Electrocardiographic patch devices and contemporary wireless cardiac monitoring.

Cardiac electrophysiologic derangements often coexist with disorders of the circulatory system. Capturing and diagnosing arrhythmias and conduction system disease may lead to a change in diagnosis, clinical management and patient outcomes. Standard 12-lead electrocardiogram (ECG), Holter monitors and event recorders have served as useful diagnostic tools over the last few decades. However, their shortcomings are only recently being addressed by emerging technologies. With advances in device miniaturization and wireless technologies, and changing consumer expectations, wearable “on-body” ECG patch devices have evolved to meet contemporary needs. These devices are unobtrusive and easy to use, leading to increased device wear time and diagnostic yield. While becoming the standard for detecting arrhythmias and conduction system disorders in the outpatient setting where continuous ECG monitoring in the short to medium term (days to weeks) is indicated, these cardiac devices and related digital mobile health technologies are reshaping the clinician-patient interface with important implications for future healthcare delivery

ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-time Constraints

Remote health monitoring is becoming indispensable, though, Internet of Things (IoTs)-based solutions have many implementation challenges, including energy consumption at the sensing node, and delay and instability due to cloud computing. Compressive sensing (CS) has been explored as a method to extend the battery lifetime of medical wearable devices. However, it is usually associated with computational complexity at the decoding end, increasing the latency of the system. Meanwhile, mobile processors are becoming computationally stronger and more efficient. Heterogeneous multicore platforms (HMPs) offer a local processing solution that can alleviate the limitations of remote signal processing. This paper demonstrates the real-time performance of compressed ECG reconstruction on ARM's big.LITTLE HMP and the advantages they provide as the primary processing unit of the IoT architecture. It also investigates the efficacy of CS in minimizing power consumption of a wearable device under real-time and hardware constraints. Results show that both the orthogonal matching pursuit and subspace pursuit reconstruction algorithms can be executed on the platform in real time and yield optimum performance on a single A15 core at minimum frequency. The CS extends the battery life of wearable medical devices up to 15.4% considering ECGs suitable for wellness applications and up to 6.6% for clinical grade ECGs. Energy consumption at the gateway is largely due to an active internet connection; hence, processing the signals locally both mitigates system's latency and improves gateway's battery life. Many remote health solutions can benefit from an architecture centered around the use of HMPs, a step toward better remote health monitoring systems.Peer reviewedFinal Published versio

Wireless body sensor networks for health-monitoring applications

This is an author-created, un-copyedited version of an article accepted for publication in Physiological Measurement. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01

Survey on wireless body area sensor networks for healthcare applications: Signal processing, data analysis and feedback

Wireless sensor networks (WSNs) technologies are considered as one of the key of the research areas in computer science and healthcare application industries.The wireless body area sensor networks (WBASNs) is a wireless network used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements.The paper surveys the state-of-the-art on WBASNs discussing the major components of research in this area including physiological sensing, data preprocessing, detection and classification of human related phenomena. We provide comparative studies of the technologies and techniques used in such systems

An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies

Autism Spectrum Disorders (ASD) are associated with physiological abnormalities, which are likely to contribute to the core symptoms of the condition. Wearable technologies can provide data in a semi-naturalistic setting, overcoming the limitations given by the constrained situations in which physiological signals are usually acquired. In this study an integrated system based on wearable technologies for the acquisition and analysis of neurophysiological and autonomic parameters during treatment is proposed and an application on five children with ASD is presented. Signals were acquired during a therapeutic session based on an imitation protocol in ASD children. Data were analyzed with the aim of extracting quantitative EEG (QEEG) features from EEG signals as well as heart rate and heart rate variability (HRV) from ECG. The system allowed evidencing changes in neurophysiological and autonomic response from the state of disengagement to the state of engagement of the children, evidencing a cognitive involvement in the children in the tasks proposed. The high grade of acceptability of the monitoring platform is promising for further development and implementation of the tool. In particular if the results of this feasibility study would be confirmed in a larger sample of subjects, the system proposed could be adopted in more naturalistic paradigms that allow real world stimuli to be incorporated into EEG/psychophysiological studies for the monitoring of the effect of the treatment and for the implementation of more individualized therapeutic programs

Wireless body area sensor networks signal processing and communication framework: Survey on sensing, communication technologies, delivery and feedback

Problem statement: The Wireless Body Area Sensor Networks (WBASNs) is a wireless network used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements.This study surveys the state-of-the-art on Wireless Body Area Networks, discussing the major components of research in this area including physiological sensing and preprocessing, WBASNs communication techniques and data fusion for gathering data from sensors.In addition, data analysis and feedback will be presented including feature extraction, detection and classification of human related phenomena.Approach: Comparative studies of the technologies and techniques used in such systems will be provided in this study, using qualitative comparisons and use case analysis to give insight on potential uses for different techniques.Results and Conclusion: Wireless Sensor Networks (WSNs) technologies are considered as one of the key of the research areas in computer science and healthcare application industries.Sensor supply chain and communication technologies used within the system and power consumption therein, depend largely on the use case and the characteristics of the application.Authors conclude that Life-saving applications and thorough studies and tests should be conducted before WBANs can be widely applied to humans, particularly to address the challenges related to robust techniques for detection and classification to increase the accuracy and hence the confidence of applying such techniques without physician intervention

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

개 심장 기능 검사를 위한 인공지능 보조 심전도 활용

학위논문(석사) -- 서울대학교대학원 : 수의과대학 수의학과, 2023. 2. 김민수.This study aimed to determine the proportion of patients with electrocardiographic changes and to suggest the clinical utility of AI-assisted single-lead ECG for primary screening of canine heart function. Data was obtained from 116 owned dogs referred to Veterinary Medical Teaching Hospital, Seoul National University from June 2020 to December 2021. Single lead ECG traces were recorded and initially interpreted by the provided machine-learning software (CARDIOBIRD®, ANIWARE Ltd., Hong Kong). Among the 116 traces, 36 (31%) had abnormal ECG findings, according to the AI software. The most common abnormalities were wide or tall QRS (n=20), atrioventricular block (AVB) (n=8), and sinus pause (n=4). All data were suitable for interpretation and compatible to manual interpretation by clinicians. Despite some limitations, the newly developed AI-assisted ECG has shown promise for the screening of heart diseases in veterinary emergency or primary hospital without board certified cardiologist.심전도 검사(ECG)는 심장질환을 진단하는데 유용한 검사이지만, 많은 임상환경에서 임상가들은 심전도의 검사해석의 모호함을 느끼고 있다. 이 연구는 인공 지능(AI) 보조 심전도 기기를 이용하여 심전도 이상이 발견된 환자의 비율을 확인하고, 응급 상황에서 간편한 스크리닝 진단도구로써 AI 보조, 단일유도(lead II) 심전도의 임상적 유용성을 제안하는 것을 목표로 하였다. 검사는 2020년 6월부터 2021년 12월까지 서울대학교 수의과대학 응급의학과에 내원한 116마리의 환자로부터 측정되었다. 본 연구에 사용된 AI 기반 심전도 기기에(CARDIOBIRD®, ANIWARE Ltd., 홍콩) 따르면 116개의 심전도 기록 중 36마리(31%)가 비정상적인 심전도 소견을 보였다. 가장 흔한 이상은 넓거나 높은 QRS (n=17), 방실 차단(AVB) (n=8), 동정지 (n=4)였다. 모든 데이터는 해석에 적합한 품질을 보였으며 임상의의 해석과도 높은 일치율을 보였다. 개의 심장기능을 스크리닝하는데 있어 새로 개발된 스마트폰 기반의 인공지능 보조 심전도의 효용성이 평가되었다. 인공지능 보조 심전도는 특히 심장전문의가 없는 일차병원이나 응급실 환경에서 유용할 것으로 기대된다.Introduction 1 Materials and Methods 2 1. Animals 2 2. Ethical statement 2 3. ECG measuring device 2 4. ECG data analysis 2 5. Clinical setting 5 6. Diagnostic criteria 5 Results 7 1. Animals 7 2. AI-assisted ECG report 9 3. Comparison between standard ECG and AI-assisted ECG 11 4. Analysis of abnormal ECG findings 13 Discussion 15 Conclusion 20 References 21 Abstract in Korean 26석

All-in-One, Wireless, Stretchable Hybrid Electronics for Smart, Connected, and Ambulatory Physiological Monitoring

Commercially available health monitors rely on rigid electronic housing coupled with aggressive adhesives and conductive gels, causing discomfort and inducing skin damage. Also, research-level skin-wearable devices, while excelling in some aspects, fall short as concept-only presentations due to the fundamental challenges of active wireless communication and integration as a single device platform. Here, an all-in-one, wireless, stretchable hybrid electronics with key capabilities for real-time physiological monitoring, automatic detection of signal abnormality via deep-learning, and a long-range wireless connectivity (up to 15 m) is introduced. The strategic integration of thin-film electronic layers with hyperelastic elastomers allows the overall device to adhere and deform naturally with the human body while maintaining the functionalities of the on-board electronics. The stretchable electrodes with optimized structures for intimate skin contact are capable of generating clinical-grade electrocardiograms and accurate analysis of heart and respiratory rates while the motion sensor assesses physical activities. Implementation of convolutional neural networks for real-time physiological classifications demonstrates the feasibility of multifaceted analysis with a high clinical relevance. Finally, in vivo demonstrations with animals and human subjects in various scenarios reveal the versatility of the device as both a health monitor and a viable research tool.ope