Sensor-based health systems can often become difficult to use, extend and sustain. The authors propose a framework for designing sensor-based health monitoring systems aiming to provide extensible and usable monitoring services in the scope of pervasive patient care. The authors’ approach relies on a distributed system for monitoring the patient health status anytime-anywhere and detecting potential health complications, for which healthcare professionals and patients are notified accordingly. Portable or wearable sensing devices measure the patient's physiological parameters, a smart mobile device collects and analyses the sensor data, a Medical Center system receives notifications on the detected health condition, and a Health Professional Platform is used by formal caregivers in order to review the patient condition and configure monitoring schemas. A Service-oriented architecture is utilised to provide extensible functional components and interoperable interactions among the diversified system components. The framework was applied within the REMOTE ambient-assisted living project in which a prototype system was developed, utilising Bluetooth to communicate with the sensors and Web services for data exchange. A scenario of using the REMOTE system and preliminary usability results show the applicability, usefulness and virtue of our approach.

Based on the demographic changes and the rapid increase of older population in Europe, major challenges are expected to rise, both in the economy as well as the society, whether the dominant care model for supporting elderly in living independently at home continues to rely on informal and formal caregivers’ assistance. To respond to the above challenges, assistive technologies are called to develop Information and Communication Technology (ICT) services for supporting seniors to remain active and independent, for as long as possible, in their chosen home environment. The work described in this Letter is based on the Miraculous-Life project and it emphasises the identification and assessment of a set of services that an ICT system for Ageing Well should support, in an actual end-users setting. The outcome of this work may inform fellow researchers and other projects in the area of Ageing Well in: (i) understanding which ICT services can be the most valuable for end-users’ Quality of Life, (ii) prioritising the development of related ICT services and (iii) facilitating better recourse allocation in order to reduce any risks associated to implementation failures of these services within their respective projects. A final trial phase is planned, aiming to validate the Miraculous Life prototype longitudinally in a naturalistic environment with a larger sample size. During this trial, it will be investigated if perceived usefulness, satisfaction and motivation could be predicted by sociodemographic variables and personality.

Heart failure (HF) is a chronic disease characterised by poor quality of life, recurrent hospitalisation and high mortality. Adherence of patient to treatment suggested by the experts has been proven a significant deterrent of the above-mentioned serious consequences. However, the non-adherence rates are significantly high; a fact that highlights the importance of predicting the adherence of the patient and enabling experts to adjust accordingly patient monitoring and management. The aim of this work is to predict the adherence of patients with HF, through the application of machine learning techniques. Specifically, it aims to classify a patient not only as medication adherent or not, but also as adherent or not in terms of medication, nutrition and physical activity (global adherent). Two classification problems are addressed: (i) if the patient is global adherent or not and (ii) if the patient is medication adherent or not. About 11 classification algorithms are employed and combined with feature selection and resampling techniques. The classifiers are evaluated on a dataset of 90 patients. The patients are characterised as medication and global adherent, based on clinician estimation. The highest detection accuracy is 82 and 91% for the first and the second classification problem, respectively.

Continuous patient monitoring systems acquire enormous amounts of data that is either manually analysed by doctors or automatically processed using intelligent algorithms. Sections of data acquired over long period of time can be corrupted with artefacts due to patient movement, sensor placement and interference from other sources. Owing to the large volume of data these artefacts need to be automatically identified so that the analysis systems and doctors are aware of them while making medical diagnosis. Three important factors are explored that must be considered and quantified for the design and evaluation of automatic artefact identification algorithms: signal quality, interpretation quality and computational complexity. The first two are useful to determine the effectiveness of an algorithm, whereas the third is particularly vital in mHealth systems where computational resources are heavily constrained. A series of artefact identification and filtering algorithms are then presented focusing on the electrocardiography data. These algorithms are quantified using the three metrics to demonstrate how different algorithms can be evaluated and compared to select the best ones for a given wireless sensor network.

In biomedical, data security is the most expensive resource for wireless body area network applications. Cryptographic algorithms are used in order to protect the information against unauthorised access. Advanced encryption standard (AES) cryptographic algorithm plays a vital role in telemedicine applications. The authors propose a novel approach for design of substitution bytes (S-Box) using second-order reversible one-dimensional cellular automata (RCA²) as a replacement to the classical look-up-table (LUT) based S-Box used in AES algorithm. The performance of proposed RCA² based S-Box and conventional LUT based S-Box is evaluated in terms of security using the cryptographic properties such as the nonlinearity, correlation immunity bias, strict avalanche criteria and entropy. Moreover, it is also shown that RCA² based S-Boxes are dynamic in nature, invertible and provide high level of security. Further, it is also found that the RCA² based S-Box have comparatively better performance than that of conventional LUT based S-Box.

The authors aimed to develop an application for producing different architectures to implement dual tree complex wavelet transform (DTCWT) having near shift-invariance property. To obtain a low-cost and portable solution for implementing the DTCWT in multi-channel real-time applications, various embedded-system approaches are realised. For comparison, the DTCWT was implemented in C language on a personal computer and on a PIC microcontroller. However, in the former approach portability and in the latter desired speed performance properties cannot be achieved. Hence, implementation of the DTCWT on a reconfigurable platform such as field programmable gate array, which provides portable, low-cost, low-power, and high-performance computing, is considered as the most feasible solution. At first, they used the system generator DSP design tool of Xilinx for algorithm design. However, the design implemented by using such tools is not optimised in terms of area and power. To overcome all these drawbacks mentioned above, they implemented the DTCWT algorithm by using Verilog Hardware Description Language, which has its own difficulties. To overcome these difficulties, simplify the usage of proposed algorithms and the adaptation procedures, a code generator program that can produce different architectures is proposed.

In this Letter, the authors describe the characterisation design and development of the authors’ wearable, multimodal vitals acquisition unit for intelligent field triage. The unit is able to record the standard electrocardiogram, blood oxygen and body temperature parameters and also has the unique capability to record up to eight custom designed acoustic streams for heart and lung sound auscultation. These acquisition channels are highly synchronised to fully maintain the time correlation of the signals. The unit is a key component enabling systematic and intelligent field triage to continuously acquire vital patient information. With the realised unit a novel data-set with highly synchronised vital signs was recorded. The new data-set may be used for algorithm design in vital sign analysis or decision making. The monitoring unit is the only known body worn system that records standard emergency parameters plus eight multi-channel auscultatory streams and stores the recordings and wirelessly transmits them to mobile response teams.

The medical emergency response comprises a domain with complex processes, encompassing multiple heterogeneous entities, from organisations involved in the response to human actors to key information sources. Due to the heterogeneity of the entities and the complexity of the domain, it is important to fully understand the individual processes in which the components are involved and their inter-operations, before attempting to design any technological tool for coordination and decision support. This work starts with the gluing together and visualisation of the interactions of involved entities into a conceptual model, along the identified five workspaces of emergency response. The modelling visualises the domain processes, in a way that reveals the necessary communication and coordination points, the required data sources and data flows, as well as the required decision support needs. Work continues with the identification and modelling of the event-driven discrete-time-based dynamics of the emergency response processes and their compositions, using Petri nets as the modelling technique. Subsequently, an integrated model of the process is presented, which facilitates the parallelisation of the tasks undertaken in an emergency incident.

Τhe performance of rescuers and personnel handling major emergencies or crisis events can be significantly improved through continuous training and through technology support. The work done in order to create a system has been discussed which can support both resources and victims during a crisis or major emergency event. More specifically, the system supports real-time management of firefighter teams, rescue teams, health services, and victims during a major disaster. It can be deployed in an ad hoc manner in the disaster area, as a stand-alone infrastructure (using its own telecommunications and power). It mainly consists of a control station, which is installed in the area command centre, the firefighters units, the rescuers units, the ambulance vehicles units, and the telemedicine units that can be used in order to support victim handling at the casualties clearing station. The system has been tested and improved through continuous communication with experts and through professional exercises; the results and conclusions are presented.

This Letter proposes an end-to-end mobile tele-echography platform using a portable robot for remote cardiac ultrasonography. Performance evaluation investigates the capacity of long-term evolution (LTE) wireless networks to facilitate responsive robot tele-manipulation and real-time ultrasound video streaming that qualifies for clinical practice. Within this context, a thorough video coding standards comparison for cardiac ultrasound applications is performed, using a data set of ten ultrasound videos. Both objective and subjective (clinical) video quality assessment demonstrate that H.264/AVC and high efficiency video coding standards can achieve diagnostically-lossless video quality at bitrates well within the LTE supported data rates. Most importantly, reduced latencies experienced throughout the live tele-echography sessions allow the medical expert to remotely operate the robot in a responsive manner, using the wirelessly communicated cardiac ultrasound video to reach a diagnosis. Based on preliminary results documented in this Letter, the proposed robotised tele-echography platform can provide for reliable, remote diagnosis, achieving comparable quality of experience levels with in-hospital ultrasound examinations.

This Letter introduces a feasibility study of a scanning system for applications in biomedical bone imaging operating in the microwave range 0.5–4 GHz. Mechanical uncertainties and data acquisition time are minimised by using a fully automated scanner that controls two antipodal Vivaldi antennas. Accurate antenna positioning and synchronisation with data acquisition enables a rigorous proof-of-concept for the microwave imaging procedure of a multi-layer phantom including skin, fat, muscle and bone tissues. The presence of a suitable coupling medium enables antenna miniaturisation and mitigates the impedance mismatch between antennas and phantom. The three-dimensional image of tibia and fibula is successfully reconstructed by scanning the multi-layer phantom due to the distinctive dielectric contrast between target and surrounding tissues. These results show the viability of a microwave bone imaging technology which is low cost, portable, non-ionising, and does not require specially trained personnel. In fact, as no a-priori characterisation of the antenna is required, the image formation procedure is very conveniently simplified.

Wearable electrocardiogram (ECG) is attracting much attention in daily healthcare applications, and human body communication (HBC) technology provides an evident advantage in making the sensing electrodes of ECG also working for transmission through the human body. In view of actual usage in daily life, however, non-contact electrodes to the human body are desirable. In this Letter, the authors discussed the ECG circuit structure in the HBC-based wearable ECG for removing the common mode noise when employing non-contact capacitive coupling electrodes. Through the comparison of experimental results, they have shown that the authors’ proposed circuit structure with the third electrode directly connected to signal ground can provide an effect on common mode noise reduction similar to the usual drive-right-leg circuit, and a sufficiently good acquisition performance of ECG signals.

Hydrocephalus is a condition characterised by enlarged cerebral ventricles, which in turn affects intracranial pressure (ICP); however, the mechanisms regulating ICP are not fully understood. A nonlinear signal processing approach was applied to ICP signals measured during infusion studies from patients with two forms of hydrocephalus, in a bid to compare the differences. This is the first study of its kind. The two forms of hydrocephalus were idiopathic normal pressure hydrocephalus (iNPH) and secondary normal pressure hydrocephalus (SH). Following infusion tests, the Lempel–Ziv (LZ) complexity was calculated from the iNPH and SH ICP signals. The LZ complexity values were averaged for the baseline, infusion, plateau and recovery stages of the tests. It was found that as the ICP increased from basal levels, the LZ complexities decreased, reaching their lowest during the plateau stage. However, the complexities computed from the SH ICP signals decreased to a lesser extent when compared with the iNPH ICP signals. Furthermore, statistically significant differences were found between the plateau and recovery stage complexities when comparing the iNPH and SH results (p = 0.05). This Letter suggests that advanced signal processing of ICP signals with LZ complexity can help characterise different types of hydrocephalus in more detail.

The presence of irregularities in electroencephalographic (EEG) signals entails complexities during the Alzheimer's disease (AD) diagnosis. In addition, the uncertainty presented on EEG raises major issues in the improvement of the classification rate. The multi-resolution analysis through an optimum threshold will likely achieve better results in distinguishing AD and normal EEG signals. Hence, a fuzzy-entropy concept defined in a complex multi-resolution wavelet has been proposed to obtain the most appropriate threshold. First, the complex coefficients are fuzzified using a Gaussian membership function. Afterwards, the ability of the proposed fuzzy-entropy threshold has been compared with traditional thresholds in complex wavelet domain. Experimental results show that the authors’ methodology produces a higher signal-to-noise ratio and a lower root-mean-square error than traditional approaches. Moreover, a neural network scheme is performed along several features to classify AD from normal EEG signals obtaining a specificity of 87.5%.

In this Letter, the authors propose an efficient and robust method for automatically determining the VT and VF events in the electrocardiogram (ECG) signal. The proposed method consists of: (i) discrete cosine transform (DCT)-based noise suppression; (ii) addition of bipolar sequence of amplitudes with alternating polarity; (iii) zero-crossing rate (ZCR) estimation-based VTVF detection; and (iv) peak-to-peak interval (PPI) feature based VT/VF discrimination. The proposed method is evaluated using 18,000 episodes of different ECG arrhythmias taken from 6 PhysioNet databases. The method achieves an average sensitivity (Se) of 99.61%, specificity (Sp) of 99.96%, and overall accuracy (OA) of 99.92% in detecting VTVF and non-VTVF episodes by using a ZCR feature. Results show that the method achieves a Se of 100%, Sp of 99.70% and OA of 99.85% for discriminating VT from VF episodes using PPI features extracted from the processed signal. The robustness of the method is tested using different kinds of ECG beats and various types of noises including the baseline wanders, powerline interference and muscle artefacts. Results demonstrate that the proposed method with the ZCR, PPI features can achieve significantly better detection rates as compared with the existing methods.

The main aim of root canal irrigation is to eliminate micro-organisms. Sodium hypochlorite (NaOCl) is considered to be the ideal material and raising its temperature potentiates the antimicrobial activity. NaOCl may lead to localised tissue necrosis when extruded past the root apex. This study analyses the use of high temperature root canal irrigation as an alternative process for the elimination of microorganisms from the root canal system. An experimental set-up was designed where a constant supply of heat was passed from a heat source through a copper wire inside the root canal. The data acquired together with known constants pertaining to enamel and dentine was used to numerically model the thermal changes in a tooth using a finite element method. Results obtained from the finite element thermal model of the tooth were repeatable and were validated with the experimental results. The thermo-physical properties of the tooth were varied and convergence criteria met. The temperatures reached were below what has been reported to cause irreversible damage to the bone. This was further confirmed from a series of simulations that were undertaken. The temperatures achieved were suitable for the elimination of microorganisms during root canal therapy.