MOTIVATION: The American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines represent the gold standard for clinical variant interpretation. Despite the widespread adoption of ACMG/AMP guidelines, a comprehensive comparison of the software tools designed to implement them has been lacking. This represents a significant gap, as clinicians require evidence-based guidance on which tools to use in their practice. RESULTS: We benchmarked four ACMG/AMP-based tools (Franklin, InterVar, TAPES, Genebe) selected from 22 tools, and compared their performance with LIRICAL, a top-performing phenotype-driven tool, using 151 expert-curated datasets from Mendelian disorders. Selection criteria included free availability, VCF compatibility, operational reliability, and not being disease-specific. Our evaluation framework assessed top-N accuracy (N = 1, 5, 10, 20, 50), retention rates, precision, recall, F1 scores, and area under the curve (AUC). Statistical validation employed bootstrap confidence intervals (n = 1000) and Friedman tests. LIRICAL (68.21%) and Franklin (61.59%) demonstrated superior top-10 variant prioritization accuracy in Mendelian disorders, significantly outperforming other tools (P = .0000). Results demonstrate that tools with advanced phenotypic integration significantly outperform those relying primarily on genomic features. AVAILABILITY AND IMPLEMENTATION: All data and source code required to reproduce the findings of this study are openly available in the Code Ocean repository at https://doi.org/10.24433/CO.6562438.v1.

While wearable robots that utilize intrinsically soft materials for actuation offer enhanced safety and biological compatibility, the challenges of sensing and control significantly affect their performance. The control problem in such systems is inherently complex, and the inclusion of 'softness' introduces additional nonlinearities, hysteresis, and uncertainties. Furthermore, the effectiveness of control strategies is highly dependent on sensor selection and integration, which presents its own challenges. Most robotic systems require separate sensors for control purposes. In this study, a new sensing and control scheme are introduced for soft wearable robots, leveraging the intrinsic soft-sensing capability of fluidic filament actuators without adding computational complexity. This method enables simultaneous sensing and actuation with$\mathbf{9 6 \%}$position accuracy, even under physical disturbances. This approach is demonstrated with a soft assistive device for elbow flexion/extension, achieving 70.5% tracking accuracy and a 0.09s response delay to human intention, ensuring the system provides minimal resistance when assistance is not needed, while delivering the required support when necessary.

Aneurysms, balloon-like bulges in blood vessels, present a significant health risk due to their potential to rupture, leading to life-threatening internal bleeding. Current treatments often involve delivering embolic materials or metal coils to fill these bulges, occluding them from the pressure of blood flow. However, clinical micro-catheters that deploy embolic materials used today face limitations, primarily their rigidity and the lack of active control over the bending tip of the catheter. This paper introduces a new soft micro-robotics catheter, with diameter of only 0.8 mm, equipped with a hollow channel. With this new design, the new device can induce bending motions at its tip for active steerability to reach desired aneurysm targets and then perform the delivery of embolic materials and tools. To enhance the control and precise navigation during procedures, a robust mathematical model and image processing techniques are also introduced and validated. Experiments are also performed to characterise and validate the model’s accuracy and the steerability and navigation capabilities of the new micro-catheter.

Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective. The unique challenges posed by the specialized nature of SRT data have led the scientific community to explore more sophisticated analytical avenues. Recent trends indicate an increasing reliance on deep learning algorithms, especially in areas such as spatial clustering, identification of spatially variable genes and data alignment tasks. In this manuscript, we provide a rigorous critique of these advanced deep learning methodologies, probing into their merits, limitations and avenues for further refinement. Our in-depth analysis underscores that while the recent innovations in deep learning tailored for SRT have been promising, there remains a substantial potential for enhancement. A crucial area that demands attention is the development of models that can incorporate intricate biological nuances, such as phylogeny-aware processing or in-depth analysis of minuscule histology image segments. Furthermore, addressing challenges like the elimination of batch effects, perfecting data normalization techniques and countering the overdispersion and zero inflation patterns seen in gene expression is pivotal. To support the broader scientific community in their SRT endeavors, we have meticulously assembled a comprehensive directory of readily accessible SRT databases, hoping to serve as a foundation for future research initiatives.

Oral squamous cell carcinoma (OSCC) has the characteristics of early regional lymph node metastasis. OSCC patients often have poor prognoses and low survival rates due to cervical lymph metastases. Therefore, it is necessary to rely on a reasonable screening method to quickly judge the cervical lymph metastastic condition of OSCC patients and develop appropriate treatment plans. In this study, the widely used pathological sections with hematoxylin-eosin (H&E) staining are taken as the target, and combined with the advantages of hyperspectral imaging technology, a novel diagnostic method for identifying OSCC lymph node metastases is proposed. The method consists of a learning stage and a decision-making stage, focusing on cancer and non-cancer nuclei, gradually completing the lesions' segmentation from coarse to fine, and achieving high accuracy. In the learning stage, the proposed feature distillation-Net (FD-Net) network is developed to segment the cancerous and non-cancerous nuclei. In the decision-making stage, the segmentation results are post-processed, and the lesions are effectively distinguished based on the prior. Experimental results demonstrate that the proposed FD-Net is very competitive in the OSCC hyperspectral medical image segmentation task. The proposed FD-Net method performs best on the seven segmentation evaluation indicators: MIoU, OA, AA, SE, CSI, GDR, and DICE. Among these seven evaluation indicators, the proposed FD-Net method is 1.75%, 1.27%, 0.35%, 1.9%, 0.88%, 4.45%, and 1.98% higher than the DeepLab V3 method, which ranks second in performance, respectively. In addition, the proposed diagnosis method of OSCC lymph node metastasis can effectively assist pathologists in disease screening and reduce the workload of pathologists.

Following biology's lead, soft robotics has emerged as a perfect candidate for actuation within complex environments. While soft actuation has been developed intensively over the last few decades, soft sensing has so far slowed to catch up. A largely unresearched area is the change of the soft material properties through prestress to achieve a degree of mechanical sensitivity tunability within soft sensors. Here, a new 3D force sensor which employs novel hydraulic filament artificial muscles capable of in-situ sensitivity tunability is introduced. Using a neural network (NN) model, the new soft 3D sensor can precisely detect external forces based on the change of the hydraulic pressures with error of$\sim 1.0, \sim 1.3$, and$\sim 0.94$% in the$\text{x, y}$, and z-axis directions, respectively. The sensor is also able to sense large force ranges, comparable to other similar sensors available in the literature. The sensor is then integrated into a soft robotic surgical arm for monitoring the tool-tissue interaction during an ablation process.

Atrial fibrillation (AF) is mostly treated via robotic catheter-based cardiac ablation procedures. Over the last few decades, cables or tendon mechanisms are at the core of available cardiac catheters. Despite advances, the use of cables often results in considerable force loss, nonlinear hysteresis, and control challenges. Most catheters are not equipped with force sensing, which increases the risk of the ablation process and decreases their efficacy in clinical settings. In addition, current catheters have a poor user interface and therefore the ablation process requires skilled or trained surgeons to steer the complex motion of the catheter tip within the heart chambers. To improve the cardiac ablation procedure, a new robotic catheter that has the ability to extend its working space without moving its flexible body and a real-time force sensor for safe operation is highly desired. In this work, a new handheld and soft robotic catheter for AF ablation is introduced. The new device consists of several improved components such as a soft manipulator for navigation and bending motion, an ergonomic handheld controller, and a soft force sensor for monitoring tool-tissue contact. The design, modeling, and fabrication of the device are presented and followed by experimental characterizations and ex-vivo validation.

There is an increasing interest in soft robotic grippers as they exhibit an ability to grip objects of differing shapes, sizes, textures, and even deformable materials, all of which present a difficult challenge to traditional rigid grippers. An ideal soft gripper would exhibit universal gripping with high gripping force and consists of low-cost materials with simple fabrication processes. This paper investigates the development of a strong and scalable hydraulic soft tubular gripper (HSTG) using facile fabrication method and low-cost materials. The HSTG which consists of a single long hydraulically actuated artificial muscle, soft 3D printed element, and commercial weaving yarn can expand and contract its orifice to grasp objects using a miniature hydraulic syringe. Grasping experiments show that the new HSTG can successfully grasp convex, nonconvex, and flat objects as well as the ones with cavity. The soft gripper uniquely exhibits high normal contact force at minimal pressure and energy use due to the nature of its working principle. A 26 g HSTG can produce at least 40 N of gripping force, can hold at least 88 N in external gripping mode (~346 times of its weight), 0.34 N in internal mode, and 1.74 N in suction gripping mode. The design and mechanical properties of its components can be fine-tuned to produce tailored performance for different grasping tasks.

Heart rhythm disorders are becoming increasingly prevalent with population aging. Atrial fibrillation ablation (AFA) is a procedure used to treat an irregular heart rhythm (arrhythmia) that starts in the heart's upper chambers. The AFA works by scarring or destroying heart tissue to disrupt aberrant conduction pathways causing the arrhythmia. In hospital cardiac units, a flexible catheter with integrated metal electrode is currently used for the AFA procedure. Despite advances, existing cardiac catheter tips are driven by cable mechanisms which are associated with high nonlinear hysteresis and force loss. In addition, they are also limited to rigid components which require multiple actuators to control the bending tip to reach the complex anatomical corners of the heart. This paper introduces a new soft hydraulic catheter that can achieve bidirectional bending motion via a single soft artificial muscle. The new catheter is also equipped with a portable handle as an ergonomic control interface. To validate the design concept, various prototypes are fabricated and tested including bending angles and generated force capability. Mathematical models for the bending arm are also developed and experimentally validated. The new soft catheter will enable rapid and precise manipulation to reach any target within the cardiac chambers, offering more rapid and focused ablation therapy to improve patient outcomes.

BACKGROUND: Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Recent studies have observed causative mutations in susceptible genes related to colorectal cancer in 10 to 15% of the patients. This highlights the importance of identifying mutations for early detection of this cancer for more effective treatments among high risk individuals. Mutation is considered as the key point in cancer research. Many studies have performed cancer subtyping based on the type of frequently mutated genes, or the proportion of mutational processes. However, to the best of our knowledge, combination of these features has never been used together for this task. This highlights the potential to introduce better and more inclusive subtype classification approaches using wider range of related features to enable biomarker discovery and thus inform drug development for CRC. RESULTS: In this study, we develop a new pipeline based on a novel concept called 'gene-motif', which merges mutated gene information with tri-nucleotide motif of mutated sites, for colorectal cancer subtype identification. We apply our pipeline to the International Cancer Genome Consortium (ICGC) CRC samples and identify, for the first time, 3131 gene-motif combinations that are significantly mutated in 536 ICGC colorectal cancer samples. Using these features, we identify seven CRC subtypes with distinguishable phenotypes and biomarkers, including unique cancer related signaling pathways, in which for most of them targeted treatment options are currently available. Interestingly, we also identify several genes that are mutated in multiple subtypes but with unique sequence contexts. CONCLUSION: Our results highlight the importance of considering both the mutation type and mutated genes in identification of cancer subtypes and cancer biomarkers. The new CRC subtypes presented in this study demonstrates distinguished phenotypic properties which can be effectively used to develop new treatments. By knowing the genes and phenotypes associated with the subtypes, a personalized treatment plan can be developed that considers the specific phenotypes associated with their genomic lesion.

The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as they can be used without expertise, and BP measurement can be performed by patients at home. Non-invasive cuff-based monitoring is the dominant method for BP measurement. While the oscillometric technique is most common, a few automated NIBP measurement methods have been developed based on the auscultatory technique. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification, and feature extraction problems. This paper proposes a novel automated AI-based technique for NIBP estimation from auscultatory waveforms (AWs) based on converting the NIBP estimation problem to a sequence-to-sequence classification problem. To do this, a sequence of segments was first formed by segmenting the AWs, and their corresponding decomposed detail, and approximation parts obtained by wavelet packet decomposition method, and extracting features from each segment. Then, a label was assigned to each segment, i.e. (i) between systolic, and diastolic segments, and (ii) otherwise, and a bidirectional long short term memory recurrent neural network (BiLSTM-RNN) was devised to solve the resulting sequence-to-sequence classification problem. Adopting a 5-fold cross-validation scheme, and using a data base of 350 NIBP recordings gave an average mean absolute error of 1.7±3.7 mmHg for systolic BP (SBP), and 3.4 ±5.0 mmHg for diastolic BP (DBP) relative to reference values. Based on the results achieved, and comparisons made with the existing literature, it is concluded that the proposed automated BP estimation algorithm based on deep learning methods, and auscultatory waveform brings plausible benefits to the field of BP estimation.

Because of the rapid and serious nature of acute cardiovascular disease (CVD) especially ST segment elevation myocardial infarction (STEMI), a leading cause of death worldwide, prompt diagnosis and treatment is of crucial importance to reduce both mortality and morbidity. During a pandemic such as coronavirus disease-2019 (COVID-19), it is critical to balance cardiovascular emergencies with infectious risk. In this work, we recommend using wearable device based mobile health (mHealth) as an early screening and real-time monitoring tool to address this balance and facilitate remote monitoring to tackle this unprecedented challenge. This recommendation may help to improve the efficiency and effectiveness of acute CVD patient management while reducing infection risk.

The traditional differential diagnosis of membranous nephropathy (MN) mainly relies on clinical symptoms, serological examination and optical renal biopsy. However, there is a probability of false positives in the optical inspection results, and it is unable to detect the change of biochemical components, which poses an obstacle to pathogenic mechanism analysis. Microscopic hyperspectral imaging can reveal detailed component information of immune complexes, but the high dimensionality of microscopic hyperspectral image brings difficulties and challenges to image processing and disease diagnosis. In this paper, a novel classification framework, including spatial-spectral density peaks-based discriminant analysis (SSDP), is proposed for intelligent diagnosis of MN using a microscopic hyperspectral pathological dataset. SSDP constructs a set of graphs describing intrinsic structure of MHSI in both spatial and spectral domains by employing density peak clustering. In the process of graph embedding, low-dimensional features with important diagnostic information in the immune complex are obtained by compacting the spatial-spectral local intra-class pixels while separating the spectral inter-class pixels. For the MN recognition task, a support vector machine (SVM) is used to classify pixels in the low-dimensional space. Experimental validation data employ two types of MN that are difficult to distinguish with optical microscope, including primary MN and hepatitis B virus-associated MN. Experimental results show that the proposed SSDP achieves a sensitivity of 99.36%, which has potential clinical value for automatic diagnosis of MN.

Kidney development is key to the long-term health of the fetus. Renal volume and vascularity assessed by 3D ultrasound (3D-US) are known markers of wellbeing, however, a lack of real-time image segmentation solutions preclude these measures being used in a busy clinical environment. In this work, we aimed to automate kidney segmentation using fully convolutional neural networks (fCNNs). We used multi-parametric input fusion incorporating 3D B-Mode and power Doppler (PD) volumes, aiming to improve segmentation accuracy. Three different fusion strategies and their performance were assessed versus a single input (B-Mode) network. Early input-level fusion provided the best segmentation accuracy with an average Dice similarity coefficient (DSC) of 0.81 and Hausdorff distance (HD) of 8.96 mm, an improvement of 0.06 DSC and reduction of 1.43 mm HD compared to our baseline network. Compared to manual segmentation for all models, repeatability was assessed by intra-class correlation coefficients (ICC) indicating good to excellent reproducibility (ICC 0.93). The framework was extended to support multiple graphics processing units (GPUs) to better handle volumetric data, dense fCNN models, batch normalization and complex fusion networks. This work and available source code provides a framework to increase the parameter space of encoder-decoder style fCNNs across multiple GPUs and shows that application of multi-parametric 3D-US in fCNN training improves segmentation accuracy.

Despite many advances in the development of retinal prostheses, clinical reports show that current retinal prosthesis subjects can only perceive prosthetic vision with poor visual acuity. A possible approach for improving visual acuity is to produce virtual electrodes (VEs) through electric field modulation. Generating controllable and localized VEs is a crucial factor in effectively improving the perceptive resolution of the retinal prostheses. In this paper, we aimed to design a microelectrode array (MEA) that can produce converged and controllable VEs by current steering stimulation strategies. Through computational modeling, we designed a three-dimensional concentric ring-disc MEA and evaluated its performance with different stimulation strategies. Our simulation results showed that electrode-retina distance (ERD) and inter-electrode distance (IED) can dramatically affect the distribution of electric field. Also the converged VEs could be produced when the parameters of the three-dimensional MEA were appropriately set. VE sites can be controlled by manipulating the proportion of current on each adjacent electrode in a current steering group (CSG). In addition, spatial localization of electrical stimulation can be greatly improved under quasi-monopolar (QMP) stimulation. This study may provide support for future application of VEs in epiretinal prosthesis for potentially increasing the visual acuity of prosthetic vision.

Cell classification, especially that of white blood cells, plays a very important role in the field of diagnosis and control of major diseases. Compared to traditional optical microscopic imaging, hyperspectral imagery, combined with both spatial and spectral information, provides more wealthy information for recognizing cells. In this paper, a novel blood cell classification framework, which combines a modulated Gabor wavelet and deep convolutional neural network (CNN) kernels, named as MGCNN, is proposed based on medical hyperspectral imaging. For each convolutional layer, multi-scale and orientation Gabor operators are taken dot product with initial CNN kernels. The essence is to transform the convolutional kernels into the frequency domain to learn features. By combining characteristics of Gabor wavelets, the features learned by modulated kernels at different frequencies and orientations are more representative and discriminative. Experimental results demonstrate that the proposed model can achieve better classification performance than traditional CNNs and widely used support vector machine approaches, especially as training small-sample-size situations.

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.

The eleven papers presented in this special issue provide a snapshot of the latest advances in the field of enabling technologies in health engineering and health informatics for the new revolution of Healthcare 4.0, hoping to further enable, drive and accelerate the research, development, and application of key technologies into healthcare systems.

A pilot study was conducted to determine if a smartphone-based adjunct to standard care could increase the completion rate of a cardiac rehabilitation program (CRP). Based on historical completion rates, 66 participants who were about to commence a hospital-based CRP were randomized so that half received three devices embedded with near-field communication, namely, a smartphone [pre-installed with an application (app) designed specifically for cardiac rehabilitation], portable blood pressure monitor, and weight scale while completing the CRP. The completion rate among participants who were randomized to the intervention group was 88%, compared to 67% in the control group ( = 0.038). This combined with the week-to-week frequency with which participants in the intervention group measured their blood pressure ( 5/week) demonstrated the ability of the intervention to increase the proportion of patients who completed the CRP. No significant differences were found between the treatment groups for the measurements taken at baseline and prior to discharge from the CRP. A statistically significant correlation ( = 0.472; = 0.013) was found between the average time participants walked each day (as estimated via the smartphone app) and participants' six minute walking distance (6MWD) before they were discharged from the CRP (a clinically validated measurement).

Falls in older people are a major challenge to public health. A wearable fall detector can detect falls automatically based on kinematic information of the human body, allowing help to arrive sooner. To date, most studies have focused on the accuracy of an offline algorithm to distinguish real-world or simulated falls from activities of daily living, while neglecting the false alarm rate and battery life of a real device. To address these two important metrics, which significantly influence user compliance, this paper proposes a low-power fall detector using triaxial accelerometry and barometric pressure sensing. This fall detector minimizes power consumption using both hardware- and firmware-based techniques. Additionally, the fall detection algorithm used in this device is optimized to achieve a balance between sensitivity and false alarm rate, while minimizing the power consumption due to algorithm execution. The fall detector achieved a high sensitivity (91%) with a low false alarm rate (0.1149 alarms per hour), and a commercially-viable battery life (1125 days).

We used body-worn inertial sensors to quantify differences in semi-free-living gait between stairs and on normal flat ground in older adults, and investigated the utility of assessing gait on these terrains for predicting the occurrence of multiple falls. Eighty-two community-dwelling older adults wore two inertial sensors, on the lower back and the right ankle, during several bouts of walking on flat surfaces and up and down stairs, in between rests and activities of daily living. Derived from the vertical acceleration at the lower back, step rate was calculated from the signal's fundamental frequency. Step rate variability was the width of this fundamental frequency peak from the signal's power spectral density. Movement vigor was calculated at both body locations from the signal variance. Partial Spearman correlations between gait parameters and physiological fall risk factors (components from the Physiological Profile Assessment) were calculated while controlling for age and gender. Overall, anteroposterior vigor at the lower back in stair descent was lower in subjects with longer reaction times. Older adults walked more slowly on stairs, but they were not significantly slower on flat surfaces. Using logistic regression, faster step rate in stair descent was associated with multiple prospective falls over 12 months. No significant associations were shown from gait parameters derived during walking upstairs or on flat surfaces. These results suggest that stair descent gait may provide more insight into fall risk than regular walking and stair ascent, and that further sensor-based investigation into unsupervised gait on different terrains would be valuable.

Falls are the number one cause of injuries in the elderly. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or a medical rescue group for immediate assistance, mitigating fall-related injuries. However, most studies on fall detection to date have focused on the accuracy of detection while neglecting power efficiency and battery life, and hence the developed fall detectors usually cannot operate for a long period (a year or more) without recharging or replacing their batteries. This paper presents a low-power fall detector that utilizes triaxial accelerometry and barometric pressure sensing. This fall detector reduces its power consumption through both hardware- and firmware-based approaches. This study also incorporates several human trials to develop and evaluate the device, including simulated falls and activities of daily living. A benchtop power measurement test is also conducted to estimate the battery life with data from a one-week free-living trial. These experiments show that the fall detector achieves high sensitivity (97.5% and 93.0%) and specificity (93.2% and 87.3%) on training and testing datasets, while providing an estimated battery life of 664.9 days.

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.

The ten papers from this special sectoin were presented at the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’14).

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.

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.

The special section contains seven contributions to knowledge in the areas of hemodynamics, wearable technologies, management of chronic diseases, health information systems, and clinical decision support systems.

Delay elements are used in integrated circuits (ICs) to meet design specific timing requirements. Delays are often generated by increasing the input transition times. For long delays, such a signal generally results in prolonged short-circuit current either within the delay element itself or at the subsequent stage, elevating the overall power consumption of the system. In this paper, a novel CMOS semi-static threshold triggered delay element architecture is proposed, that can also be configured to work with other conventional delay elements, to minimize the short-circuit current over a wide delay range resulting in a predictable output delay and reduced power consumption. The semi-static threshold-triggered delay element is fabricated in a commercial 0.35 μm CMOS technology and comparative results show significant improvements in operating range and power consumption over other well-known delay elements.

The state of the art of biological-machine systems integration (BMSI) with an emphasis on neural interfacing is reported. The goal of BMSI from a medical viewpoint is to effectively replace or facilitate activation of, or recording from, neural elements in a part of the nervous system. BMSI will be firstly examined from a generic level whereby the current technologies for noninvasive and invasive neural recording and neural stimulation will be detailed. A case study in the area of visual neuroprosthesis will be presented to elucidate the current and future issues facing BMSI. This will include a discussion on biocompatibility, design of stimulating electrodes, the implanted microelectronic neurostimulator, and parallelization of stimulus encoding and delivery.

Telehealth is the provision of health services at a distance. Typically, this occurs in unsupervised or remote environments, such as a patient's home. We describe one such telehealth system and the integration of extracted clinical measurement parameters with a decision-support system (DSS). An enterprise application-server framework, combined with a rules engine and statistical analysis tools, is used to analyze the acquired telehealth data, searching for trends and shifts in parameter values, as well as identifying individual measurements that exceed predetermined or adaptive thresholds. An overarching business process engine is used to manage the core DSS knowledge base and coordinate workflow outputs of the DSS. The primary role for such a DSS is to provide an effective means to reduce the data overload and to provide a means of health risk stratification to allow appropriate targeting of clinical resources to best manage the health of the patient. In this way, the system may ultimately influence changes in workflow by targeting scarce clinical resources to patients of most need. A single case study extracted from an initial pilot trial of the system, in patients with chronic obstructive pulmonary disease and chronic heart failure, will be reviewed to illustrate the potential benefit of integrating telehealth and decision support in the management of both acute and chronic disease.

The use of a wearable triaxial accelerometer for unsupervised monitoring of human movement has become a major research focus in recent years. In this paper, the relationship between accelerometry signals and human gait is analysed using a linear prediction (LP) model. We explore the use of the LP model for analysing five gait patterns and show that the LP cepstrum can be used for gait pattern classification with high accuracy. This is then compared to a filterbank based approach to estimate the cepstral coefficients. Fifty subjects participated in collection of gait pattern data involving walking on level surfaces, and walking up and down stairs and ramps. The results show that an overall accuracy of 93% can be achieved using features derived from the cepstral coefficients for the five different walking patterns.

This paper describes accelerometry based classification of walking patterns. A feature extraction technique based on empirical mode decomposition (EMD) is proposed for the classification of unsupervised walking activities from accelerometry data. The front-end 20 dimensional features representing the gait patterns were obtained from the first three modes of decomposition of the acceleration data in anterior-posterior, medio-lateral, and vertical direction. The back-end of the system was a 64-mixture Gaussian Mixture Model (GMM) classifier. Overall classification accuracy of 96.02% was achieved for the five different human gait patterns including walking on flat surfaces, walking up and down paved ramps and walking up and down stairways.

In this paper, we discuss system architectures, design challenges and circuit implementation principles for intraocular epiretinal neuro-stimulators to be used as part of vision prostheses in partially restoring vision to the blind. Our unique hexagonal electrode placement allows focused simultaneous stimulation which allow the electrode count to scale. A new dual-channel transcutaneous inductive link is used to transfer power and data efficiently to the implant, and a new dual-voltage power rectifier provides two implant supplies without the need for a DC-DC converter. Low-power designs of current stimulators and ECAP amplifiers are also outlined in the paper.

Granting those heart failure patients who are recipients of an implantable rotary blood pump (iRBP) greater functionality in daily activities is a key long-term strategy currently being pursued by many research groups. A reliable technique for noninvasive detection of the various pumping states, most notably that of ventricular collapse or suction, is an essential component of this strategy. Presented in this study is such a technique, whereby various indicators are derived from the noninvasive pump feedback signals, and a suitable computational methodology developed to classify the pumping states of interest. Clinical telemetry data from ten implant recipients was categorized (with the aid of trans-oesophageal echocardiography) into the normal and suction states. These data are used to develop a pumping state classifier based on an artificial neural network (ANN). Nine indices, derived from the noninvasive impeller speed signal, form the inputs to this ANN classifier. During validation, the resulting ANN classifier achieved a maximum sensitivity of 98.54% (609/618 samples of 5 s in length) and specificity of 99.26% (12,123/12,213 samples) for correct detection of the suction state. The ability to detect the suction state with such a high degree of accuracy provides a critical parameter both for control strategy development, and for clinical care of the implant recipient.

The real-time monitoring of human movement can provide valuable information regarding an individual's degree of functional ability and general level of activity. This paper presents the implementation of a real-time classification system for the types of human movement associated with the data acquired from a single, waist-mounted triaxial accelerometer unit. The major advance proposed by the system is to perform the vast majority of signal processing onboard the wearable unit using embedded intelligence. In this way, the system distinguishes between periods of activity and rest, recognizes the postural orientation of the wearer, detects events such as walking and falls, and provides an estimation of metabolic energy expenditure. A laboratory-based trial involving six subjects was undertaken, with results indicating an overall accuracy of 90.8% across a series of 12 tasks (283 tests) involving a variety of movements related to normal daily activities. Distinction between activity and rest was performed without error; recognition of postural orientation was carried out with 94.1% accuracy, classification of walking was achieved with less certainty (83.3% accuracy), and detection of possible falls was made with 95.6% accuracy. Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence.