Medical Cyber-physical Systems Research Articles

Review of Medical Cyber Physical System based Infusion Pump-perception on control strategy

The revolutionary change in recent technologies has dominated the Medical field. Huge medical devices are deployed in hospitals and in-home environment. The innovations of smart medical devices are prominent which can be used in the fully automated and semi- automated environment. For several decades, numerous medical devices are designed and utilized effectively. These devices are designed for the past few years, manufactured in micro and nanotechnologies with smart sensing techniques. Such devices are network-enabled and integrated into the cyber world in physical means, leading to a medical cyber physical system. The design, working, and optimal control of smart medical devices are essential. In this paper, the emphasis on the review of infusion pump control mechanism with essential parameters such as drug delivery, flow rate, pump control has been considered.

Detecting insider attacks in medical cyber–physical networks based on behavioral profiling

Cyber–physical systems (CPS) have been widely used in medical domains to provide high-quality patient treatment in complex clinical scenarios. With more medical devices being connected in industry, the security of medical cyber–physical systems has received much attention. Medical smartphones are one of the widely adopted facilities in the healthcare industry aiming to improve the quality of service for both patients and healthcare personnel. These devices construct an emerging CPS network architecture, called medical smartphone networks (MSNs). Similar to other distributed networks, MSNs also suffer from insider attacks, where the intruders have authorized access to the network resources, resulting in the leakage of patient information. In this work, we focus on the detection of malicious devices in MSNs and design a trust-based intrusion detection approach based on behavioral profiling. A node’s reputation can be judged by identifying the difference in Euclidean distance between two behavioral profiles. In the evaluation, we evaluate our approach in a real MSN environment by collaborating with a practical healthcare center. Experimental results demonstrate that our approach can identify malicious MSN nodes faster than other similar approaches.

Distributed Access Control Based on Proxy Signature in Medical Cyber-Physical Systems

Distributed Access Control Based on Proxy Signature in Medical Cyber-Physical Systems

Medical cyber-physical systems: A survey.

Medical cyber-physical systems (MCPS) are healthcare critical integration of a network of medical devices. These systems are progressively used in hospitals to achieve a continuous high-quality healthcare. The MCPS design faces numerous challenges, including inoperability, security/privacy, and high assurance in the system software. In the current work, the infrastructure of the cyber-physical systems (CPS) are reviewed and discussed. This article enriched the researches of the networked Medical Device (MD) systems to increase the efficiency and safety of the healthcare. It also can assist the specialists of medical device to overcome crucial issues related to medical devices, and the challenges facing the design of the medical device's network. The concept of the social networking and its security along with the concept of the wireless sensor networks (WSNs) are addressed. Afterward, the CPS systems and platforms have been established, where more focus was directed toward CPS-based healthcare. The big data framework of CPSs is also included.

Sustainable securing of Medical Cyber-Physical Systems for the healthcare of the future

Over the last years, solutions based on Information and Communications Technologies (ICT) have opted for the deployment of eHealth services to provide a higher level of quality and safety to medical processes, with which to mitigate human errors. As a result, Medical Cyber-Physical Systems (MCPS) is a new disruptive approach oriented to enable smart healthcare systems to monitor, process and make autonomous decisions without the need to involve doctors and caregivers. Yet, current MCPS pose several open challenges, the security of medical devices being one of the most critical ones because of its close relationship with patients’ safety. To deal with these issues, mobile edge cloud computing – or fog computing – can be used to improve the security of MCPS by enabling the deployment and management of efficient resources into the network edge. In this context, we propose the novel concept of Virtual Medical Device as a sustainable and efficient mechanism deployed in the network edge to ease and provide dynamism and low latency for achieving security in MCPS. Additionally, we also propose a fog computing-oriented framework to enable real-time and dynamic management of physical and virtual medical devices as well as network infrastructure composing MCPS. To show our solution feasibility, we designed a reactive and proactive use case depicting security concerns of current MCPS. We also describe how our framework arranges MCPS with management policies that consider patients’ vital signs, network communications, and location of medical devices and patients.

Machine Intelligence in Healthcare and Medical Cyber Physical Systems: A Survey

Today, the US healthcare industry alone can save $300 B per year by using machine intelligence to analyze a rich set of existing medical data; results from these analyses can lead to breakthroughs such as more accurate medical diagnoses, discovery of new cures for diseases, and cost savings in the patient admission process at healthcare organizations. Because healthcare applications intrinsically imply a vast amount of data, the execution of any algorithm on medical data is computationally intensive. Significant advancements made in computational power in the past decade have provided the opportunity for many researchers to successfully implement various machine intelligence-based healthcare applications, which didn’t run efficiently on earlier computational platforms. In this paper, we provide a survey of machine intelligence algorithms within the context of healthcare applications; our survey includes a comprehensive list of the most commonly used computational models and algorithms. We view the application of these algorithms in multiple steps, namely, data acquisition, feature extraction, and aggregation, modeling, algorithm training, and algorithm execution and provide details—as well as representative case studies—for each step. We provide a set of metrics that are used to evaluate modeling and algorithmic performance, which facilitate the comparison of the presented models and algorithms. Medical cyber-physical systems are presented as an emerging application case study of machine intelligence in healthcare. We conclude our paper by providing a list of opportunities and challenges for incorporating machine intelligence in healthcare applications and provide an extensive list of tools and databases to help other researchers.

Towards False Alarm Reduction Using Fuzzy If-Then Rules for Medical Cyber Physical Systems

Cyber-Physical Systems (CPS) are integrations of computation, networking, and physical processes. Its process control is often referred to as embedded systems. Generally, CPS and Internet of Things have the same basic architecture, whereas the former shows a higher combination and coordination between physical and computational elements, i.e., wireless sensor networks can be a vital part of CPS applications. With the rapid development, CPS has been applied to healthcare industry, where a wide range of medical sensors are used within a healthcare organization. However, these sensors may generate a large number of false alarms in practice, which could significantly reduce the system effectiveness. Targeting on this issue, in this work, we attempt to design a Medical Fuzzy Alarm Filter (named MFAFilter) for healthcare environments by means of fuzzy logic, especially fuzzy if-then rules, which could handle the vague and imprecise among data. In the evaluation, we conducted two major experiments to explore the performance of our approach in a simulated and a real network environment, respectively. Experimental results demonstrate that the use of fuzzy if-then rules could achieve a better accuracy as compared to the traditional supervised algorithms, and that our designed filter is effective in the practical environment.

A Cyber-Physical System Framework for Early Detection of Paroxysmal Diseases

Paroxysmal diseases of inpatients are globally recognized as one of the top challenges in medicine. Poor clinical outcomes are primarily caused by delayed recognition, especially due to diverse clinical diagnostic criteria with complex manifestations, irregular episodes, and already overloaded clinical activities. With the proliferation of measuring devices and increased computational capabilities, cyber-physical characterization plays an increasingly important role in many domains to provide enabling technologies. This paper presents a cyber-physical system (CPS) framework to assist physicians in making earlier diagnoses of paroxysmal sympathetic hyperactivity based on existing medical knowledge. We propose a configurable diagnostic knowledge model to characterize clinical criteria to reduce domain knowledge deficiency between physicians and computer scientists. We present a component-based medical CPS framework to employ the knowledge models and integrate medical devices. Our approach aims to relieve medical staff from the heavy burden of clinical activities and to provide timely decision support. We evaluate our approach on 128 real-world clinical cases. Compared with the state-of-the-art approach, the results demonstrate that we enable early detection in 11.02% more patients and detect the condition 16.57 hours earlier on average.

Cyber-Physical Systems Security—A Survey

With the exponential growth of cyber-physical systems (CPS), new security challenges have emerged. Various vulnerabilities, threats, attacks, and controls have been introduced for the new generation of CPS. However, there lack a systematic study of CPS security issues. In particular, the heterogeneity of CPS components and the diversity of CPS systems have made it very difficult to study the problem with one generalized model. In this paper, we capture and systematize existing research on CPS security under a unified framework. The framework consists of three orthogonal coordinates: (1) from the \emph{security} perspective, we follow the well-known taxonomy of threats, vulnerabilities, attacks and controls; (2)from the \emph{CPS components} perspective, we focus on cyber, physical, and cyber-physical components; and (3) from the \emph{CPS systems} perspective, we explore general CPS features as well as representative systems (e.g., smart grids, medical CPS and smart cars). The model can be both abstract to show general interactions of a CPS application and specific to capture any details when needed. By doing so, we aim to build a model that is abstract enough to be applicable to various heterogeneous CPS applications; and to gain a modular view of the tightly coupled CPS components. Such abstract decoupling makes it possible to gain a systematic understanding of CPS security, and to highlight the potential sources of attacks and ways of protection.

Toward Physiology-Aware DASH: Bandwidth-Compliant Prioritized Clinical Multimedia Communication in Ambulances

The ultimate objective of medical cyber-physical systems is to enhance the safety and effectiveness of patient care. To ensure safe and effective care during emergency patient transfer from rural areas to center tertiary hospitals, reliable and real-time communication is essential. Unfortunately, real-time monitoring of patients involves transmission of various clinical multimedia data including videos, medical images, and vital signs, which requires use of mobile network with high-fidelity communication bandwidth. However, the wireless networks along the roads in rural areas range from 4G to 2G to low speed satellite links, which poses a significant challenge to transmit critical patient information. In this paper, we present a bandwidth-compliant criticality-aware system for transmission of massive clinical multimedia data adaptive to varying bandwidths during patient transport. Model-based clinical automata are used to determine the criticality of clinical multimedia data. We borrow concepts from DASH, and propose physiology-aware adaptation techniques to transmit more critical clinical data with higher fidelity in response to changes in disease, clinical states, and bandwidth condition. In collaboration with Carle's ambulance service center, we develop a bandwidth profiler, and use it as proof of concept to support our experiments. Our preliminary evaluation results show that our solutions ensure that most critical patient's clinical data are communicated with higher fidelity.

Data-Centered Runtime Verification of Wireless Medical Cyber-Physical System

Wireless medical cyber-physical systems are widely adopted in the daily practices of medicine, where huge amounts of data are sampled by the wireless medical devices and sensors, and is passed to the decision support systems (DSSs). Many text-based guidelines have been encoded for work-flow simulation of DSS to automate health care based on those collected data. But for some complex and life-critical diseases, it is highly desirable to automatically rigorously verify some complex temporal properties encoded in those data, which brings new challenges to current simulation-based DSS with limited support of automatical formal verification and real-time data analysis. In this paper, we conduct the first study on applying runtime verification to cooperate with current DSS based on real-time data. Within the proposed technique, a user-friendly domain specific language, named DRTV, is designed to specify vital real-time data sampled by medical devices and temporal properties originated from clinical guidelines. Some interfaces are developed for data acquisition and communication. Then, for medical practice scenarios described in DRTV model, we will automatically generate event sequences and runtime property verifier automata. If a temporal property violates, real-time warnings will be produced by the formal verifier and passed to medical DSS. We have used DRTV to specify different kinds of medical care scenarios and have applied the proposed technique to assist existing wireless medical cyber-physical system. As presented in experiment results, in terms of warning detection, it outperforms the only use of DSS or human inspection, and improves the quality of clinical health care of hospital.

Design of a secure and effective medical cyber‐physical system for ubiquitous telemonitoring pregnancy

SummaryIn recent years, ubiquitous‐based telemedicine has become increasingly popular in many countries; this is a new discipline that can break distance barriers in order to improve access to various medical services. Due to recent progress in telecare services, scientists provided those pregnant women with advanced medical care via medical cyber‐physical systems (MCPS) to achieve eugenics. The MCPS presents a new level of integrated intelligence that is characterized by interaction and coordination of computing processes with physical processes. In an environment of MCPS, security is one of the key objectives for ubiquitous telemonitoring. In this paper, we take advantage of the cloud characteristics and the high efficiency of MCPS to achieve the desired medical monitoring and also to effectively protect pregnancy‐related privacy. We establish a cloud computing platform for all users to access the cloud information at any time in accordance with their own demands. We also used cryptography to design the medical system. Identity‐based combined encryption and signature cryptosystem were proposed as the best ways to ensure the security and privacy of healthcare data in the cloud.

A smart medical system for dynamic closed-loop blood glucose-insulin control

Abstract Recent years have witnessed the rapid development of healthcare devices. The connections between patients and medical cyber-physical systems are becoming tighter and many patients have got enormous benefits from these convenient systems. Our research, mainly refer to blood glucose-insulin control system, is a hot topic in the field of connected health as the development of diabetes has attracted more and more attention during the past several decades. Mainstream methods for treating diabetes in hospitals is mainly confined to manually injecting insulin to patients, which is inconvenient and highly expensive. Meanwhile, they are not fine-grained for doctors to accurately control insulin levels, so we try to improve the whole system for blood glucose-insulin control. Medical cyber-physical system on blood glucose-insulin control consists of three parts; CGMS (Continuous Glucose Monitoring System), insulin pump and closed-loop control algorithm. CGSM and insulin pump have made great advances in recent years, but we are still trying to find a better method to decrease errors introduced by mechanical measurement. Closed-loop control algorithm is vitally important and complex to study in this system. We introduce a novel algorithm which can better control blood glucose and insulin levels. To optimize this algorithm and solve storage problem, we also add back end analysis to this system. Our simulations are based on real data from patients in Shanghai No.9 hospital. We have finally concluded that our system performs well.

Survey on Emerging Security Mechanisms for Medical Cyber Physical Systems

Medical Cyber Physical System (MCPS) is able to transmit and process the data collected from health monitoring systems, which consists of BAN. The acquired data is transmitted to private or public cloud which contains set of algorithms for analysing the patient data. These medical data should be kept secret. After analysing these data, the feedback is given to the doctors to take corrective action. This system includes data acquisition which is capable of acquiring data from body area networks, data aggregation which concentrate the gathered signal information, cloud processing which includes many analysis algorithms and action layer which produce either physical action or decision support. Each of its layer contain hardware such as sensors and cloud computing architectures etc. So, the data should be secured in each layers. So many normal encipher mechanisms are used. Also, emerging encryption technology such as homomorphic encryption standards used in MCPS.

Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System

With the recent development in information and communication technology, more and more smart devices penetrate into people's daily life to promote the life quality. As a growing healthcare trend, medical cyber-physical systems (MCPSs) enable seamless and intelligent interaction between the computational elements and the medical devices. To support MCPSs, cloud resources are usually explored to process the sensing data from medical devices. However, the high quality-of-service of MCPS challenges the unstable and long-delay links between cloud data center and medical devices. To combat this issue, mobile edge cloud computing, or fog computing, which pushes the computation resources onto the network edge (e.g., cellular base stations), emerges as a promising solution. We are thus motivated to integrate fog computation and MCPS to build fog computing supported MCPS (FC-MCPS). In particular, we jointly investigate base station association, task distribution, and virtual machine placement toward cost-efficient FC-MCPS. We first formulate the problem into a mixed-integer non-linear linear program and then linearize it into a mixed integer linear programming (LP). To address the computation complexity, we further propose an LP-based two-phase heuristic algorithm. Extensive experiment results validate the high-cost efficiency of our algorithm by the fact that it produces near optimal solution and significantly outperforms a greedy algorithm.

Medical Cyber Physical System Security—Mitigating Attacks Using Trust Model

Medical Cyber Physical System Security—Mitigating Attacks Using Trust Model

Prediction of Critical Pulmonary Shunts in Infants

As a first step toward the development of closed-loop medical cyber-physical systems, this paper presents a monitor for blood oxygen concentration that predicts critical drops in oxygen levels caused by pulmonary shunts in infants. Although blood oxygen concentration is one of the most closely monitored vital signs in modern operating rooms, it cannot be measured noninvasively and is currently monitored by a time-delayed proxy—the hemoglobin oxygen saturation. To predict sharp drops in blood oxygen concentration, we employ available noninvasive respiratory measurements and build a parameterized physiological model of the circulation of these gases through the cardiopulmonary system. Since the model parameters (e.g., metabolic rate) are unknown and vary greatly across patients, we utilize a parameter-invariant detector designed to provide a constant false alarm rate for different patients regardless of the values of the parameters and robust to missing measurements. Finally, we evaluate the performance of the detector on real patient data collected during surgeries performed at the Children’s Hospital of Philadelphia. As evaluated on 61 patients experiencing a drop in blood oxygen concentration, the detector achieves a detection rate of about 85% with a potentially life-saving early warning of 90 s on average. In addition, it achieves a false alarm rate of 0.95 false alarms per hour (about 0.5% of the tests) across 314 patients who did not experience a pulmonary shunt.

Data-driven Adaptive Safety Monitoring Using Virtual Subjects in Medical Cyber-Physical Systems: A Glucose Control Case Study

Medical cyber-physical systems (MCPS) integrate sensors, actuators, and software to improve patient safety and quality of healthcare. These systems introduce major challenges to safety analysis because the patient's physiology is complex, nonlinear, unobservable, and uncertain. To cope with the challenge that unidentified physiological parameters may exhibit short-term variances in certain clinical scenarios, we propose a novel run-time predictive safety monitoring technique that leverages a maximal model coupled with online training of a computational virtual subject (CVS) set. The proposed monitor predicts safety-critical events at run-time using only clinically available measurements. We apply the technique to a surgical glucose control case study. Evaluation on retrospective real clinical data shows that the algorithm achieves 96% sensitivity with a low average false alarm rate of 0.5 false alarm per surgery.

Selected design issues of the medical cyber-physical system for telemonitoring pregnancy at home

The telemonitoring problem of high-risk pregnancies at home is introduced, and some design issues of the monitoring system are identified. A Medical Cyber-Physical System (MCPS) approach has been taken. Various MCPS design issues and requirements, like: interaction of caregivers and a patient with the MCPS system, Plug-and-Play architecture, maintenance support for caregivers, interoperability of medical devices, medical workflows automation, dependability of the system, smart alerting, and intelligent acquisition of biosignals, have been addressed. The telecare system consists of the Body Area Network (BAN) of advanced sensors that are interconnected on a body of a pregnant woman, the Personal Area Network (PAN) that is responsible for embedded processing of physical signals, smart alerting, an intelligent human-machine-interface, and a reliable transmission channel to the Surveillance Centre located in a hospital or a local medical centre. The system integrates the new strategy for abdominal signal acquisition and analysis based on the smart selection of algorithms realized in the mobile instrumentation of PAN. Dependable telemedical systems, when broadly deployed, will provide a high societal value to high-risk pregnant women, especially those in dispersed rural areas.

Emerging security mechanisms for medical cyber physical systems

The following decade will witness a surge in remote health-monitoring systems that are based on body-worn monitoring devices. These Medical Cyber Physical Systems (MCPS) will be capable of transmit...