Search

The computer-based approach is required for the non-invasive detection of chronic liver diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we review the computer-aided diagnosis of hepatic lesions in view of diffuse- and focal liver disorders. This survey mainly focuses on three image acquisition modalities: ultrasonography, computed tomography, and magnetic resonance imaging. We present the insightful analysis with pros and cons for each preliminary step, particularly preprocessing, attribute analysis, and classification techniques to accomplish clinical diagnostic tasks. In preprocessing, we explore and compare commonly used denoising, deblurring and segmentation methods. Denoising is mainly performed with nonlinear models. In contrast, deep neural networks are frequently applied for deblurring and automatic segmentation of region-of-interest. In attribute analysis, the most common approach comprises texture properties. For classification, the support vector machine is mainly utilized across three image acquisition modalities. However, comparative analysis shows the best performance is obtained by deep learning-based convolutional neural networks. Considering biopsy samples or pathological factors such as overall stage, margin, and differentiation can be helpful for improving the prediction performance. In addition, technique breakthrough is expected soon with advances in machine learning models to address data limitation problems and improve the prediction performance.

A state-of-the-art brain&ndash, computer interface (BCI) system includes brain signal acquisition, noise removal, channel selection, feature extraction, classification, and an application interface. In functional near-infrared spectroscopy-based BCI (fNIRS-BCI) channel selection may enhance classification performance by identifying suitable brain regions that contain brain activity. In this study, the z-score method for channel selection is proposed to improve fNIRS-BCI performance. The proposed method uses cross-correlation to match the similarity between desired and recorded brain activity signals, followed by forming a vector of each channel&rsquo, s correlation coefficients&rsquo, maximum values. After that, the z-score is calculated for each value of that vector. A channel is selected based on a positive z-score value. The proposed method is applied to an open-access dataset containing mental arithmetic (MA) and motor imagery (MI) tasks for twenty-nine subjects. The proposed method is compared with the conventional t-value method and with no channel selected, i.e., using all channels. The z-score method yielded significantly improved (p &lt, 0.0167) classification accuracies of 87.2 &plusmn, 7.0%, 88.4 &plusmn, 6.2%, and 88.1 &plusmn, 6.9% for left motor imagery (LMI) vs. rest, right motor imagery (RMI) vs. rest, and mental arithmetic (MA) vs. rest, respectively. The proposed method is also validated on an open-access database of 17 subjects, containing right-hand finger tapping (RFT), left-hand finger tapping (LFT), and dominant side foot tapping (FT) tasks.The study shows an enhanced performance of the z-score method over the t-value method as an advancement in efforts to improve state-of-the-art fNIRS-BCI systems&rsquo, performance.

Functional near-infrared spectroscopy (fNIRS) is one of the latest noninvasive brain function measuring technique that has been used for the purpose of brain-computer interfacing (BCI). In this paper, we compare and analyze the effect of six most commonly used filtering techniques (i.e., Gaussian, Butterworth, Kalman, hemodynamic response filter (hrf), Wiener, and finite impulse response) on classification accuracies of fNIRS-BCI. To conclude with the best optimal filter for a specific cortical task owing to a specific cortical region, we divided our experimental tasks according to the three main cortical regions: prefrontal, motor, and visual cortex. Three different experiments were performed for prefrontal and motor execution tasks while one for visual stimuli. The tasks performed for prefrontal include rest (R) vs mental arithmetic (MA), R vs object rotation (OB), and OB vs MA. Similarly, for motor execution, R vs left finger tapping (LFT), R vs right finger tapping (RFT), and LFT vs RFT. Likewise, for the visual cortex, R vs visual stimuli (VS) task. These experiments were performed for ten trials with five subjects. For consistency among extracted data, six statistical features were evaluated using oxygenated hemoglobin, namely, slope, mean, peak, kurtosis, skewness, and variance. Combination of these six features was used to classify data by the nonlinear support vector machine (SVM). The classification accuracies obtained from SVM by using hrf and Gaussian were significantly higher for R vs MA, R vs OB, R vs RFT, and R vs VS and Wiener filter for OB vs MA. Similarly, for R vs LFT and LFT vs RFT, hrf was found to be significant p<0.05. These results show the feasibility of using hrf for effective removal of noises from fNIRS data.

This chapter presents a functional near-infrared spectroscopy study to differentiate between drowsy and active states in drivers. Passive brain signals were acquired from the dorsolateral prefrontal cortex of five healthy subjects. After preprocessing the acquired signals, mean and slope values were extracted as features for classification. A support vector machine (SVM) and linear discriminant analysis (LDA) were used as classifiers to test and train the data. The average classification accuracies acquired using SVM and LDA were 74.3 ± 2.5% and 73.0 ± 2.7%, respectively, using signal mean and signal peak calculated within a 0–7 s time window. The results demonstrate the suitability of the proposed method for drowsiness detection with minimum delay, thereby adding another attribute for driver safety.

Objective. In this paper, a novel methodology for feature extraction to enhance classification accuracy of functional near-infrared spectroscopy (fNIRS)-based two-class and three-class brain–computer interface (BCI) is presented. Approach. Novel features are extracted using vector-based phase analysis method. Changes in oxygenated Δ H b O and de-oxygenated ( Δ H b R ) haemoglobin are used to calculate four novel features: change in cerebral blood volume ( Δ C B V ), change in cerebral oxygen exchange ( Δ C O E ), vector magnitude (|L|) and angle (k). Δ C B V is the sum and Δ C O E is difference of Δ H b O and Δ H b R , whereas |L| is magnitude and k is angle of vector. fNIRS signals of seven healthy subjects, corresponding to left-hand index finger tapping (LFT), right-hand index finger tapping (RFT) and rest are acquired from motor cortex using multi-channel continuous-wave imaging system. After removing physiological and instrumental noises from the acquired signals, the four novel features are calculated. For validation, conventional temporal, spatial and spatiotemporal features; mean, peak, slope, variance, kurtosis and skewness are also calculated using Δ H b O and Δ H b R . All possible two-feature and three-feature combinations of the novel and conventional features are then used to classify two-class (LFT vs RFT) and three-class (LFT vs RFT vs rest) fNIRS-BCI using linear discriminant analysis. Main results. Results demonstrate that combination of four novel features yields significantly higher average classification accuracies of 98.7 ± 1.0% and 85.4 ± 1.4% as compared to 68.7 ± 6.9% and 53.6 ± 10.6% using conventional features for two-class and three-class problem, respectively. Validation of proposed method on an open access database containing RFT, LFT and dominant side foot tapping tasks for 30 subjects also shows improvement in average classification accuracies for two-class and three-class fNIRS-BCIs. Significance. This study provides a step forward in improving the classification accuracies of state-of-the-art fNIRS-BCIs by showing significant improvement in classification accuracies of two-class and three-class fNIRS-BCIs using novel features extracted by vector-based phase analysis.

Research on robotic hand for prosthesis is widely being carried out. This paper attempts to provide an enhanced control of individual fingers of robotic hand using electromyography (EMG). Eight-channel surface EMG is to acquire signals from widest part of forearm of 10 subjects. These signals are corresponding to movement of five individual fingers, which are thumb, index finger, middle finger, ring finger, and little finger. Features are extracted from these signals and Deep Neural Network is applied as classifier to distinguish between the five signals with an average accuracy of around 95%. The classified signal is then interfaced with the robotic hand to perform mimicking of individual finger motion. This work can be effectively used for hand rehabilitation with flexibility of actively controlling individual finger motion.

Shortage of electricity is one of the main problem in the development of rural areas in Pakistan. Photovoltaic techniques are generally attaining an escalating attention and they're to become very competitive alternative, additionally, environmental issues such as population and global warming impact are attracting experts towards green electricity sources such as solar systems. A standout amongst the most imperative provisions of photovoltaic frameworks is for water pumping, especially in rural areas that have a considerable amount of solar radiation and have no access to national grids. They are generally utilized within household and animals water supplies and small-scale irrigation systems [1]. Water pumping from a PV array is a valid option to pollution-generating diesel and human-powered water pumps. PV-array water pumping can be fulfilled with or without a backup module. With a backup module, energy generated by the sun can be stored in the backup; hence, the application of the water pump can be fulfilled, even to a cloudy day, or at night to operate small loads. The application of photovoltaic is increasing in rural areas due to shortage, unavailability and costly electricity. Solar powered water pumps are commonly used in agriculture and residential level. This designed model deals with solar powered submersible pump. Among many available schemes, it consists of a PV panel, a storing backup, a variable-frequency inverter, a charge controller and induction motor coupled with a water pump. The inverter drives the induction motor, which drives the water pump. To obtain maximumoutput power of the Solar panel, the inverter is operated at variable frequency or soft start to minimize stall current of induction motor [5]. This designed model powers 0.75hp water pump. Different types of controllers can be used to increase frequency gradually from 0 to 50 Hz with 5 sec of delay each, thus voltage is controlled from 0 to 220V AC.

Background In this paper, a novel functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI) framework for control of prosthetic legs and rehabilitation of patients suffering from locomotive disorders is presented. Methods fNIRS signals are used to initiate and stop the gait cycle, while a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control the torques of hip and knee joints for minimization of position error. In the present study, the brain signals of walking intention and rest tasks were acquired from the left hemisphere’s primary motor cortex for nine subjects. Thereafter, for removal of motion artifacts and physiological noises, the performances of six different filters (i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass, finite impulse response) were evaluated. Then, six different features were extracted from oxygenated hemoglobin signals, and their different combinations were used for classification. Also, the classification performances of five different classifiers (i.e. k-Nearest Neighbour, quadratic discriminant analysis, linear discriminant analysis (LDA), Naïve Bayes, support vector machine (SVM)) were tested. Results The classification accuracies obtained from SVM using the hrf were significantly higher (p

In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class (motor imagery and rest; mental rotation and rest) brain-computer interface (BCI) is presented. First, fNIRS signals corresponding to motor imagery and mental rotation are acquired from the motor and prefrontal cortex respectively afterwards, filtered to remove physiological noises. Then, the signals are modelled using the general linear model (GLM), the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for motor imagery versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for mental rotation versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine (SVM). These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher (p < 0.05). These results serve to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.

In this paper, a novel technique for determination of the optimal feature combinations and, thereby, acquisition of the maximum classification performance for a functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI), is proposed. After obtaining motor-imagery and rest signals from the motor cortex, filtering is applied to remove the physiological noises. Six features (signal slope, signal mean, signal variance, signal peak, signal kurtosis and signal skewness) are then extracted from the oxygenated hemoglobin (HbO). Afterwards, the hybrid genetic algorithm (GA)-support vector machine (SVM) is applied in order to determine and classify 2- and 3-feature combinations across all subjects. The SVM classifier is applied to classify motor imagery versus rest. Moreover, four time windows (0-20s, 0-10s, 11-20s and 6-15s) are selected, and the hybrid GA-SVM is applied in order to extract the optimal 2- and 3-feature combinations. In the present study, the 11-20s time window showed significantly higher classification accuracies - the minimum accuracy was 91% - than did the other time windows (p

One of the pivotal issues which must be tackled when an effective brain-computer interface (BCI) is to be designed, is to reduce the enormous space of features extracted from fNIRS signals. BCI researchers often use genetic algorithms (GA) as the technique to extract features. The classic genetic algorithm obtains a feature set with the high classification accuracy; however, it is unable to create a reliable classifier due to the large dimensionality of this set. Herein, we propose a refined version of the genetic algorithm, which chooses a small number of features and determines the optimal feature combination for classification of fNIRS signals for a BCI system.