Neuroscience and Biomedical Engineering (Discontinued) - Volume 2, Issue 1, 2014

Vital signs are often considered as critical information to assess initial health condition and underlying health issues. Vital signs can contribute towards early detection, early diagnosis and risk reduction of fatal incidents. Today’s advanced monitoring systems incorporate the balanced combination of clinical and technological aspects to give an innovative healthcare outcome. Vital signs monitoring systems are rapidly becoming the core of today’s healthcare deliveries. The paradigm shifted from traditional and manual recording to computer based electronic records and further to smartphones as versatile and innovative healthcare monitoring systems. In this paper, the vital signs monitoring systems are classified as wearable, wireless and mobile monitoring systems and patient acceptability of some of these systems has been evaluated using 30 participants. Moreover, a comprehensive review of related literature in the context of acceptability, mobility, reliability and efficiency of vital signs monitoring systems in healthcare delivery and handling physiological measurements is presented. The outcome of this study indicates that despite some limitations commented by patients and clinicians, these systems should be more compact and simple to operate and they should be available to healthcare professionals with minimum interruption to normal daily life activities (ADLs).

This work presents a method for early detection of epileptic seizures from EEG data, taking into account information about both the temporal and the spatial evolution of the seizures. The system was designed using over 8 hours of EEG, including 10 seizures in 5 patients. Seizure detection was accomplished in three main stages: multiresolution overcomplete decomposition by the à-trous algorithm, feature extraction by computing power spectral density and sample entropy values of subbands and detection by using z-test and support vector machines (SVM). Results highlight large differences between the sub-band sample entropy values during ictal and normal EEG epochs, respectively, reveling a substantial increase of such parameter during the seizure. This enables high detection accuracy and specificity especially in beta and gamma bands (16-125 Hz). The detection performance of the proposed method was evaluated based on the ground truth provided by the expert neurophysiologist, and the results show that our technique is capable to obtain a high accuracy (above the 95% on average), with a high temporal resolution. This enables reaching very low detection latency and early detection of the seizures onset. Furthermore, spatial information, within the limits of the acquisition, on the evolution of the seizure is maintained since all the channels are separately processed.

Top-down attention to spatial and temporal cues has been thoroughly studied in the visual and auditory domains. However, the neural systems that are important for tactile top-down temporal attention (i.e., attention based on time interval cues) remain undefined. Thus, the differences in brain activity between directed attention to tactile spatial location and time intervals are unclear. We measured brain activity during a task in sixteen healthy volunteers with functional magnetic resonance imaging (fMRI). The task manipulated cued attention to spatial locations (S) and temporal intervals (T) in a factorial design. Symbolic central cues oriented the subjects toward S only (left or right), toward T only (600 msec or 1800 msec), or toward both S and T simultaneously (no information was provided regarding S or T). The behavioral data indicated that the benefits and costs of performance during temporal attention were similar to those established for tactile spatial attention. The brain-imaging data revealed a partial overlap between neural systems involved in the performance of spatial versus temporal orientation of tactile attention tasks.

Pain is a well-known non-motor symptom of Parkinson’s disease (PD) and might be related to not only peripheral factors but also abnormal processing in the central nervous system (CNS). The aim of this study is to investigate dysfunction of the central processing of pain, and examine the relationships between abnormal processing of pain and motor or non-motor symptoms of PD. To induce pain-related evoked potentials in 23 PD patients and 12 healthy controls, we activated Aδ fibers using a push-pin type needle electrode inserted in the epidermis. Evoked potentials were recorded from the Cz electrode at the index finger and second toe. The Hoehn-Yahr stage, Unified Parkinson’s Disease Rating Scale (UPDRS), Self-rating Depression Scale (SDS), and Mini Mental State Examination (MMSE) were evaluated. In addition, MIBG myocardial scintigraphy was performed and early and delayed heart-to-mediastinum (H/M) ratios were examined. The N1-P1 peak-to-peak amplitudes, which are thought to originate from the anterior cingulate cortex and insula, were significantly lower in PD patients than in the controls for both the upper and lower limbs (both P<0.01). The N1-P1 amplitudes for the upper limbs were significantly correlated with the H/M ratio (P<0.05). The N1-P1 amplitudes did not correlate with the severity of clinical parameters such as the Hoehn-Yahr stage or UPDRS and SDS or MMSE. These results may reflect abnormal central processing of pain in PD patients, which appears to be independent of the clinical features and severity of motor and nonmotor symptoms except for degeneration of the myocardial sympathetic nerve.

To deal with the problem of detecting driver fatigue, we propose a vigilance estimation method using electrooculogram (EOG) and investigate the correlation between multiple EOG features and vigilance. We examine four kinds of features extracted from EOG: slow eye movement (SEM), saccade, blink, and EOG energy. First, we introduce three eye movement detection algorithms to identify SEMs, saccades, and blinks from EOGs. Then we define and extract twenty different features from those eye movements. Second, the features are processed by a linear dynamic system (LDS) approach, which can effectively remove noises and some EOG components that are irrelevant to vigilance. Finally, we analyze the de-noised features with the vigilance reference obtained from a monotonous visual task. Our experimental results on an EOG data set of twentytwo subjects indicate that combination of SEMs, saccades, blinks, and energy features of EOGs has a high correlation coefficient with the vigilance references, up to the average 0.75. This work provides a potent support to developing vigilance estimation system based on multiple EOG features for detecting driver fatigue.