Moreover, degree (the number of connections for each node), and betweenness centrality (how connected a particular region is to other regions) differed between the empty bladder, the catheterized empty bladder, and the catheterized and partially filled bladder. Comparing resting state data before and after an interoceptive task (repeated intravesical infusion and drainage) further showed increased average path length for the salience networks and decreased clustering coefficient of the DMN. These results suggest visceral interoception influences brain topological properties of resting state networks.Deep brain stimulation (DBS) is used to treat a range of neurologic conditions. Determining the anatomic location of the DBS lead and inferring the microelectrode recording track from co-registered pre-operative and post-operative scans is important for stereotactic surgery and neurophysiology research. Reslicing images with the DBS lead in-plane while maintaining mirror symmetry is not possible with current clinical navigation software. Therefore, we developed an open source software tool in Matlab for visualizing DBS lead placement and anatomic segmentation with computed tomography and magnetic resonance images. The code and graphical user interface are available at github.com/camplaboratory/DBS_reslice.Reconstructing the perceived faces from brain signals has become a promising work recently. However, the reconstruction accuracies rely on a large number of brain signals collected for training a stable reconstruction model, which is really time consuming, and greatly limits its application. In our current study, we develop a new framework that can efficiently perform high-quality face reconstruction with only a small number of brain signals as training samples. The framework consists of three mathematical models principle component analysis (PCA), linear regression (LR) and conditional generative adversarial network (cGAN). We conducted a functional Magnetic Resonance Imaging (fMRI) experiment in which two subjects’ brain signals were collected to test the efficiency of our proposed method. Results show that we can achieve state-of-the-art reconstruction performance from brain signals with a very limited number of fMRI training samples.Alzheimer’s disease (AD) is progressive neurodegenerative disease. It is important to identify effective biomarkers to explore changes of complex functional brain networks in AD patients based on functional magnetic resonance imaging (fMRI). Recently, four fMRI brain network parameters were frequently used, including regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (f/ALFF) and degree centrality (DC). However, these parameters only present the changes of brain networks in a full time quantum, but ignore changes over a short period of time and lack space information. In this study we propose a new brain network parameter for fMRI, called multilayer network modularity and spatiotemporal network switching rate (stNSR). This parameter is calculated combing Pearson correlation sliding Hamming window and the Louvain algorithm. To verify the efficiency of stNSR, we selected 61 AD patients and 110 healthy controls (HC) from Xuanwu Hospital, Beijing, China. First, we used two-sample t test to identify regions of interest (ROI) between AD patients and HCs. Second, we calculated the stNSR values in these ROIs, and compared them with ReHo, ALFF, f/ALFF, and DC values between AD and HC groups. The results showed that, stNSR values in left calcimine fissure and surrounding cortex, left Lingual gyrus and left cerebellum inferior significantly increased, while stNSR values significantly decreased in left Para hippocampal gyrus, left temporal and superior temporal gyrus. As a comparison, changes in these ROIs could not be observed using ReHo, ALFF, f/ALFF, and DC. The results indicated that stNSR may reflect differences of brain networks between AD patients and HCs.Alzheimer’s disease (AD) is a degenerative brain disease and the most common cause of dementia. Early stage β-amyloid oligomers (AβOs) and late stage Aβ plaques are the pathological hallmarks of AD brains. AβOs are known to be more neurotoxic and contribute to neuronal damage. Most current approaches are focused on detecting Aβ plaques, which occurs at the late stage of AD, and are limited by poor sensitivity and/or contrast agent toxicity. In previous studies, we developed a new curcumin-conjugated magnetic nanoparticle (Cur-MNPs) to target the Aβ pathologies. In this study, we investigate the in vivo feasibility of this novel Cur-MNPs to detect Aβ pathologies at the early and late stages of AD in transgenic AD mice and perform immunohistochemical examinations to validate the specific targeting of various form of Aβ pathologies.Simultaneously resting brain glucose metabolism and intrinsic functional activity, by integrated PET/MRI scans, both reflect nerve actions. Studies showed that there existed relevance between two phenotypes of neuros in normal human brains. However, whether the relevance will change in cognitive dysfunction (CD) brains is still unknown. The aim of this study therefore is to explore the relevance between voxel-wise glucose metabolism and functional connectivity in Chinese CD people. The dataset in this study included two imaging modalities and clinical information of 21 healthy control (HC) individuals and 15 CD patients, from Xuanwu hospital, Beijing, China. Firstly, we calculated the standardized uptake value rate (SUVR) from positron emission tomography (PET), and three parameters for intrinsic functional activity from functional magnetic resonance imaging (fMRI), including amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity (ReHo). Second, the two sample t-test was used to compare each parameter between HC and CD groups respectively. Third, the relevance between SUVR and the three fMRI parameters were measured by Spearman’s rank correlation. The results of t-test showed that glucose metabolism consumption decreased in Default Mode Network (DMN) (p less then 0.01), and the damage of functional connection also happened DMN area in CD group. The correlation between glucose metabolism and functional activity in CD group was lower than that in HC group in DMN. Especially, the correlation between SUVR and ReHo was significantly reduced (p less then 0.05). Above results promoted a deeper understanding on the pathogenesis of cognitive impairment, and providing new biomarkers to discriminate CD and HC subjects.Neuronal-related activity can be estimated from functional magnetic resonance imaging (fMRI) data with no knowledge of the timings of blood oxygenation level-dependent (BOLD) events by means of deconvolution with regularized least-squares. This work proposes two improvements on the deconvolution algorithm of sparse paradigm free mapping (SPFM) a new formulation that enables the estimation of neuronal events with long, sustained activity; and the implementation of a subsampling approach based on stability selection that avoids the choice of any regularization parameter. The proposed method is evaluated on real fMRI data and compared with both the original SPFM algorithm and conventional analysis with a general linear model (GLM) that is aware of the temporal model of the neuronal-related activity. We demonstrate that the novel stability-based SPFM algorithm yields activation maps with higher resemblance to the maps obtained with GLM analyses and offers improved detection of neuronal-related events over SPFM, particularly in scenarios with low contrast-to-noise ratio.A unified framework for the analysis of fluorescence data taken by a two-photon imaging system is presented. As in the processing of blood-oxygen-level-dependent signals of functional magnetic resonance imaging, the acquired functional images have to be co-registered with a structural brain atlas before delineating the regions activated by a given stimulus. The voxels whose calcium traces are highly correlated with the predicted responses are demarcated without the need for subjective reasoning. Experimental data acquired while presenting olfactory stimuli are used to demonstrate the efficacy of the proposed schemes. The results indicate that the functional images of a Drosophila individual can be normalized into a standard stereotactic space, and the expected brain regions can be delineated adequately. This framework provides an opportunity to enable the development of a Drosophila functional connectome database.Recently, more evidences manifest that the subjective cognitive decline (SCD) of unimpaired individual may represent first symptom of Alzheimer’s disease (AD). This study investigated the differences of intrinsic glucose metabolic functional connectivity between SCD and healthy subject (HC) groups from the perspective of brain network topology. In this study we attained 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) scans from Xuanwu Hospital, Beijing, China, including 85 SCD subjects (male = 16, mean age = 66, MMSE = 28.4) and 74 HC subjects (male = 37, mean age = 65,MMSE=29.0). Graph theory method has been used in this study. Network parameters, including global efficiency, local efficiency, characteristic path length, clustering coefficient, betweenness centrality, sigma and modularity were calculated and compared between two groups. As a result, both SCD and HC groups showed the small-world property. Meanwhile, SCD showed loss of small-world properties, for example, sigma in SCD was significantly lower than HC (p less then 0.05). In addition, the clustering coefficient and local efficiency of SCD were both higher than HC significantly (p less then 0.05). In contrast, the characteristic path length and global efficiency of SCD were lower than HC, which led to the regularization of brain network in SCD group. Furthermore, we found global modularity of SCD was lower than HC and the number of modules also decreased. Our findings suggested that there exist differences in glucose metabolic brain network between two groups, demonstrating that the graph theory analysis method could be useful and helpful to predict risks in the preclinical stage of AD.Cerebral vascular territories are related to the clinical progression and outcome of ischemic stroke. The vascular territory map (VTM) helps to understand stroke pathophysiology and potentially the clinical prognosis. A VTM can be generated from the bolus arrival time map. However, previous methods require initial seed points to be chosen manually, and the region inferior to the circle of Willis is not included. In this paper, we propose a method to automatically generate a map of the whole cerebral vascular territory from CT perfusion imaging. We applied the proposed method to 19 cases of ischemic stroke to generate VTM for each case.Clinical Relevance- The proposed map may improve the interpretation of the physiological status of collateral flow for ischemic stroke, and aid in treatment decision making.Brain tumor is among the deadliest cancers, whose effective treatment is partially dependent on the accurate diagnosis of the tumor type. Convolutional neural networks (CNNs), which have been the state-of-the-art in brain tumor classification, fail to identify the spatial relations in the image. Capsule networks, proposed to overcome this drawback, are sensitive to miscellaneous backgrounds and cannot manage to focus on the main target. To address this shortcoming, we have recently proposed a capsule network-based architecture capable of taking both brain images and tumor rough boundary boxes as inputs, to have access to the surrounding tissue as well as the main target. Similar to other architectures, however, this network requires extensive search within the space of all possible configurations, to find the optimal architecture. To eliminate this need, in this study, we propose a boosted capsule network, referred to as BoostCaps, which takes advantage of the ability of boosting methods to handle weak learners, by gradually boosting the models. BoosCaps, to the best of our knowledge, is the first capsule network model that incorporates an internal boosting mechanism. Our results show that the proposed BoostCaps framework outperforms its single capsule network counterpart.While Deep Learning methods have been successfully applied to tackle a wide variety of prediction problems, their application has been mostly limited to data structured in a grid-like fashion. However, the study of the human brain „connectome” involves the representation of the brain as a graph with interacting nodes. In this paper, we extend the Graph Attention Network (GAT), a novel neural network (NN) architecture acting on the features of the nodes of a binary graph, to handle a set of graphs provided with node features and non-binary edge weights. We demonstrate the effectiveness of our architecture by training it multimodal data collected from a large homogeneous fMRI dataset (n=1003 individuals with multiple fMRI sessions per subject) made publicly available by the Human Connectome Project (HCP), demonstrating good performance and seamless integration of multimodal neuroimaging data. Our adaptation provides a powerful and flexible deep learning tool to integrate multimodal neuroimaging connectomics data in a predictive context.Accurate segmentation of brain tumors is a challenging task and also a crucial step in diagnosis and treatment planning for cancer patients. Magnetic resonance imaging (MRI) is the standard imaging modality for detection, characterization, treatment planning and outcome evaluation of brain tumors. MRI scans are usually acquired at multiple sessions before and after the treatment. An automatic segmentation framework is highly desirable to segment brain tumors in MR images as it streamlines the image-guided radiation therapy workflow considerably. Automatic segmentation of brain tumors also facilitates an incremental development of data-driven systems for therapy outcome prediction based on radiomics analysis. In this study, an outlier-detection-based segmentation framework is proposed to delineate brain tumors in magnetic resonance (MR) images automatically. The proposed method considers the tumor and edema pixels in an MR image as outliers compared to the pixels associated with the healthy tissue. The framework generates two outlier masks using independent one-class support vector machines that operate on post-contrast T1-weighted (T1w) and T2-weighted-fluid-attenuation-inversion-recovery (T2-FLAIR) images. The outlier masks are subsequently refined and fused using a number of morphological and logical operators to estimate a tumor mask for each image slice. The framework was constructed and evaluated using the MRI data acquired from 35 and 5 patients with brain metastasis, respectively. The obtained results demonstrated an average Dice similarity coefficient and Hausdorff distance of 0.84 ± 0.06 and 1.85 ± 0.48 mm, respectively, between the manual (ground truth) and automatic tumor contours, on the independent test set.Radiation therapy is a major treatment option for brain metastasis. For radiation treatment planning and outcome evaluation, magnetic resonance (MR) images are acquired before and at multiple sessions after the treatment. Accurate segmentation of brain tumors on MR images is crucial for treatment planning, response evaluation, and developing data-driven models for outcome prediction. Due to the high volume of imaging data acquired from each patient at multiple follow-up sessions, manual tumor segmentation is resource- and time-consuming in clinic, hence developing an automatic segmentation framework is highly desirable. In this work, we proposed a cascaded 2D-3D Unet framework to segment brain tumors automatically on contrast-enhanced T1- weighted images acquired before and at multiple scan sessions after radiotherapy. 2D Unet is a well-known structure for medical image segmentation. 3D Unet is an extension of 2D Unet with a volumetric input image to provide richer spatial information. The limitation of 3D Unet is that it is memory consuming and cannot process large volumetric images. To address this limitation, a large volumetric input of 3D Unet is often patched to smaller volumes which leads to loss of context. To overcome this problem, we proposed using two cascaded 2D Unets to crop the input volume around the tumor area and reduce the input size of the 3D Unet, obviating the need to patch the input images. The framework was trained using images acquired from 96 patients before radiation therapy and tested using images acquired from 10 patients before and at four follow-up scans after radiotherapy. The segmentation results for the images of independent test set demonstrated that the cascaded framework outperformed the 2D and 3D Unets alone, with an average Dice score of 0.9 versus 0.86 and 0.88 for the baseline, and 0.87 versus 0.83 and 0.84 for the first followup. Similar results were obtained for the other follow-up scans.Recent advances in medical image segmentation have largely been driven by the success of deep learning algorithms. However, one main challenge for the training of one- stage segmentation networks is the serious imbalance between the number of examples that are easy and hard to classify or in positive and negative classes. In this paper, we first investigate and compare the strategies that were proposed parallelly to handle one or two of these imbalance problems. And we propose a hybrid loss that addresses these two imbalance problems together by combining the merits of Exponential logarithmic Dice and weighted Cross entropy Loss (EDCL). Without any whistles and bells, the proposed EDC loss with 3D Unet achieves mean dice of 57.38%, which surpasses the other state-of-the- art methods with 5-fold cross-validation on a public dataset for 3D brain lesion segmentation, Anatomical Tracings of Lesions After Stroke (ATLAS) v1.2.Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. In this paper, we propose a fully automated two-stage integrated deep learning approach for efficient CMBs detection, which combines a regional-based You Only Look Once (YOLO) stage for potential CMBs candidate detection and three-dimensional convolutional neural networks (3D-CNN) stage for false positives reduction. Both stages are conducted using the 3D contextual information of microbleeds from the MR susceptibility-weighted imaging (SWI) and phase images. However, we average the adjacent slices of SWI and complement the phase images independently and utilize them as a two- channel input for the regional-based YOLO method. The results in the first stage show that the proposed regional-based YOLO efficiently detected the CMBs with an overall sensitivity of 93.62% and an average number of false positives per subject (FPavg) of 52.18 throughout the five-folds cross-validation. The 3D-CNN based second stage further improved the detection performance by reducing the FPavg to 1.42. The outcomes of this work might provide useful guidelines towards applying deep learning algorithms for automatic CMBs detection.Oxygen deprivation (hypoxia) and reduced blood supply (ischemia) can occur before, during or shortly after birth and can result in death, brain damage and long-term disability. Assessing neuronal survival after hypoxia-ischemia in the near-term fetal sheep brain model is essential for the development of novel treatment strategies. As manual quantification of neurons in histological images varies between different assessors and is extremely time-consuming, automation of the process is needed and has not been currently achieved. To achieve automation, successfully segmenting the neurons from the background is very important. Due to presence of densely populated overlapping cells and with no prior information of shapes and sizes, the segmentation of neurons from the image is complex. Initially, we segmented the RGB images by using K-means clustering to primarily segment the neurons from the background based on their colour value, a distance transform for seed detection and watershed method for separating overlapping objects. However, this resulted in unsatisfactory sensitivity and performance due to over-segmentation if we use the RGB image directly. In this paper, we propose a semi-automated modified approach to segment neurons that tackles the over-segmentation issue that we encountered. Initially, we separated the red, green and blue colour channel information from the RGB image. We determined that by applying the same segmentation method first to the blue channel image, then by performing segmentation on the green channel for the neurons that remain unsegmented from the blue channel segmentation and finally by performing segmentation on red channel for neurons that were still unsegmented from the green channel segmentation, improved performance results could be achieved. The modified approach increased performance for the healthy and ischemic animal images from 89.7% to 98.08% and from 94.36% to 98.06% respectively as compared to using RGB image directly.The present study proposes a new personalized sleep spindle detection algorithm, suggesting the importance of an individualized approach. We identify an optimal set of features that characterize the spindle and exploit a support vector machine to distinguish between spindle and nonspindle patterns. The algorithm is assessed on the open source DREAMS database, that contains only selected part of the polysomnography, and on whole night polysomnography recordings from the SPASH database. We show that on the former database the personalization can boost sensitivity, from 84.2% to 89.8%, with a slight increase in specificity, from 97.6% to 98.1%. On a whole night polysomnography instead, the algorithm reaches a sensitivity of 98.6% and a specificity of 98.1%, thanks to the personalization approach. Future work will address the integration of the spindle detection algorithm within a sleep scoring automated procedure.Studies that evaluate human emotions from biological signals have been actively conducted, with many using images or sounds to induce emotions passively. However, few studies utilized the action of working to elicit emotions (especially positive ones) actively. Hence, in this study, emotions were examined during working (a puzzle was used in this study) from the psychological viewpoint of the Profile of Mood States 2nd Edition and the physiological viewpoint of electroencephalograms (EEGs). As a result, different time-dependent changes of power change rate in the theta band in the frontal region were observed between the presence and absence of the emotion „fatigue-inertia.” Those in the alpha band in the frontal region were observed between the existence and nonexistence of the emotion „vigor-activity.” Therefore, it is suggested that we can evaluate the emotion of a subject while working by a spatiotemporal pattern of band power obtained by EEG.Neonatal hypoxic-ischemic encephalopathy (HIE) evolves over different phases of time during recovery. Some neuroprotection treatments are only effective for specific, short windows of time during this evolution of injury. Clinically, we often do not know when an insult may have started, and thus which phase of injury the brain may be experiencing. To improve diagnosis, prognosis and treatment efficacy, we need to establish biomarkers which denote phases of injury. Our pre-clinical research, using preterm fetal sheep, show that micro-scale EEG patterns (e.g. spikes and sharp waves), superimposed on suppressed EEG background, primarily occur during the early recovery from an HI insult (0-6 h), and that numbers of events within the first 2 h are strongly predictive of neural survival. Thus, real-time automated algorithms that could reliably identify EEG patterns in this phase will help clinicians to determine the phases of injury, to help guide treatment options. We have previously developed successful automated machine learning approaches for accurate identification and quantification of HI micro-scale EEG patterns in preterm fetal sheep post-HI. This paper introduces, for the first time, a novel online fusion strategy that employs a high-level wavelet-Fourier (WF) spectral feature extraction method in conjunction with a deep convolutional neural network (CNN) classifier for accurate identification of micro-scale preterm fetal sheep post-HI sharp waves in 1024Hz EEG recordings, along with 256Hz down-sampled data. The classifier was trained and tested over 4120 EEG segments within the first 2 hours latent phase recordings. The WF-CNN classifier can robustly identify sharp waves with considerable high-performance of 99.86% in 1024Hz and 99.5% in 256Hz data. The method is an alternative deep-structure approach with competitive high-accuracy compared to our computationally-intensive WS-CNN sharp wave classifier.During gambling, humans often begin by making decisions based on expected rewards and expected risks. However, expectations may not match actual outcomes. As gamblers keep track of their performance, they may feel more or less lucky, which then influences future betting decisions. Studies have identified the orbitofrontal cortex (OFC) as a brain region that plays a significant role during risky decision making in humans. However, most human studies infer neural activation from functional magnetic resonance imaging (fMRI), which has a poor temporal resolution. In particular, fMRI cannot detect activity from neuronal populations in the OFC, which may encode specific information about how a subject reacts to mismatched outcomes. In this preliminary study, four human subjects participated in a gambling task while local field potentials (LFPs), captured at a millisecond resolution, were recorded from the OFC. We analyzed high-frequency activity (HFA >70 Hz) in the LFPs, as HFA has been shown to correlate to activation of neuronal populations. In 3 out of 4 subjects, HFA in OFC modulated between matched and mismatched trials as soon as the outcome of each bet was revealed, with modulations occurring at different times and directions depending on the anatomical location within the OFC.Blood infection due to different circumstances could immediately develop to an extreme body reaction that leads to a serious life-threatening condition, called Sepsis. Currently, therapeutic protocols through timely antibiotic resuscitation strategies play an important role to fight against the adverse conditions and improve survival. Therefore, timing, and more specifically early diagnosis of the illness, is crucially important for an effective treatment. Studies have indicated that vital signals such as heart rate variability (HRV) could provide potential prognostic biological markers that can help with early detection of sepsis before it is clinically diagnosed through its actual symptoms. Therefore, this study employs neonatal and pediatric electrocardiogram (ECG) to extract 52 hourly sets of linear and non-linear features from the HRV, starting from 24 hours prior to the clinical diagnosis of sepsis in patients with positive blood cultures (n=14). Similar sets of features were also obtained from a non-sepsis control group to create an evaluation benchmark (n=14).In particular, this study initially demonstrates how the variations within the 24 hours values of specific HRV feature-sets could effectively reveal prognostic information about the evolution of sepsis, prior to the actual clinical diagnosis. Moreover, this study demonstrates that differences in the values of a particular set of features at 22 hours before the actual clinical diagnosis/symptoms can be reliably used to train a convolutional neural network for automatic classification between the individuals in the sepsis and non-sepsis groups with 88.89±7.86% accuracy.Empathy which can understand and respond to the unique affective experiences of others plays an essential role in social interaction. Although many neuroimaging studies have investigated the neural mechanisms underlying empathy for social pain, how its mechanisms are modulated by trait empathy remains unknown. The present event-related potential (ERP) study used Chatroom Interact Task to examine how trait empathy modulates brain response to empathy for social rejection. The behavior results showed that participants were less pleasant when observing rejection compared to observing acceptance in both high- and low-levels empathy groups. The ERP results revealed more negative-going N2 for social acceptance compared to rejection in both groups, but there was no difference in N2 between high- and low- empathy group. However, the late components, i.e., the P3b, N400 and LPP, revealed significant difference between social acceptance and rejection in high empathic participants rather than low empathic participants. These findings suggested that individuals with high empathic traits could devote more attention and mental resources to process observing ostracism.Short-duration bursts of spontaneous activity are important markers of maturation in the electroencephalogram (EEG) of premature infants. This paper examines the application of a feature-less machine learning approach for detecting these bursts. EEGs were recorded over the first 3 days of life for infants with a gestational age below 30 weeks. Bursts were annotated on the EEG from 36 infants. In place of feature extraction, the time-series EEG is transformed into a time-frequency distribution (TFD). A gradient boosting machine is then trained directly on the whole TFD using a leave-one-out procedure. TFD kernel parameters, length of the Doppler and lag windows, are selected within a nested cross-validation procedure during training. Results indicate that detection performance is sensitive to Doppler-window length but not lag-window length. Median area under the receiver operator characteristic for detection is 0.881 (inter-quartile range 0.850 to 0.913). Examination of feature importance highlights a critical wideband region less then 15 Hz in the TFD. Burst detection methods form an important component in any fully-automated brain-health index for the vulnerable preterm infant.The N2pc event-related potential component measures direction and time course of selective visual attention and represents an important biomarker in cognitive neuroscience. While its subtractive origin strongly influences the amplitude, thus hindering its detection, other external factors, such as subject’s inefficiency to allocate attention to the cued target, or the heterogeneity of the visual context, may strongly affect the elicitation of the component itself. It would therefore be extremely important to create a tool that, using as few sweeps as possible, could reliably establish whether an N2pc is present in an individual subject. In the present work, we propose an approach by resorting to a time-frequency analysis of N2pc individual signals; in particular, power at each frequency band (α/β/δ/θ) was computed in the N2 time range and correlated to the estimated amplitude of the N2pc. Preliminary results on fourteen human volunteers of a visual search design showed a very high correlation coefficient (over 0.9) between the low frequency bands power and the mean absolute amplitude of the component, using only 40 sweeps. Results also seemed to suggest that N2pc amplitude values higher than 0.5 μV could be accurately classified according to time-frequency indices.Clinical Relevance – The online detection of the N2pc presence in individual EEG datasets would allow not only to study the factors responsible of N2pc variability across subjects and conditions, but also to investigate novel search variants on participants with a predisposition to show an N2pc, reducing time and costs and the possibility to obtain biased results.Diagnosis of hypoxic-ischemic encephalopathy (HIE) is currently limited and prognostic biological markers are required for early identification of at risk infants at birth. Using pre-clinical data from our fetal sheep models, we have shown that micro-scale EEG patterns, such as high-frequency spikes and sharp waves, evolve superimposed on a significantly suppressed background during the early hours of recovery (0-6 h), after an HI insult. In particular, we have demonstrated that the number of micro-scale gamma spike transients peaks within the first 2-2.5 hours of the insult and automatically quantified sharp waves in this period are predictive of neural outcome. This period of time is optimal for the initiation of neuroprotection treatments such as therapeutic hypothermia, which has a limited window of opportunity for implementation of 6 h or less after an HI insult. Clinically, it is hard to determine when an insult has started and thus the window of opportunity for treatment. Thus, reliable automatic algorithms that could accurately identify EEG patterns that denote the phase of injury is a valuable clinical tool. We have previously developed successful machine-learning strategies for the identification of HI micro-scale EEG patterns in a preterm fetal sheep model of HI. This paper employs, for the first time, reverse biorthogonal Wavelet-Scalograms (WS) as the inputs to a 17-layer deep-trained convolutional neural network (CNN) for the precise identification of high-frequency micro-scale spike transients that occur in the 80-120Hz gamma band during first 2 h period of an HI insult. The rbio-WS-CNN classifier robustly identified spike transients with an exceptionally high-performance of 99.82%.Clinical relevance-The suggested classifier would effectively identify and quantify EEG patterns of a similar morphology in preterm newborns during recovery from an HI-insult.Early diagnosis and prognosis of babies with signs of hypoxic-ischemic encephalopathy (HIE) is currently limited and requires reliable prognostic biomarkers to identify at risk infants. Using our pre-clinical fetal sheep models, we have demonstrated that micro-scale patterns evolve over a profoundly suppressed EEG background within the first 6 hours of recovery, post HI insult. In particular, we have shown that high-frequency micro-scale spike transients (in the gamma frequency band, 80-120Hz) emerge immediately after an HI event, with much higher numbers around 2-2.5 h of the insult, with numbers gradually declining thereafter. We have also shown that the automatically quantified sharp waves in this phase are predictive of neural outcome. Initiation of some neuroprotective treatments within this limited window of opportunity, such as therapeutic hypothermia, optimally reduces neural injury. In clinical practice, it is hard to determine the exact timing of the injury, therefore, reliable automatic identification of EEG transients could be beneficial to help specify the phases of injury. Our team has previously developed successful machine- and deep-learning strategies for the identification of post-HI EEG patterns in an HI preterm fetal sheep model.This paper introduces, for the first time, a novel online fusion approach to train an 11-layers deep convolutional neural network (CNN) classifier using Wavelet-Fourier (WF) spectral features of EEG segments for accurate identification of high-frequency micro-scale spike transients in 1024Hz EEG recordings in our preterm fetal sheep. Sets of robust features were extracted using reverse biorthogonal wavelet (rbio2.8 at scale 7) and considering an 80-120Hz spectral frequency range. The WF-CNN classifier was able to accurately identify spike transients with a reliable high-performance of 99.03±0.86%.Clinical relevance-Results confirm the expertise of the method for the identification of similar patterns in the EEG of neonates in the early hours after birth.Muscle activation during sleep is an important biomarker in the diagnosis of several sleep disorders and neurodegenerative diseases. Muscle activity is typically assessed manually based on the EMG channels from polysomnography recordings. Ear-EEG provides a mobile and comfortable alternative for sleep assessment. In this study, ear-EEG was used to automatically detect muscle activities during sleep. The study was based on a dataset comprising four full night recordings from 20 healthy subjects with concurrent polysomnography and ear-EEG. A binary label, active or relax, extracted from the chin EMG was assigned to selected 30 s epoch of the sleep recordings in order to train a classifier to predict muscle activation. We found that the ear-EEG based classifier detected muscle activity with an accuracy of 88% and a Cohen’s kappa value of 0.71 relative to the labels derived from the chin EMG channels. The analysis also showed a significant difference in the distribution of muscle activity between REM and non-REM sleep.This research focuses on the gait phase recognition using different sEMG and EEG features. Seven healthy volunteers, 23-26 years old, were enrolled in this experiment. Seven phases of gait were divided by three-dimensional trajectory of lower limbs during treadmill walking and classified by Library for Support Vector Machines (LIBSVM). These gait phases include loading response, mid-stance, terminal Stance, pre-swing, initial swing, mid-swing, and terminal swing. Different sEMG and EEG features were assessed in this study. Gait phases of three kinds of walking speed were analyzed. Results showed that the slope sign change (SSC) and mean power frequency (MPF) of sEMG signals and SSC of EEG signals achieved higher accuracy of gait phase recognition than other features, and the accuracy are 95.58% (1.4 km/h), 97.63% (2.0 km/h) and 98.10% (2.6 km/h) respectively. Furthermore, the accuracy of gait phase recognition in the speed of 2.6 km/h is better than other walking speeds.Voice command is an important interface between human and technology in healthcare, such as for hands-free control of surgical robots and in patient care technology. Voice command recognition can be cast as a speech classification task, where convolutional neural networks (CNNs) have demonstrated strong performance. CNN is originally an image classification technique and time-frequency representation of speech signals is the most commonly used image-like representation for CNNs. Various types of time-frequency representations are commonly used for this purpose. This work investigates the use of cochleagram, utilizing a gammatone filter which models the frequency selectivity of the human cochlea, as the time-frequency representation of voice commands and input for the CNN classifier. We also explore multi-view CNN as a technique for combining learning from different time-frequency representations. The proposed method is evaluated on a large dataset and shown to achieve high classification accuracy.Technology is rapidly changing the health care industry. As new systems and devices are developed, validating their effectiveness in practice is not trivial, yet it is essential for assessing their technical and clinical capabilities. Digital auscultations are new technologies that are changing the landscape of diagnosis of lung and heart sounds and revamping the centuries old original design of the stethoscope. Here, we propose a methodology to validate a newly developed digital stethoscope, and compare its effectiveness against a market-accepted device, using a combination of signal properties and clinical assessments. Data from 100 pediatric patients is collected using both devices side by side in two clinical sites. Using the proposed methodology, we objectively compare the technical performance of the two devices, and identify clinical situations where performance of the two devices differs. The proposed methodology offers a general approach to verify a new digital auscultation device as clinically-viable; while highlighting the important consideration for clinical conditions in performing these evaluations.The acoustoelectric (AE) effect is that ultrasonic wave causes the conductivity of electrolyte to change in local position. AE imaging is an imaging method that utilizes AE effect. The decoding accuracy of AE signal is of great significance to improve the decoded signal quality and resolution of AE imaging. At present, the envelope function is adopted to decode AE signal, but the timing characteristics of the decoded signal and the source signal are not very consistent. In order to further improve the decoding accuracy, based on envelope decoding, the decoding process of AE signal is investigated. Considering with the periodic property of AE signal in time series, the upper envelope signal is further fitted by Fourier approximation. Phantom experiment validates the feasibility of AE signal decoding by Fourier approximation. And the time sequence diagram decoded with envelope is also compared. The fitted curve can represent the overall trend curve of low-frequency current signal, which has a significant correspondence with the current source signal. The main performance is of the same frequency and phase. Experiment results validate that the proposed decoding algorithm can improve the decoding accuracy of AE signal and be of potential for the clinical application of AE imaging.This paper presents a signal analysis approach to identify the contact objects at the tip of a flexible ureteroscope. First, a miniature triaxial fiber optic sensor based on Fiber Bragg Grating(FBG) is devised to measure the interactive force signals at the ureteroscope tip. Due to the multidimensional properties of these force signals, the principal components analysis(PCA) method is introduced to reduce dimensions. The signal features are then extracted from the representative principal component signals using the wavelet transform(WT) method. Experimental results show that the contact objects at the tip of a ureteroscope are readily discriminated from the measured force signals with the proposed approach.Clinical Relevance-This work commits to analyze the contact force signals at the tip of a flexible ureteroscope for the purpose of contact objects identification.Gamers with Internet gaming disorder (IGD) dynamically regulate their psychophysiological responses during playing; however, analyzing instantaneous psychophysiological responses in these gamers has been limited by a lack of appropriate methods. We propose combining the Complementary Ensemble Empirical Mode Decomposition and Direct Quadrature methods to overcome this limitation. The related effect of abdominal breathing (AB) training (as a relaxing psychology method) on the distribution of instantaneous frequency (IF) was investigated by calculating median (IFmed), kurtosis (IFkurt) and skewness (IFskew), and 19 participants with high-risk IGD (HIGD) were found to have increased IFmed [massively multiplayer online role-playing game (MMORPG) 0.36 ± 0.08; first-person shooter game (FPSG) 0.34 ± 0.08] but decreased IFkurt (MMORPG 5.98 ± 2.31; FPSG 6.84 ± 4.61) and IFskew (MMORPG 0.40 ± 0.69; FPSG 0.64 ±1.04) during game-film stimuli compared with baseline and recovery states. After AB training, IFmed of these 19 participants (MMORPG 0.24 ± 0.11; FPSG 0.18 ± 0.06) decreased significantly. This study is firstly to observe the IF distribution of respiratory signal in gamers with HIGD; thus, this distribution may be used as a respiratory physiological marker of IGD risk.Assessment of the pharyngeal airway is becoming important for delivering personalized treatment and better management of sleep apnea. However, evaluation of the pharyngeal airway area is difficult in the current state of the art. It is essential to use simple and accessible technology to measure the pharyngeal airway area. As vowel sounds are generated by vocal cords vibration and characterized by the pharyngeal airway, vowel sounds have the potential to evaluate the pharyngeal airway area. The objective of this study was to investigate the relationship between acoustic features of vowel sounds and the pharyngeal airway cross-sectional area (PAXSA) between soft palate and glottis. Twenty subjects were included in this study whose PAXSA was measured by acoustic pharyngometry. Vowel sounds were recorded with a microphone while lying supine. Vowel sound average power was calculated in different frequency ranges of 100-3000 Hz, 100-500 Hz, 500-1000 Hz, 1000-1500 Hz, 1500-2000 Hz, 2000-2500 Hz and 2500-3000 Hz. Statistical analysis showed that the decreases in the PAXSA were strongly correlated with the higher average power of vowel sounds in all frequency ranges. These results showed that individuals with low PAXSA might articulate the vowel in higher intensity. Clinical Relevance – This study demonstrates that the pharyngeal airway cross-sectional area during normal breathing has a significant effect on vowel articulation. Thus, vowel sound features can be used to estimate the resting pharyngeal airway cross-sectional area.Acoustic feedback cancellation is a challenging problem in the design of sound reinforcement systems, hearing aids, etc. Acoustic feedback is inevitable when the acoustic signal path forms a loop between the microphone and loudspeaker. An efficient short duration noise injection algorithm is proposed in this paper to estimate the impulse response of the acoustic feedback path model. The algorithm does not require any prior information about the acoustic feedback path. It is capable of optimally estimate the acoustic feedback path for cancellation, and avoid the occurrence of any howling episode, in varying acoustic environments. Presented algorithm is efficiently implemented on smartphone device having close proximity of loudspeaker and microphone to emulate the feedback condition. The algorithm being platform-independent can also be implemented for any set-up or system. The experimental results of the proposed method shows satisfying results and its ability to track and cancel the acoustic feedback in changing characteristics of the acoustic path.A compressor in hearing aid devices (HADs) is responsible for mapping the dynamic range of input signals to the residual dynamic range of hearing-impaired (HI) patients. Gains and parameters of the compressor are set according to the HI patient’s preferences. In different surroundings depending upon noise level, the patient may seek to tune the parameters to improve performance. Traditionally, fitting of the hearing aids is done by an audiologist using hearing aid software and the HI patient’s opinion at a clinic. In this paper, we propose a frequency-based multi-band compressor implemented as a smartphone application, which can be used as an alternative to that of the traditional HADs. The proposed solution allows the user to tune the compression parameters for each band along with a choice of compression speed and fitting strategy. Exploiting smartphone processing and hardware capabilities, the application can be used for bilateral hearing loss. The performance of this easy-to-use smartphone-based application is compared with traditional HADs using a hearing aid test system. Objective and subjective evaluations are also carried out to quantify the performance.A child having a delayed development in language skills without any reason is known to be suffering from specific language impairment (SLI). Unfortunately, almost 7% kindergarten children are reported with SLI in their childhood. The SLI could be treated if identified at an early stage, but diagnosing SLI at early stage is challenging. In this article, we propose a machine learning based system to screen the SLI speech by analyzing the texture of the speech utterances. The texture of speech signals is extracted from the popular time-frequency representation called spectrograms. These spectrogram acts like a texture image and the textural features to capture the change in audio quality such as Haralick’s feature and local binary patterns (LBPs) are extracted from these textural images. The experiments are performed on 4214 utterances taken from 44 healthy and 54 SLI speakers. Experimental results with 10-fold cross validation, indicates that a very good accuracy up to 97.41% is obtained when only 14 dimensional Haralick’s feature is used. The accuracy is slightly boosted up to 99% when the 59-dimensional LBPs are amalgamated with Haralick’s features. The sensitivity and specificity of the whole system is up to 98.96% and 99.20% respectively. The proposed method is gender and speaker independent and invariant to examination conditions.A good understanding of the origin of stimulus-frequency otoacoustic emission (SFOAE) fine structure in human ears and its probe level-dependency has potential clinical significance. In this study, we develop a two-component additive model, with total SFOAE unmixed into short- and long-latency components (or reflections) using time windowing method, to investigate the origin of SFOAE fine structure in humans from 40 to 70 dB SPL. The two-component additive model predicts that a spectral notch seen in the amplitude fine structure is produced when short- and long-latency components have opposite phases and comparable magnitudes. And the depth of spectral notch is significantly correlated with the amplitude difference between the two separated components, as well as their degree of opposite phase. Our independent evidence for components contributing to SFOAE fine structure suggests that amplitude, phase and delay fine structure in the human SFOAEs are a construct of the complex addition of two or more internal reflections with different phase slops in the cochlea.Deep neural networks (DNNs) have been useful in solving benchmark problems in various domains including audio. DNNs have been used to improve several speech processing algorithms that improve speech perception for hearing impaired listeners. To make use of DNNs to their full potential and to configure models easily, automated machine learning (AutoML) systems are developed, focusing on model optimization. As an application of AutoML to audio and hearing aids, this work presents an AutoML based voice activity detector (VAD) that is implemented on a smartphone as a real-time application. The developed VAD can be used to elevate the performance of speech processing applications like speech enhancement that are widely used in hearing aid devices. The classification model generated by AutoML is computationally fast and has minimal processing delay, which enables an efficient, real-time operation on a smartphone. The steps involved in real-time implementation are discussed in detail. The key contribution of this work include the utilization of AutoML platform for hearing aid applications and the realization of AutoML model on smartphone. The experimental analysis and results demonstrate the significance and importance of using the AutoML for the current approach. The evaluations also show improvements over the state of art techniques and reflect the practical usability of the developed smartphone app in different noisy environments.