2,098 research outputs found
Review of sensors for remote patient monitoring
Remote patient monitoring (RPM) of physiological
measurements can provide an efficient method and high
quality care to patients. The physiological signals
measurement is the initial and the most important factor
in RPM. This paper discusses the characteristics of the
most popular sensors, which are used to obtain vital
clinical signals in prevalent RPM systems.
The sensors discussed in this paper are used to measure
ECG, heart sound, pulse rate, oxygen saturation, blood
pressure and respiration rate, which are treated as the
most important vital data in patient monitoring and
medical examination
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
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
Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function
Cardiovascular disease is a major threat to health and one of the primary
causes of death globally. The 12-lead ECG is a cheap and commonly accessible
tool to identify cardiac abnormalities. Early and accurate diagnosis will allow
early treatment and intervention to prevent severe complications of
cardiovascular disease. In the PhysioNet/Computing in Cardiology Challenge
2020, our objective is to develop an algorithm that automatically identifies 27
ECG abnormalities from 12-lead ECG recordings
HEART MONITORING VIA WIRELESS ECG
The monitoring of heart had been a complex task. Acquiring ECG of the chronic patient spending most of their time outside the hospital had been a trivial task. Recording of ECG of such patients using wireless method is further challenging. This paper presents various methods of wireless ECG acquisition, their limitations and challenges. Cardiomobile, Flexible wireless ECG are the examples of such systems that are available in the medical world for wireless ECG
Enhancing Cardiovascular Disease Prediction Based on AI and IoT Concepts
One-third of all deaths worldwide yearly are attributable to cardiovascular disease (CVD). In contrast to the 7% of the wealthy who experience premature death, 43% of the poor do. Lifestyle diseases like obesity and diabetes are to blame. The importance of early identification of heart disease was demonstrated, and premature mortality was kept to a minimum. Combining clinical and biochemical data is essential for the early diagnosis of heart illness. Numerous IoT-enabled wearable healthcare applications have been created and released in recent years. Although the ability of wearable devices to share patient health data is expanding, it remains challenging to predict and identify health problems. Security, data storage, and patient monitoring are all part of the system. Artificial intelligence (AI) therapies may one day change the face of cardiology by providing doctors with cutting-edge data analysis and therapeutic decision-making resources. As the volume and complexity of data continue to increase, AI tools like machine learning (ML) and deep learning (DL) can assist medical professionals in learning more. Suppose we want to provide medical care to the elderly and those with chronic illnesses in the comfort of their own homes. In that case, we must upgrade our communication and information technology systems. The implemented DNN model's accuracy is amazing at 95.34 % and can yield other noteworthy outcomes when used to identify CVDs. We discuss and suggest the most suitable AI-IoT models for early CVD prediction and detection to reduce computational costs and increase time efficiency
Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review
The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/
AI Techniques for Efficient Healthcare Systems in ECG Wave Based Cardiac Disease Detection by High Performance Modelling
Heart disease (HD) is extremely lethal by nature and claims a disproportionately large number of lives worldwide. Early and reliable detection techniques are necessary to prevent fatalities from HD. Clinical test results, electrocardiogram (ECG) signal, the heart sound signal, impedance cardiography (ICG), magnetic resonance imaging, and computer tomography (CT) can all be used to determine whether an individual has HD. This research propose novel technique in efficient healthcare system by ECG wave based cardiac disease detection using deep learning architecture with high performance modelling. Here the input is collected as ECG waves which has been processed and obtained as ECG wave fragments. This ECG fragment features has been extracted using deep belief kernel principal neural network. Based on this extracted features the patients 3D heart image has been collected and classified using deep quantum multilayer convolutional neural networks. Here the experimental analysis has been carried out in terms of accuracy, precision, recall, F-score, SNR, RMSE. Proposed technique attained accuracy of 95%, precision of 81%, recall of 69%, F-1score of 73%, SNR of 59% and RMSE of 62%.  
Three-Heartbeat Multilead ECG Recognition Method for Arrhythmia Classification
Electrocardiogram (ECG) is the primary basis for the diagnosis of cardiovascular diseases. However, the amount of ECG data of patients makes manual interpretation time-consuming and onerous. Therefore, the intelligent ECG recognition technology is an important means to decrease the shortage of medical resources. This study proposes a novel classification method for arrhythmia that uses for the very first time a three-heartbeat multi-lead (THML) ECG data in which each fragment contains three complete heartbeat processes of multiple ECG leads. The THML ECG data pre-processing method is formulated which makes use of the MIT-BIH arrhythmia database as training samples. Four arrhythmia classification models are constructed based on one-dimensional convolutional neural network (1D-CNN) combined with a priority model integrated voting method to optimize the integrated classification effect. The experiments followed the recommended inter-patient scheme of the Association for the Advancement of Medical Instrumentation (AAMI) recommendations, and the practicability and effectiveness of THML ECG data are proved with ablation experiments. Results show that the average accuracy of the N, V, S, F, and Q classes is 94.82%, 98.10%, 97.28%, 98.70%, and 99.97%, respectively, with the positive predictive value of the N, V, S, and F classes being 97.0%, 90.5%, 71.9%, and 80.4%, respectively. Compared with current studies, the THML ECG data can effectively improve the morphological integrity and time continuity of ECG information and the 1D-CNN model of ECG sequence has a higher accuracy for arrhythmia classification. The proposed method alleviates the problem of insufficient samples, meets the needs of medical ECG interpretation and contributes to the intelligent dynamic research of cardiac disease
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