With the growth in Internet and digital technology, Internet of Medical Things (IoMT) and Telemedicine have become buzzwords in healthcare. A large number of medical images and information is shared through a public network in these applications. This paper proposes a region-based hybrid Medical Image Watermarking (MIW) scheme to ensure the authenticity, authorization, integrity, and confidentiality of the medical images transmitted through a public network in IoMT. In the proposed scheme, medical images are partitioned into Region of Interest (RoI) and Region of Non-Interest (RoNI). To ascertain integrity of RoI, tamper detection and recovery bits are embedded in RoI in the medical image. RoI is watermarked using adaptive Least Significant Bit (LSB) substitution with respect to the hiding capacity map for higher RoI imperceptibility and accuracy in tamper detection and recovery. Electronic Patient Record (EPR) is compressed using Huffman coding and encrypted using a pseudo-random key (secret key) to provide higher confidentiality and payload. QR code of hospital logo, Encrypted EPR, and RoI recovery bits are interleaved in RoNI using Discrete Wavelet Transform-Singular Value Decomposition (DWT-SVD) hybrid transforms to achieve a robust watermark. The proposed scheme is tested under various geometric and non-geometric attacks such as filtering, compression, rotation, salt and pepper noise and shearing. The evaluation results demonstrate that the proposed scheme has high imperceptibility, robustness, security, payload, tamper detection, and recovery accuracy under image processing attacks. Therefore, the proposed scheme can be used in the transmission of medical images and EPR in IoMT. Relevance of the proposed scheme is established by its superior performance in comparison to some of the popular existing schemes.

In the current digital era, personal data storage on public platforms is a major cause of concern with severe security and privacy ramifications. This is true especially in e-health data management since patient’s health data must be managed following a slew of established standards. The Cloud Service Providers (CSPs) primarily provide computing and storage resources. However, data security in the cloud is still a major concern. In several instances, Blockchain technology rescues the CSPs by providing the robust security to the underlying data by encrypting data using the unique and secret keys. Each network user in Blockchain has its own unique and secret keys linked directly to the transaction keys as a digital signature to protect the data. However, Blockchain technology suffers from the latency and throughput issues in high workload scenarios. To overcome e-healthcare records privacy issues in a third-party cloud, we designed a Patient’s E-Healthcare Records Management System (PRMS) that focuses on latency and throughput. A comprehensive performance analysis of PRMS is carried out on different third-party clouds to validate its applicability. Moreover, the proposed PRMS system is compared with Blockchain platforms such as Hyperledger Fabric v0.6 and Etherium 1.5.8 against latency and throughput by adjusting the workload for each platform up to 10,000 transactions per second. The proposed PRMS is compared to the Secure and Robust Healthcare-Based Blockchain (SRHB) approach using Yahoo Cloud Serving Benchmark (YCSB) and small bank datasets. The experimental results indicate that deploying PRMS on Amazon Web Services decreases System Execution Time (SET) and the Average Delay (AD) time by 2.4%, 8.33%, and 25.15%, 15.26%, respectively. Additionally, deploying PRMS on the Google Cloud Platform decreases System Execution Time (SET) and Average Delay (AD) by 2.27%, 2.4%, and 2.72%, 4.73% AD, respectively. The experimental results confirm the superiority of the PRMS under the high workload scenario over SRHB and its applicability in cloud data centers.

In recent years, cardiovascular diseases are on the rise, and they entail enormous health burdens on global economies. Cardiac vibrations yield a wide and rich spectrum of essential information regarding the functioning of the heart, and thus it is necessary to take advantage of this data to better monitor cardiac health by way of prevention in early stages. Specifically, seismocardiography (SCG) is a noninvasive technique that can record cardiac vibrations by using new cutting-edge devices as accelerometers. Therefore, providing new and reliable data regarding advancements in the field of SCG, i.e., new devices and tools, is necessary to outperform the current understanding of the State-of-the-Art (SoTA). This paper reviews the SoTA on SCG and concentrates on three critical aspects of the SCG approach, i.e., on the acquisition, annotation, and its current applications. Moreover, this comprehensive overview also presents a detailed summary of recent advancements in SCG, such as the adoption of new techniques based on the artificial intelligence field, e.g., machine learning, deep learning, artificial neural networks, and fuzzy logic. Finally, a discussion on the open issues and future investigations regarding the topic is included.

Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the standalone use of ECG and SCG.