar, and Vinod Kumar Giri. "An Approach of Neural Network For Electrocardiogram Classification." APTIKOM Journal on Computer Science and Information Technologies 1, no. 3 (January 16, 2020): 119–27. http://dx.doi.org/10.34306/csit.v1i3.57.

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ECG is basically the graphical representation of the electrical activity of cardiac muscles duringcontraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to thisearly detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated bythe cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiologicalparameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG playsa vital role in the manual as well as automatic analysis of ECG. In this paper the study of the concept of patternrecognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes ofpredefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generatedwaveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters suchas spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includesartificial neural network as a classifier for identifying the abnormalities of heart disease.
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Gautam, Mayank Kumar, and Vinod Kumar Giri. "An Approach of Neural Network For Electrocardiogram Classification." APTIKOM Journal on Computer Science and Information Technologies 1, no. 3 (November 1, 2016): 119–27. http://dx.doi.org/10.11591/aptikom.j.csit.120.

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ECG is basically the graphical representation of the electrical activity of cardiac muscles during contraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to this early detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated by the cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiological parameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG plays a vital role in the manual as well as automatic analysis of ECG. In this paper the study of the concept of pattern recognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes of predefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generated waveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters such as spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includes artificial neural network as a classifier for identifying the abnormalities of heart disease.
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Aditya Mahendra Oka, Gede, and Andjar Pudji. "Design of Vital Sign Monitor with ECG, BPM, and Respiration Rate Parameters." Indonesian Journal of electronics, electromedical engineering, and medical informatics 3, no. 1 (February 22, 2021): 34–38. http://dx.doi.org/10.35882/ijeeemi.v3i1.6.

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Vital sign monitor is a device used to monitor a patient's vital sign, in the form of a heartbeat, pulse, blood pressure, temperature of the heart's pulse form continuously. Condition monitoring in patients is needed so that paramedics know the development of the condition of inpatients who are experiencing a critical period. Electrocardiogram (ECG) is a physiological signal produced by the electrical activity of the heart. Recording heart activity can be used to analyze how the characteristics of the heart. By obtaining respiration from outpatient electrocardiography, which is increasingly being used clinically to practice to detect and characterize the abnormal occurrence of heart electrical behavior during normal daily activities. The purpose of this study is to determine that the value of the Repiration Rate is taken from ECG signals because of its solidity. At the peak of the R ECG it has several respiratory signals such as signals in fluctuations. An ECG can be used to determine breathing numbers. This module utilizes leads ECG signals to 1 lead, namely lead 2, respiration rate taken from the ECG, BPM in humans displayed on a TFT LCD. This research module utilizes the use of filters to obtain ECG signals, and respiration rates to display the results on a TFT LCD. This module has the highest error value of 0.01% compared to the Phantom EKG tool. So this module can be used for the diagnosis process.ECG, Respiration Rate, Filter
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Proffitt, A., and P. Rees. "The athletic ECG." Journal of The Royal Naval Medical Service 102, no. 1 (June 2016): 50–55. http://dx.doi.org/10.1136/jrnms-102-50.

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AbstractThe electrocardiogram (ECG) is the most frequently performed basic cardiology investigation. Correct interpretation of the ECG is vital, both to confirm acute diagnoses such as myocardial infarction, and in the elective setting to diagnose previous or underlying cardiac abnormalities. Normal electrocardiographic parameters for the multiple components of the ECG have been identified and are applied to the general population, but it is acknowledged that cardiac conditioning occurs with frequent and sustained aerobic exercise, in turn leading to physiological changes in the ECG. Service personnel may perform exercise at a level that leads to cardiac conditioning with associated ECG changes. This clinical review will briefly address the normal ECG and consider changes associated with aerobic cardiac conditioning. By identifying what constitutes physiological non-pathological changes in the athletic ECG, this clinical review aims to assist those who interpret ECGs in Service personnel.
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Kim, Ju-Yeon, Jae-Hyun Park, Se-Young Jang, and Jong-Ryul Yang. "Peak Detection Algorithm for Vital Sign Detection Using Doppler Radar Sensors." Sensors 19, no. 7 (April 1, 2019): 1575. http://dx.doi.org/10.3390/s19071575.

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An accurate method for detecting vital signs obtained from a Doppler radar sensor is proposed. A Doppler radar sensor can remotely obtain vital signs such as heartbeat and respiration rate, but the vital signs obtained by using the sensor do not show clear peaks like in electrocardiography (ECG) because of the operating characteristics of the radar. The proposed peak detection algorithm extracts the vital signs from the raw data. The algorithm shows the mean accuracy of 96.78% compared to the peak count from the reference ECG sensor and a processing time approximately two times faster than the gradient-based algorithm. To verify whether heart rate variability (HRV) analysis similar to that with an ECG sensor is possible for a radar sensor when applying the proposed method, the continuous parameter variations of the HRV in the time domain are analyzed using data processed with the proposed peak detection algorithm. Experimental results with six subjects show that the proposed method can obtain the heart rate with high accuracy but cannot obtain the information for an HRV analysis because the proposed method cannot overcome the characteristics of the radar sensor itself.
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Elangovan, Ramanujam, and Padmavathi S. "A Review on Time Series Motif Discovery Techniques an Application to ECG Signal Classification." International Journal of Artificial Intelligence and Machine Learning 9, no. 2 (July 2019): 39–56. http://dx.doi.org/10.4018/ijaiml.2019070103.

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Cardiovascular disease diagnosis from an ECG signal plays an important and significant role in the health care system. Recently, numerous researchers have developed an automatic time series-based multi-step diagnosis system for the fast and accurate diagnosis of ECG abnormalities. The multi-step procedure involves ECG signal acquisition, signal pre-processing, feature extraction, and classification. Among which, the feature extraction plays a vital role in the field of accurate diagnosis. The features may be different types such as statistical, morphological, wavelet or any other signal-based approach. This article discusses various time series motif-based feature extraction techniques with respect to a different dimension of ECG signal.
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Dissertations / Theses on the topic "VITAL-ECG"

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Holzhausen, Rudolf. "A clinical patient vital signs parameter measurement, processing and predictive algorithm using ECG." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/6466.

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In the modern clinical and healthcare setting, the electronic collection and analysis of patient related vital signs and parameters are a fundamental part of the relevant treatment plan and positive patient response. Modern analytical techniques combined with readily available computer software today allow for the near real time analysis of digitally acquired measurements. In the clinical context, this can directly relate to patient survival rates and treatment success. The processing of clinical parameters, especially the Electrocardiogram (ECG) in the critical care setting has changed little in recent years and the analytical processes have mostly been managed by highly trained and experienced cardiac specialists. Warning, detection and measurement techniques are focused on the post processing of events relying heavily on averaging and analogue filtering to accurately capture waveform morphologies and deviations. This Ph. D. research investigates an alternative and the possibility to analyse, in the digital domain, bio signals with a focus on the ECG to determine if the feasibility of bit by bit or near real time analysis is indeed possible but more so if the data captured has any significance in the analysis and presentation of the wave patterns in a patient monitoring environment. The research and experiments have shown the potential for the development of logical models that address both the detection and short term predication of possible follow-on events with a focus on Myocardial Ischemic (MI) and Infraction based deviations. The research has shown that real time waveform processing compared to traditional graph based analysis, is both accurate and has the potential to be of benefit to the clinician by detecting deviations and morphologies in a real time domain. This is a significant step forward and has the potential to embed years of clinical experience into the measurement processes of clinical devices, in real terms. Also, providing expert analytical and identification input electronically at the patient bedside. The global human population is testing the healthcare systems and care capabilities with the shortage of clinical and healthcare providers in ever decreasing coverage of treatment that can be provided. The research is a moderate step in further realizing this and aiding the caregiver by providing true and relevant information and data, which assists in the clinical decision process and ultimately improving the required standard of patient care.
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Lee, Y. D. (Young-Dong). "Wireless vital signs monitoring system for ubiquitous healthcare with practical tests and reliability analysis." Doctoral thesis, Oulun yliopisto, 2010. http://urn.fi/urn:isbn:9789514263880.

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Abstract The main objective of this thesis project is to implement a wireless vital signs monitoring system for measuring the ECG of a patient in the home environment. The research focuses on two specific research objectives: 1) the development of a distributed healthcare system for vital signs monitoring using wireless sensor network devices and 2) a practical test and performance evaluation for the reliability for such low-rate wireless technology in ubiquitous health monitoring applications. The first section of the thesis describes the design and implementation of a ubiquitous healthcare system constructed from tiny components for the home healthcare of elderly persons. The system comprises a smart shirt with ECG electrodes and acceleration sensors, a wireless sensor network node, a base station and a server computer for the continuous monitoring of ECG signals. ECG data is a commonly used vital sign in clinical and trauma care. The ECG data is displayed on a graphical user interface (GUI) by transferring it to a PDA or a terminal PC. The smart shirt is a wearable T-shirt designed to collect ECG and acceleration signals from the human body in the course of daily life. In the second section, a performance evaluation of the reliability of IEEE 802.15.4 low-rate wireless ubiquitous health monitoring is presented. Three scenarios of performance studies are applied through practical tests: 1) the effects of the distance between sensor nodes and base-station, 2) the deployment of the number of sensor nodes in a network and 3) data transmission using different time intervals. These factors were measured to analyse the reliability of the developed technology in low-rate wireless ubiquitous health monitoring applications. The results showed how the relationship between the bit-error-rate (BER) and signal-to-noise ratio (SNR) was affected when varying the distance between sensor node and base-station, through the deployment of the number of sensor nodes in a network and through data transmission using different time intervals.
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RANDAZZO, VINCENZO. "Novel neural approaches to data topology analysis and telemedicine." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850610.

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Lin, Bo-Kai, and 林柏凱. "Integration of Wireless ECG and Skin Temperature Signals for Developing a Clinical Vital Sign Monitoring System." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/23278920984954726182.

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Book chapters on the topic "VITAL-ECG"

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Sonkusale, Sameer. "Sensors for Vital Signs: ECG Monitoring Systems." In Handbook of Biochips, 1–23. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-6623-9_2-1.

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Sonkusale, Sameer. "Sensors for Vital Signs: ECG Monitoring Systems." In Handbook of Biochips, 221–43. New York, NY: Springer New York, 2022. http://dx.doi.org/10.1007/978-1-4614-3447-4_2.

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Papaioannou, Maria, George Mandellos, Theodor Panagiotakopoulos, and Dimitrios Lymperopoulos. "Handling ECG Vital Signs in Personalized Ubiquitous Telemedicine Services." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 85–94. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05195-2_9.

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Alrubaish, Hind, and Nazar Saqib. "Your Vital Signs as Your Password?" In Recent Advances in Biometrics [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.104783.

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Cognitive biometrics (vital signs) indicate the individual’s authentication using his/her mental and emotional status specifically, electrocardiogram (ECG) and electroencephalogram (EEG). The motivation behind cognitive biometrics is their uniqueness, their absolute universality in each living individual, and their resistance toward spoofing and replaying attacks in addition to their indication of life. This chapter investigates the ability to use the vital sign as unimodal authentication in its status by surveying the recent techniques, their requirements and limitation, and whether it is ready to be used in the real market or not. Our observations state—that the vital signs can be considered as a PASSWORD due to their uniqueness, but it needs more improvements to be deployed to the market.
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Majumder, Swanirbhar, and Saurabh Pal. "Trends of ECG Analysis and Diagnosis." In Handbook of Research on Trends in the Diagnosis and Treatment of Chronic Conditions, 185–210. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8828-5.ch009.

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Any biomedical signal has the specialty in terms of the remoteness and nature of their source as an advantage over other natural signals. The analysis of biomedical signal plays a significant role in medical, and to be exact cardiological decision making, provided, the subject information is accurate and reliable. Normally experienced and trained medical practitioners, are known to study and know them better, but in this age of technology computerized expert system are better for long term continuous monitoring and automatic decision making. This led to evolution of biomedical engineering as a separate wing where parts of engineering under automatic signal processing and analysis studies are done. ECG being the most vital physiological signal, its acquisition technique, noise and artifacts elimination methodologies are discussed in this chapter. A brief description on ECG and its usage as biometric and analysis of Atrial Fibrillation is presented.
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Arun, Uma, and Natarajan Sriraam. "A Wearable ECG Monitoring System for Resource-Constrained Settings." In Biomedical and Clinical Engineering for Healthcare Advancement, 1–16. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0326-3.ch001.

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Recent advancement in wearable technology has created a huge impact in healthcare delivery and clinical diagnosis. Remote access of physiological, vital parameters from patients and improvement in their day-to-day quality of life were the significant indicators due to this availability of wearable technology. Though wearable physiological monitoring systems for long-term monitoring of Electro cardiogram (ECG) were developed at high-cost involvement, there is a huge need for such technology for resource-constrained settings, at a low cost. This chapter suggests a wearable ECG monitoring system by making use of single channel textile sensors for screening of cardiac episodes. The proposed Cardiac signal framework (CARDIF) with chest textile-based sensors ensures the required qualitative signal for clinical assessment and the evaluation of fidelity measures confirms its suitability for early screening of cardiac episodes. The proposed CARDIF framework involves low-cost design without sacrificing the required clinical diagnosis requirement and can be extended for long-term, continuous monitoring in resource-constrained settings.
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R., Jegan, and Nimi W. S. "Sensor Based Smart Real Time Monitoring of Patients Conditions Using Wireless Protocol." In Biotechnology, 720–43. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8903-7.ch029.

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This article describes how physiological signal monitoring plays an important role in identifying the health condition of heart. In recent years, online monitoring and processing of biomedical signals play a major role in accurate clinical diagnosis. Therefore, there is a requirement for the developing of online monitoring systems that will be helpful for physicians to avoid mistakes. This article focuses on the method for real time acquisition of an ECG and PPG signal and it's processing and monitoring for tele-health applications. This article also presents the real time peak detection of ECG and PPG for vital parameters measurement. The implementation and design of the proposed wireless monitoring system can be done using a graphical programming environment that utilizes less power and a minimized area with reasonable speed. The advantages of the proposed work are very simple, low cost, easy integration with programming environment and continuous monitoring of physiological signals.
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Chouhan, Kuldeep Singh, Jyoti Gajrani, Bhavna Sharma, and Satya Narayan Tazi. "Arrhythmia Classification Using Deep Learning Architecture." In Real-Time Applications of Machine Learning in Cyber-Physical Systems, 148–72. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9308-0.ch010.

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As cardiovascular diseases (CVDs) are a serious concern to modern medical science to diagnose at an early stage, it is vital to build a classification model that can effectively reduce mortality rates by treating millions of people in a timely manner. An electrocardiogram (ECG) is a specialized instrument that measures the heart's physiological responses. To accurately diagnose a patient's acute and chronic heart problems, an in-depth examination of these ECG signals is essential. The proposed model consists of a convolutional neural network having three convolutional, two pooling, and two dense layers. The proposed model is trained and evaluated on the MIT-BIH arrhythmia and PTB diagnostic datasets. The classification accuracy is 99.16%, which is higher than state-of-the-art studies on similar arrhythmias. Recall, precision, and F1 score of the proposed model are 96.53%, 95.15%, and 99.17%, respectively. The proposed model can aid doctors explicitly for the detection and classification of arrhythmias.
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Nambakhsh, Mohammad Saleh, and M. Shiva. "A Novel Blind Wavelet Base Watermarking of ECG Signals on Medical Images Using EZW Algorithm." In Encyclopedia of Healthcare Information Systems, 1004–15. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-889-5.ch125.

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Exchange of databases between hospitals needs efficient and reliable transmission and storage techniques to cut down the cost of health care. This exchange involves a large amount of vital patient information such as biosignals and medical images. Interleaving one form of data such as 1-D signal over digital images can combine the advantages of data security with efficient memory utilization (Norris, Englehart & Lovely, 2001), but nothing prevents the user from manipulating or copying the decrypted data for illegal uses. Embedding vital information of patients inside their scan images will help physicians make a better diagnosis of a disease. In order to solve these issues, watermark algorithms have been proposed as a way to complement the encryption processes and provide some tools to track the retransmission and manipulation of multimedia contents (Barni, Podilchuk, Bartolini & Delp, 2001; Vallabha, 2003). A watermarking system is based on an imperceptible insertion of a watermark (a signal) in an image. This technique is adapted here for interleaving graphical ECG signals within medical images to reduce storage and transmission overheads as well as helping for computer-aided diagnostics system. In this chapter, we present a new wavelet-based watermarking method combined with the EZW coder. The principle is to replace significant wavelet coefficients of ECG signals by the corresponding significant wavelet coefficients belonging to the host image, which is much bigger in size than the mark signal. This chapter presents a brief introduction to watermarking and the EZW coder that acts as a platform for our watermarking algorithm.
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Seoane, Fernando, Sibrecht Bouwstra, Juan Carlos Marquez, Johan Löfhede, and Kaj Lindecrantz. "Smart Textiles in Neonatal Monitoring." In Neonatal Monitoring Technologies, 41–55. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0975-4.ch003.

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Prematurely born and critically ill babies admitted to the Neonatal Intensive Care Unit require round-the-clock monitoring of vital signs and in special cases additional parameters such as brain functioning monitoring. Although close monitoring is fundamental for a good developmental outcome, the monitor systems are obtrusive, causing stress for the baby and hampering parent-child contact. New developments in textile and electronics offer opportunity in greatly improving the comfort and appearance of the monitoring systems for ECG as well as EEG monitoring by replacing the adhesive electrodes with textile electrodes. The authors present the designs of a neonatal jacket containing textile electrodes for ECG monitoring and textile electrodes intended to be integrated in a cap for brain functioning monitoring. The initial results presented show good prospect for further development. Accuracy and reliability are challenges specific for the medical application of smart textiles such as in neonatal monitoring. Furthermore, the mass-production of smart textiles requires improvement before smart garments can be introduced to the practice of neonatal care.
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Conference papers on the topic "VITAL-ECG"

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Randazzo, Vincenzo, Eros Pasero, and Silvio Navaretti. "VITAL-ECG: A portable wearable hospital." In 2018 IEEE Sensors Applications Symposium (SAS). IEEE, 2018. http://dx.doi.org/10.1109/sas.2018.8336776.

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Paviglianiti, Annunziata, Vincenzo Randazzo, Eros Pasero, and Alberto Vallan. "Noninvasive Arterial Blood Pressure Estimation using ABPNet and VITAL-ECG." In 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2020. http://dx.doi.org/10.1109/i2mtc43012.2020.9129361.

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Mandellos, George J., Michael N. Koukias, and Dimitrios K. Lymberopoulos. "Structuring the e-SCP-ECG+ protocol for multi vital-sign handling." In 2008 8th IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2008. http://dx.doi.org/10.1109/bibe.2008.4696777.

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Paviglianiti, Annunziata, and Eros Pasero. "VITAL-ECG: a de-bias algorithm embedded in a gender-immune device." In 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT). IEEE, 2020. http://dx.doi.org/10.1109/metroind4.0iot48571.2020.9138291.

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Yumang, Analyn N., Geraldo C. Talisic, Lorenz F. Oripaypay, Jessie R. Balbin, Christel Evance V. Lopez, Janette C. Fausto, and Christopher James P. Mabbagu. "Vital Signs Determination from ECG and PPG Signals Obtained from Arduino Based Sensors." In the 2019 9th International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3326172.3326202.

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Chowdhury, Souma, and Ali Mehmani. "Optimal Metamodeling to Interpret Activity-Based Health Sensor Data." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68385.

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Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features — heart rate, QRS time, and QR ratio in each heartbeat period — models with median error of <25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers.
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Sugano, Hiroto, Shinsuke Hara, Tetsuo Tsujioka, Shigeyoshi Nakajima, Tadayuki Inoue, Kazuhide Takeuchi, and Hajime Nakamura. "Continuous ECG data gathering by a wireless vital sensor — Evaluation of its sensing and transmission capabilities." In Applications (ISSSTA). IEEE, 2010. http://dx.doi.org/10.1109/isssta.2010.5649925.

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Luo, Yuxuan, Kok-Hin Teng, Yongfu Li, Wei Mao, Chun-Huat Heng, and Yong Lian. "A 93μW 11Mbps wireless vital signs monitoring SoC with 3-lead ECG, bio-impedance, and body temperature." In 2017 IEEE Asian Solid-State Circuits Conference (A-SSCC). IEEE, 2017. http://dx.doi.org/10.1109/asscc.2017.8240208.

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Hassanuzzaman, Md, Purnendu Biswas, and Tanzilur Rahman. "End to End Solution for Continuous Monitoring and Real-Time Analysis of Vital Signs from ECG Signal." In 2019 IEEE R10 Humanitarian Technology Conference (R10-HTC). IEEE, 2019. http://dx.doi.org/10.1109/r10-htc47129.2019.9042478.

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Cheng, Mu-Huo, Li-Chung Chen, Ying-Che Hung, Chang Ming Yang, and Tzu Lin Yang. "A Real-Time Heart-Rate Estimator from Steel Textile ECG Sensors in a Wireless Vital Wearing System." In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.664.

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