Evaluation of international standards for ECG-recording and storage for use in tele-medical services

This report is written to clarify which of the international standards for ECG recordings that can be used in tele-medical services, where the recordings should be transmitted by wireless telecommunication facilities and finally stored as information integrated into the patients Electronic Health Record (EHR). Some principals for recording, transmission and storage of digital vital signs parameters are highlighted and important aspects of wireless communication of recorded signals from biomedical sensors are described, in order to understand the significance and differences in the storing formats to be used. Even if most of the relevant standards are not yet ratified (the last meeting in ISO TC 251 WH6 was held in October 2005), the actual international standards SCP-ECG, MFER, FDAXML and DICIOM are defined and already widely adopted. In this report, these standards are briefly described and evaluated with respect to possible use in tele-medical services, and recommendations are given in order to obtain a reliable and secure communication solution. Requirements for integration of the ECG file formats into the EHR are briefly described, and it is given some recommendations for actual standards to be used in future solutions

Clinical evaluation of a wireless ECG sensor system for arrhythmia diagnostic purposes

In a clinical study, a novel wireless electrocardiogram (ECG) recorder has been evaluated with regard to its ability to perform arrhythmia diagnostics. As the ECG recorder will detect a "non-standard" ECG signal, it has been necessary to compare those signals to "standard" ECG recording signals in order to evaluate the arrhythmia detection ability of the new system. Simultaneous recording of ECG signals from both the new wireless ECG recorder and a conventional Holter recorder was compared by two independent cardiology specialists with regard to signal quality for performing arrhythmia diagnosis. In addition, calculated R-R intervals from the two systems were correlated. A total number of 16 patients participated in the study. It can be considered that recorded ECG signals obtained from the wireless ECG system had an acceptable quality for arrhythmia diagnosis. Some of the patients used the wireless sensor while doing physical sport activities, and the quality of the recorded ECG signals made it possible to perform arrhythmia diagnostics even under such conditions. Consequently, this makes possible improvements in correlating arrhythmias to physical activities

Ubiquitous Computing for Remote Cardiac Patient Monitoring: A Survey

New wireless technologies, such as wireless LAN and sensor networks, for telecardiology purposes give new possibilities for monitoring vital parameters with wearable biomedical sensors, and give patients the freedom to be mobile and still be under continuous monitoring and thereby better quality of patient care. This paper will detail the architecture and quality-of-service (QoS) characteristics in integrated wireless telecardiology platforms. It will also discuss the current promising hardware/software platforms for wireless cardiac monitoring. The design methodology and challenges are provided for realistic implementation

Design Requirements for a Patient Administered Personal Electronic Health Record

Published version of a chapter in the book: Biomedical engineering, trends in electronics, communications and software. Intech, 2011 Open Acces

Context-aware system for cardiac condition monitoring and management: a survey

Health monitoring assists physicians in the decision-making process, which in turn, improves quality of life. As technology advances, the usage and applications of context-aware systems continue to spread across different areas in patient monitoring and disease management. It provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters. In this survey, we consider context-aware systems proposed by researchers for health monitoring and management. More specifically, we investigate different technologies and techniques used for cardiac condition monitoring and management. This paper also propose "mCardiac", an enhanced context-aware decision support system for cardiac condition monitoring and management during rehabilitation

REAL-TIME ARRHYTHMIA MONITORING AND DETECTION SYSTEM FOR REMOTE DIAGNOSIS

Arrhythmia is a kind of heart disease with the implication of abnormal heart beat rhythm; either it is too fast, too slow or with an irregular rhythm which can lead to death if the patient did not go for appropriate treatment within the time. The symptoms of this disease is atypical, thus continuous monitoring system is needed in order to pre-detect the disease followed by earlier treatment so that unwanted end of someone life can be avoided. This project is proposing a real-time, continuously monitoring and detection system for arrhythmia patient which will allow the collected data from patient being analyzed on the exact time. Throughout this paper, the proposed system will be designed for home-based usage with remote diagnosis feature to give more comfort to the patient and flexibility to the doctor or cardiologist. The system will be programmed in LabVIEW; a graphical programming software from National Instruments with the ECG sensor attached to the PC via DAQ board to allow real-time data collecting and analysis as well as the add-on feature to enable the web-based monitoring system for remote observation

A Novel Real-Time Intelligent Tele Cardiology System Using Wireless Technology to Detect Cardiac Abnormalities

This study presents a novel wireless, ambulatory,real- time, and auto alarm intelligent telecardiology system to improve healthcare for cardiovascular disease, which is one of the most prevalent and costly health problems in the world.This system consists of a lightweight and power-saving wireless ECG device equipped with a built-in automatic warning expert system. A temperature sensor is fixed to the user2019;s body, which senses temperature in the body, and delivers it to the ECG device. This device is connected to a microcontroller and ubiquitous real-time display platform. The acquired ECG signals which are transmitted to the microcontroller is then, processed by the expert system in order to detect the abnormality. An alert signal is sent to the remote database server, which can be accessed by an Internet browser, once an abnormal ECG is detected. The current version of the expert system can identify five types of abnormal cardiac rhythms in real-time, including sinus tachycardia, sinus bradycardia, wide QRS complex, atrial fibrillation (AF), and cardiac asystole, which is very important for both the subjects who are being monitored and the healthcare personnel tracking cardiac-rhythm disorders. The proposed system also activates an emergency medical alarm system when problems occur. We believe that in the future a business-card-like ECG device, accompanied with a Personal Computer, can make universal cardiac protection service possible

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