Popular Documents (March 2018)
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Deep Learning for Health Informatics
Publication Year: 2017, Page(s):4 - 21
Cited by: Papers (6)With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful too... View full abstract»
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A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices
Publication Year: 2017, Page(s):56 - 64
Cited by: Papers (2)The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has ... View full abstract»
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Atrial Fibrillation Detection via Accelerometer and Gyroscope of a Smartphone
Publication Year: 2018, Page(s):108 - 118We present a smartphone-only solution for the detection of atrial fibrillation (AFib), which utilizes the built-in accelerometer and gyroscope sensors [inertial measurement unit, (IMU)] in the detection. Depending on the patient's situation, it is possible to use the developed smartphone application either regularly or occasionally for making a measurement of the subject. The smartphone is placed ... View full abstract»
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Bayesian Optimization of Personalized Models for Patient Vital-Sign Monitoring
Publication Year: 2018, Page(s):301 - 310Gaussian process regression (GPR) provides a means to generate flexible personalized models of time series of patient vital signs. These models can perform useful clinical inference in ways that population-based models cannot. A challenge for the use of personalized models is that they must be amenable to a wide range of parameterizations, to accommodate the plausible physiology of any patient in ... View full abstract»
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A Novel Continuous Blood Pressure Estimation Approach Based on Data Mining Techniques
Publication Year: 2017, Page(s):1730 - 1740Continuous blood pressure (BP) estimation using pulse transit time (PTT) is a promising method for unobtrusive BP measurement. However, the accuracy of this approach must be improved for it to be viable for a wide range of applications. This study proposes a novel continuous BP estimation approach that combines data mining techniques with a traditional mechanism-driven model. First, 14 features de... View full abstract»
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Big Data for Health
Publication Year: 2015, Page(s):1193 - 1208
Cited by: Papers (59)This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a dive... View full abstract»
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DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices
Publication Year: 2018, Page(s):98 - 107
Cited by: Papers (1)In this paper, we present DREAMER, a multimodal database consisting of electroencephalogram (EEG) and electrocardiogram (ECG) signals recorded during affect elicitation by means of audio-visual stimuli. Signals from 23 participants were recorded along with the participants self-assessment of their affective state after each stimuli, in terms of valence, arousal, and dominance. All the signals were... View full abstract»
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Mobile Stride Length Estimation With Deep Convolutional Neural Networks
Publication Year: 2018, Page(s):354 - 362Objective: Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the-art double integration approaches to gait patterns with a clear zero-velocity phase. Methods: We describe a novel approach to stride length estimatio... View full abstract»
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Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review
Publication Year: 2016, Page(s):1521 - 1537
Cited by: Papers (8)After decades of evolution, measuring instruments for quantitative gait analysis have become an important clinical tool for assessing pathologies manifested by gait abnormalities. However, such instruments tend to be expensive and require expert operation and maintenance besides their high cost, thus limiting them to only a small number of specialized centers. Consequently, gait analysis in most c... View full abstract»
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Big Data, Big Knowledge: Big Data for Personalized Healthcare
Publication Year: 2015, Page(s):1209 - 1215
Cited by: Papers (31)The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be succ... View full abstract»
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Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease
Publication Year: 2018, Page(s):173 - 183
Cited by: Papers (2)The accurate diagnosis of Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal neuroimaging data, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), has shown its effectiveness for AD diagnosis. The deep polynomial networks (DPN) is a recently proposed deep learn... View full abstract»
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On the Accuracy and Scalability of Probabilistic Data Linkage Over the Brazilian 114 Million Cohort
Publication Year: 2018, Page(s):346 - 353Data linkage refers to the process of identifying and linking records that refer to the same entity across multiple heterogeneous data sources. This method has been widely utilized across scientific domains, including public health where records from clinical, administrative, and other surveillance databases are aggregated and used for research, decision making, and assessment of public policies. ... View full abstract»
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A Survey on Ambient-Assisted Living Tools for Older Adults
Publication Year: 2013, Page(s):579 - 590
Cited by: Papers (223)In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summa... View full abstract»
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Automated Breast Ultrasound Lesions Detection using Convolutional Neural Networks
Publication Year: 2018, Page(s): 1Breast lesion detection using ultrasound imaging is considered an important step of Computer-Aided Diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for brea... View full abstract»
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HyCLASSS: A Hybrid Classifier for Automatic Sleep Stage Scoring
Publication Year: 2018, Page(s):375 - 385Automatic identification of sleep stage is an important step in a sleep study. In this paper, we propose a hybrid automatic sleep stage scoring approach, named HyCLASSS, based on single channel electroencephalogram (EEG). HyCLASSS, for the first time, leverages both signal and stage transition features of human sleep for automatic identification of sleep stages. HyCLASSS consists of two parts: A r... View full abstract»
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A Scalable and Pragmatic Method for the Safe Sharing of High-Quality Health Data
Publication Year: 2018, Page(s):611 - 622The sharing of sensitive personal health data is an important aspect of biomedical research. Methods of data de-identification are often used in this process to trade the granularity of data off against privacy risks. However, traditional approaches, such as HIPAA safe harbor or k-anonymization, often fail to provide data with sufficient quality. Alternatively, data can be de-identified only to a ... View full abstract»
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Predicting Asthma-Related Emergency Department Visits Using Big Data
Publication Year: 2015, Page(s):1216 - 1223
Cited by: Papers (34) | Patents (1)Asthma is one of the most prevalent and costly chronic conditions in the United States, which cannot be cured. However, accurate and timely surveillance data could allow for timely and targeted interventions at the community or individual level. Current national asthma disease surveillance systems can have data availability lags of up to two weeks. Rapid progress has been made in gathering nontrad... View full abstract»
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A Food Recognition System for Diabetic Patients Based on an Optimized Bag-of-Features Model
Publication Year: 2014, Page(s):1261 - 1271
Cited by: Papers (26)Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the ... View full abstract»
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Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
Publication Year: 2017, Page(s): 1The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHR). While primarily designed for archiving patient information and performing administrative healthcare tasks like billing, many researchers have found secondary use of these records for various clinical informatics applications. Over the same period, the machine learning community has... View full abstract»
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An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification
Publication Year: 2017, Page(s):31 - 40
Cited by: Papers (6)The availability of medical imaging data from clinical archives, research literature, and clinical manuals, coupled with recent advances in computer vision offer the opportunity for image-based diagnosis, teaching, and biomedical research. However, the content and semantics of an image can vary depending on its modality and as such the identification of image modality is an important preliminary s... View full abstract»
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Predicting Mood Changes in Bipolar Disorder Through Heartbeat Nonlinear Dynamics
Publication Year: 2016, Page(s):1034 - 1043
Cited by: Papers (9)Bipolar disorder (BD) is characterized by an alternation of mood states from depression to (hypo)mania. Mixed states, i.e., a combination of depression and mania symptoms at the same time, can also be present. The diagnosis of this disorder in the current clinical practice is based only on subjective interviews and questionnaires, while no reliable objective psycho-physiological markers are availa... View full abstract»
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Epileptic Seizure Classification of EEGs Using Time–Frequency Analysis Based Multiscale Radial Basis Functions
Publication Year: 2018, Page(s):386 - 397The automatic detection of epileptic seizures from electroencephalography (EEG) signals is crucial for the localization and classification of epileptic seizure activity. However, seizure processes are typically dynamic and nonstationary, and thus, distinguishing rhythmic discharges from nonstationary processes is one of the challenging problems. In this paper, an adaptive and localized time-freque... View full abstract»
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Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge
Publication Year: 2018, Page(s):503 - 515Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes the left ventricular remodeling after... View full abstract»
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Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain
Publication Year: 2017, Page(s):41 - 47
Cited by: Papers (3)Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide. Although polypectomy at early stage reduces CRC incidence, 90% of the polyps are small and diminutive, where removal of them poses risks to patients that may outweigh the benefits. Correctly detecting and predicting polyp type during colonoscopy allows endoscopists to resect and discard the tissue without submitting it for hist... View full abstract»
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Signal-Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring
Publication Year: 2015, Page(s):832 - 838
Cited by: Papers (24)The identification of invalid data in recordings obtained using wearable sensors is of particular importance since data obtained from mobile patients is, in general, noisier than data obtained from nonmobile patients. In this paper, we present a signal quality index (SQI), which is intended to assess whether reliable heart rates (HRs) can be obtained from electrocardiogram (ECG) and photoplethysmo... View full abstract»
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High Bit-Depth Medical Image Compression With HEVC
Publication Year: 2018, Page(s):552 - 560Efficient storing and retrieval of medical images has direct impact on reducing costs and improving access in cloud-based health care services. JPEG 2000 is currently the commonly used compression format for medical images shared using the DICOM standard. However, new formats such as high efficiency video coding (HEVC) can provide better compression efficiency compared to JPEG 2000. Furthermore, J... View full abstract»
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An Emerging Era in the Management of Parkinson's Disease: Wearable Technologies and the Internet of Things
Publication Year: 2015, Page(s):1873 - 1881
Cited by: Papers (24)Current challenges demand a profound restructuration of the global healthcare system. A more efficient system is required to cope with the growing world population and increased life expectancy, which is associated with a marked prevalence of chronic neurological disorders such as Parkinson's disease (PD). One possible approach to meet this demand is a laterally distributed platform such as the In... View full abstract»
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Platform for Automated Real-Time High Performance Analytics on Medical Image Data
Publication Year: 2018, Page(s):318 - 324Biomedical data are quickly growing in volume and in variety, providing clinicians an opportunity for better clinical decision support. Here, we demonstrate a robust platform that uses software automation and high performance computing (HPC) resources to achieve real-time analytics of clinical data, specifically magnetic resonance imaging (MRI) data. We used the Agave application programming inter... View full abstract»
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Depth-Based Human Fall Detection via Shape Features and Improved Extreme Learning Machine
Publication Year: 2014, Page(s):1915 - 1922
Cited by: Papers (37)Falls are one of the major causes leading to injury of elderly people. Using wearable devices for fall detection has a high cost and may cause inconvenience to the daily lives of the elderly. In this paper, we present an automated fall detection approach that requires only a low-cost depth camera. Our approach combines two computer vision techniques-shape-based fall characterization and a learning... View full abstract»
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Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings
Publication Year: 2013, Page(s):828 - 834
Cited by: Papers (47)There has been an increased interest in speech pattern analysis applications of Parkinsonism for building predictive telediagnosis and telemonitoring models. For this purpose, we have collected a wide variety of voice samples, including sustained vowels, words, and sentences compiled from a set of speaking exercises for people with Parkinson's disease. There are two main issues in learning from su... View full abstract»
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Heartbeat Classification Using Abstract Features From the Abductive Interpretation of the ECG
Publication Year: 2018, Page(s):409 - 420Objective: This paper aims to prove that automatic beat classification on ECG signals can be effectively solved with a pure knowledge-based approach, using an appropriate set of abstract features obtained from the interpretation of the physiological processes underlying the signal. Methods: A set of qualitative morphological and rhythm features are obtained for each heartbeat as a result of the ab... View full abstract»
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A Solitary Feature-Based Lung Nodule Detection Approach for Chest X-Ray Radiographs
Publication Year: 2018, Page(s):516 - 524Lung cancer is one of the most deadly diseases. It has a high death rate and its incidence rate has been increasing all over the world. Lung cancer appears as a solitary nodule in chest x-ray radiograph (CXR). Therefore, lung nodule detection in CXR could have a significant impact on early detection of lung cancer. Radiologists define a lung nodule in CXR as “solitary white nodule-like blob... View full abstract»
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Validation of an Accelerometer to Quantify a Comprehensive Battery of Gait Characteristics in Healthy Older Adults and Parkinson's Disease: Toward Clinical and at Home Use
Publication Year: 2016, Page(s):838 - 847
Cited by: Papers (22)Measurement of gait is becoming important as a tool to identify disease and disease progression, yet to date its application is limited largely to specialist centers. Wearable devices enables gait to be measured in naturalistic environments, however questions remain regarding validity. Previous research suggests that when compared with a laboratory reference, measurement accuracy is acceptable for... View full abstract»
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Segmentation of the Blood Vessels and Optic Disk in Retinal Images
Publication Year: 2014, Page(s):1874 - 1886
Cited by: Papers (38)Retinal image analysis is increasingly prominent as a nonintrusive diagnosis method in modern ophthalmology. In this paper, we present a novel method to segment blood vessels and optic disk in the fundus retinal images. The method could be used to support nonintrusive diagnosis in modern ophthalmology since the morphology of the blood vessel and the optic disk is an important indicator for disease... View full abstract»
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A Natural Language Processing Framework for Assessing Hospital Readmissions for Patients With COPD
Publication Year: 2018, Page(s):588 - 596With the passage of recent federal legislation, many medical institutions are now responsible for reaching target hospital readmission rates. Chronic diseases account for many hospital readmissions and chronic obstructive pulmonary disease has been recently added to the list of diseases for which the United States government penalizes hospitals incurring excessive readmissions. Though there have b... View full abstract»
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Deep Learning for Fall Detection: 3D-CNN Combined with LSTM on Video Kinematic Data
Publication Year: 2018, Page(s): 1Fall detection is an important public healthcare problem. Timely detection could enable instant delivery of medical service to the injured. A popular non-intrusive solution for fall detection is based on videos obtained through ambient camera, and the corresponding methods usually require a large dataset to train a classifier and are inclined to be influenced by the image quality. However, it is h... View full abstract»
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Fall Detection in Homes of Older Adults Using the Microsoft Kinect
Publication Year: 2015, Page(s):290 - 301
Cited by: Papers (107)A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble o... View full abstract»
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HEp-2 Cell Image Classification With Deep Convolutional Neural Networks
Publication Year: 2017, Page(s):416 - 428
Cited by: Papers (4)Efficient Human Epithelial-2 cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper proposes an automatic framework for this classification task, by utilizing the deep convolutional neural networks (CNNs) which have recently attracted intensive attention in visual recognition. In addition to describing the proposed classification framework, this paper elabor... View full abstract»
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DeepPap: Deep Convolutional Networks for Cervical Cell Classification
Publication Year: 2017, Page(s):1633 - 1643
Cited by: Papers (1)Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into “abnormal” and “normal” categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in t... View full abstract»
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Identification of Location Specific Feature Points in a Cardiac Cycle Using a Novel Seismocardiogram Spectrum System
Publication Year: 2018, Page(s):442 - 449Seismocardiogram (SCG) or mechanocardiography is a noninvasive cardiac diagnostic method; however, previous studies used only a single sensor to detect cardiac mechanical activities that will not be able to identify location-specific feature points in a cardiac cycle corresponding to the four valvular auscultation locations. In this study, a multichannel SCG spectrum measurement system was propose... View full abstract»
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Sensor-Based Gait Parameter Extraction With Deep Convolutional Neural Networks
Publication Year: 2017, Page(s):85 - 93
Cited by: Papers (2)Measurement of stride-related, biomechanical parameters is the common rationale for objective gait impairment scoring. State-of-the-art double-integration approaches to extract these parameters from inertial sensor data are, however, limited in their clinical applicability due to the underlying assumptions. To overcome this, we present a method to translate the abstract information provided by wea... View full abstract»
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Alignment-Free, Self-Calibrating Elbow Angles Measurement Using Inertial Sensors
Publication Year: 2017, Page(s):312 - 319
Cited by: Papers (5)Due to their relative ease of handling and low cost, inertial measurement unit (IMU)-based joint angle measurements are used for a widespread range of applications. These include sports performance, gait analysis, and rehabilitation (e.g., Parkinson's disease monitoring or poststroke assessment). However, a major downside of current algorithms, recomposing human kinematics from IMU data, is that t... View full abstract»
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Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports
Publication Year: 2018, Page(s):244 - 251Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this study, we investigated deep learning and a convolutional neural network (CNN), for extracting ICD-O-3 topographic codes from a corpus of breast and lung cancer pathology reports. We perf... View full abstract»
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Ballistocardiography and Seismocardiography: A Review of Recent Advances
Publication Year: 2015, Page(s):1414 - 1427
Cited by: Papers (69) | Patents (2)In the past decade, there has been a resurgence in the field of unobtrusive cardiomechanical assessment, through advancing methods for measuring and interpreting ballistocardiogram (BCG) and seismocardiogram (SCG) signals. Novel instrumentation solutions have enabled BCG and SCG measurement outside of clinical settings, in the home, in the field, and even in microgravity. Customized signal process... View full abstract»
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Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis
Publication Year: 2017, Page(s):76 - 84
Cited by: Papers (3)Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns, and generates a map of path... View full abstract»
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Segmentation of Intra-Retinal Cysts from Optical Coherence Tomography Images using a Fully Convolutional Neural Network Model
Publication Year: 2018, Page(s): 1Optical Coherence Tomography (OCT) is an imaging modality that is used extensively for ophthalmic diagnosis, near-histological visualization and quantification of retinal abnormalities such as cysts, exudates, retinal layer disorganization, etc. Intra-retinal cysts (IRCs) occur in several macular disorders such as, diabetic macular edema, retinal vascular disorders, age-related macular degeneratio... View full abstract»
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Variation of the Korotkoff Stethoscope Sounds During Blood Pressure Measurement: Analysis Using a Convolutional Neural Network
Publication Year: 2017, Page(s):1593 - 1598Korotkoff sounds are known to change their characteristics during blood pressure (BP) measurement, resulting in some uncertainties for systolic and diastolic pressure (SBP and DBP) determinations. The aim of this study was to assess the variation of Korotkoff sounds during BP measurement by examining all stethoscope sounds associated with each heartbeat from above systole to below diastole during ... View full abstract»
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Modeling Clinically Validated Physical Activity Assessments Using Commodity Hardware
Publication Year: 2018, Page(s):335 - 345Consumer-grade wearable activity devices such as Fitbits are increasingly being used in research settings to promote physical activity (PA) due to their low-cost and widespread popularity. However, Fitbit-derived measures of activity intensity are consistently reported to be less accurate than intensity estimates obtained from research-grade accelerometers (i.e., ActiGraph). As such, the potential... View full abstract»
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Real-Time Robust Heart Rate Estimation From Wrist-Type PPG Signals Using Multiple Reference Adaptive Noise Cancellation
Publication Year: 2018, Page(s):450 - 459Heart rate (HR) monitoring using photoplethysmographic (PPG) signals recorded from wearers' wrist greatly facilitates design of wearable devices and maximizes user experience. However, placing PPG sensors in wrist causes much stronger and complicated motion artifacts (MA) due to loose interface between sensors and skin. Therefore, developing robust HR estimation algorithms for wrist-type PPG signa... View full abstract»
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A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound
Publication Year: 2017, Page(s):48 - 55
Cited by: Papers (2)Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estimation of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of ... View full abstract»
Aims & Scope
J-BHI publishes original papers describing recent advances in the field of biomedical and health informatics where information and communication technologies intersect with health, healthcare, life sciences and biomedicine. Papers must contain original content in theoretical analysis, methods, technical development, and/or novel clinical applications of information systems.
Meet Our Editors
Editor-in-Chief
DIMITRIOS I. FOTIADIS
Unit of Medical Technology and Intelligent Information Systems
Dept. of Materials Science and Engineering University of Ioannina, Greece fotiadis@cc.uoi.gr, jbhi-eic@embs.org