The relation between fetal heart rate and the value of umbilical cord artery pH is not something new for the scientific community. However, the problem has not been investigated sufficiently. One reason for that is the lack of open databases with a large number of recordings. Such a database is used here, recently publicly available, with cardiotocographic data recorded approximately two hours before delivery and until the end of the delivery. We use entropy measures to investigate how the value of umbilical cord artery pH is correlated to the variability of the fetal heart rhythm.

Health organizations are collecting more data from a wider array of sources at greater speed every day. The analysis of this vast amount and array of data creates new opportunities to deliver modern personalized health and social care services. Big Data Analytics and underlying technologies have the potential to process and analyze these data to extract meaningful insights for improving quality of care, efficiency and sustainability of health and social care systems.

Although the genome sequences of most studied organisms, like human, E. coli, and others are already known, de novo genome sequencing remains popular as a majority of genomes remains unknown. Unfortunately, sequencing machines are able to read only short fragments of DNA. Therefore, one of the basic steps in reconstructing novel genomes lies in putting these pieces of DNA, called ‘reads’, together into complete genome sequences using a process known as genome assembly. Reads joining, however, requires efficient detection of their overlaps.

"In [1], a reasonably fast method of Fractal Dimension estimation was presented. However, it has a weak point: After each subdivision of the partition table, pixels that might have been quite far apart originally might end up needing to be merged. Thus, after each subdivision, the partition table needs to be re-sorted, which is a computationally expensive operation. The ideal solution would be to order the pixels of the image in such a way, that a) pixels that are in the same cube are consecutive and b) they remain in that order even after each subdivision.

Deciding for Cervical Cancer (CxCa) treatment is not a simple task. There are several competing factors that arise from the perspective of survival, treatment, toxicity, quality of patient’s life, as well as the geographic location of the patient, which indicates access to specific healthcare resources. All of these factors play a significant role in the ultimate decision to pursue surgery, chemotherapy, and radiation therapy.

A large amount of data is continuously generated in intensive health care. An analysis of these streaming data can provide important information to improve the monitoring of the health conditions of patients. The volume, velocity and complexity of these data, which come unlabeled, make their analysis a challenging task. Machine learning techniques have been successfully used for data stream mining.

Sexual contact networks for disease transmission have been used extensively with HIV and provide valuable insight into the way the disease spreads through a population. These computationally intensive models often suffer from lack of reusability which makes them expensive to create and use. We take an innovated approach to build an HIV transmission model designed for expansion and reusability, and add additional functionality for model realism.

For a cyber-physical system, its execution behaviors are often impacted by human interactive behaviors. However, the assumptions about a cyber-physical system’s expected human interactive behaviors are often informally documented, or even left implicit and unspecified in system design. Unfortunately, such implicit human interaction assumptions made by safety critical cyber-physical systems, such as medical cyber-physical systems (M-CPS), can lead to catastrophes. Several recent U.S. Food and Drug Administration (FDA) medical device recalls are due to implicit human interaction assumptions.

"Post-concussion syndrome (PCS) is associated with incomplete recovery following a mild traumatic brain injury (mTBI). Often, PCS is characterised by microstructural damage to white matter tracts in the brain. Currently, there is no biomarker to screen for such damage. In this paper we present preliminary result for a novel and simple to administer paradigm that can be used to test the microstructural integrity of the corpus callosum. The novel paradigm is based on the Matching Familiar Figures Test (MFFT).

Computed tomography (CT) stands out among the exams used by computer-aided diagnosis in medical imaging as it provides the visualization of internal organs such as the lungs and their structures. This paper focuses on the segmentation of lungs using a three-dimensional region growing (3D RG) method and the registration toolkit ITK library. To evaluate the proposed segmentation method, we used 30 exams from the LAPISCO image database.

Mathematical Anxiety is the feeling of fear or dislike when dealing with mathematically rich situations. Although MA seems innocent in general, it can seriously compromise math performance, lead to avoidance, affect the learning procedure, as well as influence future career choices and directions. The accurate recognition of MA, apart from diagnostic purposes, is considered to be very important for e-learning systems as well. This work presents an automatic system for the detection of MA based on electroencephalographic (EEG) signals.

Motor Imagery (MI), the mental execution of an action, is widely applied as a control modality for electroencephalography (EEG) based Brain-Computer Interfaces (BCIs). Different approaches to MI have been implemented, namely visual observation (VMI) or kinesthetic rehearsal (KMI) of movements. Although differences in brain activity during VMI or KMI have been studied, no investigation with regards to their suitability for BCI applications has been made.

A plethora of studies has shown that working memory, processing speed and flowing intelligence are diminished with aging. However, emotional processing remains relatively stable even though young and old seem to process emotions differently. Neurophysiological studies have employed emotional stimuli to investigate age differences through Event Related Potentials (ERPs). The present approach used affective visual word stimuli derived from the Greek language. Healthy young and elderly volunteers passively viewed the stimuli which were divided into pleasant, unpleasant and neutral.

Elementary school students experience significant personal changes; their bodies change, as well as their inner selves. Their emotional status can be affected by both intristic and extrinsic factors; therefore, it is vital to highlight the emotional factors that influence the learning process, as a negative emotional status may lead to reduced motivation and low school performance, whereas a positive one may bring the opposite results. The aim of this study is to evaluate the effects of the teaching process onto the students’ emotional status, and how it affects their academic performance.

Assistant Robots can be an efficient and low-cost solution to Patient-Care. One important aspect of Assistant Bots is succesful as well as Socially Intelligent Communication with the Patient. A new Conditional Entropy Retrieval Based model is proposed and also an Attitude Modeling based on Popitz Powers. The Conditional Entropy Model and the Attitude Model are combined in order to record Attitude Changes in Dialogue Interactions between Patients and Doctors.

Note keeping is an indispensable ingredient of successful research. Although traditionally performed on paper, recently the task is increasingly facilitated by Electronic Lab Notebooks, i.e., ICT programs that allow their users to make electronic observation in laboratory contexts. Owing to recent advances in mobile technologies, Smartphones and Tablets have emerged as promising alternatives for electronic note keeping.

Electronic Health Records (EHR) are structured and unstructured data generated during routine clinical care. EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the pace of translation and enable precision medicine at scale. One of the main use-cases of EHR is creating algorithms to define disease status, onset and severity from diagnoses, prescriptions, laboratory tests, symptoms and other EHR elements - a process known as phenotyping.

Three components are embedded in relearning of motor skills: error detection, training to reduce errors, and memory consolidation of the improved motor execution. We integrate here three components that we previously reported to suggest a three stage paradigm for error detection via EEG, correction through BCI and memory consolidation of the corrected motor motion through neurofeedback memory consolidation. In our earlier work we that the characteristics of the error potential is associated with the type of error, rather than being generic, by that providing a BCI system for error correction.