The medical field is creating large amount of data that physicians are unable to comprehend and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. It is a representation learning method that consists of layers that transform the data non-linearly, thus revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.

The provision of effective emergency telemedicine and home monitoring solutions are the major fields of interest discussed in this study. Ambulances, Rural Health Centers (RHC) or other remote health location such as Ships navigating in wide seas are common examples of possible emergency sites, while critical care telemetry and telemedicine home follow-ups are important issues of telemonitoring. In order to support the above different growing application fields we created a combined real-time and store and forward facility that consists of a base unit and a telemedicine (mobile) unit. This integrated system: can be used when handling emergency cases in ambulances, RHC or ships by using a mobile telemedicine unit at the emergency site and a base unit at the hospital-expert's site, enhances intensive health care provision by giving a mobile base unit to the ICU doctor while the telemedicine unit remains at the ICU patient site and enables home telemonitoring, by installing the telemedicine unit at the patient's home while the base unit remains at the physician's office or hospital. The system allows the transmission of vital biosignals (3–12 lead ECG, SPO2, NIBP, IBP, Temp) and still images of the patient. The transmission is performed through GSM mobile telecommunication network, through satellite links (where GSM is not available) or through Plain Old Telephony Systems (POTS) where available. Using this device a specialist doctor can telematically "move" to the patient's site and instruct unspecialized personnel when handling an emergency or telemonitoring case. Due to the need of storing and archiving of all data interchanged during the telemedicine sessions, we have equipped the consultation site with a multimedia database able to store and manage the data collected by the system. The performance of the system has been technically tested over several telecommunication means; in addition the system has been clinically validated in three different countries using a standardized medical protocol.

Virtual reality (VR) constitutes an alternative, effective, and increasingly utilized treatment option for people suffering from psychiatric and neurological illnesses. However, the currently available VR simulations provide a predetermined simulative framework that does not take into account the unique personality traits of each individual; this could result in inaccurate, extreme, or unpredictable responses driven by patients who may be overly exposed and in an abrupt manner to the predetermined stimuli, or result in indifferent, almost non-existing, reactions when the stimuli do not affect the patients adequately and thus stronger stimuli are recommended. In this study, we present a VR system that can recognize the individual differences and readjust the VR scenarios during the simulation according to the treatment aims. To investigate and present this dynamically adaptive VR system we employ an Anxiety Disorder condition as a case study, namely arachnophobia. This system consists of distinct anxiety states, aiming to dynamically modify the VR environment in such a way that it can keep the individual within a controlled, and appropriate for the therapy needs, anxiety state, which will be called “desired states” for the study. This happens by adjusting the VR stimulus, in real-time, according to the electrophysiological responses of each individual. These electrophysiological responses are collected by an external electrodermal activity biosensor that serves as a tracker of physiological changes. Thirty-six diagnosed arachnophobic individuals participated in a one-session trial. Participants were divided into two groups, the Experimental Group which was exposed to the proposed real-time adaptive virtual simulation, and the Control Group which was exposed to a pre-recorded static virtual simulation as proposed in the literature. These results demonstrate the proposed system’s ability to continuously construct an updated and adapted virtual environment that keeps the users within the appropriately chosen state (higher or lower intensity) for approximately twice the time compared to the pre-recorded static virtual simulation. Thus, such a system can increase the efficiency of VR stimulations for the treatment of central nervous system dysfunctions, as it provides numerically more controlled sessions without unexpected variations.

Gravity constituted the only constant environmental parameter, during the evolutionary period of living matter on Earth. However, whether gravity has affected the evolution of species, and its impact is still ongoing. The topic has not been investigated in depth, as this would require frequent and long-term experimentations in space or an environment of altered gravity. In addition, each organism should be studied throughout numerous generations to determine the profound biological changes in evolution. Here, we review the significant abnormalities presented in the cardiovascular, immune, vestibular and musculoskeletal systems, due to altered gravity conditions. We also review the impact that gravity played in the anatomy of snakes and amphibians, during their evolution. Overall, it appears that gravity does not only curve the space–time continuum but the biological continuum, as well.

Nowadays, Virtual Reality (VR) is bringing great benefits to Anxiety Disorder treatments, as well as to other brain cognitive dysfunctions. The advantage of VR is that it can provoke stimuli to the same degree as real-life situations. However, measurement methods of physiological changes caused by the aforementioned stimuli, which apply to VR Anxiety Disorder treatments, have not been examined extensively. As a result, clinicians who use biosignal sensors tend to ask their patients to remain motionless during simulations in order to achieve accurate measurements from the sensors. It is clear that this practice limits the level and range of benefits yielded when using VR simulation. As a consequence, the patients' experience is restricted and so is the potential of the sensors' application in the treatment methods. Furthermore, the data gathered from the sensors is handled using conventional analysis affecting the conclusions drawn about the patients' state. This study aims to emphasise the importance of interacting with the stimuli during the VR treatment through the proposal of an Electrodermal Activity (EDA) Sensor System architecture that can be combined with VR simulations while still allowing the patient to move and interact within the Virtual Environment, without compromising the sensor's measurements. Continuous Deconvolution Analysis is used to draw conclusions from the gathered biosensor data.

Ambient-assisted living (AAL) is currently one of the important research and development areas, where accessibility, usability, and learning play a major role and where future interfaces are an important concern for applied engineering. The general goal of AAL solutions is to apply ambient intelligence technology to enable people with specific demands, e.g., handicapped or elderly, to live in their preferred environment longer. The term “Internet of Things” (IoT) is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. In this context, we propose a new methodology based on self organizing maps (SOMs) and fuzzy C-means (FCM) algorithms for profile generation as regards the activities of the user and their correlation with the available sensors. Moreover, we utilize the provided context to assign the generated profiles to more contextually complex activities. Our methodology is being evaluated into an AAL structure equipped with several sensors. More precisely, we assess the proposed method in a data set generated by accelerometers and its performance over a number of everyday activities.