Preload-based starling-like control for rotary blood pumps: numerical comparison with pulsatility control and constant speed operation

In this study, we evaluate a preload-based Starling-like controller for implantable rotary blood pumps (IRBPs) using left ventricular end-diastolic pressure (PLVED) as the feedback variable. Simulations are conducted using a validated mathematical model. The controller emulates the response of the natural left ventricle (LV) to changes in PLVED. We report the performance of the preload-based Starling-like controller in comparison with our recently designed pulsatility controller and constant speed operation. In handling the transition from a baseline state to test states, which include vigorous exercise, blood loss and a major reduction in the LV contractility (LVC), the preload controller outperformed pulsatility control and constant speed operation in all three test scenarios. In exercise, preload-control achieved an increase of 54% in mean pump flow ([Formula: see text]) with minimum loading on the LV, while pulsatility control achieved only a 5% increase in flow and a decrease in mean pump speed. In a hemorrhage scenario, the preload control maintained the greatest safety margin against LV suction. PLVED for the preload controller was 4.9 mmHg, compared with 0.4 mmHg for the pulsatility controller and 0.2 mmHg for the constant speed mode. This was associated with an adequate mean arterial pressure (MAP) of 84 mmHg. In transition to low LVC, [Formula: see text] for preload control remained constant at 5.22 L/min with a PLVED of 8.0 mmHg. With regards to pulsatility control, [Formula: see text] fell to the nonviable level of 2.4 L/min with an associated PLVED of 16 mmHg and a MAP of 55 mmHg. Consequently, pulsatility control was deemed inferior to constant speed mode with a PLVED of 11 mmHg and a [Formula: see text] of 5.13 L/min in low LVC scenario. We conclude that pulsatility control imposes a danger to the patient in the severely reduced LVC scenario, which can be overcome by using a preload-based Starling-like control approach.

Classification of Implantable Rotary Blood Pump States With Class Noise

A medical case study related to implantable rotary blood pumps is examined. Five classifiers and two ensemble classifiers are applied to process the signals collected from the pumps for the identification of the aortic valve nonopening pump state. In addition to the noise-free datasets, up to 40% class noise has been added to the signals to evaluate the classification performance when mislabeling is present in the classifier training set. In order to ensure a reliable diagnostic model for the identification of the pump states, classifications performed with and without class noise are evaluated. The multilayer perceptron emerged as the best performing classifier for pump state detection due to its high accuracy as well as robustness against class noise.

Predicting days in hospital using health insurance claims

Health-care administrators worldwide are striving to lower the cost of care while improving the quality of care given. Hospitalization is the largest component of health expenditure. Therefore, earlier identification of those at higher risk of being hospitalized would help health-care administrators and health insurers to develop better plans and strategies. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. We utilized a regression decision tree algorithm, along with insurance claim data from 242 075 individuals over three years, to provide predictions of number of days in hospital in the third year, based on hospital admissions and procedure claims data. The proposed method performs well in the general population as well as in subpopulations. Results indicate that the proposed model significantly improves predictions over two established baseline methods (predicting a constant number of days for each customer and using the number of days in hospital of the previous year as the forecast for the following year). A reasonable predictive accuracy (AUC =0.843) was achieved for the whole population. Analysis of two subpopulations-namely elderly persons aged 63 years or older in 2011 and patients hospitalized for at least one day in the previous year-revealed that the medical information (e.g., diagnosis codes) contributed more to predictions for these two subpopulations, in comparison to the population as a whole.

A cortical integrate-and-fire neural network model for blind decoding of visual prosthetic stimulation

We present a computational model of the optic pathway which has been adapted to simulate cortical responses to visual-prosthetic stimulation. This model reproduces the statistically observed distributions of spikes for cortical recordings of sham and maximum-intensity stimuli, while simultaneously generating cellular receptive fields consistent with those observed using traditional visual neuroscience methods. By inverting this model to generate candidate phosphenes which could generate the responses observed to novel stimulation strategies, we hope to aid the development of said strategies in-vivo before being deployed in clinical settings.

Towards photorealistic and immersive virtual-reality environments for simulated prosthetic vision: integrating recent breakthroughs in consumer hardware and software

Simulated prosthetic vision (SPV) in normally sighted subjects is an established way of investigating the prospective efficacy of visual prosthesis designs in visually guided tasks such as mobility. To perform meaningful SPV mobility studies in computer-based environments, a credible representation of both the virtual scene to navigate and the experienced artificial vision has to be established. It is therefore prudent to make optimal use of existing hardware and software solutions when establishing a testing framework. The authors aimed at improving the realism and immersion of SPV by integrating state-of-the-art yet low-cost consumer technology. The feasibility of body motion tracking to control movement in photo-realistic virtual environments was evaluated in a pilot study. Five subjects were recruited and performed an obstacle avoidance and wayfinding task using either keyboard and mouse, gamepad or Kinect motion tracking. Walking speed and collisions were analyzed as basic measures for task performance. Kinect motion tracking resulted in lower performance as compared to classical input methods, yet results were more uniform across vision conditions. The chosen framework was successfully applied in a basic virtual task and is suited to realistically simulate real-world scenes under SPV in mobility research. Classical input peripherals remain a feasible and effective way of controlling the virtual movement. Motion tracking, despite its limitations and early state of implementation, is intuitive and can eliminate between-subject differences due to familiarity to established input methods.

The unique characteristics of ON and OFF retinal ganglion cells: a modeling study

Retinal ganglion cells (RGCs) demonstrate a large range of variation in their ionic channel properties and morphologies. These cell-specific properties are responsible for the unique way they process synaptic inputs. A cell-specific modeling approach allows us to examine the functional significance of regional membrane channel expression and cell morphology. ON and OFF RGC models based on accurate biophysics and realistic representation of morphologies were used to study the contribution of different ion channel properties and spatial structure of neurons to RGC electrical activity. Using this approach, morphologically-complex retinal neurons such as amacrine cells or RGCs can be modelled and their interactions and processing can be better understood.

A low-power fall detection algorithm based on triaxial acceleration and barometric pressure

This paper proposes a low-power fall detection algorithm based on triaxial accelerometry and barometric pressure signals. The algorithm dynamically adjusts the sampling rate of an accelerometer and manages data transmission between sensors and a controller to reduce power consumption. The results of simulation show that the sensitivity and specificity of the proposed fall detection algorithm are both above 96% when applied to a previously collected dataset comprising 20 young actors performing a combination of simulated falls and activities of daily living. This level of performance can be achieved despite a 10.9% reduction in power consumption.

Simulation of motor current waveform as an index for aortic valve open-close condition during ventricular support

Monitoring of aortic valve (AV) opening and closure during heart pump support by a left ventricular assist device (LVAD) is crucial in preventing adverse events such as thrombus formation near the AV. In preventing adverse events such as thrombus formation near the AV. In this paper, simulations of LVAD motor current waveform were undertaken to evaluate its suitability for ascertaining aortic valve status. A two-dimensional fluid-structure interaction finite-element model is presented to predict AV closure during LVAD outflow, useful in the development of a pump speed controller.

Pilot evaluation of an unobtrusive system to detect falls at nighttime

Research shows that older people (aged 65 years and over) suffer many unintentional indoor falls which often lead to severe injuries. As a result of an increasingly aged population in developed countries, a sizable portion of healthcare funding is consumed in the treatment of fall-related injuries and associated long-term care. Detecting falls soon after they occur can be potentially live saving. In addition, early treatment of fall-related injuries can reduce treatment costs by minimizing health deterioration resulting from long periods spent incapacitated on the floor after a fall (a scenario known as a `long lie') and decreasing the number of hospital bed-days required. In this study, a previously proposed unobtrusive nighttime fall detection system based on wireless passive infrared sensors and furniture load sensors is evaluated in a pilot study involving three older subjects, monitored for a combined total of 174 days. No falls occurred during the study. The system reported a false alarm rate of 0.53 falls per day, which is comparable with similar unobtrusive and wearable sensor fall detection solutions.

Predicting number of hospitalization days based on health insurance claims data using bagged regression trees

Healthcare administrators worldwide are striving to both lower the cost of care whilst improving the quality of care given. Therefore, better clinical and administrative decision making is needed to improve these issues. Anticipating outcomes such as number of hospitalization days could contribute to addressing this problem. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. We utilized a regression decision tree algorithm, along with insurance claim data from 300,000 individuals over three years, to provide predictions of number of days in hospital in the third year, based on medical admissions and claims data from the first two years. Our method performs well in the general population. For the population aged 65 years and over, the predictive model significantly improves predictions over a baseline method (predicting a constant number of days for each patient), and achieved a specificity of 70.20% and sensitivity of 75.69% in classifying these subjects into two categories of 'no hospitalization' and 'at least one day in hospital'.

Producing 3D neuronal networks in hydrogels for living bionic device interfaces

Hydrogels hold significant promise for supporting cell based therapies in the field of bioelectrodes. It has been proposed that tissue engineering principles can be used to improve the integration of neural interfacing electrodes. Degradable hydrogels based on poly (vinyl alcohol) functionalised with tyramine (PVA-Tyr) have been shown to support covalent incorporation of non-modified tyrosine rich proteins within synthetic hydrogels. PVA-Tyr crosslinked with such proteins, were explored as a scaffold for supporting development of neural tissue in a three dimensional (3D) environment. In this study a model neural cell line (PC12) and glial accessory cell line, Schwann cell (SC) were encapsulated in PVA-Tyr crosslinked with gelatin and sericin. Specifically, this study aimed to examine the growth and function of SC and PC12 co-cultures when translated from a two dimensional (2D) environment to a 3D environment. PC12 differentiation was successfully promoted in both 2D and 3D at 25 days post-culture. SC encapsulated as a single cell line and in co-culture were able to produce both laminin and collagen-IV which are required to support neuronal development. Neurite outgrowth in the 3D environment was confirmed by immunocytochemical staining. PVA-Tyr/sericin/gelatin hydrogel showed mechanical properties similar to nerve tissue elastic modulus. It is suggested that the mechanical properties of the PVA-Tyr hydrogels with native protein components are providing with a compliant substrate that can be used to support the survival and differentiation of neural networks.

Impact of hierarchies of clinical codes on predicting future days in hospital

Health insurance claims contain valuable information for predicting the future health of a population. Nowadays, with many mature machine learning algorithms, models can be implemented to predict future medical costs and hospitalizations. However, it is well-known that the way in which the data are represented significantly affects the performance of machine learning algorithms. In health insurance claims, key clinical information mainly comes from the associated clinical codes, such as diagnosis codes and procedure codes, which are hierarchically structured. In this study, it is investigated whether the hierarchies of such clinical codes can be utilized to improve predictive performance in the context of predicting future days in hospital. Empirical investigations were done on data sets of different sizes, considering that the frequency of the appearance of lower-level (more specific) clinical codes could vary significantly in populations of different sizes. The use of bagged trees with feature sets that include only basic demographic features, low-level, medium-level, high-level clinical codes, and a full feature set were compared. The main finding from this study is that different hierarchies of clinical codes do not have a significant impact on the predictive power. Some other findings include: 1) Sample size greatly affects the predictive outcome (more observations result in more stable and more accurate outcomes); 2) Combined use of enriched demographic features and clinical features give better performance as compared to using them separately.

Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data

BACKGROUND

The use of telehealth technologies to remotely monitor patients suffering chronic diseases may enable preemptive treatment of worsening health conditions before a significant deterioration in the subject's health status occurs, requiring hospital admission.

OBJECTIVE

The objective of this study was to develop and validate a classification algorithm for the early identification of patients, with a background of chronic obstructive pulmonary disease (COPD), who appear to be at high risk of an imminent exacerbation event. The algorithm attempts to predict the patient's condition one day in advance, based on a comparison of their current physiological measurements against the distribution of their measurements over the previous month.

METHOD

The proposed algorithm, which uses a classification and regression tree (CART), has been validated using telehealth measurement data recorded from patients with moderate/severe COPD living at home. The data were collected from February 2007 to January 2008, using a telehealth home monitoring unit.

RESULTS

The CART algorithm can classify home telehealth measurement data into either a 'low risk' or 'high risk' category with 71.8% accuracy, 80.4% specificity and 61.1% sensitivity. The algorithm was able to detect a 'high risk' condition one day prior to patients actually being observed as having a worsening in their COPD condition, as defined by symptom and medication records.

CONCLUSION

The CART analyses have shown that features extracted from three types of physiological measurements; forced expiratory volume in 1s (FEV1), arterial oxygen saturation (SPO2) and weight have the most predictive power in stratifying the patients condition. This CART algorithm for early detection could trigger the initiation of timely treatment, thereby potentially reducing exacerbation severity and recovery time and improving the patient's health. This study highlights the potential usefulness of automated analysis of home telehealth data in the early detection of exacerbation events among COPD patients.

Activation and inhibition of retinal ganglion cells in response to epiretinal electrical stimulation: a computational modelling study

OBJECTIVE

Retinal prosthetic devices aim to restore sight in visually impaired people by means of electrical stimulation of surviving retinal ganglion cells (RGCs). This modelling study aims to demonstrate that RGC inhibition caused by high-intensity cathodic pulses greatly influences their responses to epiretinal electrical stimulation and to investigate the impact of this inhibition on spatial activation profiles as well as their implications for retinal prosthetic device design. Another aim is to take advantage of this inhibition to reduce axonal activation in the nerve fibre layer.

APPROACH

A three-dimensional finite-element model of epiretinal electrical stimulation was utilized to obtain RGC activation and inhibition threshold profiles for a range of parameters.

MAIN RESULTS

RGC activation and inhibition thresholds were highly dependent on cell and stimulus parameters. Activation thresholds were 1.5, 3.4 and 11.3 μA for monopolar electrodes with 5, 20 and 50 μm radii, respectively. Inhibition to activation threshold ratios were mostly within the range 2-10. Inhibition significantly altered spatial patterns of RGC activation. With concentric electrodes and appropriately high levels of stimulus amplitudes, activation of passing axons was greatly reduced.

SIGNIFICANCE

RGC inhibition significantly impacts their spatial activation profiles, and therefore it most likely influences patterns of perceived phosphenes induced by retinal prosthetic devices. Thus this inhibition should be taken into account in future studies concerning retinal prosthesis development. It might be possible to utilize this inhibitory effect to bypass activation of passing axons and selectively stimulate RGCs near their somas and dendrites to achieve more localized phosphenes.

Performance optimization of current focusing and virtual electrode strategies in retinal implants

The electrode configuration in an implanted visual prosthesis array affects the spatial electric field distribution within the retina, contributing to current focusing and virtual electrode (VE) stimulation strategies. In this paper, a finite element model incorporating various electrode configurations was used to study the interaction between electrode size and electrode-to-cell distance in current focusing and VE stimulation paradigms. The electrode array unit comprises an active electrode, six flanking return electrodes and a distant monopolar return. A quasi-monopolar (QMP) fraction is defined as the proportion of current which can be preferentially returned through the distant return, in comparison with the more adjacent flanking electrodes. The simulation results indicate that current focusing and VE strategies can be optimized by tuning the QMP fraction. The QMP fraction is adjusted to optimize the electric field spread based on retinal ganglion cell (RGC) density in the degenerate retina, thereby offsetting the effect of inhomogeneous distribution of surviving RGCs and leading to a uniform stimulation paradigm across electrodes. Importantly, there is negligible difference in functional performance across electrode configurations for distances less than the electrode diameter, implying that the stimulation mode does not significantly affect activation threshold or activated retinal area for electrode diameters greater than the retinal thickness. Furthermore, the QMP fraction has a significant effect on VE performance, defined by activation threshold and activated retinal area, when threshold current is evenly divided between two adjacent active electrodes.

Signal quality measures on pulse oximetry and blood pressure signals acquired from self-measurement in a home environment

Recently, decision support system (DSSs) have become more widely accepted as a support tool for use with telehealth systems, helping clinicians to summarize and digest what would otherwise be an unmanageable volume of data. One of the pillars of a home telehealth system is the performance of unsupervised physiological self-measurement by patients in their own homes. Such measurements are prone to error and noise artifact, often due to poor measurement technique and ignorance of the measurement and transduction principles at work. These errors can degrade the quality of the recorded signals and ultimately degrade the performance of the DSS system, which is aiding the clinician in their management of the patient. Developed algorithms for automated quality assessment for pulse oximetry and blood pressure (BP) signals were tested retrospectively with data acquired from a trial that recorded signals in a home environment. The trial involved four aged subjects who performed pulse oximetry and BP measurements by themselves at their home for ten days, three times per day. This trial was set up to mimic the unsupervised physiological self-measurement as in a telehealth system. A manually annotated "gold standard" (GS) was used as the reference against which the developed algorithms were evaluated after analyzing the recordings. The assessment of pulse oximetry signals shows 95% of good sections and 67% of noisy sections were correctly detected by the developed algorithm, and a Cohen's Kappa coefficient (κ) of 0.58 was obtained in 120 pooled signals. The BP measurement evaluation demonstrates that 75% of the actual noisy sections were correctly classified in 120 pooled signals, with 97% and 91% of the signals correctly identified as worthy of attempting systolic and/or diastolic pressure estimation, respectively, with a mean error and standard deviation of 2.53±4.20 mmHg and 1.46±5.29 mmHg when compared to a manually annotated GS. These results demonstrate the feasibility, and highlight the potential benefit, of incorporating automated signal quality assessment algorithms for pulse oximetry and BP recording within a DSS for telehealth patient management.

Convergence across tactile afferent types in primary and secondary somatosensory cortices

Integration of information by convergence of inputs onto sensory cortical neurons is a requisite for processing higher-order stimulus features. Convergence across defined peripheral input classes has generally been thought to occur at levels beyond the primary sensory cortex, however recent work has shown that this does not hold for the convergence of slowly-adapting and rapidly-adapting inputs in primary somatosensory cortex. We have used a new analysis method for multi-unit recordings, to show convergence of inputs deriving from the rapidly-adapting and Pacinian channels in a proportion of neurons in both primary and secondary somatosensory cortex in the anaesthetised cat. We have validated this method using single-unit recordings. The secondary somatosensory cortex has a greater proportion of sites that show convergence of this type than primary somatosensory cortex. These findings support the hypothesis that the more complex features processed in higher cortical areas require a greater degree of convergence across input classes, but also shows that this convergence is apparent in the primary somatosensory cortex.

Laser patterning of platinum electrodes for safe neurostimulation

OBJECTIVE

Laser surface modification of platinum (Pt) electrodes was investigated for use in neuroprosthetics. Surface modification was applied to increase the surface area of the electrode and improve its ability to transfer charge within safe electrochemical stimulation limits.

APPROACH

Electrode arrays were laser micromachined to produce Pt electrodes with smooth surfaces, which were then modified with four laser patterning techniques to produce surface structures which were nanosecond patterned, square profile, triangular profile and roughened on the micron scale through structured laser interference patterning (SLIP). Improvements in charge transfer were shown through electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and biphasic stimulation at clinically relevant levels. A new method was investigated and validated which enabled the assessment of in vivo electrochemically safe charge injection limits.

MAIN RESULTS

All of the modified surfaces provided electrical advantage over the smooth Pt. The SLIP surface provided the greatest benefit both in vitro and in vivo, and this surface was the only type which had injection limits above the threshold for neural stimulation, at a level shown to produce a response in the feline visual cortex when using an electrode array implanted in the suprachoroidal space of the eye. This surface was found to be stable when stimulated with more than 150 million clinically relevant pulses in physiological saline.

SIGNIFICANCE

Critical to the assessment of implant devices is accurate determination of safe usage limits in an in vivo environment. Laser patterning, in particular SLIP, is a superior technique for improving the performance of implant electrodes without altering the interfacial electrode chemistry through coating. Future work will require chronic in vivo assessment of these electrode patterns.

What Does Big Data Mean for Wearable Sensor Systems? Contribution of the IMIA Wearable Sensors in Healthcare WG

OBJECTIVES

The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare.

METHODS

The article draws on the scientific literature to support the opinions presented by the IMIA Wearable Sensors in Healthcare Working Group.

RESULTS

The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring.

CONCLUSIONS

Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals.

Organic electrode coatings for next-generation neural interfaces

Traditional neuronal interfaces utilize metallic electrodes which in recent years have reached a plateau in terms of the ability to provide safe stimulation at high resolution or rather with high densities of microelectrodes with improved spatial selectivity. To achieve higher resolution it has become clear that reducing the size of electrodes is required to enable higher electrode counts from the implant device. The limitations of interfacing electrodes including low charge injection limits, mechanical mismatch and foreign body response can be addressed through the use of organic electrode coatings which typically provide a softer, more roughened surface to enable both improved charge transfer and lower mechanical mismatch with neural tissue. Coating electrodes with conductive polymers or carbon nanotubes offers a substantial increase in charge transfer area compared to conventional platinum electrodes. These organic conductors provide safe electrical stimulation of tissue while avoiding undesirable chemical reactions and cell damage. However, the mechanical properties of conductive polymers are not ideal, as they are quite brittle. Hydrogel polymers present a versatile coating option for electrodes as they can be chemically modified to provide a soft and conductive scaffold. However, the in vivo chronic inflammatory response of these conductive hydrogels remains unknown. A more recent approach proposes tissue engineering the electrode interface through the use of encapsulated neurons within hydrogel coatings. This approach may provide a method for activating tissue at the cellular scale, however, several technological challenges must be addressed to demonstrate feasibility of this innovative idea. The review focuses on the various organic coatings which have been investigated to improve neural interface electrodes.

Quasi-monopolar electrical stimulation of the retina: a computational modelling study

OBJECTIVE

In this study we investigated the feasibility of quasi-monopolar (QMP) electrical stimulation for retinal implant devices, using a computational model of the retinal ganglion cell layer.

APPROACH

When used with hexagonally arrayed multiple electrodes, QMP stimulation is a hybrid of hexapolar and conventional monopolar stimulus modes. In hexapolar mode, each active electrode is surrounded by six guards which collectively return the stimulus current, whereas in monopolar mode the injected stimulus current is returned through a distant return electrode. The QMP paradigm, on the other hand, distributes the return current between the guard electrodes as well as the distant return. The electrodes tested were 25, 50 and 100 µm in diameter, with hexagonally arranged centre-to-centre spacing of either double or quadruple this diameter.

MAIN RESULTS

Simulation results indicated that electrode size had minimal effects on subretinal threshold currents, whilst electrode configuration and centre-to-centre spacing played major roles in determining thresholds and spatial activation patterns. Threshold charge densities for 50 and 100 µm electrodes were generally within the safe limit.

SIGNIFICANCE

We found that QMP stimulation offers greater advantages compared to monopolar and hexapolar stimulation, in that it combines the low thresholds of monopolar stimulation with the localized spatial activation achieved with hexapolar electrodes during parallel stimulation.

Gait as a biomarker? Accelerometers reveal that reduced movement quality while walking is associated with Parkinson's disease, ageing and fall risk

Humans are living longer but morbidity has also increased; threatening to create a serious global burden. Our approach is to monitor gait for early warning signs of morbidity. Here we present highlights from a series of experiments into gait as a potential biomarker for Parkinson's disease (PD), ageing and fall risk. Using body-worn accelerometers, we developed several novel camera-less methods to analyze head and pelvis movements while walking. Signal processing algorithms were developed to extract gait parameters that represented the principal components of vigor, head jerk, lateral harmonic stability, and oscillation range. The new gait parameters were compared to accidental falls, mental state and co-morbidities. We observed: 1) People with PD had significantly larger and uncontrolled anterioposterior (AP) oscillations of the head; 2) Older people walked with more lateral head jerk; and, 3) the combination of vigorous and harmonically stable gait was demonstrated by non-fallers. Our findings agree with research from other groups; changes in human gait reflect changes to well-being. We observed; different aspects of gait reflected different functional outcomes. The new gait parameters therefore may be complementary to existing methods and may have potential as biomarkers for specific disorders. However, further research is required to validate our observations, and establish clinical utility.

Inertial measurements of free-living activities: assessing mobility to predict falls

An exploratory analysis was conducted into how simple features, from acceleration at the lower back and ankle during simulated free-living walking, stair ascent and descent, correlate with age, the overall fall risk from a clinically validated Physiological Profile Assessment (PPA), and its sub-components. Inertial data were captured from 92 older adults aged 78-95 (42 female, mean age 84.1, standard deviation 3.9 years). The dominant frequency, peak width from Welch's power spectral density estimate, and signal variance along each axis, from each sensor location and for each activity were calculated. Several correlations were found between these features and the physiological risk factors. The strongest correlations were from the dominant frequency at the ankle along the mediolateral direction during stair ascent (Spearman's correlation coefficient p = - 0.45) with anterioposterior sway, and signal variance of the anterioposterior acceleration at the lower back during stair descent (p = - 0.45) with age. These findings should aid future attempts to classify activities and predict falls in older adults, based on true free-living data from a range of activities.

Physiological control of implantable rotary blood pumps for heart failure patients

In general, patient variability and diverse environmental operation makes physiological control of a left ventricular assist device (LVAD) a complex and complicated problem. In this work, we implement a Starling-like controller which adjusts mean pump flow using pump flow pulsatility as the feedback parameter. The linear relationship between mean pump flow and pump flow pulsatility forms the desired flow of the Starling-like controller. A tracking control algorithm based on sliding mode control (SMC) has been implemented. The controller regulates the estimated mean pulsatile flow (Qp) and flow pulsatility (PIQp) generated from a model of the assist device. A lumped parameter model of the cardiovascular system (CVS) was used to test the control strategy. The immediate response of the controller was evaluated by inducing a fall in left ventricle (LV) preload following a reduction in circulating blood volume. The simulation supports the speed and robustness of the proposed strategy.

Estimation of cardiac output and total peripheral resistance in preterm infants by arterial waveform analysis

This study investigated whether arterial blood pressure waveform analysis could be useful for estimating left ventricular outflow (LVO) and total peripheral resistance (TPR) in preterm infants. A cohort of 27 infants were studied, with 89 measurements of left ventricular outflow (LVO) using Doppler echocardiography and arterial pressure using catheters, performed in 0, 12, 24 and 36 hours after birth. TPR was computed as mean arterial pressure divided by LVO. The diastolic decay rate (1/τ) was obtained via fitting an exponential function to the last one third of each arterial pulse, with the mean rate computed from 50 pulses selected from each infant. This decay rate was considered to be inversely related to TPR while positively related to LVO. The results of regression analysis have confirmed that the diastolic decay rate had significant positive and negative relationships with LVO and TPR respectively(r = 0.383, P = 0.0002 and r = -0.379, P = 0.0002 respectively). These preliminary results demonstrated the potential utility of arterial pressure waveform analysis for estimating LVO and TPR in preterm infants, but more advanced multi-parameter models may be needed to improve accuracy of the estimation.

A continuum neuronal tissue model based on a two-compartmental representation of cells

Although significant advances have been made in continuum modeling of cardiac and smooth muscle tissue, the progress in neuronal continuum modeling has been slower. In this paper, a continuum neuronal tissue model based on a two-compartmental representation of cells is presented. Each neuron is described using both a somatic compartment modeled by the classical Hodgkin-Huxley current kinetics and a dendritic compartment based on a passive RC formulation. In addition, a synaptic current is fed into the dendritic compartment to account for the presynaptic influence of cells located within the dendritic field of each soma. A number of cases are simulated, including intracellular current injection into either the dendritic or somatic compartments, as well as extracellular current stimulation with and without synaptic input into neurons. The model incorporates a number of parameters controlling neuronal excitability which can be adjusted to validate each neuron's responses against experimental data, allowing for the modeling of different neuronal cell types and behaviors.

Modeling aortic valve closure under the action of a ventricular assist device

The support of a failing heart with pump devices has been an essential element in cardiac health care for several decades. It is therefore important to understand the left ventricular response to the pumping action of these devices when connected to the native heart. Furthermore, monitoring of aortic valve opening and closure is important in avoiding valve stenosis and thrombogenesis during pump support. This paper reports the first steps in simulating the effects of outlet pump pressure on aortic valve closure of the heart assisted by an implantable blood pump. A two-dimensional fluid structure interaction aortic valve model is presented with blood flow in left ventricular chamber using the Arbitrary Lagrangian-Eulerian Finite Element Method formulation to predict the AV closure during outflow of blood from the left ventricle into the left ventricular assist device (LVAD).

Generating tactile afferent stimulation patterns for slip and touch feedback in neural prosthetics

Current prosthetic limbs are limited by a lack of tactile feedback. Slip feedback is particularly important to inform grip. Object slip is marked by both a change in the normal grip force applied and a change in force tangential to the fingertips. In this study, we demonstrate that a new multi-axial tactile sensor composed of gold nanoparticle strain gauges is able to record slip and reconstruct the X, Y, and Z forces incident on the sensor's surface due to a slipping object. We entered the X, Y, and Z force components generated by the slip event into a noisy leaky integrate and fire model to simulate the firing responses of SA1 and FA1 afferents. We also recorded a uniaxial normal force input representative of tactile contact. A single set of SA1 model and FA1 model parameters generated realistic firing patterns for both the slip and normal force recordings. These results suggest that canonical SA1 and FA1 afferent models could be used to generate biomimetic electrical stimulation patterns for both slip and touch stimuli. When used to activate the tactile afferents of an amputee, these electrical stimulation patterns could create natural and distinguishable slip and touch percepts for closed loop control of an upper limb neural prosthesis.

Polymer micro-fiber Bragg grating

Polymer micro-fibers with inscribed Bragg gratings are reported in this Letter. Starting with a single-mode polymer optical fiber and implementing a two-stage tapering process, a 16 μm diameter micro-fiber is fabricated and a Bragg grating is inscribed in it that exhibits a peak reflected wavelength circa 1530 nm. The growth dynamics of the polymer micro-fiber Bragg grating are also observed and analyzed. A maximum reflectivity of 5% is obtained after an exposure time of 3 min to a 50 mW power He-Cd laser of 325 nm wavelength. The temperature and strain characterization results of the micro-fiber Bragg grating with different diameters are also presented. Such polymer micro-fiber Bragg gratings can be used as sensors for high-sensitivity measurements in a number of application areas.

Applications of supervised learning to biological signals: ECG signal quality and systemic vascular resistance

Discovering information encoded in non-invasively recorded biosignals which belies an individual's well-being can help facilitate the development of low-cost unobtrusive medical device technologies, or enable the unsupervised performance of physiological assessments without excessive oversight from trained clinical personnel. Although the unobtrusive or unsupervised nature of such technologies often results in less accurate measures than their invasive or supervised counterparts, this disadvantage is typically outweighed by the ability to monitor larger populations than ever before. The expected consequential benefit will be an improvement in healthcare provision and health outcomes for all. The process of discovering indicators of health in unsupervised or unobtrusive biosignal recordings, or automatically ensuring the validity and quality of such signals, is best realized when following a proven systematic methodology. This paper provides a brief tutorial review of supervised learning, which is a sub-discipline of machine learning, and discusses its application in the development of algorithms to interpret biosignals acquired in unsupervised or semi-supervised environments, with the aim of estimating well-being. Some specific examples in the disparate application areas of telehealth electrocardiogram recording and calculating post-operative systemic vascular resistance are discussed in the context of this systematic approach for information discovery.

Study of cardiac pacemaker excitation using generic ionic models and realistic cell distribution

Generic ionic models optimized to replicate experimentally recorded cardiac action potentials (APs) from the central and peripheral sinoatrial node (SAN), the natural pacemaker of the heart, as well as atrial intact-myocytes are implemented in a realistic 2D model of rabbit SAN geometry. The model was used to investigate two frequently-proposed modes of SAN architecture: the gradient and mosaic hypotheses. In a simplified gradient arrangement, the peripheral SAN region acts as a transition zone between the central SAN and atrium and is required for spontaneous rhythmic initiation of APs from central SAN into the atria. Furthermore, the application of optimized single cell parameters to the realistic 2D rabbit geometry did not accurately replicate experimentally recorded APs. On the other hand, in an adapted mosaic geometry, peripheral SAN cells were not required to produce spontaneous regular excitation.

Simulation of left ventricle flow dynamics with dilated cardiomyopathy during the filling phase

Dilated cardiomyopathy (DCM) is a common cardiac disease which leads to the deterioration in cardiac performance. A computational fluid dynamics (CFD) approach can be used to enhance our understanding of the disease, by providing us with a detailed map of the intraventricular flow and pressure distributions. In the present work, effect of ventricular size on the intraventricular flow dynamics and intraventricular pressure gradients (IVPGs) was studied using two different implementation methods, i.e. the geometry-prescribed and the fluid structure interaction (FSI) methods. Results showed that vortex strength and IVPGs are significantly reduced in a dilated heart, leading to an increased risk of thrombus formation and impaired ventricular filling. We suggest FSI method as the ultimate method in studying ventricular dysfunction as it provides additional cardiac disease prognostic factors and more realistic model implementation.

Characterization of memory load in an arithmetic task using non-linear analysis of EEG signals

In this paper, we investigate non-linear analysis of electroencephalogram (EEG) signals to examine changes in working memory load during the performance of a cognitive task with varying difficulty levels. EEG signals were recorded during an arithmetic task while the induced load was varying in seven levels from very easy to extremely difficult. The EEG signals were analyzed using three different non-linear/dynamic measures; namely: correlation dimension, Hurst exponent and approximate entropy. Experimental results show that the values of the measures extracted from the delta frequency band of signals acquired from the frontal and occipital lobes of the brain vary in accordance with the task difficulty level induced. The values of the correlation dimension increased as the task difficulty increased, showing a rise in complexity of the EEG signals, while the values of the Hurst exponent and approximate entropy decreased as task difficulty increased, indicating more regularity and predictability in the signals.

Electrical stimulation of inner retinal neurons in wild-type and retinally degenerate (rd/rd) mice

Electrical stimulation of the retina following photoreceptor degeneration in diseases such as retinitis pigmentosa and age-related macular degeneration has become a promising therapeutic strategy for the restoration of vision. Many retinal neurons remain functional following photoreceptor degeneration; however, the responses of the different classes of cells to electrical stimuli have not been fully investigated. Using whole-cell patch clamp electrophysiology in retinal slices we investigated the response to electrical stimulation of cells of the inner nuclear layer (INL), pre-synaptic to retinal ganglion cells, in wild-type and retinally degenerate (rd/rd) mice. The responses of these cells to electrical stimulation were extremely varied, with both extrinsic and intrinsic evoked responses observed. Further examination of the intrinsically evoked responses revealed direct activation of both voltage-gated Na⁺ channels and K⁺ channels. The expression of these channels, which is particularly varied between INL cells, and the stimulus intensity, appears to dictate the polarity of the eventual response. Retinally degenerate animals showed similar responses to electrical stimulation of the retina to those of the wild-type, but the relative representation of each response type differed. The most striking difference between genotypes was the existence of a large amplitude oscillation in the majority of INL cells in rd/rd mice (as previously reported) that impacted on the signal to noise ratio following electrical stimulation. This confounding oscillation may significantly reduce the efficacy of electrical stimulation of the degenerate retina, and a greater understanding of its origin will potentially enable it to be dampened or eliminated.

Sensitivity analysis of left ventricle with dilated cardiomyopathy in fluid structure simulation

Dilated cardiomyopathy (DCM) is the most common myocardial disease. It not only leads to systolic dysfunction but also diastolic deficiency. We sought to investigate the effect of idiopathic and ischemic DCM on the intraventricular fluid dynamics and myocardial wall mechanics using a 2D axisymmetrical fluid structure interaction model. In addition, we also studied the individual effect of parameters related to DCM, i.e. peak E-wave velocity, end systolic volume, wall compliance and sphericity index on several important fluid dynamics and myocardial wall mechanics variables during ventricular filling. Intraventricular fluid dynamics and myocardial wall deformation are significantly impaired under DCM conditions, being demonstrated by low vortex intensity, low flow propagation velocity, low intraventricular pressure difference (IVPD) and strain rates, and high-end diastolic pressure and wall stress. Our sensitivity analysis results showed that flow propagation velocity substantially decreases with an increase in wall stiffness, and is relatively independent of preload at low-peak E-wave velocity. Early IVPD is mainly affected by the rate of change of the early filling velocity and end systolic volume which changes the ventriculo:annular ratio. Regional strain rate, on the other hand, is significantly correlated with regional stiffness, and therefore forms a useful indicator for myocardial regional ischemia. The sensitivity analysis results enhance our understanding of the mechanisms leading to clinically observable changes in patients with DCM.

Design of a Decision Support System for a Home Telehealth Application

A decision support system (DSS) that has been designed to manage patients using a home telehealth system is presented. The DSS has been developed to assist home telehealth clinical support staff with their workload, and to provide more effective communication between multiple home telehealth users. The three-tier system architecture that consists of a data layer; a business logic layer; and a front-end layer employs business processes and uses a rule engine for its logic and knowledge base. This paper discusses the design considerations involved in the construction of a DSS for the purpose of home telehealth, and illustrates how it may be developed using entirely open source software.

Current steering in retinal stimulation via a quasimonopolar stimulation paradigm

PURPOSE

Research to restore some degree of vision to patients suffering from retinal degeneration is becoming increasingly more promising. Several groups have chosen electrical stimulation of the remaining network of a degenerate retina as a means to generate discrete light percepts (phosphenes). Approaches vary significantly, with the greatest difference being the location of the stimulating electrode itself.

METHODS

Suprachoroidal positioning offers excellent mechanical stability and surgical simplicity; however, at the cost of activation thresholds and focused stimulation due to the distance from the electrodes to the target neurons. Past studies proposed a hexapolar electrode configuration to focus the cortical activation and minimize cross-talk between electrodes during concurrent stimulation. The high impedance nature of the choroid and pigment epithelium, however, cause current to shunt between the stimulating and return electrodes, resulting in even higher activation thresholds. In our study, we analyzed the effect of stimulating the feline retina using a quasimonopolar stimulation by simultaneously stimulating a hexapolar and distant monopolar return configurations.

RESULTS

Results of in vivo studies showed that quasimonopolar stimulation can be used to maintain the activation containment properties of hexapolar stimulation, while lowering the activation threshold to values almost equivalent to those of monopolar stimulation.

CONCLUSIONS

The optimal stimulus was found to be composed of a subthreshold monopolar stimulus combined with a suprathreshold hexapolar stimulation. This resulted in a decrease of activation threshold of 60% with respect to hexapolar alone, but with no discernible deleterious effect on the charge containment of a pure hexapolar stimulation.

Detrended fluctuation analysis of blood pressure in preterm infants with intraventricular hemorrhage

Very preterm infants are at high risk of death and serious permanent brain damage, as occurs with intraventricular hemorrhage (IVH). Detrended fluctuation analysis (DFA) that quantifies the fractal correlation properties of physiological signals has been proposed as a potential method for clinical risk assessment. This study examined whether DFA of the arterial blood pressure (ABP) signal could derive markers for the identification of preterm infants who developed IVH. ABP data were recorded from a prospective cohort of 30 critically ill preterm infants in the first 1-3 h of life, 10 of which developed IVH. DFA was performed on the beat-to-beat sequences of mean arterial pressure (MAP), systolic blood pressure (SBP) and pulse interval, with short-term exponent (α1, for timescale of 4-15 beats) and long-term exponent (α2, for timescale of 15-50 beats) computed accordingly. The IVH infants were found to have higher short-term scaling exponents of both MAP and SBP (α1 = 1.06 ± 0.18 and 0.98 ± 0.20) compared to the non-IVH infants (α1 = 0.84 ± 0.25 and 0.78 ± 0.25, P = 0.017 and 0.038, respectively). The results have demonstrated that fractal dynamics embedded in the arterial pressure waveform could provide useful information that facilitates early identification of IVH in preterm infants.

Assessing fall risk using wearable sensors: a practical discussion. A review of the practicalities and challenges associated with the use of wearable sensors for quantification of fall risk in older people

Identification of older people most at risk of falling may facilitate early preventative intervention to reduce the likelihood of falls occurring. While many clinical fall risk assessment techniques exist, they often require subjective assessor interpretation, or are not appropriate for unsupervised screening of larger populations owing to a number of issues including safety, ability to reliably perform the assessment, and requirements for unwieldy apparatus. Researchers have more recently attempted to address some of these deficits by instrumenting new or existing physical fall risk assessments with wearable motion sensors to make such assessments more objective, quicker to administer, and potentially more appropriate for deployment for unsupervised use in the community. The objective of this paper is to discuss various practical questions involving sensor-based fall risk assessment (SFRA). Many of the issues discussed contribute to answering the important question of whether SFRA should or can be used in either a supervised or an unsupervised manner, and what possible deployment scenarios exist for it.

Evaluation of suction detection during different pumping states in an implantable rotary blood pump

In recent times, the problem of noninvasive suction detection for implantable rotary blood pumps has attracted substantial research interest. Here, we compare the performance of various suction indices for different types of suction and non-suction events based on pump speed irregularity. A total of 171 different indices that consist of previously proposed as well as newly introduced suction indices are tested using regularized logistic regression. These indices can be classified as amplitude based (derived from the mean, maximum, and minimum values of a cycle), duration based (derived from the duration of a cycle), gradient based (derived from the first order as well as higher order differences) and frequency based (derived from the power spectral density). The non-suction event data consists of ventricular ejection with or without arrhythmia and intermittent and continuous non-opening of the aortic valve. The suction event data consists of partial ventricular collapse that occurs intermittently as well as continuously with or without arrhythmia. In addition, we also attempted to minimize the usage of multiple indices by applying the sequential forward floating selection method to find which combination of indices gives the best performance. In general, the amplitude-based and gradient-based indices performed quite well while the duration-based and frequency-based indices performed poorly. By having only two indices ([i] the maximum gradient change in positive slope; and [ii] the standard deviation of the maximum value in a cycle), we were able to achieve a sensitivity of 98.9% and a specificity of 99.7%.