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Mar 18, When the main. However, first we must trust the collected data. In this paper, we present a novel approach to improving heart rate estimates from wrist pulse photoplethysmography PPG sensors. We also discuss the impact of sensor movement on the veracity of collected heart rate data. Keywords: Physical sensors and sensor systems - Mechanical sensors and systems , Bio-electric sensors - Sensor systems , Implantable sensors Abstract: As described in this paper, we propose a sheet-type pressure sensor to support assistive technology for artificial knee joint replacement.
The proposed pressure sensor consists of two sheets: an electrode sheet with metal wiring and a flexible polymer-based insulating layer on 80 um polyimide film, as well as a pressure-sensitive conductive sheet that can function as a pressure-to-resistance sensor. We developed a 5 cm x 7 cm pressure sensor sheet with sensing points.
The multiple sensing sheet is expected to monitor the pressure distribution in an artificial knee joint during total knee arthroplasty to improve patients' quality of life. Keywords: Physiological monitoring - Novel methods Abstract: In an acute myocardial infarction AMI a blood vessel of the heart is fully or partially blocked by e. If the coronary arteries are fully blocked, the height of the observed ST elevation in electrocardiogram ECG is directly related to the severity of permanent damage to the heart muscle. A percutaneous coronary intervention PCI — an invasive procedure - can be performed to remove the blockage from coronary arteries and it should be performed rapidly after the onset of the symptoms.
However, sometimes the person suffering from AMI do not realize to acquire for help soon enough. In order to facilitate the rapid treatment we describe a smartphone-only solution for the early detection of STEMI condition. Keywords: Mechanical sensors and systems , Physiological monitoring - Novel methods , Wearable sensor systems - User centered design and applications Abstract: This study aims to annotate certain fiducial points in gyrocardiography GCG waveforms which coincide with cardiac events to evaluate the systolic and diastolic function of the heart.
Keywords: Physiological monitoring - Instrumentation , Portable miniaturized systems , Physical sensors and sensor systems - Mechanical sensors and systems Abstract: In this pilot study we investigated the correspondence between the fiducial points FPs in the seismocardiogram SCG commonly associated with the opening and closure of mitral and aortic valves, and the real valve movements as detected by M-mode echo images.
The minimal and maximal error was observed for the opening 2. These figures support the use of the traditional SCG FPs in research, but foster further investigations for their application in clinics. Keywords: Physiological monitoring - Instrumentation , Mechanical sensors and systems , Integrated wearable and portable systems Abstract: SCG could present a easy to use tool in diagnostics and continues monitoring of patient with heart conditions.
However, no description of normal time and amplitude values for healthy subjects exists. This paper describes the normal cardiac time intervals and amplitudes of the SCG signal in 44 healthy subjects. LVET was significantly different between male and females. Therefore, these organic polymers are attractive in a broad spectrum of bioelectronic applications ranging from implantable electrodes to biosensors and actuators. Patterned films of CPs, especially with various surface chemistries, provide versatile and sophisticated building-blocks for bioelectronics.
In this context, we recently introduecd a simple and efficinet technique of hydrogel-mediated electropolymerization to directly pattern films of PPy polypyrrole with spatially-addressable chemistries. This technique employs a topographically patterned hydrogel stamp to deliver polymer precursors to the surface of electrode during the PPy electropolymerization. This method enables easy incorporation of different molecules into CP film during the polymerization. Herein, we aim to extend the scope of hydrogel-mediated electropolymerization to pattern other types of CPs and to explore the potential of bio-functionalized CP films for cell adhesion studies.
The produced films were characterized for morphology, impedance, and chemical composition. Patterned CP films were bio-functionalized by incorporation of a lamimin peptide into these films. Lastly, the resultant substartes were tested for cell adhesion where laminin-doped CP showed a higher level of cell adhesion compared to PSS polystyrene sulfonate -doped CP films.
These results together demonstrate the potential application of patterned films of bio-functionalized CPs for cellular engineering. Keywords: Scaffolds in tissue engineering - Biofabrication , Scaffolds in tissue engineering - Patterned 3D , Scaffolds in tissue engineering - Rapid prototyping Abstract: Stereolithography-based bioprinting offers advantages in resolution and rapid printing time, and thus has received major attention in recent years.
However, traditional stereolithography-based bioprinting utilizes an ultraviolet light which may cause mutagenesis and carcinogenesis of cells. In this paper, we present a new visible light crosslinkable bioink that is based on cell-adhesive gelatin. We examined the feasibility of using visible light from a commercial beam projector to pattern the EY-GelMA bioink. We measured the absorbance of bioink to characterize its sensitivity to visible light and performed bioprinting to test its ability to promote cell adhesion.
It is found that the EY-GelMA bioink has an absorption peak at roughly nm, and that it can be successfully crosslinked by visible light from the commercial projector. Ultimately, the EY-GelMA bioink can support both visible light crosslinking and cell adhesion, offering great potential in bioprinting and tissue engineering. Keywords: Microfluidic applications , Microfluidic techniques, methods and systems , Biomaterials - Chemical and electrochemical sensors Abstract: Compact disk CD Microfluidic platforms are being studied for medical applications such as blood tests.
However, its size is bulky and electricity is needed to realize centrifuge force.
New tools and some omics
In this paper, bearing-based hand spinner is designed using three-dimensional printer. This spinner does not need electricity and keeps rotating direction during spinning unlike paperfuge. The properties of spinner vary depending on bearing type which is positioned at the center. The type of weighting area also affects RPM changes over time.
When separation experiment is implemented, separating mixture into red ink and oil is achieved properly with ceramic ball bearing. Previous works addressed this subject in the way of controlling cell migration by micro- or nano-patterning the substrates. However, the problem of changing spatial cell density freely under co-culture conditions is remaining. To solve this problem, in this work, we report that C2C12 spatial cell density changes by the patterning geometric boundary of the topographical structures.
This our finding will provide a new device which enables to manipulate spatial cell density under co-culture conditions for heterogeneous tissue engineering. Keywords: Scaffolds in tissue engineering - Biofabrication , Scaffolds in tissue engineering - Multiscale , Scaffolds in tissue engineering - Self-assembled Abstract: Artificial assembly of mature tissues in vitro is challenging from many viewpoints. Therefore, production of intermediate building blocks — cell spheroids expected to be a viable alternative.
The system consists of a 3D culture unit and a medium perfusion unit. The 3D culture unit is dedicated for spheroid culture without using scaffolds, eliminating concerns about biocompatibility of artificial materials. The spheroids are cultured in a sealed environment and their life are sustained by hollow fiber perfusion fluidics.
We confirmed by visual and by microscopic examination that no contamination did occur before and after spheroid inoculation. Moreover, we confirmed growth and fusion between cells when C2C12 spheroids were cultured in this system. Keywords: Nano-bio technology design , Microfluidic techniques, methods and systems , Translational issues in tissue engineering and biomaterials - Bioreactors Abstract: Advances in the areas of tissue engineering and microfabrication techniques have enabled promising in vitro platforms, known as Organs-on-Chips, with the aim of mimicking complex in vivo conditions for more accurate toxicology studies.
To analyze the physiological change induced by chemicals or toxic substances continuously, sensors can be used in order to measure the intracellular and extracellular environment of single cells, cell constructs, or tissue, and therefore the integration of monitoring techniques into 3D tissue culture platforms provides an essential step for the next generation Organ-on-Chip platforms. However, current in vitro platforms are not capable of combining the culture of 3D models with monitoring techniques. In this work, spheroid culturing protocols were developed for optimized spheroid growth and an evaluation of spheroid integrity on different porous layers was performed in order to provide a defined spheroid encapsulation on BioChip sensors.
Keywords: New technologies and methodologies in biomechanics , Joint biomechanics , Biomechanics and robotics in physical exercise Abstract: This paper offers quantification of ankle stability in relation to simulated haptic environments of varying stiffness.
This study analyzes the stability trends of male and female subjects independently over a wide range of simulated environments after subjects were exposed to vigorous position perturbation. Ankle stability was quantified for both degrees-of-freedom of the ankle in the sagittal and frontal planes. In the frontal plane, male and female subjects exhibited nearly identical stability levels. In the sagittal plane, however, male subjects demonstrated marginally more stability than female subjects in environments with negative stiffness.
Results of this study are beneficial to understanding situations in which the ankle is likely to lose stability, potentially resulting in injury. Keywords: Joint biomechanics , Applied tissue and organ models and motion analysis , New technologies and methodologies in biomechanics Abstract: Knee osteoarthritis that prevalently occurs at the medial compartment is a progressive chronic disorder affecting the articular cartilage of the knee joint, and lead to loss of joint functionality.
Valgus braces have been used as a treatment procedure to unload the medial compartment for patients with medial osteoarthritis. Valgus braces through the application of counteracting external valgus moment shift the load from medial compartment towards the lateral compartment. Previous biomechanical studies focused only on the changes in varus moments before and after wearing the brace.
The objective of this study was to investigate the influence of opposing external valgus moment applied by knee braces on the lateral tibial cartilage contact conditions using a 3D finite element model of the knee joint. Finite element simulations were performed on the knee joint model without and with the application of opposing valgus moment to mimic the unbraced and braced conditions.
Lateral tibial cartilage contact pressures and contact area, and tibial rotation varus-valgus and internal-external were estimated for the complete walking gait cycle. The opposing valgus moment increased the maximum contact pressure and contact area on the lateral tibial cartilage compared to the normal gait moment. A peak contact pressure of 8. The results show that the use of opposing valgus moment may significantly increase the maximum contact pressures and contact area on the lateral tibial cartilage and increases the risk of articular cartilage damage on the lateral compartment.
Keywords: Joint biomechanics Abstract: In this study we have described the use of statistical shape modeling SSM technique in evaluating the morphological variation of shoulder bones from a South African population. The anatomical landmark selections were carried out, followed by the registration of the meshes which were validated before establishing the dense correspondence. The SSMs were built and average shape comparison from each side for each bone were made in order to evaluate handedness. In general, there was no error found around the gleno-humeral region which may suggest that the usage of contralateral healthy shoulder could serve as an informed decision making tool for surgery and prosthesis design.
Keywords: Joint biomechanics Abstract: External hip protectors are used by the elderly in preventing hip fracture due to sideway falls. There are some commercial hip protectors which has both energy absorbing and energy shunting properties. In this study, a novel hip protector using shear thickening polymer STP is studied. The purpose of this work is to determine the optimal thickness of STP needed for maximum force attenuation. A mechanical test rig to simulate a person falling with sufficient impact energy to fracture the greater trochanter if unprotected was used together with biofidelic femur model which simulates the layer of flesh with skin.
When comparing the overall thickness with commercial hip protectors, STP hip protectors tested have much less thickness. Reduced thickness increases the compliance and comfort of STP hip protectors. Keywords: Optimization in musculoskeletal biomechanics , Modeling and simulation in musculoskeletal biomechanics , Dynamics in musculoskeletal biomechanics Abstract: This research proposes a novel method that evaluates joint reaction forces by motion analysis using a musculoskeletal model.
While general muscle tension estimations minimize the sum of the muscle tensions, the proposed method utilizes the joint reaction forces themselves in the objective function of the optimization problem in addition to conventional method. This method can estimate a pattern of the muscle tensions that maximizes or minimizes a specific joint force. As a typical outcome, the proposed method allows evaluating intervertebral disc compressive force caused by co-contraction of muscles while avoiding risk underestimation. We analyzed the actual lifting motion as an example and confirmed that the method can estimate the muscle tension distribution under different tension conditions.
Keywords: Modeling and simulation in musculoskeletal biomechanics Abstract: Subject-specific musculoskeletal models can predict accurate joint and muscle biomechanics thereby helping clinicians and surgeons. Current modeling strategies do not incorporate accurate subject-specific muscle parameters. This study reports a statistical shape model SSM based method to predict subject-specific muscle attachment regions on shoulder bones and illustrates the concurrent validity of the predictions. The regions were represented by subset of vertices on the bone meshes and were tracked using vertex identifiers.
Subject-specific muscle attachment regions were predicted using external set of bones not used in building the SSMs. Validity of predictions was determined by visual inspection and also by using four similarity measures between predicted and manually segmented regions. Excellent concurrent validity was found indicating the higher accuracy of predictions. This method can be effectively employed in modeling pipelines or in automatic segmentation of medical images. Further validations are warranted on all the muscles of the shoulder complex.
Modern hearing aids can suppress background noise; however, there is little that can be done to help a user attend to a single conversation without knowing which speaker is being attended to. Cognitively controlled hearing aids that use auditory attention decoding AAD methods are the next step in offering help.
A number of challenges exist, including the lack of access to the clean sound sources in the environment with which to compare with the neural signals. We propose a novel framework that combines single-channel speech separation algorithms with AAD. Using invasive electrophysiology recordings, our system is able to decode the attention of a subject and detect switches in attention using only the mixed audio. We also identified the regions of the auditory cortex that contribute to AAD.
Our quality assessment of the modified audio demonstrates a significant improvement in both subjective and objective speech quality measures. Our novel framework for AAD bridges the gap between the most recent advancements in speech processing technologies and speech prosthesis research and moves us closer to the development of cognitively controlled hearing aids.
Our results on a EEG-based hand squeeze task show that using a convolutional neural network and a time preserving signal representation strategy provides a good balance between high accuracy and feasibility in a real-time application. According to experiment, the ITR of the eighty-target motion checkerboard paradigm achieved These results suggest that the proposed Chinese speller can input vast number of characters with higher speed and less operations, providing a high practicability communication method for people with motor disabilities.
Subjects were required to perform three tactile attention tasks, prompted by cues presented in random order and while both wrists were simultaneously stimulated: 1 selective sensation of the left hand SS-L , 2 selective sensation of the right hand SS-R , 3 bilateral selective sensation SS-B. This non-stationarity makes the modeling of dynamics challenging. In our prior work, we developed a framework to identify time-invariant linear state-space models SSMs to describe both stationary spontaneous neural population dynamics and input-output IO neural dynamics in response to electrical stimulation.
Here, we develop an adaptive identification algorithm that estimates time-variant SSMs to track possible non-stationarity in brain network dynamics. We apply the adaptive algorithm to track high-density human ECoG dynamics in three subjects over a long time-period. We find that the adaptive identification algorithm can estimate time-variant SSMs that significantly outperform time-invariant SSMs in all subjects.
Our results demonstrate that non-stationary dynamics exist in high-dimensional human ECoG signals over long time-periods, and that the proposed adaptive SSM identification algorithm can successfully track these non-stationarities. These results have important implications for more accurate estimation of neural biomarkers for different brain states and for adaptive closed-loop stimulation therapy across a wide range of neurological disorders.
These methods aim at functional motor rehabilitation using Brain-machine interfaces to constitute an alternate pathway from the brain to the muscles. Even in patients with absence of residual finger movements, recovery could be achieved. The extent to which these interventions are affected by individual lesion topology is yet to be understood. In this study EEG was measured in 30 chronic stroke patients during movement attempts of the paretic arm.
We show that the magnitude of the event-related desynchronization was smaller in patients presenting lesions with involvement of the motor cortex. This could have important implications on the design of new rehabilitation schemes for these patients, which might benefit from carefully tailored interventions.
Keywords: Sensory neuroprostheses - Visual , Neural stimulation , Neural interfaces - Tissue-electrode interface Abstract: Neuronal excitation threshold is known to be influenced by the distance from the stimulating electrodes to the target cell. Closer proximity with the target require less electrical energy for stimulation.
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Is it possible to obtain the optimal proximity to stimulate the target cell with the minimum electrical energy? This study stimulated the retinal ganglion cells with various stimulating distances and measured the cortical electrically evoked potentials EEPs. Results showed that EEPs increased with impedance, an indirect measurement of electrode-retina distance, reached a response maximum and decreased when impedance further increased. Keywords: Neural interfaces - Implantable systems , Sensory neuroprostheses - Visual , Neural stimulation Abstract: Profoundly blind people are capturing their first glimpses of what the future holds through first-generation retinal neuroprosthesis technologies now in clinical use.
While the placement of a device at this site offers a number of unique advantages, the increased distance to the target neurons also presents a number of challenges that must be overcome in order to realize a clinically-beneficial device. Here, the advantages and challenges of the suprachoroidal approach are compared, and a case is made for its pursuit as a clinical therapy in the treatment of degenerative disorders of the retina. Keywords: Sensory neuroprostheses - Visual Abstract: The feasibility of the retinal stimulation to restore vision can be evaluated in the animal model as a pre-clinical experiment.
Rabbit is a good animal model not only for the electrophysiological but also surgical experiment, but there is no hereditary retinal degeneration model in wild type rabbit. Genetically modified retinal degeneration model was introduced but not commonly used because of the difficulty in breeding. Medically induced retinal degeneration models with systemic injection of sodium iodate deteriorate the outer retina including photoreceptor and plexiform layers and only mimicking end-stage retinal degeneration.
By using intravitreal sodium iodine injection, monocular photoreceptor degeneration can be achieved and can mimic various stages of the retinal degeneration. In this model, dose-dependent retinal degeneration and related histological and physiological changes will be introduced. Keywords: Sensory neuroprostheses - Visual , Sensory neuroprostheses , Neural interfaces - Implantable systems Abstract: We have recently a second-generation device for a suprachoroidal-transretinal stimulation STS retinal prosthesis and have recently developed a second-generation device.
Efficacy evaluations of this were performed in pre-clinical and clinical tests. The results indicated that the device is feasible for clinical applications. Keywords: General and theoretical informatics - Predictive analytics , Health Informatics - Disease profiling and personalized treatment , Bioinformatics - Gene expression pattern recognition Abstract: Recent research shows that gene expression changes appear to correlate well with the progression of many types of cancers. Using changes in gene expression as a basis, this paper proposes a data-driven 2-player game-theoretic model to predict the risk of adenocarcinoma based on Nash equilibrium.
A key innovation in this work is the pay-off function which is a weighted composite of the expression of a cohort of tumor-suppressor genes as one player and an analogous cohort of oncogenes as the other player. Another novelty of the model is its ability to predict the risk that a healthy sample will develop adenocarcinoma, if its associated gene expression is comparable to that of early-stage tumor samples. The model is validated using two of the largest publicly available adenocarcinoma datasets.
We propose a temporal transfer learning approach to utilize information from adjacent time points to yield an early cardiac arrest prediction model that is robust in predictive accuracies as well as maintains the interpretability of the model coefficients. Our model estimates the logistic regression coefficients simultaneously for various time points to share knowledge from different observation windows. This framework can overcome small sample size issues, and result in robust estimation of the model coefficients.
We find that our model consistently outperforms a logistic regression model fit only on vital sign data from a single time slice for intensive care unit patients. Moreover, we find that the estimated coefficients from our model captures temporal trends in the data. Keywords: General and theoretical informatics - Predictive analytics , General and theoretical informatics - Artificial Intelligence , General and theoretical informatics - Algorithms Abstract: In the cost sensitive healthcare industry, an unplanned downtime of diagnostic and therapy imaging devices can be a burden on the financials of both the hospitals as well as the original equipment manufacturers OEMs.
In the current era of connectivity, it is easier to get these devices connected to a standard monitoring station. Once the system is connected, OEMs can monitor the health of these devices remotely and take corrective actions by providing preventive maintenance thereby avoiding major unplanned downtime. In this article, we present an overall methodology of predicting failure of these devices well before customer experiences it.
We use data-driven approach based on machine learning to predict failures in turn resulting in reduced machine downtime, improved customer satisfaction and cost savings for the OEMs. Keywords: Health Informatics - Disease profiling and personalized treatment , General and theoretical informatics - Machine learning , General and theoretical informatics - Predictive analytics Abstract: Over the past decade, continuous glucose monitoring CGM has proven to be a very resourceful tool for diabetes management.
To date, CGM devices are employed for both retrospective and online applications. Their use allows to better describe the patients' pathology as well as to achieve a better control of patients' level of glycemia. The analysis of CGM sensor data makes possible to observe a wide range of metrics, such as the glycemic variability during the day or the amount of time spent below or above certain glycemic thresholds. However, due to the high variability of the glycemic signals among sensors and individuals, CGM data analysis is a non-trivial task.
Standard signal filtering solutions fall short when an appropriate model personalization is not applied. State-of-the-art data-driven strategies for online CGM forecasting rely upon the use of recursive filters. Each time a new sample is collected, such models need to adjust their parameters in order to predict the next glycemic level. In this paper we aim at demonstrating that the problem of online CGM forecasting can be successfully tackled by personalized machine learning models, that do not need to recursively update their parameters.
While past research groups have attempted to find predictors for performance they do not provide satisfactory predictions. We conduct in this paper a study to predict human performance by developing a regression model using neurophysiological signals collected from electroencephalogram EEG , during simulated office-work tasks under different indoor room temperatures 22 and 30 Celsius. Finally, we showed that the predictor using EEG is more robust than regression models using skin temperature and heart rate.
Our work show the potential of using brain signals to accurately predict human office work performance. Keywords: General and theoretical informatics - Predictive analytics Abstract: Acute hypotensive episodes AHE are characterized by continuously low blood pressure for prolonged time, and could be potentially fatal. We present a novel AHE detection system, by first quantizing the blood pressure data into clinically accepted severity ranges and then identifying most frequently occurring blood pressure pattern among thesewhich we call consensus motifs. We apply machine learning techniques support vector machine on these consensus motifs.
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The results show that the use of consensus motifs instead of raw time series data extends the predictability by 45 minutes beyond the 2 hours that is possible using only the raw data, yielding a significant improvement without compromising the clinical accuracy. Keywords: Vascular mechanics and hemodynamics - Vascular Hemodynamics , Pulmonary and critical care - Bioengineering applications in Intensive care , Cardiovascular and respiratory system modeling - Vascular mechanics and hemodynamics Abstract: We propose a model-based reconstruction technique to estimate radial artery blood pressure from measurements obtained by the Nexfin noninvasive blood pressure monitor.
The Nexfin monitor provides brachial artery pressure estimates by transforming a pressure measured at the finger. The estimated brachial pressure differs significantly from the radial artery pressure commonly measured in intensive care applications. Our reconstruction method is based on a transmission line model of the arterial network and transforms brachial to radial pressure estimates. Applying the method to 22 records from six patients reduced mean pulse pressure differences between reconstructed and measured radial artery pressures from Mean systolic and diastolic pressure differences changed from Our method can be applied to more general problems of estimating pressure waveforms downstream from an upstream measurement location.
Keywords: Cardiovascular and respiratory signal processing - Cardiovascular signal processing , Cardiovascular and respiratory system modeling - Cardiovascular control models , Cardiovascular and respiratory signal processing - Pulse transit time Abstract: This paper presents a practical approach to reconstruct brachial artery pressure BAP distally from digital artery pressure DAP. We hypothesize that continuous BAP can simply be approximated by sum of two halves of the continuous DAP shifted by the time delay.
In order to test it, we enrolled 30 healthy volunteers for two experiments. The errors of the proposed method in estimating systolic and diastolic pressures are 2. Our method is therefore promising in estimating continuous proximal blood pressure from peripheral blood pressure in practice. Keywords: Vascular mechanics and hemodynamics - Pulse wave velocity , Cardiovascular and respiratory signal processing - Pulse transit time , Cardiovascular and respiratory signal processing - Heart Rate and Blood Pressure Variability Abstract: We present a prototype design of dual element photoplethysmograph PPG probe along with associated measurement system for carotid local pulse wave velocity PWV evaluation in a non-invasive and continuous manner.
The PPG probe consists of two identical sensing modules placed 23 mm apart. Simultaneously measured blood pulse waveforms from these arterial sites were processed and the pulse transit time delay was resolved using the developed application-specific software. The ability of developed PPG probe and associated measurement system to detect acute changes in carotid local PWV due to blood pressure BP variations was experimentally validated by an in-vivo study.
Intra-subject carotid BP elevation was achieved by an upper arm cuff based occlusion, which offered a controlled way of local PWV escalation. The elevated carotid BP values were also recorded by a calibrated pressure tonometer prior to the study, and was used as a reference.
A significant increment 1. Study results demonstrated the feasibility of real-time signal acquisition and reliable local PWV evaluation under normal and elevated BP conditions using the developed measurement system. Keywords: Cardiovascular and respiratory signal processing - Pulse transit time , Vascular mechanics and hemodynamics - Pulse wave velocity , Cardiovascular and respiratory signal processing - Cardiovascular signal processing Abstract: The paper presents study and analysis of a Giant Magneto Resistance GMR -based magneto plethysmograph and illustrates its efficacy as a tool for real-time cuff-less measurement of Blood Pressure BP.
The proposed scheme employs two GMR sensors and associated biasing and signal conditioning in its architecture. The methodology, circuits and signal processing stages used are described in the paper. A prototype of the GMR-sensing solution is developed and tested. Initially, tests are carried out to determine the quality and characteristics of the plethysmographs produced by developed sensor unit, in different conditions such as various body positions, bias current etc. Good quality bio-signals were obtained during the above tests. Then, the experiments were conducted on 29 volunteers to find the feasibility of developed scheme as a BP monitor.
The results obtained show that the performance of developed BP monitor is within acceptable limits. These parameters extracted from the central pulse wave invasively measured were compared with the parameters measured from the brachial pulse waves by a regression model and a transfer function model.
The accuracy of the parameters which were estimated by the regression model and the transfer function model was compared too. Our findings showed that in addition to the k value, the above parameters of the central pulse wave and the brachial pulse wave invasively measured had positive correlation. Keywords: Cardiovascular and respiratory signal processing - Pulse transit time , Cardiovascular and respiratory system modeling - Cardiovascular Disease Abstract: Continuous blood pressure BP monitoring has a significant meaning to the prevention and early diagnosis of cardiovascular disease.
However, existing continuous BP monitoring approaches, especially cuff-less BP monitoring approaches, are all contraptions which complex and huge computation required. For example, for the most sophisticated cuff-less BP monitoring method using pulse transit time PTT , the simultaneous record of photoplethysmography PPG signal and electrocardiography ECG are required, and various measurement of characteristic points are needed. These issues hindered widely application of cuff less BP measurement in the wearable devices.
In this study, a novel BP estimation method using single PPG signal feature was proposed and its performance in BP estimation was also tested. The results showed that the new approach proposed in this study has a mean error Further investigation revealed that this new BP estimation approach only required measurement of one characteristic point, reducing much computation when implementing. These results demonstrated that this new approach might be more suitable implemented in the wearable BP monitoring devices. Keywords: Neural stimulation including deep brain stimulation Abstract: This abstract describes some examples of regulatory-oriented research conducted by the Food and Drug Administration FDA.
Future regulatory applications of computational modeling include use of models in virtual clinical trials, in which device testing can be simulated against population of virtual patients. Keywords: Clinical engineering , Ambulatory Therapeutic Devices - Personalized therapeutic devices and emergency response systems , Neural stimulation including deep brain stimulation Abstract: During the last decades, several brain stimulation techniques were developed which are employed for different medical and research applications.
These techniques make use of the interaction between electric fields EF and living tissue, specifically within the brain. Since it is troublesome to measure the induced EF in vivo, realistic human head models have been created and used to study its distribution. The creation of suitable head models is currently based on segmentation algorithms that mask out tissue maps from anatomical MRIs. This task is non-trivial, time-consuming and is further complicated by the presence of pathologies. Furthermore, the simulated EF is influenced by assumed model physics and the choice of appropriate dielectric properties of tissues.
Thus, one of the main objectives for investigational studies should be to develop a pipeline for fast and robust model creation of individuals including the application-specific device. Ideally, these models have a minimum degree of complexity while yielding accurate results. Possible workflows and mentioned influential aspects will be discussed with the example of Tumor Treating Fields TTFields which are low intensity alternating electric fields in the kHz frequency range that are used as anti-mitotic cancer treatment.
Keywords: Neural stimulation including deep brain stimulation Abstract: Alteration of neuron structure can induce abnormalities in signal propagation, as seen in trauma. The significance of this study is the difference in distribution of residual stresses and strains at the membrane for different sizes and types of fibres. Keywords: Diagnostic devices - Physiological monitoring , Neural stimulation including deep brain stimulation Abstract: Electroencephalography EEG source localization ESL and transcranial current stimulation TCS rely on accurate bioelectric models, individualized to each subject.
Improvements in MRI sequences and image processing have enabled construction of these models at millimeter or better accuracy, with an ever increasing number of tissue compartments. However, refinements to model accuracy often necessitate additional MR imaging, while producing ever decreasing improvements to model quality.
In a clinical environment, this tradeoff between increased cost of care and improved diagnostic capability must be weighed. Here, we present results suggesting that selection of a particular numerical technique and software implementation, an often neglected aspect of numerical modeling, can induce errors on the same scale as recent advancements in patient specific modeling. In the context of clinical individualized head models, these errors can help in establishing a basis for evaluating the tradeoffs that must be made in constructing standard imaging protocols for the construction of individualized head models.
Keywords: Clinical engineering , Ambulatory Therapeutic Devices - Personalized therapeutic devices and emergency response systems Abstract: A method for rapidly creating realistic head models of Glioblastoma patients for computational studies on the distribution of Tumor Treating Fields within the head is described. The method utilizes a model of a healthy individual as a template which is non-rigidly deformed to approximate the patient's anatomy. The tumor is segmented manually and planted into the deformed template to create the patient model.
The method does not require accurate segmentation of patient MRIs, and is therefore rapid and robust. Keywords: Clinical laboratory, assay and pathology technologies , Diagnostic devices - Physiological monitoring , Clinical engineering Abstract: A novel approach of numerical electromagnetic modeling of fibrous tissues is tested by comparison with FEM solutions and is applied to realistic fibrous structures extracted from diffusion MRI data. Its idea is a thin-fiber model of every individual bent fiber with a small number of unknowns basis functions.
Keywords: Implantable sensors - Biocompatibility , Implantable prosthetic devices Abstract: Neural activities of free-moving animals provide valuable insights into behavior, memory formation and cognitive function of the hippocampus.
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Unitary activities simultaneously recorded from multiple sub-regions of the hippocampus enable detailed study of hippocampal neural circuits, but require high fidelity recordings with high temporal and spatial resolution. In this work, we explored the possibility of using Parylene-C as the structural material for a penetrating, multi-electrode array designed to record from multiple sub-region of the rat hippocampus. A channel Parylene-based flexible electrode array was designed and fabricated. The layout of the electrode array was arranged to conform to the shape of cell body layers of the rat hippocampus.
An insertion technique of temporarily reduce the effective length of the probe with polyethylene glycol PEG was developed and tested in vivo. The multi-electrode array was implanted into a rat hippocampus for chronic experimentation and unitary activities were collected both during the implantation and after recovery while the animal ran freely in an open field.
Unitary activities with an average signal to noise ratios SNR of 3 to 4 were recorded with the Parylene probe over the period of one month after implantation. Keywords: Implantable systems Abstract: Neuronal stimulation systems design is highly impacted by the overall resolution and adaptability of the device to the targeted application and area to stimulate.
In this study, we propose a novel design for neural micro-stimulation electrode presenting high resolution and adaptability to any targeted area via a high flexibility. We introduce the use of liquid metal micro-channels encapsulated into a polymer volume, achieving micro-stimulation pads at the tip of the channels.
It presents a high degree of patternability to match different possible applications, as well as flexibility and interesting mechanic properties to make it insertable and adaptable in soft tissues. The design, fabrication process, and study of the electrical and mechanical behavior and stability of the device are discussed.
Keywords: Implantable sensors , Physiological monitoring - Instrumentation , Implantable technologies Abstract: Fracture injuries are highly prevalent worldwide, with treatment of problematic fractures causing a significant burden on the U. Physicians typically monitor fracture healing by conducting physical examinations and taking radiographic images. However, nonunions currently take over 6 months to be diagnosed because these techniques are not sensitive enough to adequately assess fracture union.
In this study, we display the utility of impedance spectroscopy to track different healing rates in a pilot study of an in vivo mouse tibia fracture model. We found that impedance magnitude increases steadily over time in healing mice but stalls in non-healing mice, and phase angle displays frequency-dependent behavior that also reflects the extent of healing at the fracture site.
Our results demonstrate that impedance can track differences in healing rates early on, highlighting the potential of this technique as a method for early detection of fracture nonunion. Keywords: Implantable sensors , Physiological monitoring - Modeling and analysis , Physiological monitoring - Instrumentation Abstract: An implantable pressure monitoring system is a compelling approach to home monitoring of intracranial pressure in the long term. In our approach, an on-body unit powers a cranially concealed system where a piezoresistive element senses the pressure.
A data transmission unit built in the same platform emits a signal at a pressure dependent frequency through a miniature far field antenna. In this work, we focus on assessing the impact of variable temperature on the pressure readout at an off-body unit through in-vitro experiments. Keywords: Implantable sensors , Physiological monitoring - Novel methods , Physical sensors and sensor systems - New sensing techniques Abstract: An accurate bladder volume monitoring system is a critical component in diagnosis and treatment of urological disorders.
Here, we report an implantable bladder volume sensor with a multi-level resistor ladder which estimates the bladder volume through discrete resistance values. Discretization allows the sensor output to be resilient to the long-term drift, hysteresis, and degradation of the sensor materials.
We also demonstrate the patterning and molding capability of this material by fabrication various structures. Keywords: Implantable sensors , Physical sensors and sensor systems - Mechanical sensors and systems , Physical sensors and sensor systems - Magnetic sensors and systems Abstract: Despite better performance over primary repairs, tension-free ventral hernia repairs with mesh still suffer from a high recurrence rate. High stress gradients in the mesh are thought to contribute to hernia recurrence.
We propose a postoperative monitoring system based on a coupled pair of magnetoelastic strain sensors to enable patients and physicians to non-invasively measure and track the strain distribution across the hernia mesh. Our design combines an encased resonator with a spring-loaded transducer to achieve high signal amplitude with a wide dynamic range. We also demonstrate a fabrication protocol to integrate the resonant strain sensors with a commercial polypropylene mesh. The packaged sensor is capable of detecting up to Keywords: Image segmentation Abstract: Detection and classification of breast lesions using mammographic images are one of the most difficult studies in medical image processing.
A number of learning and non-learning methods have been proposed for detecting and classifying these lesions. In this paper we propose a powerful classification method based on sparse learning to diagnose breast cancer in mammograms. For this purpose, a supervised discriminative dictionary learning approach is applied on dense scale invariant feature transform DSIFT features.
A linear classifier is also simultaneously learned with the dictionary which can effectively classify the sparse representations. Our experimental results show the superior performance of our method compared to existing approaches. Keywords: Retinal imaging , Image feature extraction , Image segmentation Abstract: Exudate detection is an essential task for computer-aid diagnosis of diabetic retinopathy DR , so as to monitor the progress of DR. In this paper, deep convolutional neural network CNN is adopted to achieve pixel-wise exudate identification.
The CNN model is first trained with expert labeled exudates image patches and then saved as off-line classifier. In order to achieve pixel-level accuracy meanwhile reduce computational time, potential exudate candidate points are first extracted with morphological ultimate opening algorithm. A pixel-wise accuracy of Keywords: Image feature extraction , Image classification Abstract: The contrast-enhanced ultrasound CEUS has been a widely accepted imaging modality for diagnosis of liver cancers.
In clinical practice, several typical images selected from enhancement patterns of the arterial, portal venous and late phases can provide reliable information basis for diagnosis. In this work, we propose to develop a CEUS-based computer-aided diagnosis CAD for liver cancers with only three typical CEUS images selected from three phases, which simulates the clinical diagnosis mode of radiologists. The generated six-view features are then fed to a multiple kernel learning MKL classifier to further promote the predictive diagnosis result.
The experimental results indicate that the proposed DCCA-MKL algorithm achieves best performance for discriminating benign liver tumors from malignant liver cancers. The MV-CNN specialized in capturing a diverse set of nodule-sensitive features from axial, coronal and sagittal views in CT images simultaneously. The proposed network architecture consists of three CNN branches, where each branch includes seven stacked layers and takes multi-scale nodule patches as input.
The three CNN branches are then integrated with a fully connected layer to predict whether the patch center voxel belongs to the nodule. We showed that MV-CNN demonstrated encouraging performance for segmenting various type of nodules including juxta-pleural, cavitary, and non-solid nodules, achieving an average dice similarity coefficient DSC of Keywords: Image classification Abstract: Laparoscopic surgery, a type of minimally invasive surgery, is used in a variety of clinical surgeries because it has a faster recovery rate and causes less pain.
However, in general, the robotic system used in laparoscopic surgery can cause damage to the surgical instruments, organs, or tissues during surgery due to a narrow field of view and operating space, and insufficient tactile feedback. This study proposes real-time models for the detection of surgical instruments during laparoscopic surgery by using a CNN Convolutional Neural Network.
A dataset included information of the 7 surgical tools is used for learning CNN. To track surgical instruments in real time, unified architecture of YOLO apply to the models. So as to evaluate performance of the suggested models, degree of recall and precision is calculated and compared.
Finally, we achieve Keywords: Retinal imaging , Image classification , Functional image analysis Abstract: Diabetic Retinopathy DR is a disease which affect the vision ability. The observation by an ophthalmologist usually conducted by analyzing the retinal images of the patient which are marked by some DR features. However some misdiagnosis are usually found due to human error.
Here, a deep learning-based low-cost embedded system is established to assist the doctor for grading the severity of the DR from the retinal images. A compact deep learning algorithm named Deep-DR-Net which fits on a small embedded board is afterwards proposed for such purposes. In the heart of Deep-DR-Net, a cascaded encoder-classifier network is arranged using residual style for ensuring the small model size. The usage of different types of convolutional layers subsequently guarantees the features richness of the network for differentiating the grade of the DR.
Experimental results show the capability of the proposed system for detecting the existence as well as grading the severity of the DR symptomps. A recent study showed that applying CPAP treatment to patients with sleep disordered breathing recruited by their number of apnea and hypopnea events alone, does improve sleepiness but does not improve overall cardiovascular mortality.