Identifying relevant asymmetry features of EEG for emotion processing.

The left and right hemispheres of the brain process emotion differently. Neuroscientists have proposed two models to explain this difference. The first model states that the right hemisphere is dominant over the left to process all emotions. In contrast, the second model states that the left hemisphere processes positive emotions, whereas the right hemisphere processes negative emotions. Previous studies have used these asymmetry models to enhance the classification of emotions in machine learning models. However, little research has been conducted to explore how machine learning models can help identify associations between hemisphere asymmetries and emotion processing. To address this gap, we conducted two experiments using a subject-independent approach to explore how the asymmetry of the brain hemispheres is involved in processing happiness, sadness, fear, and neutral emotions. We analyzed electroencephalogram (EEG) signals from 15 subjects collected while they watched video clips evoking these four emotions. We derived asymmetry features from the recorded EEG signals by calculating the log ratio between the relative energy of symmetrical left and right nodes. Using the asymmetry features, we trained four binary logistic regressions, one for each emotion, to identify which features were more relevant to the predictions. The average AUC-ROC across the 15 subjects was 56.2, 54.6, 51.6, and 58.4% for neutral, sad, fear, and happy, respectively. We validated these results with an independent dataset, achieving comparable AUC-ROC values. Our results showed that brain lateralization was observed primarily in the alpha frequency bands, whereas for the other frequency bands, both hemispheres were involved in emotion processing. Furthermore, the logistic regression analysis indicated that the gamma and alpha bands were the most relevant for predicting emotional states, particularly for the lateral frontal, parietal, and temporal EEG pairs, such as FT7-FT8, T7-T8, and TP7-TP8. These findings provide valuable insights into which brain areas and frequency bands need to be considered when developing predictive models for emotion recognition.

Epidemiology-based Task Assignment Algorithm for Distributed Systems.

OBJECTIVE: Design task assignment algorithms based on the patterns of disease spread among the population. SCOPE: Epidemiology studies spatiotemporal patterns of illness in populations and the factors affecting it. An epidemic emerges out of the population activities and environment. Task assignment is a common activity in many realms where sub-tasks are created, delegated, and collectively carried out to achieve the original task. Due to its complexity and context, task assignment can be a challenging activity that can result in limited outcomes. This research studies task assignment as an epidemic assigned to a distributed system. We have developed computational models to understand the outbreak of aerosol-borne diseases by using the agent-based modelling approach. Experiments are carried out to observe the patterns of emergence during the spread of disease among the individuals and get insights of their mechanisms. These mechanisms are used to design algorithms for task assignment on distributed systems. RESULTS: Understanding the emergent behaviour of diseases can provide the platform for the development of distributed algorithms that can be helpful in overcoming some of the challenges of task assignment in a distributed system.

A parallel method to improve medical image transmission.

The staggering number of images acquired by modern modalities requires new approaches for medical data transmission. There have been several attempts to improve data transmission time between medical imaging systems. These attempts were mostly based on compression. Although the compression methods can help in many cases, they are sometimes ineffectual in high-speed networks. This paper introduces parallelism to provide an effective method of medical data transmission over both local area network (LAN) and wide area network (WAN). It is based on the Digital Imaging and Communications in Medicine (DICOM) protocol and uses parallel TCP connections in storage services within the protocol. Using the proposed interface in our method, current medical imaging applications can take advantage of parallelism without any modification. Experimental results show a speedup of about 1.3 to 1.5 for CT images and relatively high speedup of about 2.2 to 3.5 times for magnetic resonance (MR) images over LAN. The transmission time is improved drastically over WAN. The speedup is about 16.1 for CT images and about 5.6 to 11.5 for MR images.

Electromagnetic interference-aware transmission scheduling and power control for dynamic wireless access in hospital environments.

We study the multiple access problem for e-Health applications (referred to as secondary users) coexisting with medical devices (referred to as primary or protected users) in a hospital environment. In particular, we focus on transmission scheduling and power control of secondary users in multiple spatial reuse time-division multiple access (STDMA) networks. The objective is to maximize the spectrum utilization of secondary users and minimize their power consumption subject to the electromagnetic interference (EMI) constraints for active and passive medical devices and minimum throughput guarantee for secondary users. The multiple access problem is formulated as a dual objective optimization problem which is shown to be NP-complete. We propose a joint scheduling and power control algorithm based on a greedy approach to solve the problem with much lower computational complexity. To this end, an enhanced greedy algorithm is proposed to improve the performance of the greedy algorithm by finding the optimal sequence of secondary users for scheduling. Using extensive simulations, the tradeoff in performance in terms of spectrum utilization, energy consumption, and computational complexity is evaluated for both the algorithms.

IEEE 802.15.4 MAC with GTS transmission for heterogeneous devices with application to wheelchair body-area sensor networks.

In wireless personal area networks, such as wireless body-area sensor networks, stations or devices have different bandwidth requirements and, thus, create heterogeneous traffics. For such networks, the IEEE 802.15.4 medium access control (MAC) can be used in the beacon-enabled mode, which supports guaranteed time slot (GTS) allocation for time-critical data transmissions. This paper presents a general discrete-time Markov chain model for the IEEE 802.15.4-based networks taking into account the slotted carrier sense multiple access with collision avoidance and GTS transmission phenomena together in the heterogeneous traffic scenario and under nonsaturated condition. For this purpose, the standard GTS allocation scheme is modified. For each non-identical device, the Markov model is solved and the average service time and the service utilization factor are analyzed in the non-saturated mode. The analysis is validated by simulations using network simulator version 2.33. Also, the model is enhanced with a wireless propagation model and the performance of the MAC is evaluated in a wheelchair body-area sensor network scenario.

An EMI-aware prioritized wireless access scheme for e-Health applications in hospital environments.

Wireless communications technologies can support efficient healthcare services in medical and patient-care environments. However, using wireless communications in a healthcare environment raises two crucial issues. First, the RF transmission can cause electromagnetic interference (EMI) to biomedical devices, which could critically malfunction. Second, the different types of electronic health (e-Health) applications require different quality of service (QoS). In this paper, we introduce an innovative wireless access scheme, called EMI-aware prioritized wireless access, to address these issues. First, the system architecture for the proposed scheme is introduced. Then, an EMI-aware handshaking protocol is proposed for e-Health applications in a hospital environment. This protocol provides safety to the biomedical devices from harmful interference by adapting transmit power of wireless devices based on the EMI constraints. A prioritized wireless access scheme is proposed for channel access by two different types of applications with different priorities. A Markov chain model is presented to study the queuing behavior of the proposed system. Then, this queuing model is used to optimize the performance of the system given the QoS requirements. Finally, the performance of the proposed wireless access scheme is evaluated through extensive simulations.

Modeling of workflow-engaged networks on radiology transfers across a metro network.

Radiology metro networks bear the challenging proposition of interconnecting several hospitals in a region to provide a comprehensive diagnostic imaging service. Consequences of a poorly designed and implemented metro network could cause delays or no access at all when health care providers try to retrieve medical cases across the network. This could translate into limited diagnostic services to patients, resulting in negative impacts to the patients' medical treatment. A workflow-engaged network (WEN) is a new network paradigm. A WEN appreciates radiology workflows and priorities in using the network. A WEN greatly improves the network performance by guaranteeing that critical image transfers experience minimal delay. It adjusts network settings to ensure the application's requirements are met. This means that high-priority image transfers will have guaranteed and known delay times, whereas lower-priority traffic will have increased delays. This paper introduces a modeling to understand the benefits that WEN brings to a radiology metro network. The modeling uses actual data patterns and flows found in a hospital metro region. The workflows considered are based on the Integrating the Healthcare Enterprise profiles. This modeling has been applied to metropolitan workflows of a health region. The modeling helps identify the kind of metro network that supports data patterns and flows in a metro area. The results of the modeling show that a 155-Mb/s metropolitan area network (MAN) with WEN operates virtually equal to a normal 622-Mb/s MAN without WEN, with potential cost savings for leased line services measured in the millions of dollars per year.

A cost-effective web-based teaching file system.

Teaching files are common in radiology. Although there is an increasing role of digital technology in radiology departments, today's teaching files have not yet seen the application of this new technology. This may have been due in part to poor or incomplete implementation in many commercial software packages. We have demonstrated that by utilizing free software from the Internet, a web-based teaching file system, which is easy to use, low cost, and secure, can be created.

Security middleware infrastructure for DICOM images in health information systems.

In health care, it is mandatory to maintain the privacy and confidentiality of medical data. To achieve this, a fine-grained access control and an access log for accessing medical images are two important aspects that need to be considered in health care systems. Fine-grained access control provides access to medical data only to authorized persons based on priority, location, and content. A log captures each attempt to access medical data. This article describes an overall middleware infrastructure required for secure access to Digital Imaging and Communication in Medicine (DICOM) images, with an emphasis on access control and log maintenance. We introduce a hybrid access control model that combines the properties of two existing models. A trust relationship between hospitals is used to make the hybrid access control model scalable across hospitals. We also discuss events that have to be logged and where the log has to be maintained. A prototype of security middleware infrastructure is implemented.