IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING
Year:2018 | Volume:12 | Issue:2 |Papers Count:7

The extracellular recording from the brain's single neurons is known as a popular method in neuroscience and neuro-rehabilitation engineering. These recordings include the activity of all neurons around the electrode, for better use of which, spike sorting methods should be utilized to obtain the activity of single neurons. Based on the structural properties of the neuron, such as its dendritic tree, and the distance and direction of it relative to the electrode, it can be claimed that the form of its spike waveform is unique and constant. However, spike sorting under low signal-to-noise ratio (SNR) conditions is always accompanied with challenges. A spike sorting algorithm usually consists of three sections including the spike detection, feature extraction, and classification. In this paper, a method based on optimization of continuous wavelet coefficients is presented which is effective in low SNR values. In the proposed method, after the calculation of the parameterized wavelet coefficients, using the Euclidean distance and the area under the receiver operator characteristic curve, the best parameters were chosen to increase the separation of the features, so that a suitable scale was first found with the Euclidean distance criterion and then the translation parameter was obtained with the second criterion. In this research k-means algorithm was used for the clustering as a simple but efficient method. For evaluation, three simulated data sets were made in 9 different SNRs with a modeled background noise. The obtained results from simulated data showed that the optimization of parameters in continuous wavelet transform using the proposed algorithm could effectively improve the spike sorting performance compared to principal component analysis method.

Over the past few decades, the brain-computer interfaces (BCI) based on motor imagery has been widely developed to help people with motor disability. The advantage of this type of BCI as an endogenous system is, no need for external stimulation, and natural control. One of the major challenges to make these systems practical is to reduce the number of recording electrodes. In this study, only two EEG channels (C3 and C4) were used for detecting the imagery of left and right-hand movements. The features used were band powers (BP), some time domain parameters (TDP) and an adaptive autoregressive model (AAR). For classification, linear discriminant analysis (LDA), a well-known and simple classifier was used. The data was taken from the third BCI Competition. Our results confirm that BP features provide the most robust and effective features for accurate recognition. It was shown that combining the BP with TDP and AAR features can improve the accuracy of classification. However, implementing BP and TDP features is proposed for online classification where short computational cost is important. A maximum steepness of the mutual information (STMI) of 0. 2582 was achieved in this study that could win the second place in the BCI Competition III. Left and right motor imagery (MI) tasks can be discriminated with an average classification accuracy of 85% and Kappa of 70%.

Early alterations of functional connectivity (FC) within the default mode network (DMN) have been reported in Alzheimer’ s disease (AD). Recently, the resting-state brain networks have been described with non-stationary profiles since inter-and intra-FC of the brain networks changes over time, even at rest. To fully understand the FC changes that characterize AD, the underlying change of its dynamic pattern needs to be captured. The purpose of this study was to evaluate dynamic FC (dFC) patterns within the DMN in patients with AD relative to healthy aging. Here, a sparse logistic regression (SLR) model was employed to estimate the dFC networks in patients with AD (n = 24) compared with healthy control group (n = 37) using resting-state functional magnetic resonance imaging (rs-fMRI) data. To analyze the dFC network, we introduced a temporal variability-functional pattern (TV-FP) score, which shows how the functional pattern of a given region changes over time. This score is able to quantify the temporal patterns of regions involved in a dFC network. We compared TV-FP score across groups. The results indicate that the main regions of DMN, such as the anterior medial prefrontal cortex (aMPFC) and lateral temporal cortex (LTC), are associated with a significantly increased TV-FP score in the AD group when compared to the HC group. The FC pattern of these regions is impaired in AD according to a conventional static functional connectivity (sFC) analysis. These findings may suggest that aMPFC and LTC may tend to reorganize their functional pattern to compensate for the related functional deficiency due to AD.

For patients with chronic pulmonary disease, artificial lungs to which right ventricular pumps blood flow is considered as a bridge to lung transplantation. The performance of this device is measured by several criteria, including the efficiency of the device in gas exchange, non-damage to blood cells and low impedance compared to normal lung. In this study, the non-Newtonian blood flow around arrays of hollow fibers, as a model of fiber bundles in artificial lungs, was numerically investigated by finite volume. Two types of square and diagonal arrangements for fibers were considered to examine the effect of arrangement, besides the inlet velocity effect on the flow distribution, shear stress and the exchanged oxygen concentration between the surface of the fibers and the blood stream. It was observed that the flow velocity and shear stress in the diagonal arrangement were far more than the square arrangement that for the maximum velocity (10/87 cm/s), the shear stress on the fibers in the diagonal arrangement was about 3. 5 times that of the square arrangement. Also, there was a significant difference between the results of this analysis and the results of other studies in which oxygen exchange was ignored, which illustrates the importance of gas exchange modeling. As a measure of the efficiency of the device, from the viewpoint of gas exchange, the mass flow rate of oxygen was investigated in the output of the domain. As a result, the diagonal arrangement is much more efficient in oxygen exchange. However, there was a higher pressure drop across the fibers, for a diagonal arrangement, in comparison with the square arrangement. The results of this simulation can be a good starting point for optimal artificial lung design and can be effective in optimizing the design of clinical trials.

Automated 3D breast ultrasound (ABUS) is a novel system for breast screening. It has been proposed as a supplementary modality to mammography for detection and diagnosis of breast cancers. Although ABUS has better performance for dense breasts, reading ABUS images is time-consuming and exhausting. A computer-aided detection (CAD) system can be helpful for interpretation of ABUS images. Mass Segmentation in CADe and CADx systems play the leading role because it affects the performance of succeeding stages. Besides, it is a very challenging task because of the vast variety in size, shape, and texture of masses. Moreover, imbalanced datasets make segmentation harder. A novel mass segmentation approach based on deep learning is introduced in this paper. The deep network that is used in this study for image segmentation is inspired by U-net which has been used broadly for dense segmentation in recent years. Performance was determined using a dataset of 50 masses including 38 malignant and 12 benign masses.

Nowadays, implantable electrical neural stimulation is extensively used to treat or alleviate certain brain-related health conditions, such as in deep brain stimulation (DBS) or in vagus nerve stimulation (VNS). In this paper, we present a digital controller block, designed for a neuroelectrical stimulator chip dedicated for a brain implant. The presented design is very power and area-efficient and provides a great flexibibity in programming the specifications of the stimulation pulses. The duration of each stimulation pulse can programmed to be from 4 µ s to 4 ms, and the amplitude of each pulse could be from 4 µ A to 1 mA. The stimulation pulses could be either monophasic or biphasic, In addition, in biphasic stimulation, the priority of the cathodic pulse over the anodic pulse, or vice versa, could be pragrammed. The interphase delay between the anodic and cathodic phases could be programmed to be between 4 µ s and 512 µ s. The controller controls 16 stimulation sites, four of which can be stimulated simoultaneualy. The 16 stimulation sites are divided into four groups, each of which is stimulated by a current-controlled stimulation circuit. Each stimulation circuit is controlled by a local digital controller (LDC), which receives its data from a global digital controller (GDC). The designed controller blocks have been implemented and tested on a Spartan-6 field-programmable gate array (FPGA) board, before being implemented as an application-specific integrated circuit (ASIC) layout. The ASIC circuit has been designed using 0. 18-µ m CMOS technology. Based on the layout, each LDC occupies an area of 19, 160 µ m2 and consumes 12 µ W of power from a 1. 8V supply. On the other hand, the GDC takes up an area of 4, 246 µ m2 and consumes 8. 2 µ W of power. We have also created a graphical user interface (GUI) to be able to program the stinulation chip.

Steady state somatosensory evoked potential (SSSEP) is one of the control signals of brain-computer interfaces (BCI), based on the reflection of skin vibrational stimulation with specific frequencies in brain signals. BCI systems based on SSSEP do not cause visual fatigue in comparison with SSVEP based BCI systems, and they can be used for locked-in or amyotrophic lateral sclerosis (ALS) patients. So far, few studies have been done on SSSEP and its applications in BCI systems, because the hardware implementation of this system is challenging. In this paper, a vibrational stimulation device based on vibrational motor has been developed. This device has two separate output channels for applying vibrational stimulation to two different points of the body. The output frequency of each channel is adjustable in the range of 15 to 35 Hz with a step of 1 Hz. All parts of the device and the actuators have been shielded to prevent the emission of electromagnetic noise.