E-healthcare System Research Articles

Low-latency and High-Reliability FBMC Modulation scheme using Optimized Filter design for enabling NextG Real-time Smart Healthcare Applications.

This paper presents a prototype filter design using the orthant optimization technique to assist a filter bank multicarrier (FBMC) modulation scheme of a NextG smart e-healthcare network framework. Low latency and very high reliability are one of the main requirements of a real-time e-healthcare system. In recent times, FBMC modulation has gotten more attention due to its spectral efficiency. The characteristics of a filter bank are determined by t’s, prototype filter. A prototype filter cannot be designed to achieve an arbitrary time localization (for low latency) and frequency localization (spectral efficiency), as time and frequency spreading are conflicting goals. Hence, an optimum design needed to be achieved. In this paper, a constraint for perfect or nearly perfect reconstruction is formulated for prototype filter design and an orthant-based enriched sparse ℓ1-optimization method is applied to achieve the optimum performance in terms of higher availability of subcarrier spacing for the given requirement of signal-to-interference ratio. Larger subcarrier spacing ensures lower latency and better performance in real-time applications. The proposed FBMC system, based on an optimum design of the prototype filter, also supports a higher data rate as compared to traditional FBMC and OFDM systems, which is another requirement of real-time communication. In this paper, the solution for the different technical issues of physical layer design is provided. The presented modulation scheme through the proposed prototype filter-based FBMC can suppress the side lobe energy of the constituted filters up to large extent without compromising the recovery of the signal at the receiver end. The proposed system provides very high spectral efficiency; it can sacrifice large guard band frequencies to increase the subcarrier spacing to provide low-latency communication to support the real-time e-healthcare network.

A blockchain-based fine-grained data sharing scheme for e-healthcare system

The cloud-aided sharing of e-healthcare data has great positive significance for research. However, due to the privacy consideration, these data are usually encrypted before uploading to the cloud server which impedes data sharing between different medical institutions. Conditional proxy re-encryption (CPRE) allows the proxy to converse ciphertext, especially by specifying a condition embed in the re-encryption key to achieve fine-grained access control over the ciphertext. Unfortunately, existing CPRE schemes cannot ensure the privacy of the condition, which may contain some sensitive private information. Furthermore, a malicious proxy server may return part of the results and even false data to save its computation or bandwidth. To solve these problems, we propose a blockchain-based condition invisible proxy re-encryption scheme for the e-healthcare system. The proposed scheme guarantees the confidentiality of the data by hiding the condition in the re-encryption key so that the proxy cannot learn anything about the condition. Moreover, the ciphertext-searching algorithm is leveraged by executing the smart contract in the blockchain which ensures the results are correct and complete. Finally, experiment results demonstrate the practicability of the proposed scheme in applications.

Diagnose Diabetic Mellitus Illness Based on IoT Smart Architecture

Obtaining a quick remote diagnosis of heart disease has proven problematic in recent days. To overcome such issues in e-Healthcare systems, Internet of Things (IoT) applications have been deployed using cloud computing (CC) approaches. There are still a number of disadvantages to using CC, including latency, bandwidth, energy usage, and security and privacy concerns. Fog computing (FC), a CC development, may be able to overcome these obstacles. DiaFog enabling remote users for real-time diagnosis of diabetic mellitus disease (DMD) has been proposed in this study, which is based on the combined ideas of IoT, cloud, and fog computing, as well as an ensemble deep learning (EDL) technique. The proposed system is trained with EDL approaches on the integrated dataset of two diabetes mellitus disease datasets (DMDDs), namely, Pima Indians Diabetes Dataset (PIDD) and Hospital Frankfurt Germany Diabetes Dataset (HFGDD), obtained from the UCI-ML and Kaggle repository, respectively, and the integrated dataset of these two. The suggested system has been used to demonstrate accuracy, precision, recall, F -measure, latency, arbitration time, jitter, processing time, throughput, energy consumption, bandwidth utilization, network utilization, scalability, and more. In the remote instantaneous diagnosis of diabetic patients, the integration of IoT-fog-cloud is useful. The results of the trials show the value of employing FC principles and their applicability for speedy diabetic patient remote diagnosis. PACS-key is describing text of that key PACS-key describing text of that key.

Automatic Distributed Deep Learning Using Resource-Constrained Edge Devices

Processing data generated at high volume and speed from the Internet of Things, smart cities, domotic, intelligent surveillance, and e-healthcare systems require efficient data processing and analytics services at the Edge to reduce the latency and response time of the applications. The fog computing edge infrastructure consists of devices with limited computing, memory, and bandwidth resources, which challenge the construction of predictive analytics solutions that require resource-intensive tasks for training machine learning models. In this work, we focus on the development of predictive analytics for urban traffic. Our solution is based on deep learning techniques localized in the Edge, where computing devices have very limited computational resources. We present an innovative method for efficiently training the gated recurrent-units (GRUs) across available resource-constrained CPU and GPU Edge devices. Our solution employs distributed GRU model learning and dynamically stops the training process to utilize the low-power and resource-constrained Edge devices while ensuring good estimation accuracy effectively. The proposed solution was extensively evaluated using low-powered ARM-based devices, including Raspberry Pi v3 and the low-powered GPU-enabled device NVIDIA Jetson Nano, and also compared them with Single-CPU Intel Xeon machines. For the evaluation experiments, we used real-world Floating Car Data. The experiments show that the proposed solution delivers excellent prediction accuracy and computational performance on the Edge when compared to the baseline methods.

Privacy-preserving multi-factor authentication and role-based access control scheme for the E-healthcare system

E-healthcare assists medical specialists in remotely collecting patient health data and providing remote health diagnoses. The roles are distributed among the system's users, contrasted between admin to data entry within certain rules and policies. Role-based access control (RBAC) is a technique of advanced access control that restricts key operations of users (addition, deletion and modification) access based on a user's role within a healthcare system. This paper proposes a privacy-preserving using RBAC and smart multi-factor authentication for the healthcare system to overcome the limitation flaw in previous schemes such as security risk tolerance, scalability and dynamism. This work relies on low-complexity cryptographic hash functions and symmetric operations to authenticate users while using an asymmetric cryptosystem based on the Schnorr digital signature lightweight operation to authenticate the administrator to provide multi-factor authentication. The administrator represents the system's core, and any his information leak could attack the entire system and its components. The proposed scheme conducted two thorough formal security proofs for the proposed work based on informal analysis and the Scyther tool. Furthermore, comparisons with other schemes reveal that the proposed scheme provides greater security features, and resisting attacks than the others while also being efficient in computing and communication costs.

A survey of deep learning techniques based Parkinson’s disease recognition methods employing clinical data

Parkinson’s disease (PD) is a critical neurological ailment that affects millions of individuals worldwide. A correct diagnosis of Parkinson’s disease is required for effective treatment. Deep learning (DL) algorithms based on various diagnostic methodologies have been developed to detect PD and resolve related diagnostic issues. This research study offers a complete assessment of published surveys and DL-based diagnosis methodologies for PD recognition. The techniques of DL-based diagnostic approaches for PD recognition, such as PD dataset pre-processing, extraction and selection of features, and classification, are all included in this survey. The limitations and benefits of the proposed methods have also been examined and explored. In addition, we discussed the various datasets used to evaluate the suggested PD recognition algorithms in order to better understand these datasets. The model evaluation metrics and cross-validation techniques used by different studies in this domain have also been explored in this survey. In light of the evaluated literature, we also examined hot upcoming research issues and related solutions. Finally, we came up with several trends and areas for future study that will aid progress in automatic disease recognition, particularly in detecting Parkinson’s disease and its implementation in E-healthcare systems.

The Achievements and Future of Information Technology related Patient Safety

<p>隨著科技的進步與智慧醫療的蓬勃發展，因資訊系統問題而導致的病人安全事件層出不窮，引起國際間重視。2020年醫策會與ECRI合作，建置台灣第一個醫療科技問題與病人安全風險學習平台(Information Technology related Patient Safety, ITPS)，由醫院通報以收集因資訊科技所導致的病人安全事件。本平台於2021年共收案50件，事件類別以檢驗／檢查／病理切片事件(40%)及藥物事件(38%)最多，在資通訊事件分類方面，以資訊系統問題(48%)、系統介面問題(26%)最多。為確保通報資料正確性與完整性，由專人客製化進行問題的溝通與釐清，並由專家群進行深度剖析，挖掘資訊系統潛藏的病人安全風險，提供即時的回饋與建議，並辦理醫院通報及改善經驗標竿分享，共同研討此新興領域，共創安全E世代。</p> <p> </p><p>With the rapid technology advancement and intense development of smart healthcare, patient safety incidents triggered by information technology related problems emerge in an endless stream, attracting international attention. In 2020, the Joint Commission of Taiwan (JCT) collaborated with the Emergency Care Research Institute (ECRI) to establish Taiwan’s first Information Technology related Patient Safety (ITPS). ITPS aims to collect information technology related patient safety incidents of healthcare organizations via reporting. In 2021, ITPS received a total of 50 incident cases, with the highest number of incidents in categories of inspection/examination/pathology (40%) and medication (38%). In terms of cause classification, the information system (48%) and system interface (26%) problems were the most common. In order to ensure the accuracy and integrity of the reported incident, a dedicated specialist will communicate with the hospital and clarify specific details of the incident. Then, an expert team will conduct in-depth analysis to identify potential patient safety risks in the hospital’s information system and provide immediate feedback and recommendations with final reports. The expert team will also invite hospitals to share their improvement benchmark experience, jointly explore this emerging field, and create a safe e-healthcare system within the country.</p> <p> </p>

FEDGAN-IDS: Privacy-preserving IDS using GAN and Federated Learning

Federated Learning (FL) is a promising distributed training model that aims to minimize the data sharing to enhance privacy and performance. FL requires sufficient and diverse training data to build efficient models. Lack of data balance as seen in rare classes affects the model accuracy. Generative Adversarial Networks (GAN) are remarkable in data augmentation to balance the available training data. In this article, we propose a novel Federated Deep Learning (DL) Intrusion Detection System (IDS) using GAN, named FEDGAN-IDS, to detect cyber threats in smart Internet of Things (IoT) systems; smarthomes, smart e-healthcare systems and smart cities. We distribute the GAN network over IoT devices to act as a classifier and train using augmented local data. We compare the convergence and accuracy of our model with other federated intrusion detection models. Extensive experiments with multiple datasets demonstrates the effectiveness of the proposed FEDGAN-IDS. The model performs better and converges earlier than the state-of-the-art standalone IDS.

Hybrid Cryptographic Scheme for Secure Communication in Mobile Ad Hoc Network-Based E-Healthcare System

The COVID-19 pandemic has affected people’s lives in all aspects. This pandemic has raised the usage of ubiquitous networks such as mobile ad hoc networks (MANETs) for information exchange in various domains. MANET is a group of versatile nodes that communicates with each other without relying on a fixed physical framework. One of the prominent features of MANET is its versatile topology. Because of this striking feature, MANETs are employed in various domains like defense and combat operations, disaster management, healthcare, and environmental monitoring. In this paper, we enlighten the significance of MANET in the smart healthcare system. The COVID-19 pandemic outbreak demanded the reshaping of the healthcare systems to combat the pandemic and similar cataclysms. Existing healthcare systems are proved inefficient in dealing with pandemic situations, because they are not fully automated and also vulnerable to various security attacks. Therefore, it is vital to empower the healthcare sectors by integrating ubiquitous networks and other emerging technologies. In this paper, we proposed a MANET-based secure healthcare system to exchange medical data among portable nodes. Ensuring secure communication in the MANET-based healthcare system is one of the challenging issues. Healthcare system demands the sharing of confidential medical data among mobile nodes. So it is essential to provide secure information exchange in healthcare system by using strong cryptographic schemes. In this paper, we proposed a hybrid cryptographic algorithm for secure medical information exchange among mobile healthcare nodes. The proposed cryptographic scheme uses logistic map for key generation. Logistic map exhibits high security with less computational power. Our simulation results show that the proposed hybrid cryptographic scheme exhibits better security against various attacks in MANET-based healthcare systems.

E-Healthcare Privacy Data Sharing with Fine-Grained Access Control

The E-Healthcare Cloud system has shown that it can improve healthcare quality as well as individual quality of life. Unfortunately, concerns about security and privacy prevent it from being widely adopted and used. Several studies have been carried out in order to protect the privacy of electronic health record (EHR) data. We start with a two-layer encryption scheme. We create first-layer encryption to ensure efficient and fine-grained access control over EHR data, in which we create a highly specialised access policy for each data attribute in the EHR and encrypt them individually with high efficiency. To protect the privacy of role attributes and access policies used in the first-layer encryption, we construct the second-layer encryption systematically. We made a recommendation. User revocation is commonly supported in such schemes, as users' group memberships may change for a variety of reasons. Prior to now, the computational overhead for Auto user revocation. Binary key generation is included for file storage. We proposed enabling file encryption alongside proxy re encryption.

Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL Intrusion Attack Detection Model.

The exponential rise in advanced software computing and low-cost hardware has broadened the horizon for the Internet of Medical Things (IoMT), interoperable e-Healthcare systems serving varied purposes including electronic healthcare records (EHRs) and telemedicine. However, being heterogeneous and dynamic in nature, their database security remains a challenge forever. Numerous intrusion attacks including bot-attack and malware have confined major classical databases towards e-Healthcare. Despite the robustness of NoSQL over the structured query language databases, the dynamic data nature over a heterogeneous environment makes it vulnerable to intrusion attacks, especially over interoperable e-Healthcare systems. Considering these challenges, this work proposed a first of its kind semantic feature-driven NoSQL intrusion attack (NoSQL-IA) detection model for interoperable e-Healthcare systems. This work assessed the efficacy of the different semantic feature-extraction methods like Word2Vec, Continuous Bag of Words, N-Skip Gram (SKG), Count Vectorizer, TF-IDF, and GLOVE towards NoSQL-IA prediction. Subsequently, to minimize computational exhaustion, different feature selection methods including Wilcoxon Rank Sum Test (WRST), significant predictor test, principal component analysis, Select K-Best, and variance threshold feature selection algorithms were employed. To alleviate the data imbalance problem, it applied different resampling methods including upsampling, downsampling, and synthetic minority oversampling technique (SMOTE) over the selected features. Later, Min–Max normalization was performed over the input feature vectors to alleviate any possibility of overfitting. Towards NoSQL-IA prediction, different machine learning methods like Multinomial Naïve Bayes, decision tree, logistic regression, support vector machine, k-NN, AdaBoost, Extra Tree Classifier, random forest ensemble, and XG-Boost were applied, which classified each input query as the regular query or the NoSQL-IA attack query. The depth performance assessment revealed that the use of Word2Vec features SKG in sync with VTFS feature selection and SMOTE resampling processed with the bootstrapped random forest classifier can provide the best performance in terms of high accuracy (98.86%), F-Measure (0.974), and area under the curve (AUC) (0.981), thus enabling it suitable for interoperable e-Healthcare database security.

A lightweight block cipher technique for IoT based E-healthcare system security

A lightweight block cipher technique for IoT based E-healthcare system security

Factors Influencing Patient Adoption of the IoT for E-Health Management Systems (e-HMS) Using the UTAUT Model

This study examines factors influencing patients' adoption of the IoT for e-Health Management System (e-HMS). A conceptual framework is built by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) model to achieve the research objectives. The constructs viz. performance expectations, perceived healthcare threat, facilitation, and trust were employed to analyze the behaviour intention of healthcare IoT devices in the medical context. A questionnaire was prepared and circulated to 210 IoT healthcare users. Further, a synergy of Smart-PLS and ANN was used to determine factors influencing adopting the IoT for e-HMS. The study yields a novel insight that would render vital benefits to users and service providers. The outcomes of this research can assist IoT inventors, medicinal specialists, and vendors to enhance the optimization of e-HMS. The results make some new reporting facts to improve IoT 4.0 practices in the e-healthcare system and structural model assessment.

Efficient Ring-Topology Decentralized Federated Learning with Deep Generative Models for Medical Data in eHealthcare Systems

By leveraging deep learning technologies, data-driven-based approaches have reached great success with the rapid increase of data generated for medical applications. However, security and privacy concerns are obstacles for data providers in many sensitive data-driven scenarios, such as rehabilitation and 24 h on-the-go healthcare services. Although many federated learning (FL) approaches have been proposed with DNNs for medical applications, these works still suffer from low usability of data due to data incompleteness, low quality, insufficient quantity, sensitivity, etc. Therefore, we propose a ring-topology-based decentralized federated learning (RDFL) scheme for deep generative models (DGM), where DGM is a promising solution for solving the aforementioned data usability issues. Our RDFL schemes provide communication efficiency and maintain training performance to boost DGMs in target tasks compared with existing FL works. A novel ring FL topology and a map-reduce-based synchronizing method are designed in the proposed RDFL to improve the decentralized FL performance and bandwidth utilization. In addition, an inter-planetary file system (IPFS) is introduced to further improve communication efficiency and FL security. Extensive experiments have been taken to demonstrate the superiority of RDFL with either independent and identically distributed (IID) datasets or non-independent and identically distributed (Non-IID) datasets.

Verification approach for medical data in e-healthcare system based on biometric and watermarking

Medical information is crucial in the healthcare system, and its manipulation can lead to misdiagnosis. Medical images also contain personal information for patients; hence, information security and privacy protection are paramount when transferring medical images over the Internet. Biometric approach and watermarking techniques are used to achieve this purpose. The focus of this paper was on a biometric watermarking system with a frequency domain in which the sender's iris code is employed as a sender authentication key. The privacy of the patient's information is preserved by encrypting it and embedding the key in the cover medical image created by the Discrete Wavelet Transform. The algorithm has shown that the proposed system has met previous requirements.

A Reliable and Smart E-Healthcare System for Monitoring Intravenous Fluid Level, Pulse, and Respiration Rate

The paper presents reliable and quality maintenance of intravenous fluid level, pulse rate and respiration rate measurement system in healthcare networks. Implementing information and communication technology becomes essential to monitor an elderly patient’s health conditions in the hospital environment. In this paper, a continuous monitoring system is being developed to monitor the level of the intravenous fluid, pulse rate and respiration rate during pandemic situations with an alarm indication. The integration of pressure sensor, Strain gauge sensor, PPG sensor, and Piezo sensor with low-cost microcontroller provides a reliable and quality maintenance of an intravenous fluid level. Also, it gives an accurate measurement of pulse rate and respiration rate. Advanced signal processing tools have been used in this paper for processing and feature extraction. The hardware implementation of the proposed wireless monitoring system is done using a microcontroller programming environment that consumes meager power and provides reliable monitoring.

Prioritizing and Overcoming Barriers to e-Health Use among Elderly People: Implementation of the Analytical Hierarchical Process (AHP).

Rise in the aging population brings new challenges to modern societies. Old age is associated with several morbidities and usual issues related to health. Therefore, the provision of healthy and timely care has become the dire need to maintain their quality of life and wellbeing. The evolution of the e-health care system put pressure on societies to implement it successfully to ensure a safe and prompt provision of care services to the most vulnerable population successfully. Therefore, the provision and implementation of the e-health care system is a challenge for the health industry in terms of multi-objective decision-making. Multicriteria decision-making is a generalizable approach to making decisions with dependence and feedback and is known as an effective tool in decision-making processes, particularly in the healthcare sector. The present study aims to present an e-healthcare framework by identifying and prioritizing potential barriers towards the use of e-health by the elderly population. The analytical hierarchy process approach is adopted to calculate weights of identified potential barriers, respectively, and then rank them based on their degree of significance. The findings show that health and the ability-related barrier is ranked highest, followed by socio-environmental and attitudinal barriers. This research contributes to healthcare decision-making regarding e-health usage by implementing MCDA techniques. Our study will assist the public health practitioners and policymakers in drawing decisions on the best strategy to minimize the risks in using the e-healthcare system by the aging population, which significantly contributes to the smart healthcare system.

Reliable and Privacy-Preserving Top-k Disease Matching Schemes for E-Healthcare Systems

The integration of body sensors, cloud computing, and mobile communication technologies has significantly improved the development and availability of e-healthcare systems. In an e-healthcare system, health service providers upload real patients’ clinical data and diagnostic treatments to the cloud server. Afterward, the users can submit queries with specific body sensor parameters, to obtaining pertinent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> diagnostic files. The results are ranked based on ranking algorithms that match the query parameters to the ones in diagnostic files. However, privacy concerns arise while matching disease, since the clinical data and diagnostic files contain sensitive information. In this work, we propose two reliable and privacy-preserving Top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> disease matching schemes. The first scheme is constructed based on our proposed weighted Euclidean distance comparison algorithm under secure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbor technique to get <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> diagnostic files. It allows users to set different weights for each body indicator as per their needs. The second scheme is designed by comparing Euclidean distances under the modified Paillier homomorphic encryption algorithm where a superlinear sequence is used to reduce the computational and communication overhead. The user side incurs slightly higher computational costs, but the trusted party does not need to execute encryption operations. Hence, the proposed two schemes can be applied in different application scenarios. Simulations on synthetic and real data prove the efficiency of the schemes, and security analysis establishes the privacy-preservation properties.

Internet of Things in E-Healthcare System

Health awareness is important with increasingly complex challenges towards maintaining good health, especially during the COVID-19 pandemic. Regular health monitoring is a must for everyone during the pandemic. This project proposed an IoT based E-Healthcare system to monitor the body temperature, blood pressure and heartbeat rate. The temperature sensor was connected to a Raspberry Pi microcontroller and the readings were stored in the SQLite database management system. The blood pressure and heartbeat rate readings were retrieved from the wrist blood pressure sensor and the readings were entered into the database. The user can access the health data record through a smartphone.

Integration of Healthcare 4.0 and blockchain into secure cloud-based electronic health records systems.

Since the last decade, cloud-based electronic health records (EHRs) have gained significant attention to enable remote patient monitoring. The recent development of Healthcare 4.0 using the Internet of Things (IoT) components and cloud computing to access medical operations remotely has gained the researcher's attention from a smart city perspective. Healthcare 4.0 mainly consisted of periodic medical data sensing, aggregation, data transmission, data sharing, and data storage. The sensitive and personal data of patients lead to several challenges while protecting it from hackers. Therefore storing, accessing, and sharing the patient medical information on the cloud needs security attention that data should not be compromised by the authorized user's components of E-healthcare systems. To achieve secure medical data storage, sharing, and accessing in cloud service provider, several cryptography algorithms are designed so far. However, such conventional solutions failed to achieve the trade-off between the requirements of EHR security solutions such as computational efficiency, service side verification, user side verifications, without the trusted third party, and strong security. Blockchain-based security solutions gained significant attention in the recent past due to the ability to provide strong security for data storage and sharing with the minimum computation efforts. The blockchain made focused on bitcoin technology among the researchers. Utilizing the blockchain which secure healthcare records management has been of recent interest. This paper presents the systematic study of modern blockchain-based solutions for securing medical data with or without cloud computing. We implement and evaluate the different methods using blockchain in this paper. According to the research studies, the research gaps, challenges, and future roadmap are the outcomes of this paper that boost emerging Healthcare 4.0 technology.