Author
Ayman Ibaida
Other affiliations: RMIT University
Bio: Ayman Ibaida is an academic researcher from Victoria University, Australia. The author has contributed to research in topics: Steganography & Digital watermarking. The author has an hindex of 10, co-authored 16 publications receiving 446 citations. Previous affiliations of Ayman Ibaida include RMIT University.
Papers
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TL;DR: A wavelet-based steganography technique has been introduced which combines encryption and scrambling technique to protect patient confidential data and it is found that the proposed technique provides high-security protection for patients data with low distortion and ECG data remain diagnosable after watermarking.
Abstract: With the growing number of aging population and a significant portion of that suffering from cardiac diseases, it is conceivable that remote ECG patient monitoring systems are expected to be widely used as point-of-care (PoC) applications in hospitals around the world. Therefore, huge amount of ECG signal collected by body sensor networks from remote patients at homes will be transmitted along with other physiological readings such as blood pressure, temperature, glucose level, etc., and diagnosed by those remote patient monitoring systems. It is utterly important that patient confidentiality is protected while data are being transmitted over the public network as well as when they are stored in hospital servers used by remote monitoring systems. In this paper, a wavelet-based steganography technique has been introduced which combines encryption and scrambling technique to protect patient confidential data. The proposed method allows ECG signal to hide its corresponding patient confidential data and other physiological information thus guaranteeing the integration between ECG and the rest. To evaluate the effectiveness of the proposed technique on the ECG signal, two distortion measurement metrics have been used: the percentage residual difference and the wavelet weighted PRD. It is found that the proposed technique provides high-security protection for patients data with low (less than 1%) distortion and ECG data remain diagnosable after watermarking (i.e., hiding patient confidential data) and as well as after watermarks (i.e., hidden data) are removed from the watermarked data.
162 citations
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TL;DR: A knowledge discovery-based approach that allows the context-aware system to adapt its behaviour in runtime by analysing large amounts of data generated in Ambient assisted living systems and stored in cloud repositories is proposed.
Abstract: Context-aware monitoring is an emerging technology that provides real-time personalised health-care services and a rich area of big data application. In this paper, we propose a knowledge discovery-based approach that allows the context-aware system to adapt its behaviour in runtime by analysing large amounts of data generated in ambient assisted living (AAL) systems and stored in cloud repositories . The proposed BDCaM model facilitates analysis of big data inside a cloud environment. It first mines the trends and patterns in the data of an individual patient with associated probabilities and utilizes that knowledge to learn proper abnormal conditions. The outcomes of this learning method are then applied in context-aware decision-making processes for the patient. A use case is implemented to illustrate the applicability of the framework that discovers the knowledge of classification to identify the true abnormal conditions of patients having variations in blood pressure (BP) and heart rate (HR). The evaluation shows a much better estimate of detecting proper anomalous situations for different types of patients. The accuracy and efficiency obtained for the implemented case study demonstrate the effectiveness of the proposed model.
106 citations
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TL;DR: ECG signals are watermarked with patient biomedical information in order to confirm patient/ECG linkage integrity and it is found that a marginal amount of signal distortion that is sufficient to hold the patient information, will not affect the overall quality of the ECG.
Abstract: In Wireless telecardiology applications, an ECG signal is often transmitted without any patient details which are often supplied separately as clear text. This allows the possibility of confusion of link between signal and identity (for example, with wireless signal collision attacks). ECG data transmission can be more robustly tied to either patient identity or other patient meta-data if this meta-data is embedded within the ECG signal itself when sent. In this paper ECG signals are watermarked with patient biomedical information in order to confirm patient/ECG linkage integrity. Several cases have been tested with different degrees of signal modification due to watermarking. These show its effect on the diagnostic value of the signal (for example, the PRD as an error measure). It is found that a marginal amount of signal distortion that is sufficient to hold the patient information, will not affect the overall quality of the ECG. The proposed system will not increase the size of host signals, nor change its scaling nor bandwidth. In addition, its low complexity makes it suitable for power-limited wearable computing and sensor-net applications.
55 citations
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TL;DR: It is found that the ECG signal self-similarity characteristic can be used efficiently to achieve high compression ratios and the proposed technique can achieve a higher compression ratio of 40 with lower Percentage Residual Difference (PRD) Value less than 1%.
35 citations
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01 Jan 2009TL;DR: In wireless telecardiology applications ECG signal is compressed before transmission to support faster data delivery and reduce consumption of bandwidth as mentioned in this paper. But most of the ECG analysis and diagnosis algorithms are based on processing of the original ECG signals, therefore, compressed ECG data needs to be decompressed first before the existing algorithms and tools can be applied to detect cardiovascular abnormalities.
Abstract: In wireless telecardiology applications ECG signal is compressed before transmission to support faster data delivery and reduce consumption of bandwidth. However, most of the ECG analysis and diagnosis algorithms are based on processing of the original ECG signal. Therefore, compressed ECG data needs to be decompressed first before the existing algorithms and tools can be applied to detect cardiovascular abnormalities.
31 citations
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01 Aug 2009
TL;DR: PhysioBank是一个大型的逐渐扩增的生理学信号和相关数据的数字化记录文档;目前包含多参数的心肺。
Abstract: PhysioBank是一个大型的逐渐扩增的生理学信号和相关数据的数字化记录文档。目前包含多参数的心肺、神经和其他生物医学信号,尤以心电图(ECG)为主。信号来自健康受试者和各种疾病的患者。涉及的疾病包括心脏猝死、充血性心力衰竭、癫痫、步态不稳、睡眠呼吸暂停和衰老等。
287 citations
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TL;DR: In this article, a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-WBANs and beyond WBANs, is presented.
Abstract: The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC.
202 citations
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TL;DR: A novel healthcare monitoring framework based on the cloud environment and a big data analytics engine is proposed to precisely store and analyze healthcare data, and to improve the classification accuracy.
190 citations
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TL;DR: A cloud-based PHR system taking a radically new architectural solution to health record portability and a prototype of My PHR Machines applied to two use cases, i.e., radiology image sharing and personalized medicine are discussed.
Abstract: Personal Health Records (PHRs) should remain the lifelong property of patients, who should be able to show them conveniently and securely to selected caregivers and institutions. In this paper, we present My PHR Machines, a cloud-based PHR system taking a radically new architectural solution to health record portability. In My PHR Machines, health-related data and the application software to view and/or analyze it are separately deployed in the PHR system. After uploading their medical data to My PHR Machines, patients can access them again from remote virtual machines that contain the right software to visualize and analyze them without any need for conversion. Patients can share their remote virtual machine session with selected caregivers, who will need only a Web browser to access the pre-loaded fragments of their lifelong PHR. We discuss a prototype of My PHR Machines applied to two use cases, i.e., radiology image sharing and personalized medicine.
170 citations
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TL;DR: A wavelet-based steganography technique has been introduced which combines encryption and scrambling technique to protect patient confidential data and it is found that the proposed technique provides high-security protection for patients data with low distortion and ECG data remain diagnosable after watermarking.
Abstract: With the growing number of aging population and a significant portion of that suffering from cardiac diseases, it is conceivable that remote ECG patient monitoring systems are expected to be widely used as point-of-care (PoC) applications in hospitals around the world. Therefore, huge amount of ECG signal collected by body sensor networks from remote patients at homes will be transmitted along with other physiological readings such as blood pressure, temperature, glucose level, etc., and diagnosed by those remote patient monitoring systems. It is utterly important that patient confidentiality is protected while data are being transmitted over the public network as well as when they are stored in hospital servers used by remote monitoring systems. In this paper, a wavelet-based steganography technique has been introduced which combines encryption and scrambling technique to protect patient confidential data. The proposed method allows ECG signal to hide its corresponding patient confidential data and other physiological information thus guaranteeing the integration between ECG and the rest. To evaluate the effectiveness of the proposed technique on the ECG signal, two distortion measurement metrics have been used: the percentage residual difference and the wavelet weighted PRD. It is found that the proposed technique provides high-security protection for patients data with low (less than 1%) distortion and ECG data remain diagnosable after watermarking (i.e., hiding patient confidential data) and as well as after watermarks (i.e., hidden data) are removed from the watermarked data.
162 citations