248 research outputs found
Boosting the Battery Life of Wearables for Health Monitoring Through the Compression of Biosignals
Modern wearable Internet of Things (IoT) devices enable the monitoring of vital parameters such as heart or respiratory (RESP) rates, electrocardiography (ECG), photo-plethysmographic (PPG) signals within e-health applications. A common issue of wearable technology is that signal transmission is power-demanding and, as such, devices require frequent battery charges and this poses serious limitations to the continuous monitoring of vitals. To ameliorate this, we advocate the use of lossy signal compression as a means to decrease the data size of the gathered biosignals and, in turn, boost the battery life of wearables and allow for fine-grained and long-term monitoring. Considering 1-D biosignals such as ECG, RESP, and PPG, which are often available from commercial wearable IoT devices, we provide a thorough review of existing biosignal compression algorithms. Besides, we present novel approaches based on online dictionaries, elucidating their operating principles and providing a quantitative assessment of compression, reconstruction and energy consumption performance of all schemes. As we quantify, the most efficient schemes allow reductions in the signal size of up to 100 times, which entail similar reductions in the energy demand, by still keeping the reconstruction error within 4% of the peak-to-peak signal amplitude. Finally, avenues for future research are discussed. © 2014 IEEE
Wavelet Theory
The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior
Poisson relationships with applications to digital signal processing
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Computationally Efficient QRS Detection Analysis In Electrocardiogram Based On Dual-Slope Method
A dramatic growth of interest for wearable technology has been fostered by recent technological advances in sensors, low-power integrated circuits and wireless communications. This interest originates from the need of monitoring a patient over extensive period of time. For cardiac patients, wearable heart monitoring sensors have already become a life-saving intervention ensuring continuous monitoring during daily life. Therefore, it is essential for an accurate monitoring and diagnosis of heart patients. Patients can be equipped with wireless, miniature and lightweight sensors. The sensors temporarily store physiological data and then periodically upload the data to a database server. These recorded data sets are then analyzed to predict any possibility of worsening patient\u27s situation or explored to assess the effect of clinical intervention. To obtain accurate response with less computational complexity as well as long battery life time, there is a demand of developing fast and accurate algorithm and prototypes for wearable heart monitoring sensors. A computationally efficient QRS detection algorithm is indispensable for low power operation on electrocardiogram (ECG) signal.
In need of detecting QRS complex, most of the early works were proposed based on derivatives of ECG signal. They can be easily implemented with high computational speed. But owing to the inherent variability in ECG, these methods are highly affected by large derivatives of baseline noises. Algorithms based on neural network (NN) showed relatively robust performance against noise but requires exhaustive training and estimation of model parameter. On the other hand, wavelet based methods have the choice problem of mother wavelet. Hence, none of these methods is suitable for giving a long battery performance in wearable devices with high accuracy.
Recently, Wang et al. proposed a novel dual slope QRS detection algorithm which has less computational complexity as well as high accuracy. Considering that the width of the QRS complex is relatively fixed, this algorithm is based on the fact that the largest change of slope usually happens at the peak of QRS complex. The hardware requirement is also low. However, the method has a set of time consuming slope calculations on both sides of each sample. To avoid such time consuming slope calculation, only one sample on each side can be highlighted. In addition, the multiplication of the left and right hand side slope should give us a very high value in QRS complex.
The goal of this thesis is to develop a new computationally efficient method to detect QRS complexes and compare with the other renowned QRS detection algorithms. MIT-BIH arrhythmia database based on patients of different heart diseases and database containing ECG from healthy subjects are used. To analyze the performance, false negative (FN) and false positive (FP) are evaluated. A false negative (FN) occurs when algorithm fails to detect an actual QRS complex quoted in the corresponding annotation file of the database record and a false positive (FP) means a false beat detection. Error rate (ER) , Sensitivity (Se) and Specificity (Sp) are calculated using FP and FN
A Physiological Signal Processing System for Optimal Engagement and Attention Detection.
In today’s high paced, hi-tech and high stress environment, with extended work hours, long to-do lists and neglected personal health, sleep deprivation has become common in modern culture. Coupled with these factors is the inherent repetitious and tedious nature of certain occupations and daily routines, which all add up to an undesirable fluctuation in individuals’ cognitive attention and capacity. Given certain critical professions, a momentary or prolonged lapse in attention level can be catastrophic and sometimes deadly. This research proposes to develop a real-time monitoring system which uses fundamental physiological signals such as the Electrocardiograph (ECG), to analyze and predict the presence or lack of cognitive attention in individuals during task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the physiological parameters of the body. The system is designed using only those physiological signals that can be collected easily with small, wearable, portable and non-invasive monitors and thereby being able to predict well in advance, an individual’s potential loss of attention and ingression of sleepiness. Several advanced signal processing techniques have been implemented and investigated to derive multiple clandestine and informative features. These features are then applied to machine learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and the person not being attentive. Furthermore, Electroencephalograph (EEG) signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis. For the study, ECG signals and EEG signals of volunteer subjects are acquired in a controlled experiment. The experiment is designed to inculcate and sustain cognitive attention for a period of time following which an attempt is made to reduce cognitive attention of volunteer subjects. The data acquired during the experiment is decomposed and analyzed for feature extraction and classification. The presented results show that to a fairly reasonable accuracy it is possible to detect the presence or lack of attention in individuals with just their ECG signal, especially in comparison with analysis done on EEG signals. The continual work of this research includes other physiological signals such as Galvanic Skin Response, Heat Flux, Skin Temperature and video based facial feature analysis
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ECG analysis and classification using CSVM, MSVM and SIMCA classifiers
Reliable ECG classification can potentially lead to better detection methods and increase
accurate diagnosis of arrhythmia, thus improving quality of care. This thesis investigated the
use of two novel classification algorithms: CSVM and SIMCA, and assessed their
performance in classifying ECG beats. The project aimed to introduce a new way to
interactively support patient care in and out of the hospital and develop new classification
algorithms for arrhythmia detection and diagnosis. Wave (P-QRS-T) detection was performed
using the WFDB Software Package and multiresolution wavelets. Fourier and PCs were
selected as time-frequency features in the ECG signal; these provided the input to the
classifiers in the form of DFT and PCA coefficients. ECG beat classification was performed
using binary SVM. MSVM, CSVM, and SIMCA; these were subsequently used for
simultaneously classifying either four or six types of cardiac conditions. Binary SVM
classification with 100% accuracy was achieved when applied on feature-reduced ECG
signals from well-established databases using PCA. The CSVM algorithm and MSVM were
used to classify four ECG beat types: NORMAL, PVC, APC, and FUSION or PFUS; these
were from the MIT-BIH arrhythmia database (precordial lead group and limb lead II).
Different numbers of Fourier coefficients were considered in order to identify the optimal
number of features to be presented to the classifier. SMO was used to compute hyper-plane
parameters and threshold values for both MSVM and CSVM during the classifier training
phase. The best classification accuracy was achieved using fifty Fourier coefficients. With the
new CSVM classifier framework, accuracies of 99%, 100%, 98%, and 99% were obtained
using datasets from one, two, three, and four precordial leads, respectively. In addition, using
CSVM it was possible to successfully classify four types of ECG beat signals extracted from
limb lead simultaneously with 97% accuracy, a significant improvement on the 83% accuracy
achieved using the MSVM classification model. In addition, further analysis of the following
four beat types was made: NORMAL, PVC, SVPB, and FUSION. These signals were
obtained from the European ST-T Database. Accuracies between 86% and 94% were obtained
for MSVM and CSVM classification, respectively, using 100 Fourier coefficients for
reconstructing individual ECG beats. Further analysis presented an effective ECG arrhythmia
classification scheme consisting of PCA as a feature reduction method and a SIMCA
classifier to differentiate between either four or six different types of arrhythmia. In separate
studies, six and four types of beats (including NORMAL, PVC, APC, RBBB, LBBB, and
FUSION beats) with time domain features were extracted from the MIT-BIH arrhythmia
database and the St Petersburg INCART 12-lead Arrhythmia Database (incartdb) respectively.
Between 10 and 30 PCs, coefficients were selected for reconstructing individual ECG beats in
the feature selection phase. The average classification accuracy of the proposed scheme was
98.61% and 97.78 % using the limb lead and precordial lead datasets, respectively. In addition,
using MSVM and SIMCA classifiers with four ECG beat types achieved an average
classification accuracy of 76.83% and 98.33% respectively. The effectiveness of the proposed
algorithms was finally confirmed by successfully classifying both the six beat and four beat
types of signal respectively with a high accuracy ratio
A New Signal Processing Approach to Study Action Potential Content in Sympathetic Neural Signals
Sympathetic nerve activity plays an essential role in the normal regulation of blood pressure in humans and in the etiology and progression of many chronic diseases. Sympathetic nerve recordings associated with blood pressure regulation can be recorded directly using microneurography. A general characteristic of this signal is spontaneous burst activity of spikes (action potentials) separated by silent periods against a background of considerable gaussian noise. During measurement with electrodes, the raw muscle sympathetic nerve activity (MSNA) signal is amplified, band-pass filtered, rectified and integrated. This integration process removes important information regarding action potential content and their discharge properties.
The first objective of this thesis was to propose a new method for detecting action potentials from the raw MSNA signal to enable investigation of post-ganglionic neural discharge properties. The new method is based on the design of a mother wavelet that is matched to an actual mean action potential template extracted from a raw MSNA signal and applying it to the raw MSNA signal using a continues wavelet transform (CWT) for spike detection. The performance of the proposed method versus two previous wavelet-based approaches was evaluated using 1) MSNA recorded from seven healthy participants and, 2) simulated MSNA. The results show that the new matched wavelet performs better than the previous wavelet-based methods that use a non-matched wavelet in detecting action potentials in the MSNA signal.
The second objective of this thesis was to employ the proposed action potential detection and classification technique to study the relationship between the recruitment of sympathetic action potentials (i.e., neurons) and the size of integrated sympathetic bursts in human MSNA signal. While in other neural systems (e.g. the skeletal motor system) there is a well understood pattern of neural recruitment during activation, our understanding of how sympathetic neurons are coordinated during baseline and baroreceptor unloading are very limited. We demonstrate that there exists a hierarchical pattern of recruitment of additional faster conducting neurons of larger amplitude as the sympathetic bursts become stronger. This information has important implications for how blood pressure is controlled, and the malleability of sympathetic activation in health and disease
Electronic devices and systems for monitoring of diabetes and cardiovascular diseases
Diabetes is a serious chronic disease which causes a high rate of morbidity and mortality all
over the world. In 2007, more than 246 million people suffered from diabetes worldwide
and unfortunately the incidence of diabetes is increasing at alarming rates. The number of
people with diabetes is expected to double within the next 25 years due to a combination of
population ageing, unhealthy diets, obesity and sedentary lifestyles. It can lead to blindness,
heart disease, stroke, kidney failure, amputations and nerve damage. In women, diabetes
can cause problems during pregnancy and make it more likely for the baby to be born with
birth defects. Moreover, statistical analysis shows that 75% of diabetic patients die
prematurely of cardiovascular disease (CVD). The absolute risk of cardiovascular disease in
patients with type 1 (insulin-dependent) diabetes is lower than that in patients with type 2
(non-insulin-dependent) diabetes, in part because of their younger age and the lower
prevalence of CVD risk factors, and in part because of the different pathophysiology of the
two diseases. Unfortunately, about 9 out of 10 people with diabetes have type 2 diabetes.
For these reasons, cardiopathes and diabetic patients need to be frequently monitored and
in some cases they could easily perform at home the requested physiological measurements
(i.e. glycemia, heart rate, blood pressure, body weight, and so on) sending the measured
data to the care staff in the hospital. Several researches have been presented over the last
years to address these issues by means of digital communication systems. The largest part of
such works uses a PC or complex hardware/software systems for this purpose. Beyond the
cost of such systems, it should be noted that they can be quite accessible by relatively young
people but the same does not hold for elderly patients more accustomed to traditional
equipments for personal entertainment such as TV sets.
Wearable devices can permit continuous cardiovascular monitoring both in clinical settings
and at home. Benefits may be realized in the diagnosis and treatment of a number of major
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diseases. In conjunction with appropriate alarm algorithms, they can increase surveillance
capabilities for CVD catastrophe for high-risk subjects. Moreover, they could play an
important role in the wireless surveillance of people during hazardous operations (military,
fire-fighting, etc.) or during sport activities.
For patients with chronic cardiovascular disease, such as heart failure, home monitoring
employing wearable device and tele-home care systems may detect exacerbations in very
early stages or at dangerous levels that necessitate an emergency room visit and an
immediate hospital admission.
Taking into account mains principles for the design of good wearable devices and friendly
tele-home care systems, such as safety, compactness, motion and other disturbance
rejection, data storage and transmission, low power consumption, no direct doctor
supervision, it is imperative that these systems are easy to use and comfortable to wear for
long periods of time.
The aim of this work is to develop an easy to use tele-home care system for diabetes and
cardiovascular monitoring, well exploitable even by elderly people, which are the main
target of a telemedicine system, and wearable devices for long term measuring of some
parameters related to sleep apnoea, heart attack, atrial fibrillation and deep vein
thrombosis. Since set-top boxes for Digital Video Broadcast Terrestrial (DVB-T) are in simple computers
with their Operating System, a Java Virtual Machine, a modem for the uplink connection and
a set of standard ports for the interfacing with external devices, elderly, diabetics and
cardiopathes could easily send their self-made exam to the care staff placed elsewhere.
The wearable devices developed are based on the well known photopletysmographic
method which uses a led source/detector pair applied on the skin in order to obtain a
biomedical signal related to the volume and percentage of oxygen in blood. Such devices
investigate the possibility to obtain more information to those usually obtained by this
technique (heart rate and percentage of oxygen saturation) in order to discover new
algorithms for the continuous and remote or in ambulatory monitoring and screening of
sleep apnoea, heart attack, atrial fibrillation and deep vein thrombosis
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