1,251 research outputs found
Smart vest for respiratory rate monitoring of COPD patients based on non-contact capacitive sensing
In this paper, a first approach to the design of a portable device for non-contact monitoring
of respiratory rate by capacitive sensing is presented. The sensing system is integrated into a smart
vest for an untethered, low-cost and comfortable breathing monitoring of Chronic Obstructive
Pulmonary Disease (COPD) patients during the rest period between respiratory rehabilitation
exercises at home. To provide an extensible solution to the remote monitoring using this sensor and
other devices, the design and preliminary development of an e-Health platform based on the Internet
of Medical Things (IoMT) paradigm is also presented. In order to validate the proposed solution,
two quasi-experimental studies have been developed, comparing the estimations with respect to the
golden standard. In a first study with healthy subjects, the mean value of the respiratory rate error,
the standard deviation of the error and the correlation coefficient were 0.01 breaths per minute (bpm),
0.97 bpm and 0.995 (p < 0.00001), respectively. In a second study with COPD patients, the values
were -0.14 bpm, 0.28 bpm and 0.9988 (p < 0.0000001), respectively. The results for the rest period
show the technical and functional feasibility of the prototype and serve as a preliminary validation of
the device for respiratory rate monitoring of patients with COPD.Ministerio de Ciencia e Innovación PI15/00306Ministerio de Ciencia e Innovación DTS15/00195Junta de AndalucÃa PI-0010-2013Junta de AndalucÃa PI-0041-2014Junta de AndalucÃa PIN-0394-201
LightESD: Fully-Automated and Lightweight Anomaly Detection Framework for Edge Computing
Anomaly Detection is Widely Used in a Broad Range of Domains from Cybersecurity to Manufacturing, Finance, and So On. Deep Learning based Anomaly Detection Has Recently Drawn Much Attention Because of its Superior Capability of Recognizing Complex Data Patterns and Identifying Outliers Accurately. However, Deep Learning Models Are Typically Iteratively Optimized in a Central Server with Input Data Gathered from Edge Devices, and Such Data Transfer between Edge Devices and the Central Server Impose Substantial overhead on the Network and Incur Additional Latency and Energy Consumption. to overcome This Problem, We Propose a Fully Automated, Lightweight, Statistical Learning based Anomaly Detection Framework Called LightESD. It is an On-Device Learning Method Without the Need for Data Transfer between Edge and Server and is Extremely Lightweight that Most Low-End Edge Devices Can Easily Afford with Negligible Delay, CPU/memory Utilization, and Power Consumption. Yet, It Achieves Highly Competitive Detection Accuracy. Another Salient Feature is that It Can Auto-Adapt to Probably Any Dataset Without Manually Setting or Configuring Model Parameters or Hyperparameters, which is a Drawback of Most Existing Methods. We Focus on Time Series Data Due to its Pervasiveness in Edge Applications Such as IoT. Our Evaluation Demonstrates that LightESD Outperforms Other SOTA Methods on Detection Accuracy, Efficiency, and Resource Consumption. Additionally, its Fully Automated Feature Gives It Another Competitive Advantage in Terms of Practical Usability and Generalizability
Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios
Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which increases design time and cost, affecting deployment feasibility. This article discusses secure HMSMs' application-specific advantages, feature-specific limitations, context-specific nuances, and deployment-specific future research scopes to reduce model selection ambiguity. The models based on the Internet of Things (IoT), Machine Learning Models (MLMs), Blockchain Models, Hashing Methods, Encryption Methods, Distributed Computing Configurations, and Bioinspired Models have better Quality of Service (QoS) and security than their counterparts. Researchers can find application-specific models. This article compares the above models in deployment cost, attack mitigation performance, scalability, computational complexity, and monitoring applicability. This comparative analysis helps readers choose HMSMs for context-specific application deployments. This article also devises performance measuring metrics called Health Monitoring Model Metrics (HM3) to compare the performance of various models based on accuracy, precision, delay, scalability, computational complexity, energy consumption, and security
Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms
Myocardial infarction is the leading cause of death worldwide. In this paper,
we design domain-inspired neural network models to detect myocardial
infarction. First, we study the contribution of various leads. This systematic
analysis, first of its kind in the literature, indicates that out of 15 ECG
leads, data from the v6, vz, and ii leads are critical to correctly identify
myocardial infarction. Second, we use this finding and adapt the ConvNetQuake
neural network model--originally designed to identify earthquakes--to attain
state-of-the-art classification results for myocardial infarction, achieving
classification accuracy on a record-wise split, and
classification accuracy on a patient-wise split. These two results represent
cardiologist-level performance level for myocardial infarction detection after
feeding only 10 seconds of raw ECG data into our model. Third, we show that our
multi-ECG-channel neural network achieves cardiologist-level performance
without the need of any kind of manual feature extraction or data
pre-processing.Comment: Accepted to the European Medical and Biological Engineering
Conference (EMBEC) 202
MBFRDH: Design of a Multimodal Bioinspired Feature Representation Deep Learning Model for Identification of Heart-Diseases
Electrocardiograms (ECGs) are generated by checking different beating patterns of heart, and are widely used for identification of multiple heart-related issues. Existing deep learning models that are proposed for ECG analysis are either highly complex, or showcase lower scalability when applied to clinical scans. To overcome these issues, this text proposes design of a novel multimodal bioinspired feature representation deep learning model for identification of heart-diseases. The proposed model initially collects large-scale ECG datasets, and extracts Fourier, Cosine, iVector, Gabor, and Wavelet components. These components are given to a Grey Wolf Optimization (GWO) based feature selection model, which assists in identification of high-inter-class variance feature sets. This is done via modelling a variance-based fitness function and fusing it with an Iterative Learning Model (ILM) that use feedback-accuracy levels for optimization of selected feature sets. The extracted features are used to incrementally train a custom 1D Binary-Augmented Convolutional Neural Network (1D BACNN) that can be trained for multiclass scenarios. The BACNN Model is trained individually for each of the heart diseases. Each BACNN categorizes input ECG samples between ‘Normal’, and ‘Heart-Disease’ categories. Due to use of this binary-type classification, the proposed model is able to achieve a consistent 99.9% accuracy for multiple heart disease sets, which is found to be higher than most of the existing multiclass techniques. The model was tested for Angina, Arrhythmia, Valve disease, and Congenital heart conditions, and was observed to achieve 3.5% higher precision, 4.9% higher accuracy, with 1.2% increase is computational delay, which makes it highly suitable for real-time clinical use cases
Aerospace Medicine and Biology. A continuing bibliography (Supplement 226)
This bibliography lists 129 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1981
- …