112,816 research outputs found
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Patient Specific Congestive Heart Failure Detection From Raw ECG signal
In this study; in order to diagnose congestive heart failure (CHF) patients,
non-linear second-order difference plot (SODP) obtained from raw 256 Hz sampled
frequency and windowed record with different time of ECG records are used. All
of the data rows are labelled with their belongings to classify much more
realistically. SODPs are divided into different radius of quadrant regions and
numbers of the points fall in the quadrants are computed in order to extract
feature vectors. Fisher's linear discriminant, Naive Bayes, Radial basis
function, and artificial neural network are used as classifier. The results are
considered in two step validation methods as general k-fold cross-validation
and patient based cross-validation. As a result, it is shown that using neural
network classifier with features obtained from SODP, the constructed system
could distinguish normal and CHF patients with 100% accuracy rate. KeywordsComment: Congestive heart failure, ECG, Second-Order Difference Plot,
classification, patient based cross-validatio
Using Topological Data Analysis for diagnosis pulmonary embolism
Pulmonary Embolism (PE) is a common and potentially lethal condition. Most
patients die within the first few hours from the event. Despite diagnostic
advances, delays and underdiagnosis in PE are common.To increase the diagnostic
performance in PE, current diagnostic work-up of patients with suspected acute
pulmonary embolism usually starts with the assessment of clinical pretest
probability using plasma d-Dimer measurement and clinical prediction rules. The
most validated and widely used clinical decision rules are the Wells and Geneva
Revised scores. We aimed to develop a new clinical prediction rule (CPR) for PE
based on topological data analysis and artificial neural network. Filter or
wrapper methods for features reduction cannot be applied to our dataset: the
application of these algorithms can only be performed on datasets without
missing data. Instead, we applied Topological data analysis (TDA) to overcome
the hurdle of processing datasets with null values missing data. A topological
network was developed using the Iris software (Ayasdi, Inc., Palo Alto). The PE
patient topology identified two ares in the pathological group and hence two
distinct clusters of PE patient populations. Additionally, the topological
netowrk detected several sub-groups among healthy patients that likely are
affected with non-PE diseases. TDA was further utilized to identify key
features which are best associated as diagnostic factors for PE and used this
information to define the input space for a back-propagation artificial neural
network (BP-ANN). It is shown that the area under curve (AUC) of BP-ANN is
greater than the AUCs of the scores (Wells and revised Geneva) used among
physicians. The results demonstrate topological data analysis and the BP-ANN,
when used in combination, can produce better predictive models than Wells or
revised Geneva scores system for the analyzed cohortComment: 18 pages, 5 figures, 6 tables. arXiv admin note: text overlap with
arXiv:cs/0308031 by other authors without attributio
Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors
Auscultation is one of the most used techniques for
detecting cardiovascular diseases, which is one of the main causes
of death in the world. Heart murmurs are the most common abnormal
finding when a patient visits the physician for auscultation.
These heart sounds can either be innocent, which are harmless, or
abnormal, which may be a sign of a more serious heart condition.
However, the accuracy rate of primary care physicians and expert
cardiologists when auscultating is not good enough to avoid most
of both type-I (healthy patients are sent for echocardiogram) and
type-II (pathological patients are sent home without medication or
treatment) errors made. In this paper, the authors present a novel
convolutional neural network based tool for classifying between
healthy people and pathological patients using a neuromorphic
auditory sensor for FPGA that is able to decompose the audio into
frequency bands in real time. For this purpose, different networks
have been trained with the heart murmur information contained in
heart sound recordings obtained from nine different heart sound
databases sourced from multiple research groups. These samples
are segmented and preprocessed using the neuromorphic auditory
sensor to decompose their audio information into frequency
bands and, after that, sonogram images with the same size are
generated. These images have been used to train and test different
convolutional neural network architectures. The best results
have been obtained with a modified version of the AlexNet model,
achieving 97% accuracy (specificity: 95.12%, sensitivity: 93.20%,
PhysioNet/CinC Challenge 2016 score: 0.9416). This tool could aid
cardiologists and primary care physicians in the auscultation process,
improving the decision making task and reducing type-I and
type-II errors.Ministerio de Economía y Competitividad TEC2016-77785-
A Review of Wireless Body Area Networks for Medical Applications
Recent advances in Micro-Electro-Mechanical Systems (MEMS) technology,
integrated circuits, and wireless communication have allowed the realization of
Wireless Body Area Networks (WBANs). WBANs promise unobtrusive ambulatory
health monitoring for a long period of time and provide real-time updates of
the patient's status to the physician. They are widely used for ubiquitous
healthcare, entertainment, and military applications. This paper reviews the
key aspects of WBANs for numerous applications. We present a WBAN
infrastructure that provides solutions to on-demand, emergency, and normal
traffic. We further discuss in-body antenna design and low-power MAC protocol
for WBAN. In addition, we briefly outline some of the WBAN applications with
examples. Our discussion realizes a need for new power-efficient solutions
towards in-body and on-body sensor networks.Comment: 7 pages, 7 figures, and 3 tables. In V3, the manuscript is converted
to LaTe
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
The electrocardiogram (ECG) is one of the most extensively employed signals
used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG
signals can capture the heart's rhythmic irregularities, commonly known as
arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of
patients' acute and chronic heart conditions. In this study, we propose a
two-dimensional (2-D) convolutional neural network (CNN) model for the
classification of ECG signals into eight classes; namely, normal beat,
premature ventricular contraction beat, paced beat, right bundle branch block
beat, left bundle branch block beat, atrial premature contraction beat,
ventricular flutter wave beat, and ventricular escape beat. The one-dimensional
ECG time series signals are transformed into 2-D spectrograms through
short-time Fourier transform. The 2-D CNN model consisting of four
convolutional layers and four pooling layers is designed for extracting robust
features from the input spectrograms. Our proposed methodology is evaluated on
a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art
average classification accuracy of 99.11\%, which is better than those of
recently reported results in classifying similar types of arrhythmias. The
performance is significant in other indices as well, including sensitivity and
specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote
Sensing MDPI Journa
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Tracing diagnosis trajectories over millions of patients reveal an unexpected risk in schizophrenia.
The identification of novel disease associations using big-data for patient care has had limited success. In this study, we created a longitudinal disease network of traced readmissions (disease trajectories), merging data from over 10.4 million inpatients through the Healthcare Cost and Utilization Project, which allowed the representation of disease progression mapping over 300 diseases. From these disease trajectories, we discovered an interesting association between schizophrenia and rhabdomyolysis, a rare muscle disease (incidence < 1E-04) (relative risk, 2.21 [1.80-2.71, confidence interval = 0.95], P-value 9.54E-15). We validated this association by using independent electronic medical records from over 830,000 patients at the University of California, San Francisco (UCSF) medical center. A case review of 29 rhabdomyolysis incidents in schizophrenia patients at UCSF demonstrated that 62% are idiopathic, without the use of any drug known to lead to this adverse event, suggesting a warning to physicians to watch for this unexpected risk of schizophrenia. Large-scale analysis of disease trajectories can help physicians understand potential sequential events in their patients
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