Medical Cyber-physical Systems Research Articles

Design of a Secure Medical Data Sharing Scheme Based on Blockchain

With the rapid development of technologies such as artificial intelligence, blockchain, cloud computing, and big data, Medical Cyber Physical Systems (MCPS) are increasingly demanding data security, while cloud storage solves the storage problem of complex medical data. However, it is difficult to realize data security sharing. The decentralization feature of blockchain is helpful to solve the problem that the secure authentication process is highly dependent on the trusted third party and implement data security transmission. In this paper, the blockchain technology is used to describe the security requirements in authentication process, and a network model of MCPS based on blockchain is proposed. Through analysis of medical data storage architecture, it can ensure that data can't be tampered and untrackable. In the security authentication phase, bilinear mapping and intractable problems can be used to solve the security threat in the authentication process of medical data providers and users. It can avoid the credibility problem of the trusted third party, and also can realize the ?thyc=10?>two-way authentication between the hospital and blockchain node. Then, BAN logic is used to analyze security protocols, and formal analysis and comparison of security protocols are also made. The results show that the MCPS based on blockchain not only realizes medical treatment data sharing, but also meet the various security requirements in the security authentication phase. In addition, the storage and computing overhead costs is ideal. Therefore, the proposed scheme is more suitable for secure sharing of medical big data.

Health 4.0: On the Way to Realizing the Healthcare of the Future.

Health 4.0 establishes a new promising vision for the healthcare industry. It creatively integrates and employ innovative technologies such as the Internet of Health Things (IoHT), medical Cyber-Physical Systems (medical CPS), health cloud, health fog, big data analytics, machine learning, blockchain, and smart algorithms. The goal is to deliver improved, value-added and cost-effective healthcare services to patients and enhance the effectiveness and efficiency or the healthcare industry. Health 4.0 (adapted from the Industry 4.0 principles) changes the healthcare business model to enhance the interactions across the healthcare clients (the patients), stakeholders, infrastructure, and value chain. This effectively will improve the quality, flexibility, productivity, cost-effectiveness, and reliability of healthcare services in addition to increasing patients’ satisfaction. However, building and utilizing healthcare applications that follow the Health 4.0 concept is a non-trivial and complex endeavor. In addition, advanced potential applications based on Health 4.0 capabilities are not yet being investigated. In this paper we define the main objectives of Health 4.0 and discuss advanced potential Health 4.0 applications. To have a clear understanding of these applications, we categorize them in 4 groups based on the primary beneficiary of these applications. Thus we have patient targeted applications, applications supporting healthcare professionals, resource management applications and high-level healthcare systems management applications. In addition, as we studied the different applications, we realized that these is a certain collection of services that these most of them need regardless of their goals or business context. Services supporting data collection and transfer, security and privacy, reliable operations are some examples. As a result we propose creating a service-oriented middleware framework to offers the common services to the applications developers and facilitate the integration of different services to build applications under the Health 4.0 umbrella.

A local external coupling matrix solution and dynamic processing in medical cyber-physical cloud systems

Abstract Medical cyber-physical cloud systems (MCPCS) are new generation medical cyber-physical systems (MCPS), which introduce cloud computing to integrate multiple MCPS to improve quality of service (QoS) and expand service scope in hospitals. When used in large-scale distributed environment, MCPCS need to perform a stability analysis to guarantee collaboration among multiple MCPS. In this paper, we propose a local topology search algorithm (LTS) to obtain the local external coupling matrices, which are important input parameters for the local synchronization stability analysis of MCPCS. LTS generates an optimal multi-fork tree from the source end MCPS to multiple destination end MCPS. Based on the relationship between nodes in the optimal multi-fork tree, the local external coupling matrices applied to the local synchronization stability analysis of MCPCS are then solved. Considering the variability of network topology, we further propose a dynamic processing method called external coupling matrix change processing (ECMCP) to ensure the real-time update of the local external coupling matrices and dimensional consistency. Experimental results show that the proposed solution of LTS & ECMCP has lower costs of nodes and their communications than existing method.

Synchronization Stability Analysis of Medical Cyber-Physical Cloud System Considering Multi-Closed-Loops

Medical cyber-physical system (MCPS) is usually used in hospitals to provide continuous services (e.g., glycemic monitor and alarm) for patients. In this paper, we introduce cloud computing to integrate multiple MCPSs of hospitals to construct medical cyber-physical cloud system (MCPCS). Such complex MCPCS need stability analysis to guarantee collaboration among multiple MCPSs. We treat the MCPCS as a complex network, where each MCPS is considered as a node. Then, we introduce the Lyapunov stability theory for synchronization stability analysis of MCPCS by considering multi-closed-loops. For different network types, the dynamic equations of MCPSs were improved through introducing feedback in the multi-closed-loops. For the problem that different networks cause different delays, we select three networks to solve sufficient conditions for the synchronization stability of MCPCS. Experiments are performed to confirm the efficiency of sufficient conditions and the synchronization stability of MCPCS.

Privacy Preservation in Cloud Based Cyber Physical Systems

The Cyber physical systems (CPS) are integration of tightly coupled resource constrained physical and computational systems,which have several real time critical care applications. These systems depend on sensors to sense the physical environment. To overcome the limitations of computational power, CPS are supported by cloud computing systems as their backend. The enablement of cloud support to cyber physical cloud computing systemshave seamless advantages of improved efficiency, in same way this leads to major security and privacy concerns. These systems have decentralized and heterogeneous infrastructure and to fulfill all the security requirements, it is certainly a very complex task. In this paper we propose a generalized system model for securing different types of cloud based cyber physical systems, adversary model and holistic literature review of various CPS services, attacks, security challenges and the security schemes that can be applied to overcome a given attacks. At the end of the paper we discuss a case study on securing Medical CPS and compare the applicability of security schemes in this scenario.

Malware Detection in Embedded Systems Using Neural Network Model for Electromagnetic Side-Channel Signals

We propose a novel malware detection system for critical embedded and cyber-physical systems (CPS). The system exploits electromagnetic (EM) side-channel signals from the device to detect malicious activity. During training, the system models EM emanations from an uncompromised device using a neural network. These EM patterns act as fingerprints for the normal program activity. Next, we continuously monitor the target device’s EM emanations. Any deviation in the device’s activity causes a variation in the EM fingerprint, which in turn violates the trained model, and is reported as an anomalous activity. The system can monitor the target device remotely (without any physical contact), and does not require any modification to the monitored system. We evaluate the system with different malware behavior (DDoS, ransomware, and code modification) on different applications using an Altera Nios-II soft-processor. Experimental evaluation reveals that our framework can detect DDoS and ransomware with 100% accuracy (AUC = 1.0), and stealthier code modification (which is roughly a 5 μ s long attack) with an AUC ≈ 0.99, from distances up to 3 m. In addition, we execute control-flow hijack, DDoS, and ransomware on different applications using an A13-OLinuXino—a Cortex A8 ARM processor single board computer with Debian Linux OS. Furthermore, we evaluate the practicality and the robustness of our system on a medical CPS, implemented using two different devices (TS-7250 and A13-OLinuXino), while executing control-flow hijack attack. Our evaluations show that our framework can detect these attacks with perfect accuracy.

Towards a test and validation framework for closed-loop physiology management systems for critical and perioperative care

Many of medical devices come equipped with a communication interface. Over the years, there has been interest in leveraging these interfaces to add computers to the loop to aid in decision making and automatic application of interventions. Such systems, which we call Closed-Loop Assistants (CLAs), are intended to help clinicians manage the cognitive load that can arise as the complexity of patient management increases. We present an open-source framework for examining CLA-patient interactions through software simulations of the CLA with in-silico patients to enable early testing and validation of proposed physiology management strategies. We show how this framework can be used to test different strategies across a small patient population. Considering a patient population is important because inter-patient variability is one of the critical factors that can hamper the ability of a medical cyber-physical system like the CLA to meet its goals. The ability to explore this variability early in the design process therefore helps us in increasing robustness of the system.

Energy-Efficient ECG Signal Compression for User Data Input in Cyber-Physical Systems by Leveraging Empirical Mode Decomposition

Human physiological data are naturalistic and objective user data inputs for a great number of cyber-physical systems (CPS). Electrocardiogram (ECG) as a widely used physiological golden indicator for certain human state and disease diagnosis is often used as user data input for various CPS such as medical CPS and human–machine interaction. Wireless transmission and wearable technology enable long-term continuous ECG data acquisition for human–CPS interaction; however, these emerging technologies bring challenges of storing and wireless transmitting huge amounts of ECG data, leading to energy efficiency issue of wearable sensors. ECG signal compression technique provides a promising solution for these challenges by decreasing ECG data size. In this study, we develop the first scheme of leveraging empirical mode decomposition (EMD) on ECG signals for sparse feature modeling and compression and further propose a new ECG signal compression framework based on EMD constructed feature dictionary. The proposed method features in compressing ECG signals using a very limited number of feature bases with low computation cost, which significantly improves the compression performance and energy efficiency. Our method is validated with the ECG data from MIT-BIH arrhythmia database and compared with existing methods. The results show that our method achieves the compression ratio (CR) of up to 164 with the root mean square error (RMSE) of 3.48% and the average CR of 88.08 with the RMSE of 5.66%, which is more than twice of the average CR of the state-of-the-art methods with similar recovering error rate of around 5%. For diagnostic distortion perspective, our method achieves high QRS detection performance with the sensitivity (SE) of 99.8% and the specificity (SP) of 99.6%, which shows that our ECG compression method can preserve almost all the QRS features and have no impact on the diagnosis process. In addition, the energy consumption of our method is only 30% of that of other methods when compared under the same recovering error rate.

Design Verifiably Correct Model Patterns to Facilitate Modeling Medical Best Practice Guidelines With Statecharts

Improving patient care safety is an ultimate objective for medical cyber-physical systems. A recent study shows that the patients' death rate can be significantly reduced by computerizing medical best practice guidelines. To facilitate the development of computerized medical best practice guidelines, statecharts are often used as a modeling tool because of their high resemblances to disease and treatment models and their capabilities to provide rapid prototyping and simulation for clinical validations. However, some implementations of statecharts, such as Yakindu statecharts, are priority-based and have synchronous execution semantics which makes it difficult to model certain functionalities that are essential in modeling medical guidelines, such as two-way communications and configurable execution orders. Rather than introducing new statechart elements or changing the statechart implementation's underline semantics, we use existing basic statechart elements to design model patterns for the commonly occurring issues. In particular, we show the design of model patterns for two-way communications and configurable execution orders and formally prove the correctness of these model patterns. We further use a simplified airway laser surgery scenario as a case study to demonstrate how the developed model patterns address the two-way communication and configurable execution order issues and their impact on validation and verification of medical safety properties.

Editorial: Special Issue on Security in Medical Cyber-Physical Systems

Editorial: Special Issue on Security in Medical Cyber-Physical Systems

An IoT Based Architecture for Enhancing the Effectiveness of Prototype Medical Instruments Applied to Neurodegenerative Disease Diagnosis.

Human errors are probably the most critical cause of the large amount of medical accidents. Medical cyber-physical systems (MCPS) have been suggested as a possible approach for detecting and limiting the impact of errors and wrong procedures. However, during the initial development phase of medical instruments, regular MCPS systems are not a viable approach, because of the high costs of repeating complex validation procedures, due to modifications of the prototype instrument. In this work, a communication architecture, inspired by recent Internet of Things (IoT) advances, is proposed for connecting prototype instruments to the cloud, to allow direct and real-time interaction between developers and instrument operators. Without loss of generality, a real-world use case is addressed, dealing with the use of transcranial magnetic stimulation (TMS) for neurodegenerative disease diagnosis. The proposed infrastructure leverages on a message-oriented middleware, complemented by historical database for further data processing. Two of the most diffused protocols for cloud data exchange (MQTT and AMQP) have been investigated. The experimental setup has been focused on the real-time performance, which are the most challenging requirements. Time-related metrics confirm the feasibility of the proposed approach, resulting in an end-to-end delay on the order of few tens of milliseconds for local networks and up to few hundreds of milliseconds for geographical scale networks.

Identity-based proxy-oriented outsourcing with public auditing in cloud-based medical cyber–physical systems

Cloud-based medical cyber–physical system (MCPS) relies on cloud computing to provide powerful data storage and computing services. Based on the vital outsourced medical data, doctors can perform precise medical diagnosis for patients, thus the integrity verification of medical data has become increasingly important. In this paper, we propose an identity-based proxy-oriented outsourcing with public auditing scheme in cloud-based MCPS using elliptic curve cryptography. Our scheme enables a patient to authorize the proxy to generate and upload the signatures of medical data and corresponding encrypted medical data to cloud-based MCPS. Any third party auditor (TPA) can audit the medical data efficiently, without retrieving the entire medical data set. We provide the security proof of the proposed scheme, including the storage correctness guarantee and proxy-oriented privacy-preserving property. Moreover, our scheme is designed on identity-based systems, which can avoid complex certificates management. The efficiency comparison shows that our scheme is much more light-weight, and more suitable in cloud-based MCPS.

Intelligent and Dynamic Ransomware Spread Detection and Mitigation in Integrated Clinical Environments.

Medical Cyber-Physical Systems (MCPS) hold the promise of reducing human errors and optimizing healthcare by delivering new ways to monitor, diagnose and treat patients through integrated clinical environments (ICE). Despite the benefits provided by MCPS, many of the ICE medical devices have not been designed to satisfy cybersecurity requirements and, consequently, are vulnerable to recent attacks. Nowadays, ransomware attacks account for 85% of all malware in healthcare, and more than 70% of attacks confirmed data disclosure. With the goal of improving this situation, the main contribution of this paper is an automatic, intelligent and real-time system to detect, classify, and mitigate ransomware in ICE. The proposed solution is fully integrated with the ICE++ architecture, our previous work, and makes use of Machine Learning (ML) techniques to detect and classify the spreading phase of ransomware attacks affecting ICE. Additionally, Network Function Virtualization (NFV) and Software Defined Networking (SDN)paradigms are considered to mitigate the ransomware spreading by isolating and replacing infected devices. Different experiments returned a precision/recall of 92.32%/99.97% in anomaly detection, an accuracy of 99.99% in ransomware classification, and promising detection and mitigation times. Finally, different labelled ransomware datasets in ICE have been created and made publicly available.

Medical Cyber Physical Systems and Its Issues

Abstract In the previous decade, many technologies have attracted attention from several research communities. Internet of Things (IoT) is main invention of the recent/ past decade. When these smart devices or internet connected devices are interact together, then they create a cyber infrastructure. These cyber infrastructures face several serious concerns privacy, trust, security, etc. These smart devices make an automatic environment (executed without the intervention of a human) in applications likedefense, manufacturing, e-healthcare, etc. In e-healthcare, these devices built the structure of Medical Cyber Physical System (MCPS). MCPS are facing several critical issues and challenges in current era, i.e., several attacks, issues and challenges which we require to overcome in current and next decade to provide efficient and reliable service to patients. MCPS is need of smart healthcare and require attention from several research communities towards its raised issue. Hence, this article provides a detailed study about CPS, MCPS, mitigated attacks on same architecture (CPS and MCPS), issues and challenges in CPS/ MCPS, including several research gaps in CPS/ MCPS (with opportunities for future researchers).

On-Device AI-Based Cognitive Detection of Bio-Modality Spoofing in Medical Cyber Physical System

Bio-modalities, such as the face, iris, and fingerprint, are ideal for establishing authentication in the futuristic networks, such as the medical cyber physical systems (MCPSs). In such a network, to authenticate and classify the bio-modalities, raw data would be traditionally sent to the cloud other than the proximal devices as they are resource-constrained. Thus, the centralized cloud-based solution not only incurs significant delay but also violates the data privacy as the data are moved to the cloud. In recent years, privacy-preserving on-device AI nodes are getting attention to solve certain classification problem, which can also be applied for classifying spoofed and real bio-modalities for authentication. To this end, we propose an on-device AI-based MCPS architecture, where the on-device AI node runs a light-weight but powerful classification algorithm, as we call it the feature-augmented random forest (FA-RF). The FA-RF combines the power of random forest with feature selection and a proposed feature augmentation mechanism. Besides privacy-preserving of the raw data, the proposed approach can significantly reduce the communication delay imposed on the network as cloud computation and communication is removed. Our proposal is verified on real datasets of the face, iris, and fingerprint bio-modalities provided by the Warsaw, Replay-Attack, and LiveDet 2015 Crossmatch benchmark, respectively. The experimental results show that our model can outperform the state-of-the-art architectures in four out of six tests. Besides, we show that the FA-RF can significantly reduce the training and testing time in both the cloud and the on-device AI node.

Model Predictive Control of Blood Glucose for Type 1 Diabetic Rats in a Cyber-Physical System

Advances in sensing and communication technologies have enabled a tight integration between computational and physical elements that is known as cyber-physical systems (CPS). This integration has been also applied to medical devices and healthcare systems and referred to as medical CPS (MCPS). This paper presents development of patient models and algorithms for a MCPS to control blood glucose level (BGL) in type 1 diabetic rats, which is known as artificial pancreas system (APS). An APS consists of continuous glucose monitor (CGM), insulin infusion pump, and control algorithm that makes autonomous decisions about insulin injection to maintain BGL within a normal range. As the control method, we adopted model predictive control (MPC) due to its robustness, flexibility, and use of constraints. Cyber-physical interactions in APS occur by physiological feedback (BGL read by CGM) to the controller, and developing MPC requires identification of such a model of glucose-insulin interactions to predict BGL and take an appropriate action accordingly. In this study, a physiological patient model of diabetic rats is developed and fitted to the data collected from a subject. Using the patient model, we train an artificial neural network (ANN) that predicts BGL based on time-series input data. Considering BGL predicted by the ANN, the NN-MPC controls insulin injection so that BGL can be maintained within the normal range. A simulation study showed the NN-MPC yielded a good performance in a simulated environment. The study showed the potential of the proposed approach for developing a fully closed-loop MCPS for BGL control.

Complex Methods Applied to Data Analysis, Processing, and Visualisation

The amount of data available every day is not only enormous but growing at an exponential rate. Over the last ten years there has been an increasing interest in using complex methods to analyse and visualise massive datasets, gathered from very different sources and including many different features: social networks, surveillance systems, smart cities, medical diagnosis systems, business information, cyberphysical systems, and digital media data. Nowadays, there are a large number of researchers working in complex methods to process, analyse, and visualise all this information, which can be applied to a wide variety of open problems in different domains. This special issue presents a collection of research papers addressing theoretical, methodological, and practical aspects of data processing, focusing on algorithms that use complex methods (e.g., chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory) in a variety of domains (e.g., software engineering, digital media data, bioinformatics, health care, imaging and video, social networks, and natural language processing). A total of 27 papers were received from different research fields, but sharing a common feature: they presented complex systems that process, analyse, and visualise large amounts of data. After the review process, 8 papers were accepted for publication (around 30% of acceptance ratio).

A Systems Theoretic Approach to Safety Analysis in Medical Cyber Physical Systems

The concept of a Cyber-Physical System (CPS) has been gaining traction as a promising new field of study in recent years. It integrates digital processing and data transfer with the real environment. Healthcare, aircraft, automobiles, chemicals, civil infrastructure, energy, manufacturing, transportation, and biological systems are just a few of the many applications of cyber-physical systems. Medical Safe, context-aware, and interconnected networks of medical equipment are what we call Medical Cyber-Physical Systems (MCPS). More and more hospitals are installing these systems in order to give their patients with round-the-clock, high-quality treatment. Systems theory is an umbrella term for the study of how various parts of a system interact to provide a unified whole. Bio-electronic systems (implantable medical devices) are a common kind of MCPS. Bionic ear and eye implants, deep brain stimulators for neurological disorders, and bionic arms for amputees are all examples of computer-based bio-electronic systems designed to replace impaired human body parts. There is a lot of work being done to boost the efficiency of bionic systems so that they can operate at near 100% efficiency, at cheap cost, and in smaller, safer packages. Avoiding risks to property and human life caused by uncontrolled interactions in implanted devices of CPS makes the design of bug-free and safe medical device software in MCPS both crucial and difficult.

Biometric Authentication and Verification for Medical Cyber Physical Systems

A Wireless Body Area Network (WBAN) is a network of wirelessly connected sensing and actuating devices. WBANs used for recording biometric information and administering medication are classified as part of a Cyber Physical System (CPS). Preserving user security and privacy is a fundamental concern of WBANs, which introduces the notion of using biometric readings as a mechanism for authentication. Extensive research has been conducted regarding the various methodologies (e.g., ECG, EEG, gait, head/arm motion, skin impedance). This paper seeks to analyze and evaluate the most prominent biometric authentication techniques based on accuracy, cost, and feasibility of implementation. We suggest several authentication schemes which incorporate multiple biometric properties.

Seizure episodes detection via smart medical sensing system

Cyber-physical systems (CPS) consist of seamless network of sensors and actuators integrated with physical processes related to human activities. The CPS exploits sensors and actuators to monitor and control different physical process that can affect the computations of the devices. This paper presents the monitoring of physical activities exploiting wireless devices as sensors used in medical cyber-physical systems. Patients undergoing epileptic seizures experience involuntary body movements such as jerking, muscle twitching, falling, and convulsions. The proposed method exploits S-Band sensing used in medical CPS that leverage wireless devices such as omni-directional antenna at the transmitter side, four-beam patch antenna at the receiver side, RF signal generator and vector signal analyzer that perform signal conditioning by providing amplitude and raw phase data. The method uses wireless monitoring and recording system for measurement and classification of a clinical condition (epileptic seizures) versus normal daily routine activities. The data acquired that are perturbations of the radio signal is analyzed as amplitude, phase information, and statistical models. Extracting the statistical features, we leverage various machine learning algorithms such as support vector machine, random forest, and K-nearest neighbor that classify the data to differentiate patient’s various activities such as press-ups, walking, sitting, squatting, and seizure episodes. The performance parameters used in three machine learning algorithms are accuracy, precision, recall, Cohen’s Kappa coefficient, and F-measure. The values obtained using five performance parameters provide the accuracy of more than 90%.