31 research outputs found
Non-invasive Detection and Compression of Fetal Electrocardiogram
Noninvasive detection of fetal electrocardiogram (FECG) from abdominal ECG recordings is highly dependent on typical statistical signal processing techniques such as independent component analysis (ICA), adaptive noise filtering, and multichannel blind deconvolution. In contrast to the previous multichannel FECG extraction methods, several recent schemes for singleâchannel FECG extraction such as the extended Kalman filter (EKF), extended Kalman smoother (EKS), template subtraction (TS), and support vector regression (SVR) for detecting R waves on ECG, are evaluated via the quantitative metrics such as sensitivity (SE), positive predictive value (PPV), Fâscore, detection error rate (DER), and range of accuracy. A correlation predictor that combines with multivariable gray model (GM) is also proposed for sequential ECG data compression, which displays better percent root mean-square difference (PRD) than those of Sabahâs scheme for fixed and predicted compression ratio (CR). Automatic calculation on fetal heart rate (FHR) on the reconstructed FECG from mixed signals of abdominal ECG recordings is also experimented with sample synthetic ECG data. Sample data on FHR and T/QRS for both physiological case and pathological case are simulated in a 10-min time sequence
Detection and Processing Techniques of FECG Signal for Fetal Monitoring
Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system
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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
Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems
Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.Mohamed Elgendi, BjĂśrn Eskofier, Socrates Dokos, Derek Abbot
Estrazione non invasiva del segnale elettrocardiografico fetale da registrazioni con elettrodi posti sullâaddome della gestante (Non-invasive extraction of the fetal electrocardiogram from abdominal recordings by positioning electrodes on the pregnant womanâs abdomen)
openIl cuore è il primo organo che si sviluppa nel feto, particolarmente nelle primissime settimane di
gestazione. Rispetto al cuore adulto, quello fetale ha una fisiologia ed unâanatomia significativamente
differenti, a causa della differente circolazione cardiovascolare. Il benessere fetale si valuta
monitorando lâattivitĂ cardiaca mediante elettrocardiografia fetale (ECGf). LâECGf invasivo (acquisito
posizionando elettrodi allo scalpo fetale) è considerato il gold standard, ma lâinvasivitĂ che lo
caratterizza ne limita la sua applicabilitĂ . Al contrario, lâuso clinico dellâECGf non invasivo (acquisito
posizionando elettrodi sullâaddome della gestante) è limitato dalla scarsa qualitĂ del segnale risultante.
LâECGf non invasivo si estrae da registrazioni addominali, che sono corrotte da differenti tipi di rumore,
fra i quali lâinterferenza primaria è rappresentata dallâECG materno. Il Segmented-Beat Modulation
Method (SBMM) è stato da me recentemente proposto come una nuova procedura di filtraggio basata
sul calcolo del template del battito cardiaco. SBMM fornisce una stima ripulita dellâECG estratto da
registrazioni rumorose, preservando la fisiologica variabilitĂ ECG del segnale originale. Questa
caratteristica è ottenuta grazie alla segmentazione di ogni battito cardiaco per indentificare i segmenti
QRS e TUP, seguito dal processo di modulazione/demodulazione (che include strecciamento e
compressione) del segmento TUP, per aggiustarlo in modo adattativo alla morfologia e alla durata di
ogni battito originario. Dapprima applicato allâECG adulto al fine di dimostrare la sua robustezza al
rumore, lâSBMM è stato poi applicato al caso fetale. Particolarmente significativi sono i risultati relativi
alle applicazioni su ECGf non invasivo, dove lâSBMM fornisce segnali caratterizzati da un rapporto
segnale-rumore comparabile a quello caratterizzante lâECGf invasivo. Tuttavia, lâSBMM può
contribuire alla diffusione dellâECGf non invasiva nella pratica clinica.The heart is the first organ that develops in the fetus, particularly in the very early stages
of pregnancy. Compared to the adult heart, the physiology and anatomy of the fetal heart
exhibit some significant differences. These differences originate from the fact that the fetal
cardiovascular circulation is different from the adult circulation. Fetal well-being
evaluation may be accomplished by monitoring cardiac activity through fetal
electrocardiography (fECG). Invasive fECG (acquired through scalp electrodes) is the
gold standard but its invasiveness limits its clinical applicability. Instead, clinical use of
non-invasive fECG (acquired through abdominal electrodes) has so far been limited by its
poor signal quality. Non-invasive fECG is extracted from the abdominal recording and is
corrupted by different kind of noise, among which maternal ECG is the main interference.
The Segmented-Beat Modulation Method (SBMM) was recently proposed by myself as a
new template-based filtering procedure able to provide a clean ECG estimation from a
noisy recording by preserving physiological ECG variability of the original signal. The
former feature is achieved thanks to a segmentation procedure applied to each cardiac
beat in order to identify the QRS and TUP segments, followed by a
modulation/demodulation process (involving stretching and compression) of the TUP
segments to adaptively adjust each estimated cardiac beat to the original beat morphology
and duration. SBMM was first applied to adult ECG applications, in order to demonstrate
its robustness to noise, and then to fECG applications. Particularly significant are the
results relative to the non-invasive applications, where SBMM provided fECG signals
characterized by a signal-to-noise ratio comparable to that characterizing invasive fECG.
Thus, SBMM may contribute to the spread of this noninvasive fECG technique in the
clinical practice.INGEGNERIA DELL'INFORMAZIONEAgostinelli, AngelaAgostinelli, Angel
Secure Data Collection and Analysis in Smart Health Monitoring
Smart health monitoring uses real-time monitored data to support diagnosis, treatment, and health decision-making in modern smart healthcare systems and benefit our daily life. The accurate health monitoring and prompt transmission of health data are facilitated by the ever-evolving on-body sensors, wireless communication technologies, and wireless sensing techniques. Although the users have witnessed the convenience of smart health monitoring, severe privacy and security concerns on the valuable and sensitive collected data come along with the merit. The data collection, transmission, and analysis are vulnerable to various attacks, e.g., eavesdropping, due to the open nature of wireless media, the resource constraints of sensing devices, and the lack of security protocols. These deficiencies not only make conventional cryptographic methods not applicable in smart health monitoring but also put many obstacles in the path of designing privacy protection mechanisms.
In this dissertation, we design dedicated schemes to achieve secure data collection and analysis in smart health monitoring. The first two works propose two robust and secure authentication schemes based on Electrocardiogram (ECG), which outperform traditional user identity authentication schemes in health monitoring, to restrict the access to collected data to legitimate users. To improve the practicality of ECG-based authentication, we address the nonuniformity and sensitivity of ECG signals, as well as the noise contamination issue. The next work investigates an extended authentication goal, denoted as wearable-user pair authentication. It simultaneously authenticates the user identity and device identity to provide further protection. We exploit the uniqueness of the interference between different wireless protocols, which is common in health monitoring due to devices\u27 varying sensing and transmission demands, and design a wearable-user pair authentication scheme based on the interference. However, the harm of this interference is also outstanding. Thus, in the fourth work, we use wireless human activity recognition in health monitoring as an example and analyze how this interference may jeopardize it. We identify a new attack that can produce false recognition result and discuss potential countermeasures against this attack. In the end, we move to a broader scenario and protect the statistics of distributed data reported in mobile crowd sensing, a common practice used in public health monitoring for data collection. We deploy differential privacy to enable the indistinguishability of workers\u27 locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks
Sensor Signal and Information Processing II
In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing
Intelligent Biosignal Analysis Methods
This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others