10 research outputs found
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
Assessing Variability of EEG and ECG/HRV Time Series Signals Using a Variety of Non-Linear Methods
Time series signals, such as Electroencephalogram (EEG) and Electrocardiogram
(ECG) represent the complex dynamic behaviours of biological systems.
The analysis of these signals using variety of nonlinear methods is essential
for understanding variability within EEG and ECG, which potentially
could help unveiling hidden patterns related to underlying physiological mechanisms.
EEG is a time varying signal, and electrodes for recording EEG at different
positions on the scalp give different time varying signals. There might
be correlation between these signals. It is important to know the correlation
between EEG signals because it might tell whether or not brain activities from
different areas are related. EEG and ECG might be related to each other because
both of them are generated from one co-ordinately working body. Investigating
this relationship is of interest because it may reveal information about
the correlation between EEG and ECG signals.
This thesis is about assessing variability of time series data, EEG and ECG, using
variety of nonlinear measures. Although other research has looked into the
correlation between EEGs using a limited number of electrodes and a limited
number of combinations of electrode pairs, no research has investigated the
correlation between EEG signals and distance between electrodes. Furthermore,
no one has compared the correlation performance for participants with
and without medical conditions. In my research, I have filled up these gaps
by using a full range of electrodes and all possible combinations of electrode
pairs analysed in Time Domain (TD). Cross-Correlation method is calculated
on the processed EEG signals for different number unique electrode pairs from
each datasets. In order to obtain the distance in centimetres (cm) between
electrodes, a measuring tape was used. For most of our participants the head
circumference range was 54-58cm, for which a medium-sized I have discovered
that the correlation between EEG signals measured through electrodes
is linearly dependent on the physical distance (straight-line) distance between
them for datasets without medical condition, but not for datasets with medical
conditions.
Some research has investigated correlation between EEG and Heart Rate Variability
(HRV) within limited brain areas and demonstrated the existence of
correlation between EEG and HRV. But no research has indicated whether or
not the correlation changes with brain area. Although Wavelet Transformations
(WT) have been performed on time series data including EEG and HRV
signals to extract certain features respectively by other research, so far correlation
between WT signals of EEG and HRV has not been analysed. My research
covers these gaps by conducting a thorough investigation of all electrodes on
the human scalp in Frequency Domain (FD) as well as TD. For the reason of
different sample rates of EEG and HRV, two different approaches (named as
Method 1 and Method 2) are utilised to segment EEG signals and to calculate
Pearson’s Correlation Coefficient for each of the EEG frequencies with each
of the HRV frequencies in FD. I have demonstrated that EEG at the front area
of the brain has a stronger correlation with HRV than that at the other area in
a frequency domain. These findings are independent of both participants and
brain hemispheres.
Sample Entropy (SE) is used to predict complexity of time series data. Recent
research has proposed new calculation methods for SE, aiming to improve the
accuracy. To my knowledge, no one has attempted to reduce the computational
time of SE calculation. I have developed a new calculation method for time
series complexity which could improve computational time significantly in the
context of calculating a correlation between EEG and HRV. The results have
a parsimonious outcome of SE calculation by exploiting a new method of SE
implementation. In addition, it is found that the electrical activity in the frontal
lobe of the brain appears to be correlated with the HRV in a time domain.
Time series analysis method has been utilised to study complex systems that
appear ubiquitous in nature, but limited to certain dynamic systems (e.g. analysing
variables affecting stock values). In this thesis, I have also investigated the nature
of the dynamic system of HRV. I have disclosed that Embedding Dimension
could unveil two variables that determined HRV
Automated localisation and classification of abnormal beats in electrocardiograms using parsimonious wavelet analysis
International audienceWe report on a work in progress aiming at automatically analysing electrocardiograms with the help of wavelet bases. The objective we have fixed was to introduce a parsimonious and computationally efficient method --- that can be ultimately incorporated on standard ECG recording devices --- to automatically localise points of interest and decide whether they are normal or not and, for abnormal points, assign them a given class of abnormality. The task we have already achieved is the localisation and identification of normal QRS complexes. Identification and localisation of other features, like premature atrial or ventricular beats, fibrillation, noise, bundle branch block beats, ectopic beats, etc. is in progress but the so far obtained preliminary results are very encouraging
Emotion and Stress Recognition Related Sensors and Machine Learning Technologies
This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective
Epidemiology of Injury in English Women's Super league Football: A Cohort Study
INTRODUCTION: The epidemiology of injury in male professional football has been well documented (Ekstrand, Hägglund, & Waldén, 2011) and used as a basis to understand injury trends for a number of years. The prevalence and incidence of injuries occurring in womens super league football is unknown. The aim of this study is to estimate the prevalence and incidence of injury in an English Super League Women’s Football squad. METHODS: Following ethical approval from Leeds Beckett University, players (n = 25) signed to a Women’s Super League Football club provided written informed consent to complete a self-administered injury survey. Measures of exposure, injury and performance over a 12-month period was gathered. Participants were classified as injured if they reported a football injury that required medical attention or withdrawal from participation for one day or more. Injuries were categorised as either traumatic or overuse and whether the injury was a new injury and/or re-injury of the same anatomical site RESULTS: 43 injuries, including re-injury were reported by the 25 participants providing a clinical incidence of 1.72 injuries per player. Total incidence of injury was 10.8/1000 h (95% CI: 7.5 to 14.03). Participants were at higher risk of injury during a match compared with training (32.4 (95% CI: 15.6 to 48.4) vs 8.0 (95% CI: 5.0 to 10.85)/1000 hours, p 28 days) of which there were three non-contact anterior cruciate ligament (ACL) injuries. The epidemiological incidence proportion was 0.80 (95% CI: 0.64 to 0.95) and the average probability that any player on this team will sustain at least one injury was 80.0% (95% CI: 64.3% to 95.6%) CONCLUSION: This is the first report capturing exposure and injury incidence by anatomical site from a cohort of English players and is comparable to that found in Europe (6.3/1000 h (95% CI 5.4 to 7.36) Larruskain et al 2017). The number of ACL injuries highlights a potential injury burden for a squad of this size. Multi-site prospective investigations into the incidence and prevalence of injury in women’s football are require