1,487 research outputs found
Mobihealth: mobile health services based on body area networks
In this chapter we describe the concept of MobiHealth and the approach developed during the MobiHealth project (MobiHealth, 2002). The concept was to bring together the technologies of Body Area Networks (BANs), wireless broadband communications and wearable medical devices to provide mobile healthcare services for patients and health professionals. These technologies enable remote patient care services such as management of chronic conditions and detection of health emergencies. Because the patient is free to move anywhere whilst wearing the MobiHealth BAN, patient mobility is maximised. The vision is that patients can enjoy enhanced freedom and quality of life through avoidance or reduction of hospital stays. For the health services it means that pressure on overstretched hospital services can be alleviated
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
Integrated heart failure telemonitoring system for homecare
The integrated telemonitoring system (ITS) for homecare has been designed to improve quality
of care as measured by increased nursing productivity, improved patients’ clinical and
behavioral outcomes and reduction of cost. The system incorporates managerial, organizational,
operational and clinical tasks optimized for delivery of quality care through telemonitoring.
A secure, multi-modal computer network that integrates homecare nurses, patients and those
who care into one seamless environment has been developed. The network brings together
a new generation of small, hand-held, wireless terminals used by nurses and patients with
a HIPPA-compliant electronic patient record system at the caregiver’s site. Wireless terminals
use Gobi multi-standard networking technology for connectivity to any available wireless network.
The unique features of ITS include a) picture recognition technology capable of extracting
numeric data from in-home physiological signal monitor displays that include blood pressure,
weight, oxygen saturation, transmission of lung sounds, and capturing echocardiography and
electrocardiography data from mobile units; b) in-home caregiver-assisted interactive examinations
of signs and symptoms that include visual impressions of ankle swelling, jugular vein
distension measurement, and weight gain; c) video-conference capability, facilitating face-to-face
two-way communication of nursing personnel with the patients.
The ITS network has been designed to improve patients’ clinical and behavioral outcomes,
increase nursing productivity, and reduce the cost of homecare. Patients’ co-operation and compliance
has been achieved through use of easy-to-use videoconferencing terminals. (Cardiol J
2010; 17, 2: 200-204
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MobiHealth-Innovative 2.5/3G mobile services and applications for health care
MobiHealth aims at introducing new mobile value added services in the area of healthcare, based on 2.5 (GPRS) and 3G (UMTS) technologies, thus promoting the use and deployment of GPRS and UMTS. This will be achieved by the integration of sensors and actuators to a Wireless Body Area Network (BAN). These sensors and actuators will continuously measure and transmit vital constants along with audio and video to health service providers and brokers, improving on one side the life of patients and allowing on the other side the introduction of new value-added services in the areas of disease prevention and diagnostic, remote assistance, para-health services, physical state monitoring (sports) and even clinical research. Furthermore, the MobiHealth BAN system will support the fast and reliable application of remote assistance in case of accidents by allowing the paramedics to send reliable vital constants data as well as audio and video directly from the accident site
Telemedicine
Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios
Impact of Mobile and Wireless Technology on Healthcare Delivery services
Modern healthcare delivery services embrace the use of leading edge technologies and new
scientific discoveries to enable better cures for diseases and better means to enable early
detection of most life-threatening diseases. The healthcare industry is finding itself in a
state of turbulence and flux. The major innovations lie with the use of information
technologies and particularly, the adoption of mobile and wireless applications in
healthcare delivery [1]. Wireless devices are becoming increasingly popular across the
healthcare field, enabling caregivers to review patient records and test results, enter
diagnosis information during patient visits and consult drug formularies, all without the
need for a wired network connection [2]. A pioneering medical-grade, wireless
infrastructure supports complete mobility throughout the full continuum of healthcare
delivery. It facilitates the accurate collection and the immediate dissemination of patient
information to physicians and other healthcare care professionals at the time of clinical
decision-making, thereby ensuring timely, safe, and effective patient care. This paper
investigates the wireless technologies that can be used for medical applications, and the
effectiveness of such wireless solutions in a healthcare environment. It discusses challenges
encountered; and concludes by providing recommendations on policies and standards for
the use of such technologies within hospitals
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