Design and evaluation of a person-centric heart monitoring system over fog computing infrastructure

Heart disease and stroke are becoming the leading cause of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that helps physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECG have limited accuracy and rely on external resources to analyze the signal and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyze the signal and identify abnormal behavior. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud and (c) the overall power consumption. Based on this concept, the HEART platform is presented that combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate and instant monitoring of the heart. The performance of the system is evaluated concerning the accuracy of detecting abnormal events and the power consumption of the wearable device. Results indicate that a very high percentage of success can be achieved in terms of event detection ratio and the device being operative up to a several days without the need for a recharge

Hybrid Nanostructured Textile Bioelectrode for Unobtrusive Health Monitoring

Coronary heart disease, cardiovascular diseases and strokes are the leading causes of mortality in United States of America. Timely point-of-care health diagnostics and therapeutics for person suffering from these diseases can save thousands of lives. However, lack of accessible minimally intrusive health monitoring systems makes timely diagnosis difficult and sometimes impossible. To remedy this problem, a textile based nano-bio-sensor was developed and evaluated in this research. The sensor was made of novel array of vertically standing nanostructures that are conductive nano-fibers projecting from a conductive fabric. These sensor electrodes were tested for the quality of electrical contact that they made with the skin based on the fundamental skin impedance model and electromagnetic theory. The hybrid nanostructured dry electrodes provided large surface area and better contact with skin that improved electrode sensitivity and reduced the effect of changing skin properties, which are the problems usually faced by conventional dry textile electrodes. The dry electrodes can only register strong physiological signals because of high background noise levels, thus limiting the use of existing dry electrodes to heart rate measurement and respiration. Therefore, dry electrode systems cannot be used for recording complete ECG waveform, EEG or measurement of bioimpedance. Because of their improved sensitivity these hybrid nanostructured dry electrodes can be applied to measurement of ECG and bioimpedance with very low baseline noise. These textile based electrodes can be seamlessly integrated into garments of daily use such as vests and bra. In combination with embedded wireless network device that can communicate with smart phone, laptop or GPRS, they can function as wearable wireless health diagnostic systems

IoT and fog computing-based monitoring system for cardiovascular patients with automatic ECG classification using deep neural networks

[EN] Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The system sends the ECG signal to a Fog layer service by using the LoRa communication protocol. Also, it includes an AI algorithm based on deep learning for the detection of Atrial Fibrillation and other heart rhythms. The automatic detection of arrhythmias can be complementary to the diagnosis made by the physician, achieving a better clinical vision that improves therapeutic decision making. The performance of the proposed system is evaluated on a dataset of 8.528 short single-lead ECG records using two merge MobileNet networks that classify data with an accuracy of 90% for atrial fibrillation.This work was partly supported by the Spanish Government (RTI2018-095390-B-C31), Universitat Politecnica de Valencia Research Grant PAID-10-19. S.G-O has been funded by grant PDBCEx COLDOC 679, scholarship programme from COLCIENCIAS (Administrative Department of Science, Technology and Innovation of Colombia).Rincón-Arango, JA.; Guerra-Ojeda, S.; Carrascosa Casamayor, C.; Julian, V. (2020). IoT and fog computing-based monitoring system for cardiovascular patients with automatic ECG classification using deep neural networks. Sensors. 20(24):1-19. https://doi.org/10.3390/s20247353119202

A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments

The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.

Preface: a special selection on biomechanical applications in medical science - Part I

This special selection on Biomechanical Applications in Medical Science presents the ongoing and active research in Biomechanics area. The overall goal is to summarize recent discoveries and groundbreaking studies that will account for new challenge research in the broad and relevant area of Biomechanics used for medical science.Peer ReviewedPostprint (published version

Wireless biomedical sensor networks: the technology

The increase in research in the area of wireless sensor networks (WSN) has brought a whole new meaning to medical devices. This is mainly due to advances in microcontroller technologies. The WSN are cited as one of the major technologies of this century and hence it assumes importance in areas such as health, psychology, fire prevention, security and even the military. The great advantage of this technology is the ability to track, monitor, study, understand and act on a particular phenomenon or event. The primary purpose of a wireless health system is reliable data transfer with minimum delay. This work is a synthesis of vast research done as Wireless Biomedical Sensor Networks (WBSN), including experimental and non-experimental investigations as well as data from the theoretical and empirical literature which incorporates a wide range of purposes: definition of concepts, review theories and evidence analysis of methodological problems, seeking to generate a consistent and understandable overview of WBSN. Such systems are already being marketed, some are still under investigation. It is also the aim of this study to identify the characteristics of a WSN applied to health.info:eu-repo/semantics/publishedVersio

Algorithms design for improving homecare using Electrocardiogram (ECG) signals and Internet of Things (IoT)

Due to the fast growing of population, a lot of hospitals get crowded from the huge amount of patients visits. Moreover, during COVID-19 a lot of patients prefer staying at home to minimize the spread of the virus. The need for providing care to patients at home is essential. Internet of Things (IoT) is widely known and used by different fields. IoT based homecare will help in reducing the burden upon hospitals. IoT with homecare bring up several benefits such as minimizing human exertions, economical savings and improved efficiency and effectiveness. One of the important requirement on homecare system is the accuracy because those systems are dealing with human health which is sensitive and need high amount of accuracy. Moreover, those systems deal with huge amount of data due to the continues sensing that need to be processed well to provide fast response regarding the diagnosis with minimum cost requirements. Heart is one of the most important organ in the human body that requires high level of caring. Monitoring heart status can diagnose disease from the early stage and find the best medication plan by health experts. Continues monitoring and diagnosis of heart could exhaust caregivers efforts. Having an IoT heart monitoring model at home is the solution to this problem. Electrocardiogram (ECG) signals are used to track heart condition using waves and peaks. Accurate and efficient IoT ECG monitoring at home can detect heart diseases and save human lives. As a consequence, an IoT ECG homecare monitoring model is designed in this thesis for detecting Cardiac Arrhythmia and diagnosing heart diseases. Two databases of ECG signals are used; one online which is old and limited, and another huge, unique and special from real patients in hospital. The raw ECG signal for each patient is passed through the implemented Low Pass filter and Savitzky Golay filter signal processing techniques to remove the noise and any external interference. The clear signal in this model is passed through feature extraction stage to extract number of features based on some metrics and medical information along with feature extraction algorithm to find peaks and waves. Those features are saved in the local database to apply classification on them. For the diagnosis purpose a classification stage is made using three classification ways; threshold values, machine learning and deep learning to increase the accuracy. Threshold values classification technique worked based on medical values and boarder lines. In case any feature goes above or beyond these ranges, a warning message appeared with expected heart disease. The second type of classification is by using machine learning to minimize the human efforts. A Support Vector Machine (SVM) algorithm is proposed by running the algorithm on the features extracted from both databases. The classification accuracy for online and hospital databases was 91.67% and 94% respectively. Due to the non-linearity of the decision boundary, a third way of classification using deep learning is presented. A full Multilayer Perceptron (MLP) Neural Network is implemented to improve the accuracy and reduce the errors. The number of errors reduced to 0.019 and 0.006 using online and hospital databases. While using hospital database which is huge, there is a need for a technique to reduce the amount of data. Furthermore, a novel adaptive amplitude threshold compression algorithm is proposed. This algorithm is able to make diagnosis of heart disease from the reduced size using compressed ECG signals with high level of accuracy and low cost. The extracted features from compressed and original are similar with only slight differences of 1%, 2% and 3% with no effects on machine learning and deep learning classification accuracy without the need for any reconstructions. The throughput is improved by 43% with reduced storage space of 57% when using data compression. Moreover, to achieve fast response, the amount of data should be reduced further to provide fast data transmission. A compressive sensing based cardiac homecare system is presented. It gives the channel between sender and receiver the ability to carry small amount of data. Experiment results reveal that the proposed models are more accurate in the classification of Cardiac Arrhythmia and in the diagnosis of heart diseases. The proposed models ensure fast diagnosis and minimum cost requirements. Based on the experiments on classification accuracy, number of errors and false alarms, the dictionary of the compressive sensing selected to be 900. As a result, this thesis provided three different scenarios that achieved IoT homecare Cardiac monitoring to assist in further research for designing homecare Cardiac monitoring systems. The experiment results reveal that those scenarios produced better results with high level of accuracy in addition to minimizing data and cost requirements