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The gain in performance comes from the SSL stage, which we base on a general purpose pseudo-task that limits the overfitting to the subject-specific patterns present in the training dataset.

We show that S2MAML outperforms standard supervised learning and MAML on the SC, ST, ISRUC, UCD and CAP datasets. Keywords: Sensor Informatics - Physiological monitoring , Sensor Informatics - Sensor-based mHealth applications , Imaging Informatics - Image registration, segmentation, and compression Abstract: In-vehicle health monitoring allows for continuous vital sign measurement in everyday life.

Eventually, this could lead to early detection of cardiovascular diseases. In this work, we propose non-contact heart rate HR monitoring utilizing near-infrared NIR camera technology. Ten healthy volunteers are monitored in a realistic driving simulator during resting 5 min and driving 10 min. We synchronously acquire videos using an out-of-the-shelf, low-cost NIR camera and 3-lead electrocardiography ECG serves as ground truth.

The MediaPipe face detector delivers the region of interest ROI and we determine the HR from the peak with maximum amplitude within the power spectrum of skin color changes. We compare video-based with ECG-based HR, resulting in a mean absolute error MAE of 7. As we apply only a simple signal processing pipeline without sophisticated filtering, we conclude that NIR camera-based HR measurements enables unobtrusive and non-contact monitoring to a certain extent, but artifacts from subject movement pose a challenge.

If these issues can be addressed, continuous vital sign measurement in everyday life could become reality. Keywords: Sensor Informatics - Sensors and sensor systems , General and theoretical informatics - Data storage , Sensor Informatics - Wireless sensors and systems Abstract: With the enormous amount of data collected by unobtrusive sensors, the potential of utilizing these data and applying various multi-modal advanced analytics on them is numerous and promising.

However, taking advantage of the ever-growing data requires high-performance data-handling systems to enable high data scalability and easy data accessibility.

This paper demonstrates robust design, developments, and techniques of a hierarchical time-indexed database for decision support systems leveraging irregular and sporadic time series data from sensor systems, e. We propose a technique that leverages the flexibility of general purpose, high-scalability database systems, while integrating data analytics focused column stores that leverage hierarchical time indexing, compression, and dense raw numeric data storage.

We have evaluated the performance characteristics and tradeoffs of each to understand the data access latencies and storage requirements, which are key elements for capacity planning for scalable systems. Keywords: Sensor Informatics - Wearable systems and sensors , Sensor Informatics - Physiological monitoring , Sensor Informatics - Sensor-based mHealth applications Abstract: In modern psychotherapy, digital health technology offers advanced and personalized therapy options, increasing availability as well as ecological validity.

These aspects have proven to be highly relevant for children and adolescents with obsessive-compulsive disorder OCD. Exposure and Response Prevention therapy, which is the state-of-the-art treatment for OCD, builds on the reconstruction of everyday life exposure to anxious situations. However, while compulsive behavior predominantly occurs in home environments, exposure situations during therapy are limited to clinical settings. Telemedical treatment allows to shift from this limited exposure reconstruction to exposure situations in real life.

In the SSTeP KiZ study smart sensor technology in telepsychotherapy for children and adolescents with OCD , we combine video therapy with wearable sensors delivering physiological and behavioral measures to objectively determine the stress level of patients.

The setup allows to gain information from exposure to stress in a realistic environment both during and outside of therapy sessions. In a first pilot study, we explored the sensitivity of individual sensor modalities to different levels of stress and anxiety. For this, we captured the obsessive-compulsive behavior of five adolescents with an ECG chest belt, inertial sensors capturing hand movements, and an eye tracker. Despite their prototypical nature, our results deliver strong evidence that the examined sensor modalities yield biomarkers allowing for personalized detection and quantification of stress and anxiety.

This opens up future possibilities to evaluate the severity of individual compulsive behavior based on multi-variate state classification in real-life situations. Keywords: Sensor Informatics - Wearable systems and sensors , Sensor Informatics - Multi-sensor data fusion , Sensor Informatics - Intelligent medical devices and sensors Abstract: In this work, a novel multi-modal device that allows data to simultaneously be collected from three noninvasive sensor modalities was created.

Force myography FMG , surface electromyography sEMG , and inertial measurement unit IMU sensors were integrated into a wearable armband and used to collect signal data while subjects performed gestures important for the activities of daily living ADL. Using all three modalities provided statistically-significant improvements over most other modality combinations, as it provided the most accurate and consistent classification results.

Clinical relevance—The use of three sensing modalities can improve gesture-based control of upper-limb prosthetics. Keywords: Sensor Informatics - Wearable systems and sensors , General and theoretical informatics - Machine learning Abstract: Understanding how macronutrients e. The general effects are well known, e. However, there are large individual differences in food metabolism, to where the same meal can lead to different glucose responses across individuals.

On an experimental dataset containing glucose responses to a variety of mixed meals, the technique is able to extract basis functions for the macronutrients that are consistent with their hypothesized effects on PPGRs, and also characterize how macronutrients affect individuals differently.

Keywords: Sensor Informatics - Wearable systems and sensors , Sensor Informatics - Data inference, mining, and trend analysis , General and theoretical informatics - Pattern recognition Abstract: The choice of appropriate machine learning algorithms is crucial for classification problems.

This study compares the performance of state-of-the-art time-series deep learning algorithms for classifying food intake using sensor signals. The sensor signals were collected with the help of a wearable sensor system the Automatic Ingestion Monitor v2, or AIM AIM-2 used an optical and 3-axis accelerometer sensor to capture temporalis muscle activation. Raw signals from those sensors were used to train five classifiers multilayer perceptron MLP , time Convolutional Neural Network time-CNN , Fully Convolutional Neural Network FCN , Residual Neural Network ResNet , and Inception network to differentiate food intake eating and drinking from other activities.

Data were collected from 17 pilot subjects over the course of 23 days in free-living conditions. A leave one subject out cross-validation scheme was used for training and testing. Time-CNN, FCN, ResNet, and Inception achieved average balanced classification accuracy of The results indicate that ResNet outperforms other state-of-the-art deep learning algorithms for this specific problem.

In our previous work we studied, both through simulations and in vitro experiments, the uncertainty associated with the model-to-phantom registration, as well as the camera-tracker calibration and video-guided navigation. In this work, we characterize the overall navigation uncertainty using tissue emulating patient-specific kidney phantoms featuring both virtual and physical internal targets. Pre-procedural models of the kidney phantoms and internal targets are generated from cone-beam CT images, and are registered to their intra-operative physical counterparts.

The user then guides the needle insertion to reach the internal targets using video-based imaging augmented with a virtual representation of the needle tracked in real time. Following navigation, we acquire post-procedural cone-beam CT images of the phantoms and inserted needles.

These images are used to determine the ground truth needle navigation accuracy i. We also explore a method to update the pre-procedural model to physical phantom registration intra-operatively using tracked video imaging, with the overall goal to improve overall navigation accuracy in the event of sub-optimal initial image-to-phantom registration.

Our results showed a navigation error of less than 3. Following registration correction intra-operatively, we showed an overall improvement in navigation from roughly 6 mm RMS to approximately 2 mm RMS error, which is acceptable given the inherent tracking, 3D printing and phantom manufacturing limitations.

Here, we describe the development of liver phantom of varying fat concentration using saturated fat mimicking liver steatosis. Followed by a pilot study in the human liver donor setting. Later, handheld device based on Infrared-LED and Photodetector for real-time time assessment of live donor liver and fat assessment. Clinical Relevance— This device can be used in the development of an accurate and non-invasive for quantification of liver fat in the deceased donor selection process.

Device therapies need to be adjusted for individual patients and evolving patient conditions, which can be achieved by adjusting device parameter settings. However, there are no validated clinical guidelines for parameter personalization, especially for patients with complex and rare conditions. In this paper, we propose a reinforcement learning framework for online parameter personalization of ICDs. Heart states can be inferred from ECG signals from ECG patches, which can be used to create a digital twin of the patient.

Reinforcement learning then use the digital twin as environment to explore parameter settings with less misdiagnosis. Experiments were performed on three virtual patients with specific and evolving heart conditions, and the result shows that our proposed approach can identify ICD parameter settings that can achieve better performance compared to default parameter settings.

Home Blood Pressure Monitoring HBPM has the potential to help diagnose patients experiencing isolated nocturnal hypertension who may otherwise be missed. This paper investigates potential diagnostic thresholds for diagnosing isolated nocturnal hypertension using dawn and dusk HBPM measurements in the BP-Eth ambulatory blood pressure monitoring ABPM database.

Depending on whether European or American diagnostic guidelines for hypertension were used, incidence of isolated nocturnal hypertension in the BP-Eth database was Using averaged dawn and dusk HBPM measurements to diagnose isolated nocturnal hypertension yielded an AUROC of 0. These results demonstrate the potential for HBPM to function as an intermediate step in screening patients, determining which patients require more intensive ABPM monitoring for detection of isolated nocturnal hypertension.

Keywords: Models and simulations of therapeutic devices and systems , Cardiovascular assessment and diagnostic technologies Abstract: Aortic valvuloplasty is a minimally invasive procedure for the dilatation of stenotic aortic valves. Rapid ventricular pacing is an established technique for balloon stabilization during this procedure. However, low cardiac output due to the pacing is one of the inherent risks, which is also associated with several potential complications.

This paper proposes a numerical modelling approach to understand the effect of different inflation levels of a valvuloplasty balloon catheter on the positional instability caused by a pulsating blood flow.

An unstretched balloon catheter model was crimped into a tri-folded configuration and then inflated to several levels. Ten different inflation levels were then tested, and a Fluid-Structure Interaction model was built to solve interactions between the balloon and the blood flow modelled in an idealised aortic arch. This work represents a substantial progress towards the use of simulations to solve the interactions between a balloon catheter and pulsating blood flow.

Keywords: Transdermal drug delivery , Infusion pumps , Robotic-aided therapies - Wireless therapeutic systems Abstract: Micro Transdermal Interface Platforms MicroTIPs will combine minimally invasive microneedle arrays with highly miniaturized sensors, actuators, control electronics, wireless communications and artificial intelligence. These patch-like devices will be capable of autonomous physiological monitoring and transdermal drug delivery, resulting in increased patient adherence and devolved healthcare.

In this paper, we experimentally demonstrate the feasibility of controlled transdermal drug delivery using a combination of um tall silicon microneedles, a commercial micropump, pressure and flow sensors, and bespoke electronics. The imaging speed of high-dimensional MRI is often limited, which constrains its practical utility. Recently, low-rank tensor models have been exploited to enable fast MR imaging with sparse sampling.

Most existing methods use some pre-defined sampling design, and active sensing has not been explored for low-rank tensor imaging. In this paper, we introduce an active low-rank tensor model for fast MR imaging. We propose an active sampling method based on a Query-by-Committee model, making use of the benefits of low-rank tensor structure. Numerical experiments on a 3-D MRI data set with Cartesian sampling designs demonstrate the effectiveness of the proposed method.

Balanced steady-state free precession bSSFP -based MR Fingerprinting has excellent signal-to-noise characteristics and also allows for acquiring both tissue parameter maps and field inhomogeneity maps. However, field inhomogeneity often results in complex magnetization evolutions in bSSFP-based MR Fingerprinting, which creates significant challenges in image reconstruction.

In this paper, we introduce a new method to address the image reconstruction problem. The proposed method incorporates a low-dimensional nonlinear manifold learned from an ensemble of magnetization evolutions using a deep autoencoder. It provides much better representation power than a low-dimensional linear subspace in capturing complex magnetization evolutions. We formulate the image reconstruction problem with this nonlinear model and solve the resulting optimization problem using an algorithm based on variable splitting and the alternating direction method of multipliers.

We evaluate the performance of the proposed method using numerical experiments and demonstrate that it significantly outperforms the state-of-art method using a linear subspace model. Low-resolution MR imaging, by contrast, is faster than its high-resolution counterpart, but it compromises on fine details necessary for a more precise diagnosis.

Super-resolution SR , when applied to low-resolution MR images, can help increase their utility by synthetically generating high-resolution images with little additional time. In this paper, we present a SR technique for MR images that is based on generative adversarial networks GANs , which have proven to be quite useful in generating sharp-looking details in SR.

We introduce a conditional GAN with perceptual loss, which is conditioned upon the input low-resolution image, which improves the performance for isotropic and anisotropic MRI super-resolution. The majority of works focus on exploring the dynamic factors during gait. Different from previous works, we adapt an image prediction task for anticipating the next frame in process of gait. In this work, we present a novel framework for human gait plantar pressure prediction using Spatio-temporal Transformer.

We train the model to predict the next plantar pressure image in an image series while also learning frame feature encoders that predict the features of subsequent frames in the sequence.

We proposed two new components in our loss function for considering temporality as well as smaller values in the image. Our method has the advantage over existing models in that it preserves the sequential sequence of observed images while also preserving long-range dependency, which are both important for the prediction.

Our model achieves superior results over several competitive baselines on the CAD WALK database. Clinical relevance— This work can be used in robotic exoskeleton devices which are intelligent systems designed to improve gait performance and quality of life for the wearer that are being used to assist the recovery of walking ability for patients with disorders.

Keywords: Image reconstruction and enhancement - Tomographic reconstruction , Ultrasound imaging - Breast Abstract: Ultrasound computed tomography USCT is considered to have great potential for breast cancer screening. Compared with the ray based methods, the reconstructed image using full waveform inversion FWI methods have higher spatial resolution. However, the results of FWI is difficult to converge to the real value when cycle skipping occurs.

In this paper, a cross-correlation full waveform inversion CC-FWI is proposed for USCT image reconstruction. In the first stage, the ajoint source is adjusted as the residual of predicted signal and time-shifted measured signal to avoid cycle skipping. In the remaining stage, the FWI with source encoding is employed to accelerate convergence. The simulations are conducted to demonstrate the validity of the proposed algorithm. The root mean squared error RMSE of the proposed algorithm is much smaller than that of conventional FWI.

The results suggest that CC-FWI is effective in avoiding cycle skipping. Clinical relevance— New imaging modalities of high resolution, safety to examines for early-stage breast cancer imaging are urgently needed for researching and development. Ultrasound computed tomography USCT is supposed to meet the above requirements and it can be potentially deployed in breast scanning. Keywords: Image analysis and classification - Digital Pathology , Image classification , Image feature extraction Abstract: Preterm infants in a neonatal intensive care unit NICU are continuously monitored for their vital signs, such as heart rate and oxygen saturation.

Body motion patterns are documented intermittently by clinical observations. Changing motion patterns in preterm infants are associated with maturation and clinical events such as late-onset sepsis and seizures. However, continuous motion monitoring in the NICU setting is not yet performed. Video-based motion monitoring is a promising method due to its non-contact nature and therefore unobtrusiveness.

This study aims to determine the feasibility of simple video-based methods for infant body motion detection. We investigated and compared four methods to detect the motion in videos of infants, using two datasets acquired with different types of cameras.

The thermal dataset contains 32 hours of annotated videos from 13 infants in open beds. The RGB dataset contains 9 hours of annotated videos from 5 infants in incubators.

The compared methods include background substruction BS , sparse optical flow SOF , dense optical flow DOF , and oriented FAST and rotated BRIEF ORB. The detection performance and computation time were evaluated by the area under receiver operating curves AUC and run time. We conducted experiments to detect motion and gross motion respectively.

In the thermal dataset, the best performance of both experiments is achieved by BS with mean standard deviation AUCs of 0. In the RGB dataset, SOF outperforms the other methods in both experiments with AUCs of 0. All methods are efficient to be integrated into a camera system when using low-resolution thermal cameras. Keywords: Image analysis and classification - Digital Pathology , Image feature extraction , Optical imaging Abstract: Meningioma is the most common intracranial tumor in adulthood.

Its prone to recur or progress to a higher degree is difficult to predict in the absence of obvious histological criteria. This project aims to develop an automatic methodology to aid in the diagnosis of meningiomas that is objective and easily reproducible.

The methodology is based on histopathological image analysis using artificial intelligence and machine learning algorithms. It includes a semi-automatic process of identification and cleaning of the scanned samples, an automatic detection of the nuclei of each image and, finally, the parameterization of the samples.

The obtained data together with the clinical information will be analyzed using statistical methods in order to provide a methodology to support clinical diagnosis and decision-making in patient management. The result is the development of an effective methodology that generates a set of data associated with morphological parameters with different trends according to the pathological groups studied. A tool has been developed that allows an effective semiautomatic analysis of the images to evaluate these parameters in an objective and reproducible way, helping in clinical decision-making and facilitating to undertake projects with large sample series.

The difference in procedures to acquire WSIs at various trial sites gives rise to variability in the histopathology images, thus making consistent diagnosis challenging. These differences may stem from variability in image acquisition through multi-vendor scanners, variable acquisition parameters, and differences in staining procedure; as well, patient demographics may bias the glass slide batches before image acquisition.

These variabilities are assumed to cause a domain shift in the images of different hospitals. It is crucial to overcome this domain shift because an ideal machine-learning model must be able to work on the diverse sources of images, independent of the acquisition center.

A domain generalization technique is leveraged in this study to improve the generalization capability of a Deep Neural Network DNN , to an unseen histopathology image set i. According to experimental results, the conventional supervised-learning regime generalizes poorly to data collected from different hospitals. However, the proposed hospital-agnostic learning can improve the generalization considering the low-dimensional latent space representation visualization, and classification accuracy results.

However, the visual features of the two domains are different. Rather than ImageNet pretrained weights, pre-training on a Histopathology dataset may provide better initialization. To prove this hypothesis, we train two commonly used Deep Learning model architectures - ResNet and DenseNet on a complex Histopathology classification dataset, and compare transfer learning performance with ImageNet pretrained weights. This is reflected by higher performance, lower training time as well as better feature reuse.

We employ graph neural networks to combine features obtained from tissue morphology with measurements of protein expression to profile the tumour microenvironment associated with different tumour stages. Our framework presents a new approach to analysing and processing these complex multi-dimensional datasets that overcomes some of the key challenges in analysing these data and opens up the opportunity to abstract biologically meaningful interactions.

Standard diagnosis of MSI is performed via genetic analyses, however these tests are not always included in routine care. Histopathology whole-slide images WSIs are the gold-standard for colorectal cancer diagnosis and are routinely collected. This study develops a model to predict MSI directly from WSIs. Making use of both weakly- and self-supervised deep learning techniques, the proposed model shows improved performance over conventional deep learning models.

Additionally, the proposed framework allows for visual interpretation of model decisions. These results are validated in internal and external testing datasets. Keywords: Neural interfaces - Tissue-electrode interface , Neural interfaces - Biomaterials , Neural interfaces - Bioelectric sensors Abstract: The biological response to electrodes implanted in the brain has been a long-standing barrier to achieving a stable tissue device-interface. Understanding the mechanisms underlying this response could explain phenomena including recording instability and loss, shifting stimulation thresholds, off-target effects of neuromodulation, and stimulation-induced depression of neural excitability.

Our prior work detected differential expression in hundreds of genes following device implantation. Here, we extend upon that work by providing new analyses using differential co-expression analysis, which identifies changes in the correlation structure between groups of genes detected at the interface in comparison to control tissues.

Our work adds to a growing body of literature which applies new techniques in molecular biology and computational analysis to long-standing issues surrounding electrode integration with the brain. Keywords: Neural stimulation , Neuromuscular systems - EMG models , Neuromuscular systems - Central mechanisms Abstract: Recent studies have reported that transcutaneous spinal stimulation tSCS may facilitate improved upper limb motor function in those with incomplete tetraplesia.

However, little is known about how tSCS engages upper limb motor pools. This study aimed to explore the extent to which discrete upper limb motor pools can be selectively engaged via altering stimulus location and intensity.

An incremental recruitment curve at C7 vertebral level was initially performed to attain minimal threshold intensity MTI in each muscle. Paired pulses 1ms square monophasic with inter-pulse interval of 50ms were subsequently delivered at a frequency of 0. in a random order. Evoked response to the 1st PRMR1 and 2nd PRMR2 stimuli were recorded from four upper limb muscles. A significant effect of spinal level was observed in all muscles for PRMR1 with greater responses recorded more caudally.

These results suggest that some level of unilateral motor pool selectivity may be attained via altering stimulus intensity and location during cervicothoracic tSCS. Keywords: Neural interfaces - Implantable systems , Neural stimulation , Neural interfaces - Microelectrode technology Abstract: Abstract— Optogenetics is a powerful neuroscientific tool which allows neurons to be modulated by optical stimulation.

Despite widespread optogenetic experimentation in small animal models, optogenetics in non-human primates NHPs remains a niche field, particularly at the large scales necessary for multi-regional neural research. We previously published a large-scale, chronic optogenetic cortical interface for NHPs which was successful but came with a number of limitations.

Keywords: Neurological disorders - Stroke , Neural stimulation , Neural signal processing Abstract: Brain stimulation has emerged as a novel therapy for ischemic stroke, a major cause of brain injury that often results in lifelong disability.

Although past works in rodents have demonstrated protective effects of stimulation following stroke, few of these results have been replicated in humans due to the anatomical differences between rodent and human brains and a limited understanding of stimulation-induced network changes. Therefore, we combined electrophysiology and histology to study the neuroprotective mechanisms of electrical stimulation following cortical ischemic stroke in non-human primates.

To produce controlled focal lesions, we used the photothrombotic method to induce targeted vasculature damage in the sensorimotor cortices of two macaques while collecting electrocorticography ECoG signals bilaterally.

In another two monkeys, we followed the same lesioning procedures and applied repeated electrical stimulation via an ECoG electrode adjacent to the lesion. We studied the protective effects of stimulation on neural dynamics using ECoG signal power and coherence. In addition, we performed histological analysis to evaluate the differences in lesion volume. In comparison to controls, the ECoG signals showed decreased gamma power across the sensorimotor cortex in stimulated animals.

Meanwhile, Nissl staining revealed smaller lesion volumes for the stimulated group, suggesting that electrical stimulation may exert neuroprotection by suppressing post-ischemic neural activity.

With the similarity between NHP and human brains, this study paves the path for developing effective stimulation-based therapy for acute stroke in clinical studies. Keywords: Neural stimulation , Neural interfaces - Implantable systems Abstract: Polycarbonate is a polymer that has been widely used including medical application due to its useful properties. It has high temperature resistance, biocompatibility, transparency and low water absorption rate, which are needful characteristics for packaging material of implantable neural prosthetic devices.

In this study, we investigated fabrication of neural electrode with polycarbonate film using standard photolithography process and heated hydraulic press for thermal lamination.

First, oxygen plasma surface treatment was performed to increase the adhesion between metal and polycarbonate film. Then thin layer of titanium and gold layer were deposited. Metal layer is patterned through standard photolithography techniques. After completing the metal patterning, thermal lamination was performed with site opened polycarbonate film.

Keywords: Neural interfaces - Implantable systems , Neural stimulation , Brain physiology and modeling - Neural dynamics and computation Abstract: Biphasic pulsatile stimulation is the present standard for neural prosthetic use, and it is used to understand connectivity and functionality of the brain in brain mapping studies.

While pulses have been shown to drive behavioral changes, such as biasing decision making, they have deficits. For example, cochlear implants restore hearing but lack the ability to restore pitch perception.

Recent work shows that pulses produce artificial synchrony in networks of neurons and non-linear changes in firing rate with pulse amplitude. Studies also show galvanic stimulation, delivery of current for extended periods of time, produces more naturalistic behavioral responses than pulses.

In this paper, we use a winner-take-all decision-making network model to investigate differences between pulsatile and galvanic stimulation at the single neuron and network level while accurately modeling the effects of pulses on neurons for the first time.

Results show pulses bias spike timing and make neurons more resistive to natural network inputs than galvanic stimulation at an equivalent current amplitude. Previous work has focused on development of decoding models from isolated speech data with a clean background and multiple repetitions of the material. In this study, we describe a novel approach to speech decoding that relies on a generative adversarial neural network GAN to reconstruct speech from brain data recorded during a naturalistic speech listening task watching a movie.

We compared the GAN-based approach, where reconstruction was done from the compressed latent representation of sound decoded from the brain, with several baseline models that reconstructed sound spectrogram directly.

We show that the novel approach provides more accurate reconstructions compared to the baselines. These results underscore the potential of GAN models for speech decoding in naturalistic noisy environments and further advancing of BCIs for naturalistic communication. VR experiments have already integrated non-invasive neural recording modalities such as EEG and functional MRI to explore the neural correlates of human behavior and cognition.

Integration with implanted electrodes would enable significant increase in spatial and temporal resolution of recorded neural signals and the option of direct brain stimulation for neurofeedback. In this paper, we discuss the first such implementation of a VR platform with implanted electrocorticography ECoG and stereo-electroencephalography sEEG electrodes in human, in-patient subjects.

Results demonstrate an increase in line noise power Hz while wearing the VR headset that is mitigated effectively by common average referencing CAR , and no significant change in the noise floor bandpower Hz. To our knowledge, this study represents first demonstrations of VR immersion during invasive neural recording with in-patient human subjects.

Two brain regions exhibit profound circuit remodeling through this process — the olfactory bulb and hippocampus. However, how local network changes in both regions influence global circuit rewiring and dynamic network features remain largely unexplored due to the lack of spatiotemporal resolution technology and large-scale electrophysiological activity recordings.

Here, we demonstrate large-scale recordings using a high-density neurochip to reveal multimodal circuit-wide electrophysiological properties and layer-specific functional connectivity in the olfactory bulb and hippocampal networks. Our findings illustrate simultaneous recordings from the entire network, which allows us to quantify synchronous electrophysiological parameter differences and layer-specific waveform markers. Examining pairwise cross-covariance between active electrode pairs reveals individual neuronal ensemble contributions to synchronous activation between layers and hub microcircuits, demonstrating network-wide rewiring.

Our study suggests a novel tool to address the computational implications of large-scale activity patterns in functional multimodal neurogenic circuits. Keywords: Neural stimulation - Deep brain , Motor learning, neural control, and neuromuscular systems , Brain physiology and modeling - Sensory-motor Abstract: Traditional methods to access subcortical structures involve the use of anatomical atlases and high precision stereotaxic frames but suffer from significant variations in implantation accuracy.

Here, we leveraged the use of the ROSA One R Robot Assistance Platform in non-human primates to study electrophysiological interactions of the corticospinal tract with spinal cord circuits. We were able to target and stimulate the corticospinal tract within the internal capsule with high accuracy and efficiency while recording spinal local field potentials and multi-unit spikes. Our method can be extended to any subcortical structure and allows implantation of multiple deep brain stimulation probes at the same time.

Keywords: Brain functional imaging - Source localization , Brain functional imaging - EEG Abstract: Electrophysiological brain source localization consists in estimating the source positions and activities responsible for the S EEG measurements. The localization procedure is usually carried out in the time domain, however in specific situations the activities of interest can be located at well defined frequencies, e. in response to a rhythmic stimulation. This paper addresses the problem of sparse localization of multiple sources oscillating at the same frequency.

In particular the non-unicity of the solution is emphasized, as alternative source maps involving equivalent or less number of sources can be found, challenging source localization methods based on sparsity. These limitations are illustrated under a realistic SEEG simulation framework, and the usefulness to perform localization for this modality is strengthen out. Brain-computer interfaces BCIs using local field potentials LFPs have recently achieved performance comparable to spike-BCIs [1].

With superior stability over time, LFPs may be the preferred signal for BCIs. We show that sensorimotor LFPs can provide reward level information R1 — R3 like spikes[2]. We used a cued reward-level reaching task in which reward information was temporally dissociated from movement information. This allowed the study of reward- and movement-related modulations in LFPs. We recorded simultaneously from contralateral primary -somatosensory S1 , -motor M1 , and the dorsal premotor PMd cortices in a female Macaca Mulatta.

Such modulation was consistently observed after controlling for cue color, differences in behavioral variables, and electromyogram EMG activity. Statistical amplitude analysis showed that reward level could be extracted from the simple LFP feature of beta band amplitude, even before a reaching target appeared, and no specific reach plan could be developed. Keywords: Bio-electric sensors - Sensing methods , Physiological monitoring - Instrumentation , Bio-electric sensors - Sensor systems Abstract: Dry-contact electrodes are increasingly being used for EEG recordings in both research studies and consumer products.

They are more user-friendly and better suited for long-term recordings. However, dry-contact electrodes also bring challenges with respect to the stability and impedance of the electrode-skin interface. We propose a methodology to characterize and compare dry-contact electrodes. The characterization is based on measuring the electrode-skin impedance spectrum, fit a parametric model of the electrode-skin interface to the measured spectrum, and calculate the resulting thermal noise spectrum.

Thereby it is possible to relate the noise of an EEG recording to the theoretical noise contribution from the electrode-skin interface. To demonstrate the methodology, we performed an empirical study comparing two types of dry-contact electrodes in an ear-EEG setup. Here, we related the noise floor of an auditory steady-state response ASSR to the thermal noise spectrum of the electrode-skin interface.

The study showed similar impedance and EEG recording quality for the two electrode types, and the thermal noise of the electrode-skin interface was below the noise floor of the EEG recordings for both electrode types. Keywords: Bio-electric sensors - Sensor systems , Physiological monitoring - Instrumentation , Health monitoring applications Abstract: Fetal electrocardiography fECG has gotten widespread interest in the last years as technology for fetal monitoring.

Compared to cardiotocography CTG , the current state of the art, it can be designed in smaller formfactor and is thus suited for long-term and unsupervised monitoring. To reject unreliable fetal heart rate fHR estimations, the signal processing algorithm provides a signal quality index. However, the remaining 9 patients showed low acceptance rates and high errors. Besides investigating the source of these high errors, future work includes the investigating improved signal processing algorithms, different body positions and the use of dry electrodes.

Keywords: Wearable sensor systems - User centered design and applications , Physiological monitoring - Novel methods Abstract: High quality sleep monitoring is done using EEG electrodes placed on the skin.

This has traditionally required assistance by an expert when the equipment needed to mounted. However, this creates a limitation in how cheap and easy it can be to record sleep in the subject's own home. Here we present a data set of home recordings of sleep, in which subjects use self-applied ear-EEG monitoring equipment.

We compare this data set to a previously recorded data set with both ear-EEG and polysomnography, which was applied by an expert. Clinical relevance: On all tested metrics, self applied sleep recordings behaved the same as expert applied. This indicates that ear-EEG can reliably be used as a home sleep monitor, even when subjects apply the equipment themselves. Keywords: Wearable low power, wireless sensing methods , Bio-electric sensors - Sensor systems , IoT sensors for health monitoring Abstract: In recent years, in-ear electroencephalography EEG was demonstrated to record signals of similar quality compared to standard scalp-based EEG, and clinical applications of objective hearing threshold estimations have been reported.

Existing devices, however, still lack important features. In fact, most of the available solutions are based on wet electrodes, require to be connected to external acquisition platforms, or do not offer on-board processing capabilities. Here we overcome all these limitations, presenting an ear- EEG system based on dry electrodes that includes all the acquisition, processing, and connectivity electronics directly in the ear bud.

The earpiece is equipped with an ultra-low power analog front-end for analog-to-digital conversion, a low-power MEMS microphone, a low-power inertial measurement unit,and an ARM Cortex-M4 based microcontroller enabling on- board processing and Bluetooth Low Energy connectivity.

The system can stream raw EEG data or perform data processing directly in-ear. We test the device by analysing its capability to detect brain response to external auditory stimuli, achieving 4 and 1. The latter allows for hours operation on a PR44 zinc-air battery. To the best of our knowledge, this is the first wireless and fully self-contained ear-EEG system performing on-board processing, all embedded in a single earbud.

Keywords: Physiological monitoring - Instrumentation , Optical and photonic sensors and systems , Physiological monitoring - Modeling and analysis Abstract: PhotoPlethysmoGraphy PPG is ubiquitously employed in wearable devices for health monitoring.

Photodiode signal inversion is observed in rare occasions, most of the time when the sensor is pressed against the skin. We report in this article such observations made at the right common carotid artery site. Indeed we have systematically observed a photodiode signal inversion when the PPG sensor is placed where the pulse is the best felt at the carotid. In addition to be inverted, the pulse is steeper during the systolic phase. Such inversion has implications in terms of pulse arrival time PAT measurements In our experiments, this causes a difference of 20 ms in the carotid PAT when measured at the absolute maximum slope.

The mechanical and optical properties of tissues must be better accounted to explain the PPG signal morphology. Clinical Relevance— Understanding the role of mechanical tissue properties seems relevant in order to obtain more reproducible results in PPG signal analysis. Keywords: Wearable wireless sensors, motes and systems , Physiological monitoring - Modeling and analysis , Health monitoring applications Abstract: Stress is has been classified as the health epidemic of the 21st century with an increasingly active research interest within the fields of psychology, neuroscience, medicine, and more recently affective computing.

At present, stress is identified through cortisol levels in saliva but there is no unanimously accepted standard for continuous stress evaluation. With recent development in wearable sensors, many scientist are interested in stress identification through physiological signals such as the Heart rate variability HRV.

In this paper, we present a novel supervised machine learning-based algorithm to detect stress from HRV derived from both electrocardiograms ECG and photoplethysmograms PPG. HRV features from ECG and PPG signals of 46 healthy subjects were analysed and used to separately train and test a Random Forest algorithm.

Results show that PPG is a good surrogate to ECG for HRV analysis and stress detection. The proposed algorithm has the potential to assist researchers and clinicians in the automated continuous analysis of stress. Keywords: Neural networks and support vector machines in biosignal processing and classification , Data mining and big data methods - Inter-subject variability and personalized approaches , Physiological systems modeling - Multivariate signal processing Abstract: Electroencephalography EEG is shown to be a valuable data source for evaluating subjects' mental states.

However, the interpretation of multi-modal EEG signals is challenging, as they suffer from poor signal-to-noise-ratio, are highly subject-dependent, and are bound to the equipment and experimental setup used, i. This leads to machine learning models often suffer from poor generalization ability, where they perform significantly worse on real-world data than on the exploited training data.

Recent research heavily focuses on cross-subject and cross-session transfer learning frameworks to reduce domain calibration efforts for EEG signals. We argue that multi-source learning via learning domain-invariant representations from multiple data-sources is a viable alternative, as the available data from different EEG data-source domains e.

We propose an adversarial inference approach to learn data-source invariant representations in this context, enabling multi-source learning for EEG-based brain-computer interfaces. We unify EEG recordings from different source domains i. Keywords: Neural networks and support vector machines in biosignal processing and classification , Signal pattern classification Abstract: We provide a regularization framework for subject transfer learning in which we train an encoder and classifier to minimize classification loss, subject to a penalty measuring independence between the latent representation and the subject label.

We introduce three notions of independence and corresponding penalty terms using mutual information or divergence as a proxy for independence. For each penalty term, we provide several concrete estimation algorithms, using analytic methods as well as neural critic functions.

We evaluate the performance of these individual regularization strategies and our AutoTransfer method on EEG, EMG, and ECoG datasets, showing that these approaches can improve subject transfer learning for challenging real-world datasets. Therefore, it is important to develop tools to help detecting ADHD so that treatment can start as soon as possible. Plus, the differentiation of ADHD in its subtypes is important to define the recommended treatment.

Here we present original research to investigate the hypothesis of using a Spiking Neural Networks SNN EEG signals classifier for automated diagnostic of ADHD subtypes. This research used data from patients and healthy volunteers acquired as part of the Healthy Brain Network. These resting state EEG signals were collected from 5-minutes scan with a channel Hz system. For benchmarking, we present a comparison of the SNN performance with a support vector machine, a k-nearest neighborhood, a random forest algorithm and a multi-layer perceptron.

We present experiments for both the diagnostics of ADHD and for detecting which ADHD subtype the patient has. SNN presented a Keywords: Data mining and big data methods - Machine learning and deep learning methods , Data mining and big data methods - Biosignal classification , Time-frequency and time-scale analysis - Time-frequency analysis Abstract: Among the different modalities to assess emotion, electroencephalogram EEG , representing the electrical brain activity, achieved motivating results over the last decade.

Emotion estimation from EEG could help in the diagnosis or rehabilitation of certain diseases. In this paper, we propose a dual model considering two different representations of EEG feature maps: 1 a sequential based representation of EEG band power, 2 an image-based representation of the feature vectors. We also propose an innovative method to combine the information based on a saliency analysis of the image-based model to promote joint learning of both model parts.

The model has been evaluated on four publicly available datasets: SEED-IV, SEED, DEAP and MPED. The achieved results outperform results from state-of-the-art approaches for three of the proposed datasets with a lower standard deviation that reflects higher stability. The electroencephalogram EEG has been studied extensively for AD characterization, but reliable early-stage diagnosis continues to present a challenge.

The aim of this study was to introduce a novel way of classifying between AD patients, MCI subjects, and age-matched healthy control HC subjects using EEG-derived feature images and deep learning techniques. The EEG recordings of age-matched subjects 52 AD, 37 MCI, 52 HC were converted into 2D greyscale images representing the Pearson correlation coefficients and the distance Lempel-Ziv Complexity dLZC between the 21 EEG channels. Each feature type was computed from EEG epochs of 1s, 2s, 5s and 10s segmented from the original recording.

Whilst a maximum classification accuracy of 医療用語 専門講座 における 3 の 3 コース. This final course finishes the comprehensive examination of medical terminology by introducing new roots, terms, and abbreviations related to the remaining body systems: nervous brain, spinal cord, and nerves and special senses eyes and ears , digestive, and reproductive. Very informative, easy to understand, well-organized, and confidence builder with medical terminology. Great course. Great teacher. Great experience. Incredibly interesting.

Welcome to the digestive system module. This module will introduce you to medical terms related to the mouth, esophagus, stomach, intestines, and accessory organs like the liver, gallbladder, and pancreas. Following the SOAP note format, we will look at common complaints, tests, diagnoses, and treatments used in gastroenterology. I will let you "digest" the material in this module. Bon appetite! 個人 ビジネス キャンパス 政府.

Digestive - Assessment Terms. Medical Terminology III. Filled Star Filled Star Filled Star Filled Star Filled Star.