A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.

High-Matching and Low-Cost Realization of the FHN Neuron Model on Reconfigurable FPGA Board.

The main objectives of neuromorphic engineering are the research, modeling, and implementation of neural functioning in the human brain. We provide a hardware solution that can replicate such a nature-inspired system by merging multiple scientific domains and is based on neural cell processes. This work provides a modified version of the original Fitz-Hugh Nagumo (FHN) neuron using a simple 2V term called Hybrid Piece-Wised Base-2 Model (HPWBM), which accurately reproduces numerous patterns of the original neuron model. With reduced terms, we suggest modifying the original nonlinear term to achieve high matching accuracy and little computing error. Time domain and phase portraits are used to validate the proposed model, which shows that it can reproduce all of the FHN model's properties with high accuracy and little mistake. We provide an effective digital hardware approach for large-scale neuron implementations based on resource-sharing and pipelining strategies. The Hardware Description Language (HDL) is used to construct the hardware on an FPGA as a proof of concept. The recommended model hardly uses 0.48 percent of the resources on a Virtex 4 FPGA board, according to the results of the hardware implementation. The circuit can run at a maximum frequency of 448.236 MHz, according to the static timing study.

Application of MLP neural network to predict X-ray spectrum from tube voltage, filter material, and filter thickness used in medical imaging systems.

The X-ray energy spectrum is crucial for image quality and dosage assessment in mammography, radiography, fluoroscopy, and CT which are frequently used for the diagnosis of many diseases including but not limited to patients with cardiovascular and cerebrovascular diseases. X-ray tubes have an electron filament (cathode), a tungsten/rubidium target (anode) oriented at an angle, and a metal filter (aluminum, beryllium, etc.) that may be placed in front of an exit window. When cathode electrons meet the anode, they generate X-rays with varied energies, creating a spectrum from zero to the electrons' greatest energy. In general, the energy spectrum of X-rays depends on the electron beam's energy (tube voltage), target angle, material, filter thickness, etc. Thus, each imaging system's X-ray energy spectrum is unique to its tubes. The primary goal of the current study is to develop a clever method for quickly estimating the X-ray energy spectrum for a variety of tube voltages, filter materials, and filter thickness using a small number of unique spectra. In this investigation, two distinct filters made of beryllium and aluminum with thicknesses of 0.4, 0.8, 1.2, 1.6, and 2 mm were employed to obtain certain limited X-ray spectra for tube voltages of 20, 30, 40, 50, 60, 80, 100, 130, and 150 kV. The three inputs of 150 Multilayer Perceptron (MLP) neural networks were tube voltage, filter type, and filter thickness to forecast the X-ray spectra point by point. After training, the MLP neural networks could predict the X-ray spectra for tubes with voltages between 20 and 150 kV and two distinct filters made of aluminum and beryllium with thicknesses between 0 and 2 mm. The presented methodology can be used as a suitable, fast, accurate and reliable alternative method for predicting X-ray spectrum in medical applications. Although a technique was put out in this work for a particular system that was the subject of Monte Carlo simulations, it may be applied to any genuine system.

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications.

This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where distributed into; GBO variants, GBO applications, and evaluate the efficiency of GBO compared with other metaheuristic algorithms. Finally, the conclusions concentrate on the existing work on GBO, showing its disadvantages, and propose future works. The review paper will be helpful for the researchers and practitioners of GBO belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

Assessing toxicant emissions from e-liquids with DIY additives used in response to a potential flavour ban in e-cigarettes.

SIGNIFICANCE: Electronic cigarettes (e-cigarettes) aerosolise liquids that contain nicotine, propylene glycol, glycerol and appealing flavours. In the USA, regulations have limited the availability of flavoured e-cigarettes in pod-based systems, and further tightening is expected. In response, some e-cigarette users may attempt to make their e-liquids (do-it-yourself, DIY). This study examined toxicant emissions from several aerosolised DIY e-liquids. METHODS: DIY additives were identified by reviewing users' responses to a hypothetical flavour ban, e-cigarette internet forums and DIY mixing internet websites. They include essential oils, cannabidiol, sucralose and ethyl maltol. E-liquids with varying concentrations and combinations of additives and tobacco and menthol flavours were prepared and were used to assess reactive oxygen species (ROS), carbonyl and phenol emissions in machine-generated aerosols. RESULTS: Data showed that adding DIY additives to unflavoured, menthol-flavoured or tobacco-flavoured e-liquids increases toxicant emissions to levels comparable with those from commercial flavoured e-liquids. Varying additive concentrations in e-liquids did not have a consistently significant effect on the tested emissions, yet increasing power yielded significantly higher ROS, carbonyl and phenol emissions for the same additive concentration. Adding nicotine to DIY e-liquids with sucralose yielded increase in some emissions and decrease in others, with freebase nicotine-containing e-liquid giving higher ROS emissions than that with nicotine salt. CONCLUSION: This study showed that DIY additives can impact aerosol toxicant emissions from e-cigarettes and should be considered by policymakers when restricting commercially available flavoured e-liquids.

An Edge-Based Selection Method for Improving Regions-of-Interest Localizations Obtained Using Multiple Deep Learning Object-Detection Models in Breast Ultrasound Images.

Computer-aided diagnosis (CAD) systems can be used to process breast ultrasound (BUS) images with the goal of enhancing the capability of diagnosing breast cancer. Many CAD systems operate by analyzing the region-of-interest (ROI) that contains the tumor in the BUS image using conventional texture-based classification models and deep learning-based classification models. Hence, the development of these systems requires automatic methods to localize the ROI that contains the tumor in the BUS image. Deep learning object-detection models can be used to localize the ROI that contains the tumor, but the ROI generated by one model might be better than the ROIs generated by other models. In this study, a new method, called the edge-based selection method, is proposed to analyze the ROIs generated by different deep learning object-detection models with the goal of selecting the ROI that improves the localization of the tumor region. The proposed method employs edge maps computed for BUS images using the recently introduced Dense Extreme Inception Network (DexiNed) deep learning edge-detection model. To the best of our knowledge, our study is the first study that has employed a deep learning edge-detection model to detect the tumor edges in BUS images. The proposed edge-based selection method is applied to analyze the ROIs generated by four deep learning object-detection models. The performance of the proposed edge-based selection method and the four deep learning object-detection models is evaluated using two BUS image datasets. The first dataset, which is used to perform cross-validation evaluation analysis, is a private dataset that includes 380 BUS images. The second dataset, which is used to perform generalization evaluation analysis, is a public dataset that includes 630 BUS images. For both the cross-validation evaluation analysis and the generalization evaluation analysis, the proposed method obtained the overall ROI detection rate, mean precision, mean recall, and mean F1-score values of 98%, 0.91, 0.90, and 0.90, respectively. Moreover, the results show that the proposed edge-based selection method outperformed the four deep learning object-detection models as well as three baseline-combining methods that can be used to combine the ROIs generated by the four deep learning object-detection models. These findings suggest the potential of employing our proposed method to analyze the ROIs generated using different deep learning object-detection models to select the ROI that improves the localization of the tumor region.

Needle detection using ultrasound B-mode and power Doppler analyses.

BACKGROUND: Ultrasound is employed in needle interventions to visualize the anatomical structures and track the needle. Nevertheless, needle detection in ultrasound images is a difficult task, specifically at steep insertion angles. PURPOSE: A new method is presented to enable effective needle detection using ultrasound B-mode and power Doppler analyses. METHODS: A small buzzer is used to excite the needle and an ultrasound system is utilized to acquire B-mode and power Doppler images for the needle. The B-mode and power Doppler images are processed using Radon transform and local-phase analysis to initially detect the axis of the needle. The detection of the needle axis is improved by processing the power Doppler image using alpha shape analysis to define a region of interest (ROI) that contains the needle. Also, a set of feature maps is extracted from the ROI in the B-mode image. The feature maps are processed using a machine learning classifier to construct a likelihood image that visualizes the posterior needle likelihoods of the pixels. Radon transform is applied to the likelihood image to achieve an improved needle axis detection. Additionally, the region in the B-mode image surrounding the needle axis is analyzed to identify the needle tip using a custom-made probabilistic approach. Our method was utilized to detect needles inserted in ex vivo animal tissues at shallow [ 20 ∘ - 40 ∘ $20^\circ -40^\circ$ ), moderate [ 40 ∘ - 60 ∘ $40^\circ -60^\circ$ ), and steep [ 60 ∘ - 85 ∘ $60^\circ -85^\circ$ ] angles. RESULTS: Our method detected the needles with failure rates equal to 0% and mean angle, axis, and tip errors less than or equal to 0.7°, 0.6 mm, and 0.7 mm, respectively. Additionally, our method achieved favorable results compared to two recently introduced needle detection methods. CONCLUSIONS: The results indicate the potential of applying our method to achieve effective needle detection in ultrasound images.

A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning.

The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system's performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system's improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients.

Methods to achieve effective web-based learning management modules: MyGJU versus Moodle.

Several higher education institutions have harnessed e-learning tools to empower the application of different learning models that enrich the educational process. Nevertheless, the reliance on commercial or open-source platforms, in some cases, to deliver e-learning could impact system acceptability, usability, and capability. Therefore, this study suggests design methods to develop effective learning management capabilities such as attendance, coordination, course folder, course section homepage, learning materials, syllabus, emails, and student tracking within a university portal named MyGJU. In particular, mechanisms to facilitate system setup, data integrity, information security, e-learning data reuse, version control automation, and multi-user collaboration have been applied to enable the e-learning modules in MyGJU to overcome some of the drawbacks of their counterparts in Moodle. Such system improvements are required to motivate both educators and students to engage in online learning. Besides, features comparisons between MyGJU with Moodle and in-house systems have been conducted for reference. Also, the system deployment outcomes and user survey results confirm the wide acceptance among instructors and students to use MyGJU as a first point of contact, as opposed to Moodle, for basic e-learning tasks. Further, the results illustrate that the in-house e-learning modules in MyGJU are engaging, easy to use, useful, and interactive.

Phosphocholine-Decorated PPI-Dendrimers Mimic Cell Membrane Phosphocholine Clusters and Tune the Innate Immune Activity of C-Reactive Protein.

C-reactive protein (CRP) is widely used as biomarkers of infection and inflammation. It has a well-described ability to bind phosphocholine (PC), as well as PC-clusters from compromised and inflamed cell membranes and tissues. The binding of PC-clusters to CRP is of interest as this binding determines subsequent innate immune activity. We investigated PC-decorated dendrimers as mimics for PC-clusters. Five generations of poly(propylene imine) (PPI) dendrimers were modified with PC surface groups via a three-step synthetic sequence obtaining the PC-decorated dendrimers in high purity. The dendrimers were analyzed by NMR and infrared spectroscopy as well as HPLC. We developed immunoassays to show that dendrimer-PC binding to CRP was Ca2+-dependent with an apparent overall Kd of 11.9 nM for first generation (G1) PPI-PC, while G2-PPI-PC and G3-PPI-PC had slightly higher affinities, and G4-PPI-PC and G5-PPI-PC had slightly lower affinities. For all PC-dendrimers, the affinity was orders of magnitude higher than the affinity of free phosphocholine (PC), indicating a PC-cluster effect. Next, we investigated the binding of CRP:PPI-PC complexes to complement component C1q. C1q binding to CRP was dependent on the generation of PPI-PC bound to CRP, with second and third generation PPI-PCs leading to the highest affinity. The dendrimer-based approach to PC-cluster mimics and the simple binding assays presented here hold promise as tools to screen PC-compounds for their abilities to tune the innate immune activity of CRP.

A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments.

The aim of this paper is to present a dataset for Wi-Fi-based human activity recognition. The dataset is comprised of five experiments performed by 30 different subjects in three different indoor environments. The experiments performed in the first two environments are of a line-of-sight (LOS) nature, while the experiments performed in the third environment are of a non-line-of-sight (NLOS) nature. Each subject performed 20 trials for each of the experiments which makes the overall number of recorded trials in the dataset equals to 3000 trials (30 subjects × 5 experiments × 20 trials). To record the data, we used the channel state information (CSI) tool [1] to capture the exchanged Wi-Fi packets between a Wi-Fi transmitter and receiver. The utilized transmitter and receiver are retrofitted with the Intel 5300 network interface card which enabled us to capture the CSI values that are contained in the recorded transmissions. Unlike other publicly available human activity datasets, this dataset provides researchers with the ability to test their developed methodologies on both LOS and NLOS environments, in addition to many different variations of human movements, such as walking, falling, turning, and pen pick up from the ground.

Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features.

This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classify the tumors. In particular, the deep features are extracted from a pre-trained convolutional neural network model, namely the VGG19 model, at six different extraction levels. The deep features extracted at each level are analyzed using a features selection algorithm to identify the deep feature combination that achieves the highest classification performance. Furthermore, the extracted deep features are combined with handcrafted texture and morphological features and processed using features selection to investigate the possibility of improving the classification performance. The cross-validation analysis, which is performed using 380 breast ultrasound images, shows that the best combination of deep features is obtained using a feature set, denoted by CONV features that include convolution features extracted from all convolution blocks of the VGG19 model. In particular, the CONV features achieved mean accuracy, sensitivity, and specificity values of 94.2%, 93.3%, and 94.9%, respectively. The analysis also shows that the performance of the CONV features degrades substantially when the features selection algorithm is not applied. The classification performance of the CONV features is improved by combining these features with handcrafted morphological features to achieve mean accuracy, sensitivity, and specificity values of 96.1%, 95.7%, and 96.3%, respectively. Furthermore, the cross-validation analysis demonstrates that the CONV features and the combined CONV and morphological features outperform the handcrafted texture and morphological features as well as the fine-tuned VGG19 model. The generalization performance of the CONV features and the combined CONV and morphological features is demonstrated by performing the training using the 380 breast ultrasound images and the testing using another dataset that includes 163 images. The results suggest that the combined CONV and morphological features can achieve effective breast ultrasound image classifications that increase the capability of detecting malignant tumors and reduce the potential of misclassifying benign tumors.

A dataset for Wi-Fi-based human-to-human interaction recognition.

This paper presents a dataset for Wi-Fi-based human-to-human interaction recognition that comprises twelve different interactions performed by 40 different pairs of subjects in an indoor environment. Each pair of subjects performed ten trials of each of the twelve interactions and the total number of trials recorded in our dataset for all the 40 pairs of subjects is 4800 trials (i.e., 40 pairs of subjects × 12 interactions × 10 trials). The publicly available CSI tool [1] is used to record the Wi-Fi signals transmitted from a commercial off-the-shelf access point, namely the Sagemcom 2704 access point, to a desktop computer that is equipped with an Intel 5300 network interface card. The recorded Wi-Fi signals consist of the Received Signal Strength Indicator (RSSI) values and the Channel State Information (CSI) values. Unlike the publicly available Wi-Fi-based human activity datasets, which mainly have focused on activities performed by a single human, our dataset provides a collection of Wi-Fi signals that are recorded for 40 different pairs of subjects while performing twelve two-person interactions. The presented dataset can be exploited to advance Wi-Fi-based human activity recognition in different aspects, such as the use of various machine learning algorithms to recognize different human-to-human interactions.

A Game-Based Rehabilitation System for Upper-Limb Cerebral Palsy: A Feasibility Study.

Game-based rehabilitation systems provide an effective tool to engage cerebral palsy patients in physical exercises within an exciting and entertaining environment. A crucial factor to ensure the effectiveness of game-based rehabilitation systems is to assess the correctness of the movements performed by the patient during the game-playing sessions. In this study, we propose a game-based rehabilitation system for upper-limb cerebral palsy that includes three game-based exercises and a computerized assessment method. The game-based exercises aim to engage the participant in shoulder flexion, shoulder horizontal abduction/adduction, and shoulder adduction physical exercises that target the right arm. Human interaction with the game-based rehabilitation system is achieved using a Kinect sensor that tracks the skeleton joints of the participant. The computerized assessment method aims to assess the correctness of the right arm movements during each game-playing session by analyzing the tracking data acquired by the Kinect sensor. To evaluate the performance of the computerized assessment method, two groups of participants volunteered to participate in the game-based exercises. The first group included six cerebral palsy children and the second group included twenty typically developing subjects. For every participant, the computerized assessment method was employed to assess the correctness of the right arm movements in each game-playing session and these computer-based assessments were compared with matching gold standard evaluations provided by an experienced physiotherapist. The results reported in this study suggest the feasibility of employing the computerized assessment method to evaluate the correctness of the right arm movements during the game-playing sessions.

Reliable and accurate needle localization in curvilinear ultrasound images using signature-based analysis of ultrasound beamformed radio frequency signals.

PURPOSE: Ultrasound imaging is used in many minimally invasive needle insertion procedures to track the advancing needle, but localizing the needle in ultrasound images can be challenging, particularly at steep insertion angles. Previous methods have been introduced to localize the needle in ultrasound images, but the majority of these methods are based on ultrasound B-mode image analysis that is affected by the needle visibility. To address this limitation, we propose a two-phase, signature-based method to achieve reliable and accurate needle localization in curvilinear ultrasound images based on the beamformed radio frequency (RF) signals that are acquired using conventional ultrasound imaging systems. METHODS: In the first phase of our proposed method, the beamformed RF signals are divided into overlapping segments and these segments are processed to extract needle-specific features to identify the needle echoes. The features are analyzed using a support vector machine classifier to synthesize a quantitative image that highlights the needle. The quantitative image is processed using the Radon transform to achieve a reliable and accurate signature-based estimation of the needle axis. In the second phase, the accuracy of the needle axis estimation is improved by processing the RF samples located around the signature-based estimation of the needle axis using local phase analysis combined with the Radon transform. Moreover, a probabilistic approach is employed to identify the needle tip. The proposed method is used to localize needles with two different sizes inserted in ex vivo animal tissue specimens at various insertion angles. RESULTS: Our proposed method achieved reliable and accurate needle localization for an extended range of needle insertion angles with failure rates of 0% and mean angle, axis, and tip errors smaller than or equal to 0 . 7 ∘ , 0.6 mm, and 0.7 mm, respectively. Moreover, our proposed method outperformed a recently introduced needle localization method that is based on B-mode image analysis. CONCLUSIONS: These results suggest the potential of employing our signature-based method to achieve reliable and accurate needle localization during ultrasound-guided needle insertion procedures.

EEG-based BCI system for decoding finger movements within the same hand.

Decoding the movements of different fingers within the same hand can increase the control's dimensions of the electroencephalography (EEG)-based brain-computer interface (BCI) systems. This in turn enables the subjects who are using assistive devices to better perform various dexterous tasks. However, decoding the movements performed by different fingers within the same hand by analyzing the EEG signals is considered a challenging task. In this paper, we present a new EEG-based BCI system for decoding the movements of each finger within the same hand based on analyzing the EEG signals using a quadratic time-frequency distribution (QTFD), namely the Choi-William distribution (CWD). In particular, the CWD is employed to characterize the time-varying spectral components of the EEG signals and extract features that can capture movement-related information encapsulated within the EEG signals. The extracted CWD-based features are used to build a two-layer classification framework that decodes finger movements within the same hand. The performance of the proposed system is evaluated by recording the EEG signals for eighteen healthy subjects while performing twelve finger movements using their right hands. The results demonstrate the efficacy of the proposed system to decode finger movements within the same hand of each subject.

Accurate Needle Localization Using Two-Dimensional Power Doppler and B-Mode Ultrasound Image Analyses: A Feasibility Study.

Curvilinear ultrasound transducers are commonly used in various needle insertion interventions, but localizing the needle in curvilinear ultrasound images is usually challenging. In this paper, a new method is proposed to localize the needle in curvilinear ultrasound images by exciting the needle using a piezoelectric buzzer and imaging the excited needle using a curvilinear ultrasound transducer to acquire a power Doppler image and a B-mode image. The needle-induced Doppler responses that appear in the power Doppler image are analyzed to estimate the needle axis initially and identify the candidate regions that are expected to include the needle. The candidate needle regions in the B-mode image are analyzed to improve the localization of the needle axis. The needle tip is determined by analyzing the intensity variations of the power Doppler and B-mode images around the needle axis. The proposed method is employed to localize different needles that are inserted in three ex vivo animal tissue types at various insertion angles, and the results demonstrate the capability of the method to achieve automatic, reliable and accurate needle localization. Furthermore, the proposed method outperformed two existing needle localization methods.

A hybrid camera- and ultrasound-based approach for needle localization and tracking using a 3D motorized curvilinear ultrasound probe.

Three-dimensional (3D) motorized curvilinear ultrasound probes provide an effective, low-cost tool to guide needle interventions, but localizing and tracking the needle in 3D ultrasound volumes is often challenging. In this study, a new method is introduced to localize and track the needle using 3D motorized curvilinear ultrasound probes. In particular, a low-cost camera mounted on the probe is employed to estimate the needle axis. The camera-estimated axis is used to identify a volume of interest (VOI) in the ultrasound volume that enables high needle visibility. This VOI is analyzed using local phase analysis and the random sample consensus algorithm to refine the camera-estimated needle axis. The needle tip is determined by searching the localized needle axis using a probabilistic approach. Dynamic needle tracking in a sequence of 3D ultrasound volumes is enabled by iteratively applying a Kalman filter to estimate the VOI that includes the needle in the successive ultrasound volume and limiting the localization analysis to this VOI. A series of ex vivo animal experiments are conducted to evaluate the accuracy of needle localization and tracking. The results show that the proposed method can localize the needle in individual ultrasound volumes with maximum error rates of 0.7 mm for the needle axis, 1.7° for the needle angle, and 1.2 mm for the needle tip. Moreover, the proposed method can track the needle in a sequence of ultrasound volumes with maximum error rates of 1.0 mm for the needle axis, 2.0° for the needle angle, and 1.7 mm for the needle tip. These results suggest the feasibility of applying the proposed method to localize and track the needle using 3D motorized curvilinear ultrasound probes.

EEG-Based Emotion Recognition Using Quadratic Time-Frequency Distribution.

Accurate recognition and understating of human emotions is an essential skill that can improve the collaboration between humans and machines. In this vein, electroencephalogram (EEG)-based emotion recognition is considered an active research field with challenging issues regarding the analyses of the nonstationary EEG signals and the extraction of salient features that can be used to achieve accurate emotion recognition. In this paper, an EEG-based emotion recognition approach with a novel time-frequency feature extraction technique is presented. In particular, a quadratic time-frequency distribution (QTFD) is employed to construct a high resolution time-frequency representation of the EEG signals and capture the spectral variations of the EEG signals over time. To reduce the dimensionality of the constructed QTFD-based representation, a set of 13 time- and frequency-domain features is extended to the joint time-frequency-domain and employed to quantify the QTFD-based time-frequency representation of the EEG signals. Moreover, to describe different emotion classes, we have utilized the 2D arousal-valence plane to develop four emotion labeling schemes of the EEG signals, such that each emotion labeling scheme defines a set of emotion classes. The extracted time-frequency features are used to construct a set of subject-specific support vector machine classifiers to classify the EEG signals of each subject into the different emotion classes that are defined using each of the four emotion labeling schemes. The performance of the proposed approach is evaluated using a publicly available EEG dataset, namely the DEAPdataset. Moreover, we design three performance evaluation analyses, namely the channel-based analysis, feature-based analysis and neutral class exclusion analysis, to quantify the effects of utilizing different groups of EEG channels that cover various regions in the brain, reducing the dimensionality of the extracted time-frequency features and excluding the EEG signals that correspond to the neutral class, on the capability of the proposed approach to discriminate between different emotion classes. The results reported in the current study demonstrate the efficacy of the proposed QTFD-based approach in recognizing different emotion classes. In particular, the average classification accuracies obtained in differentiating between the various emotion classes defined using each of the four emotion labeling schemes are within the range of 73.8 % ⁻ 86.2 % . Moreover, the emotion classification accuracies achieved by our proposed approach are higher than the results reported in several existing state-of-the-art EEG-based emotion recognition studies.

Investigation of Physical Phenomena Underlying Temporal-Enhanced Ultrasound as a New Diagnostic Imaging Technique: Theory and Simulations.

Temporal-enhanced ultrasound (TeUS) is a novel noninvasive imaging paradigm that captures information from a temporal sequence of backscattered US radio frequency data obtained from a fixed tissue location. This technology has been shown to be effective for classification of various in vivo and ex vivo tissue types including prostate cancer from benign tissue. Our previous studies have indicated two primary phenomena that influence TeUS: 1) changes in tissue temperature due to acoustic absorption and 2) micro vibrations of tissue due to physiological vibration. In this paper, first, a theoretical formulation for TeUS is presented. Next, a series of simulations are carried out to investigate micro vibration as a source of tissue characterizing information in TeUS. The simulations include finite element modeling of micro vibration in synthetic phantoms, followed by US image generation during TeUS imaging. The simulations are performed on two media, a sparse array of scatterers and a medium with pathology mimicking scatterers that match nuclei distribution extracted from a prostate digital pathology data set. Statistical analysis of the simulated TeUS data shows its ability to accurately classify tissue types. Our experiments suggest that TeUS can capture the microstructural differences, including scatterer density, in tissues as they react to micro vibrations.