Showing papers in "IEEE Journal of Translational Engineering in Health and Medicine in 2015"
TL;DR: The experimental results show that the proposed system is efficient, achieving classification of the benign, atypical, and melanoma images with accuracy of 96.3%, 95.7%, and 97.5%, respectively.
Abstract: Melanoma spreads through metastasis, and therefore, it has been proved to be very fatal. Statistical evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma. Further investigations have shown that the survival rates in patients depend on the stage of the cancer; early detection and intervention of melanoma implicate higher chances of cure. Clinical diagnosis and prognosis of melanoma are challenging, since the processes are prone to misdiagnosis and inaccuracies due to doctors’ subjectivity. Malignant melanomas are asymmetrical, have irregular borders, notched edges, and color variations, so analyzing the shape, color, and texture of the skin lesion is important for the early detection and prevention of melanoma. This paper proposes the two major components of a noninvasive real-time automated skin lesion analysis system for the early detection and prevention of melanoma. The first component is a real-time alert to help users prevent skinburn caused by sunlight; a novel equation to compute the time for skin to burn is thereby introduced. The second component is an automated image analysis module, which contains image acquisition, hair detection and exclusion, lesion segmentation, feature extraction, and classification. The proposed system uses PH2 Dermoscopy image database from Pedro Hispano Hospital for the development and testing purposes. The image database contains a total of 200 dermoscopy images of lesions, including benign, atypical, and melanoma cases. The experimental results show that the proposed system is efficient, achieving classification of the benign, atypical, and melanoma images with accuracy of 96.3%, 95.7%, and 97.5%, respectively.
157 citations
TL;DR: The system of in-home sensors and algorithms for automated health alerts provides a method for detecting health problems very early so that early treatment is possible and this method of passive in- home sensing alleviates compliance issues.
Abstract: We present an example of unobtrusive, continuous monitoring in the home for the purpose of assessing early health changes. Sensors embedded in the environment capture behavior and activity patterns. Changes in patterns are detected as potential signs of changing health. We first present results of a preliminary study investigating 22 features extracted from in-home sensor data. A 1-D alert algorithm was then implemented to generate health alerts to clinicians in a senior housing facility. Clinicians analyze each alert and provide a rating on the clinical relevance. These ratings are then used as ground truth for training and testing classifiers. Here, we present the methodology for four classification approaches that fuse multisensor data. Results are shown using embedded sensor data and health alert ratings collected on 21 seniors over nine months. The best results show similar performance for two techniques, where one approach uses only domain knowledge and the second uses supervised learning for training. Finally, we propose a health change detection model based on these results and clinical expertise. The system of in-home sensors and algorithms for automated health alerts provides a method for detecting health problems very early so that early treatment is possible. This method of passive in-home sensing alleviates compliance issues.
96 citations
TL;DR: An analytical model is proposed for the Artemis cloud project which will be deployed at McMaster Children's Hospital in Hamilton and the amount of storage, memory, and computation power required for the system is predicted.
Abstract: The effective use of data within intensive care units (ICUs) has great potential to create new cloud-based health analytics solutions for disease prevention or earlier condition onset detection. The Artemis project aims to achieve the above goals in the area of neonatal ICUs (NICU). In this paper, we proposed an analytical model for the Artemis cloud project which will be deployed at McMaster Children’s Hospital in Hamilton. We collect not only physiological data but also the infusion pumps data that are attached to NICU beds. Using the proposed analytical model, we predict the amount of storage, memory, and computation power required for the system. Capacity planning and tradeoff analysis would be more accurate and systematic by applying the proposed analytical model in this paper. Numerical results are obtained using real inputs acquired from McMaster Children’s Hospital and a pilot deployment of the system at The Hospital for Sick Children (SickKids) in Toronto.
57 citations
TL;DR: The feasibility of the novel ARTSENS device in performing accurate in vivo measurements of arterial stiffness is verified, a device for image free, noninvasive, automated evaluation of vascular stiffness amenable for field use.
Abstract: Vascular stiffness is an indicator of cardiovascular health, with carotid artery stiffness having established correlation to coronary heart disease and utility in cardiovascular diagnosis and screening. State of art equipment for stiffness evaluation are expensive, require expertise to operate and not amenable for field deployment. In this context, we developed ARTerial Stiffness Evaluation for Noninvasive Screening (ARTSENS), a device for image free, noninvasive, automated evaluation of vascular stiffness amenable for field use. ARTSENS has a frugal hardware design, utilizing a single ultrasound transducer to interrogate the carotid artery, integrated with robust algorithms that extract arterial dimensions and compute clinically accepted measures of arterial stiffness. The ability of ARTSENS to measure vascular stiffness in vivo was validated by performing measurements on 125 subjects. The accuracy of results was verified with the state-of-the-art ultrasound imaging-based echo-tracking system. The relation between arterial stiffness measurements performed in sitting posture for ARTSENS measurement and sitting/supine postures for imaging system was also investigated to examine feasibility of performing ARTSENS measurements in the sitting posture for field deployment. This paper verified the feasibility of the novel ARTSENS device in performing accurate in vivo measurements of arterial stiffness. As a portable device that performs automated measurement of carotid artery stiffness with minimal operator input, ARTSENS has strong potential for use in large-scale screening.
53 citations
TL;DR: It is suggested that the measurement of the CCA IMT on one side only is enough (and this is in agreement with other studies), as well as automated measurements can be used.
Abstract: The intima-media thickness (IMT) of the common carotid artery (CCA) is an established indicator of cardiovascular disease (CVD). There have been reports about the difference between the left and the right sides of the CCA IMT and their relation with CVD. In this paper, we propose an automated system based on image normalization, speckle reduction filtering, and snakes segmentation, for segmenting the CCA, perform IMT measurements, and provide the differences between the left and the right sides. The study was performed on 1104 longitudinal-section ultrasound images acquired from 568 men and 536 women out of which 125 had cardiovascular symptoms (CVD). A cardiovascular expert manually delineated the IMT for the normal and the CVD groups. The corresponding (normal versus CVD) IMT mean ± standard deviation values for the left and the right sides were 0.74 ± 0.24 versus 0.87 ± 0.24 mm and 0.70 ± 0.17 versus 0.80 ± 0.18 mm, respectively. The main findings of this paper can be summarized as follows: 1) there was no significant difference between the CCA left side IMT and the right side IMT. These findings suggest that the measurement of the CCA IMT on one side only is needed for the normal group (and this is in agreement with other studies); 2) there were statistical significant differences for the IMT measurements between the normal group and the CVD group for both the left and the right sides; 3) there was an increasing linear relationship of the left and the right IMT measurements with age for the normal group; and to a lesser extend for the CVD group; 4) no statistical significant differences were found between the manual and the automated IMT measurements for both sides; and 5) the best result for classification disease modeling, using support vector machines, to discriminate between the normal and the CVD groups was a 64%±3.5% correct classifications score when using both the left and the right IMT automated measurements. Further research is required for estimating differences and similarities between left and right intima media complex structure and morphology and their variability with texture features for differentiating between the normal and the CVD group.
53 citations
TL;DR: A novel 3-D GBM cell culture model based on microwells that could mimic in vitro environment and help to bypass the lack of suitable animal models for preclinical toxicity tests is designed and could help to reduce the time of the preclinical brain tumor growth studies.
Abstract: Glioblastoma multiforme (GBM) is the most common and malignant primary brain tumor in adults because of its highly invasive behavior. The existing treatment for GBM, which involves a combination of resection, chemotherapy, and radiotherapy, has a very limited success rate with a median survival rate of in vitro environment and help to bypass the lack of suitable animal models for preclinical toxicity tests. Microwells were fabricated from simple and inexpensive polyethylene glycol material for the control of in vitro 3-D culture. We applied the 3-D micropatterning system to GBM (U-87) cells using the photolithography technique to control the cell spheroids’ shape, size, and thickness. Our preliminary results suggested that uniform GBM spheroids can be formed in 3-D, and the size of these GBM spheroids depends on the size of microwells. The viability of the spheroids generated in this manner was quantitatively evaluated using live/dead assay and shown to improve over 21 days. We believe that in vitro 3-D cell culture model could help to reduce the time of the preclinical brain tumor growth studies. The proposed novel platform could be useful and cost-effective for high-throughput screening of cancer drugs and assessment of treatment responses.
51 citations
New Jersey Institute of Technology1, National Institutes of Health2, Northeastern University3, Johns Hopkins University4, University of Houston5, Stanford University6, University of Florida7, Baylor College of Medicine8, Apollo Hospitals9, University of New South Wales10, Georgia Institute of Technology11, All India Institute of Medical Sciences12
TL;DR: This paper summarizes the panel discussion at the IEEE Engineering in Medicine and Biology Point-of-Care Healthcare Technology Conference (POCHT 2013) held in Bangalore India from Jan 16-18, 2013.
Abstract: This paper summarizes the panel discussion at the IEEE Engineering in Medicine and Biology Point-of-Care Healthcare Technology Conference (POCHT 2013) held in Bangalore India from Jan 16–18, 2013. Modern medicine has witnessed interdisciplinary technology innovations in healthcare with a continuous growth in life expectancy across the globe. However, there is also a growing global concern on the affordability of rapidly rising healthcare costs. To provide quality healthcare at reasonable costs, there has to be a convergence of preventive, personalized, and precision medicine with the help of technology innovations across the entire spectrum of point-of-care (POC) to critical care at hospitals. The first IEEE EMBS Special Topic POCHT conference held in Bangalore, India provided an international forum with clinicians, healthcare providers, industry experts, innovators, researchers, and students to define clinical needs and technology solutions toward commercialization and translation to clinical applications across different environments and infrastructures. This paper presents a summary of discussions that took place during the keynote presentations, panel discussions, and breakout sessions on needs, challenges, and technology innovations in POC technologies toward improving global healthcare. Also presented is an overview of challenges and trends in developing and developed economies with respect to priority clinical needs, technology innovations in medical devices, translational engineering, information and communication technologies, infrastructure support, and patient and clinician acceptance of POC healthcare technologies.
45 citations
TL;DR: An integrated model of patient and staff satisfaction, the effective satisfaction level model, is proposed, connecting two important concepts, namely, experience and efficiency in care delivery and leading to a more holistic approach in designing and managing health services.
Abstract: This paper investigates the connection between patient satisfaction, waiting time, staff satisfaction, and service time. It uses a variety of models to enable improvement against experiential and operational health service goals. Patient satisfaction levels are estimated using a model based on waiting (waiting times). Staff satisfaction levels are estimated using a model based on the time spent with patients (service time). An integrated model of patient and staff satisfaction, the effective satisfaction level model, is then proposed (using queuing theory). This links patient satisfaction, waiting time, staff satisfaction, and service time, connecting two important concepts, namely, experience and efficiency in care delivery and leading to a more holistic approach in designing and managing health services. The proposed model will enable healthcare systems analysts to objectively and directly relate elements of service quality to capacity planning. Moreover, as an instrument used jointly by healthcare commissioners and providers, it affords the prospect of better resource allocation.
36 citations
TL;DR: This paper investigates the development of the Swallowscope, a smartphone-based device and a feasible real-time swallowing sound-processing algorithm for the automatic screening, quantitative evaluation, and the visualisation of swallowing ability and develops a template matching approach.
Abstract: Dysphagia can cause serious challenges to both physical and mental health. Aspiration due to dysphagia is a major health risk that could cause pneumonia and even death. The videofluoroscopic swallow study (VFSS), which is considered the gold standard for the diagnosis of dysphagia, is not widely available, expensive and causes exposure to radiation. The screening tests used for dysphagia need to be carried out by trained staff, and the evaluations are usually non-quantifiable. This paper investigates the development of the Swallowscope, a smartphone-based device and a feasible real-time swallowing sound-processing algorithm for the automatic screening, quantitative evaluation, and the visualisation of swallowing ability. The device can be used during activities of daily life with minimal intervention, making it potentially more capable of capturing aspirations and risky swallow patterns through the continuous monitoring. It also consists of a cloud-based system for the server-side analyzing and automatic sharing of the swallowing sound. The real-time algorithm we developed for the detection of dry and water swallows is based on a template matching approach. We analyzed the wavelet transformation-based spectral characteristics and the temporal characteristics of simultaneous synchronised VFSS and swallowing sound recordings of 25% barium mixed 3-ml water swallows of 70 subjects and the dry or saliva swallowing sound of 15 healthy subjects to establish the parameters of the template. With this algorithm, we achieved an overall detection accuracy of 79.3% (standard error: 4.2%) for the 92 water swallows; and a precision of 83.7% (range: 66.6%–100%) and a recall of 93.9% (range: 72.7%–100%) for the 71 episodes of dry swallows.
32 citations
TL;DR: It was concluded that the anodal tDCS can perturb the local neural and the vascular activity (via NVC) which can be used for assessing regional NVC functionality where confirmatory clinical studies are required.
Abstract: This paper presents a point of care testing device for neurovascular coupling (NVC) from simultaneous recording of electroencephalogram (EEG) and near infrared spectroscopy (NIRS) during anodal transcranial direct current stimulation (tDCS). Here, anodal tDCS modulated cortical neural activity leading to hemodynamic response can be used to identify the impaired cerebral microvessels functionality. The impairments in the cerebral microvessels functionality may lead to impairments in the cerebrovascular reactivity (CVR), where severely reduced CVR predicts the chances of transient ischemic attack and ipsilateral stroke. The neural and hemodynamic responses to anodal tDCS were studied through joint imaging with EEG and NIRS, where NIRS provided optical measurement of changes in tissue oxy-( $HbO2)$ and deoxy-( $Hb$ ) hemoglobin concentration and EEG captured alterations in the underlying neuronal current generators. Then, a cross-correlation method for the assessment of NVC underlying the site of anodal tDCS is presented. The feasibility studies on healthy subjects and stroke survivors showed detectable changes in the EEG and the NIRS responses to a 0.526 A/ $\mathrm{m}^{2}$ of anodal tDCS. The NIRS system was bench tested on 15 healthy subjects that showed a statistically significant (p $HbO2$ than the nonlesioned side in response to anodal tDCS. The EEG study on healthy subjects showed a statistically significant (p $HbO2$ response in one of the stroke survivors with white matter lesions. Therefore, it was concluded that the anodal tDCS can perturb the local neural and the vascular activity (via NVC) which can be used for assessing regional NVC functionality where confirmatory clinical studies are required.
32 citations
TL;DR: The algorithm is based on a novel cascade of computationally efficient filters, optimized adaptive thresholding, and a refined search back mechanism and validated with high clinical performance on more than 300 ECG records from 189 different subjects with a high number of different abnormal beat morphologies.
Abstract: Cardiovascular diseases are projected to remain the single leading cause of death globally. Timely diagnosis and treatment of these diseases are crucial to prevent death and dangerous complications. One of the important tools in early diagnosis of arrhythmias is analysis of electrocardiograms (ECGs) obtained from ambulatory long-term recordings. The design of novel patch-type ECG recorders has increased the accessibility of these long-term recordings. In many applications, it is furthermore an advantage for these devices that the recorded ECGs can be analyzed automatically in real time. The purpose of this study was therefore to design a novel algorithm for automatic heart beat detection, and embed the algorithm in the CE marked ePatch heart monitor. The algorithm is based on a novel cascade of computationally efficient filters, optimized adaptive thresholding, and a refined search back mechanism. The design and optimization of the algorithm was performed on two different databases: The MIT-BIH arrhythmia database ( $Se=99.90$ %, $P^{+}=99.87$ ) and a private ePatch training database ( $Se=99.88$ %, $P^{+}=99.37$ %). The offline validation was conducted on the European ST-T database ( $Se=99.84$ %, $P^{+}=99.71$ %). Finally, a double-blinded validation of the embedded algorithm was conducted on a private ePatch validation database ( $Se=99.91$ %, $P^{+}=99.79$ %). The algorithm was thus validated with high clinical performance on more than 300 ECG records from 189 different subjects with a high number of different abnormal beat morphologies. This demonstrates the strengths of the algorithm, and the potential for this embedded algorithm to improve the possibilities of early diagnosis and treatment of cardiovascular diseases.
TL;DR: A new motor imagery classification method in the context of electroencephalography (EEG)-based brain-computer interface (BCI) using a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction.
Abstract: In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s $T^{2}$ statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%.
TL;DR: In this paper, the presence and direction of the optokinetic nystagmus (OKN) was detected in young children using consumer grade video equipment. But, their results suggest that the detection of OKN can be measured objectively in children with consumer grade equipment, and readily implementable algorithms.
Abstract: The detection of vision problems in early childhood can prevent neurodevelopmental disorders such as amblyopia. However, accurate clinical assessment of visual function in young children is challenging. optokinetic nystagmus (OKN) is a reflexive sawtooth motion of the eye that occurs in response to drifting stimuli, that may allow for objective measurement of visual function in young children if appropriate child-friendly eye tracking techniques are available. In this paper, we present offline tools to detect the presence and direction of the optokinetic reflex in children using consumer grade video equipment. Our methods are tested on video footage of children ( $N = 5$ children and 20 trials) taken as they freely observed visual stimuli that induced horizontal OKN. Using results from an experienced observer as a baseline, we found the sensitivity and specificity of our OKN detection method to be 89.13% and 98.54%, respectively, across all trials. Our OKN detection results also compared well (85%) with results obtained from a clinically trained assessor. In conclusion, our results suggest that OKN presence and direction can be measured objectively in children using consumer grade equipment, and readily implementable algorithms.
TL;DR: In this article, the authors demonstrate the effective deployment of a robotic assessment tool for the evaluation of mild traumatic brain injury (mTBI) patients in a busy, resource-constrained, urban emergency department (ED).
Abstract: The objective of this paper is to demonstrate the effective deployment of a robotic assessment tool for the evaluation of mild traumatic brain injury (mTBI) patients in a busy, resource-constrained, urban emergency department (ED). Methods: Functional integration of new robotic technology for research in the ED presented several obstacles that required a multidisciplinary approach, including participation from electrical and computer engineers, emergency medicine clinicians, and clinical operations staff of the hospital. Our team addressed many challenges in deployment of this advanced technology including: 1) adapting the investigational device for the unique clinical environment; 2) acquisition and maintenance of appropriate testing space for point-of-care assessment; and 3) dedicated technical support and upkeep of the device. Upon successful placement of the robotic device in the ED, the clinical study required screening of all patients presenting to the ED with complaints of head injury. Eligible patients were enrolled and tested using a robot-assisted test battery. Three weeks after the injury, patients were contacted to complete follow-up assessments. Results: Adapting the existing technology to meet anticipated physical constraints of the ED was performed by engineering a mobile platform. Due to the large footprint of the device, it was frequently moved before ultimately being fully integrated into the ED. Over 14 months, 1423 patients were screened. Twenty-eight patients could not be enrolled because the device was unavailable due to operations limitations. Technical problems with the device resulted in failure to include 20 patients. A total of 66 mTBI patients were enrolled and 42 of them completed both robot-assisted testing and follow-up assessment. Successful completion of screening and enrollment demonstrated that the challenges associated with integration of investigational devices into the ED can be effectively addressed through a collaborative patient-oriented research model. Conclusion: Effective deployment and use of new robotic technology for research in an urban academic ED required significant planning, coordination, and collaboration with key personnel from multiple disciplines. Clinical Impact: A pilot clinical study on mTBI patients using the robotic device provided useful data without disrupting the ED workflow. Integration of this technology into the ED serves as an important step toward pursing active clinical research in an acute care setting.
TL;DR: This paper uses built-in smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately estimate EE, and develops a generic regression model for EE estimation that yields upto 96% correlation with actual EE.
Abstract: Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. Accurate and real-time EE estimation utilizing small wearable sensors is a difficult task, primarily because the most existing schemes work offline or use heuristics. In this paper, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs, or downstairs) of a typical smartphone user. We used built-in smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately estimate EE. Using a barometer sensor, in addition to an accelerometer sensor, greatly increases the accuracy of EE estimation. Using bagged regression trees, a machine learning technique, we developed a generic regression model for EE estimation that yields upto 96% correlation with actual EE. We compare our results against the state-of-the-art calorimetry equations and consumer electronics devices (Fitbit and Nike+ FuelBand). The newly developed EE estimation algorithm demonstrated superior accuracy compared with currently available methods. The results were calibrated against COSMED K4b2 calorimeter readings.
TL;DR: An improved version of the SDI parameter, measured from the electrical diastolic interval and the heart rate interval, is proposed to be more sensitive in detecting CAN progression than other ECG interval-based features traditionally used for CAN diagnosis.
Abstract: Cardiac autonomic neuropathy (CAN), one of the major complications in diabetes, if detected at the subclinical stage allows for effective treatment and avoiding further complication including cardiovascular pathology. Surface ECG (Electrocardiogram)-based diagnosis of CAN is useful to overcome the limitation of existing cardiovascular autonomic reflex tests traditionally used for CAN identification in clinical settings. The aim of this paper is to analyze the changes in the mechanical function of the ventricles in terms of systolic-diastolic interval interaction (SDI) from a surface ECG to assess the severity of CAN progression [no CAN, early CAN (ECAN) or subclinical CAN, and definite CAN (DCAN) or clinical CAN]. ECG signals recorded in supine resting condition from 72 diabetic subjects without CAN (CAN-) and 70 diabetic subjects with CAN were analyzed in this paper. The severity of CAN was determined by Ewing’s Cardiovascular autonomic reflex tests. Fifty-five subjects of the CAN group had ECAN and 15 subjects had DCAN. In this paper, we propose an improved version of the SDI parameter (i.e., TQ/RR interval ratio) measured from the electrical diastolic interval (i.e., TQ interval) and the heart rate interval (i.e., RR interval). The performance of the proposed SDI measure was compared with the performance of the existing SDI measure (i.e., QT/TQ interval ratio). The proposed SDI parameter showed significant differences among three groups (no CAN, ECAN, and DCAN). In addition, the proposed SDI parameter was found to be more sensitive in detecting CAN progression than other ECG interval-based features traditionally used for CAN diagnosis. The modified SDI parameter might be used as an alternative measure for the Ewing autonomic reflex tests to identify CAN progression for those subjects who are unable to perform the traditional tests. These findings could also complement the echocardiographic findings of the left ventricular diastolic dysfunction by providing additional information about alteration in systolic and diastolic intervals in heart failure.
TL;DR: Results indicate that the ANFs of two specific PDMs, corresponding to the delta-theta and alpha bands, can delineate the two groups well, and may assist clinical diagnosis of Alzheimer's disease (AD).
Abstract: We examine whether modeling of the causal dynamic relationships between frontal and occipital electroencephalogram (EEG) time-series recordings reveal reliable differentiating characteristics of Alzheimer’s patients versus control subjects in a manner that may assist clinical diagnosis of Alzheimer’s disease (AD). The proposed modeling approach utilizes the concept of principal dynamic modes (PDMs) and their associated nonlinear functions (ANF) and hypothesizes that the ANFs of some PDMs for the AD patients will be distinct from their counterparts in control subjects. To this purpose, global PDMs are extracted from 1-min EEG signals of 17 AD patients and 24 control subjects at rest using Volterra models estimated via Laguerre expansions, whereby the O1 or O2 recording is viewed as the input signal and the F3 or F4 recording as the output signal. Subsequent singular value decomposition of the estimated Volterra kernels yields the global PDMs that represent an efficient basis of functions for the representation of the EEG dynamics in all subjects. The respective ANFs are computed for each subject and characterize the specific dynamics of each subject. For comparison, signal features traditionally used in the analysis of EEG signals in AD are computed as benchmark. The results indicate that the ANFs of two specific PDMs, corresponding to the delta–theta and alpha bands, can delineate the two groups well.
TL;DR: It is concluded that the harmonic analysis of the radial pulse wave using the TD01C system is a feasible and reliable method to assess a hemodynamic characteristic in clinical trial.
Abstract: This study was aimed to establish a standard protocol and to quantitatively assess the reliability of harmonic analysis of the radial pulse wave measured by a harmonic wave analyzer (TD01C system). Both intraobserver and interobserver assessments were conducted to investigate whether the values of harmonics are stable in successive measurements. An intraclass correlation coefficient (ICC) and a Bland–Altman plot were used for this purpose. For the reliability assessments of the intraobserver and the interobserver, 22 subjects (mean age 45 ± 14 years; 14 males and 8 females) were enrolled. The first eleven harmonics of the radial pulse wave presented excellent repeatability ( $\text {ICCs}>0.9$ and $\text {p} ) for the intraobserver assessment and high reproducibility (ICCs range from 0.83 to 0.96 and $\text {p} ) for the interobserver assessment. The Bland–Altman plot indicated that more than 90% of harmonic values fell within two standard deviations of the mean difference. Thus, we concluded that the harmonic analysis of the radial pulse wave using the TD01C system is a feasible and reliable method to assess a hemodynamic characteristic in clinical trial.
TL;DR: A system is presented which can deliver charge-balanced, constant-current biphasic pulses, with widely adjustable parameters, to arbitrary configurations of output electrodes, and is shown to be effective in eliciting visual percepts in a patient with approximately 20 years of light perception vision only due to retinitis pigmentosa.
Abstract: To evaluate the efficacy of a suprachoroidal retinal prosthesis, a highly configurable external neurostimulator is required. In order to meet functional and safety specifications, it was necessary to develop a custom device. A system is presented which can deliver charge-balanced, constant-current biphasic pulses, with widely adjustable parameters, to arbitrary configurations of output electrodes. This system is shown to be effective in eliciting visual percepts in a patient with approximately 20 years of light perception vision only due to retinitis pigmentosa, using an electrode array implanted in the suprachoroidal space of the eye. The flexibility of the system also makes it suitable for use in a number of other emerging clinical neurostimulation applications, including epileptic seizure suppression and closed-loop deep brain stimulation. Clinical trial registration number NCT01603576 ( www.clinicaltrials.gov ).
TL;DR: An ontology is presented that specifies the relation among technological context, quality of clinical data, and patient treatment and enables the development of telemedicine systems that are capable of adapting the treatment when the quality of the clinical data degrades, and thus guaranteeing patients' safety even when technological context varies.
Abstract: Clinical data are crucial for any medical case to study and understand a patient’s condition and to give the patient the best possible treatment. Pervasive healthcare systems apply information and communication technology to enable the usage of ubiquitous clinical data by authorized medical persons. However, quality of clinical data in these applications is, to a large extent, determined by the technological context of the patient. A technological context is characterized by potential technological disruptions that affect optimal functioning of technological resources. The clinical data based on input from these technological resources can therefore have quality degradations. If these degradations are not noticed, the use of this clinical data can lead to wrong treatment decisions, which potentially puts the patient’s safety at risk. This paper presents an ontology that specifies the relation among technological context, quality of clinical data, and patient treatment. The presented ontology provides a formal way to represent the knowledge to specify the effect of technological context variations in the clinical data quality and the impact of the clinical data quality on a patient’s treatment. Accordingly, this ontology is the foundation for a quality of data framework that enables the development of telemedicine systems that are capable of adapting the treatment when the quality of the clinical data degrades, and thus guaranteeing patients’ safety even when technological context varies.
TL;DR: An overview on the background information of the transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area are provided.
Abstract: Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and the treatment of cardiovascular diseases. For example, transcatheter aortic valve implantation is an alternative to aortic valve replacement for the treatment of severe aortic stenosis, and transcatheter atrial fibrillation ablation is widely used for the treatment and the cure of atrial fibrillation. In addition, catheter-based intravascular ultrasound and optical coherence tomography imaging of coronary arteries provides important information about the coronary lumen, wall, and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial to the evaluation and the treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation and motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods. We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence, it is important to understand the application domain, clinical background, and imaging modality, so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on the background information of the transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area.
TL;DR: A novel approach to examine conduction gaps is described, by using a proof of concept device to examine local electrical activation within the cardiac areas of an applied lesion, i.e., to locate gaps in the lesion set.
Abstract: The use of therapeutic ablation in patients with atrial fibrillation has become a mainstay in the treatment of this disease, yet often these individuals require multiple procedures. In other words, successful first time treatments are impacted by challenges, including the generation of linear lesions in certain anatomies like the mitral isthmus of the left atrium. Hence, there is a need to find ways to address the presence of unwanted conduction gaps at the time of lesion creation. In this paper, we describe a novel approach to examine conduction gaps, by using a proof of concept device to examine local electrical activation within the cardiac areas of an applied lesion, i.e., to locate gaps in the lesion set. To accomplish this, both epicardial and endocardial linear ablation lines composed of spot lesions with conduction gaps were created in a porcine model. The forces necessary to elicit monophasic action potentials (MAP) were collected from >200 measurements on the epicardium of the right ventricle. Ablations were then performed on the ventricular epicardium and left atrial mitral isthmus endocardially, while recording MAPs. We were able to successfully demonstrate the use of a proof of concept device to identify conduction gaps in linear lesion sets; furthermore, we were able to determine required contact forces to appropriately determine focal electrical changes of the underlying tissues. New catheter designs that incorporate capabilities to record focal MAPs could be employed clinically to better assess a given lesion quality and/or to determine the existence of an undesired conduction gap.
TL;DR: It is demonstrated that pulmonary cooling by cooling of inhalation gases immediately before they enter the trachea can slowly reduce brain and core body temperature of newborn piglets.
Abstract: We investigate thermal effects of pulmonary cooling which was induced by cold air through an endotracheal tube via a ventilator on newborn piglets. A mathematical model was initially employed to compare the thermal impact of two different gas mixtures, O2-medical air (1:2) and O2-Xe (1:2), across the respiratory tract and within the brain. Following mathematical simulations, we examined the theoretical predictions with O2-medical air condition on nine anesthetized piglets which were randomized to two treatment groups: 1) control group ( $n = 4$ ) and 2) pulmonary cooling group ( $n = 5$ ). Numerical and experimental results using O2-medical air mixture show that brain temperature fell from 38.5 °C and 38.3 °C ± 0.3 °C to 35.7 °C ± 0.9 °C and 36.5 °C ± 0.6 °C during 3 h cooling which corresponded to a mean cooling rate of 0.9 °C/h ± 0.2 °C/h and 0.6 °C/h ± 0.1 °C/h, respectively. According to the numerical results, decreasing the metabolic rate and increasing air velocity are helpful to maximize the cooling effect. We demonstrated that pulmonary cooling by cooling of inhalation gases immediately before they enter the trachea can slowly reduce brain and core body temperature of newborn piglets. Numerical simulations show no significant differences between two different inhaled conditions, i.e., O2-medical air (1:2) and O2-Xe (1:2) with respect to cooling rate.
TL;DR: It is concluded that water was safe for drinking purposes if it has a low hardness and pH (7.1-7.4) and water with more hardness contains more fluoride concentration as compared with soft water (4 mg/ml).
Abstract: This study was conducted to analyze the impact of fluoride in the anthropogenic condition in an industrial region promoting and affecting the health of the workers. Fluoride is toxic to humans in high concentrations, such as can occur in persons working in fluoride-containing mineral industries like aluminum industries. When workers are exposed to fluoride-containing minerals, they can suffer from a variety of health problems, such as dental disease. This paper presents the relationship of different clinical conditions correlated against the fluoride level. Contributing clinical aspects, such as morbidity, dysentery, overcrowding, and skin disease, are also studied to assess the consequences of fluoride upon consistent exposure. The relationship between pH and hardness of water with fluoride was measured, and then spatial maps were generated. The investigations resulted in a conclusion that hardness of water had a more pronounced impact on the level of fluoride concentration as compared with pH. Water with more hardness contains more fluoride concentration (25 mg/ml) as compared with soft water (4 mg/ml). This paper also revealed the concentration of fluoride content in the bodies of aluminum plant workers, which varied from 0.06 to 0.17 mg/L of blood serum in the case of pot room workers and 0.01 to 0.04 mg/L in the case of non-pot room workers. In fingernails, it varied from 0.09 to 3.77 mg/L and 0.39 to 1.15 mg/L in the case of pot room and non-pot room workers, respectively. In urine, it varied from 0.53 to 9.50 mg/L in pot room workers and 0.29 to 1.80 mg/L in non-pot room workers. This paper concluded that water was safe for drinking purposes if it has a low hardness (60–140 mg/ml) and pH (7.1–7.4).