397 research outputs found

    A study on stability analysis of atrial repolarization variability using ARX model in sinus rhythm and atrial tachycardia ECGs

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    © 2016 Elsevier Ireland Ltd Background The interaction between the PTa and PP interval dynamics from the surface ECG is seldom explained. Mathematical modeling of these intervals is of interest in finding the relationship between the heart rate and repolarization variability. Objective The goal of this paper is to assess the bounded input bounded output (BIBO) stability in PTa interval (PTaI) dynamics using autoregressive exogenous (ARX) model and to investigate the reason for causing instability in the atrial repolarization process. Methods Twenty-five male subjects in normal sinus rhythm (NSR) and ten male subjects experiencing atrial tachycardia (AT) were included in this study. Five minute long, modified limb lead (MLL) ECGs were recorded with an EDAN SE-1010 PC ECG system. The number of minute ECGs with unstable segments (N us ) and the frequency of premature activation (PA) (i.e. atrial activation) were counted for each ECG recording and compared between AT and NSR subjects. Results The instability in PTaI dynamics was quantified by measuring the numbers of unstable segments in ECG data for each subject. The unstable segments in the PTaI dynamics were associated with the frequency of PA. The presence of PA is not the only factor causing the instability in PTaI dynamics in NSR subjects, and it is found that the cause of instability is mainly due to the heart rate variability (HRV). C onclusion The ARX model showed better prediction of PTa interval dynamics in both groups. The frequency of PA is significantly higher in AT patients than NSR subjects. A more complex model is needed to better identify and characterize healthy heart dynamics

    Applications of Signal Analysis to Atrial Fibrillation

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    This work was supported by projects TEC2010–20633 from the Spanish Ministry of Science and Innovation and PPII11–0194–8121 from Junta de Comunidades de Castilla-La ManchaRieta Ibañez, JJ.; Alcaraz MartĂ­nez, R. (2013). Applications of Signal Analysis to Atrial Fibrillation. En Atrial Fibrillation - Mechanisms and Treatment. InTech. 155-180. https://doi.org/10.5772/5340915518

    Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review.

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    Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned

    Current Status and Future of Cardiac Mapping in Atrial Fibrillation

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    Spectroscopic photoacoustic imaging of radiofrequency ablation in the left atrium

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    Catheter-based radiofrequency ablation for atrial fibrillation has long-term success in 60-70% of cases. A better assessment of lesion quality, depth, and continuity could improve the procedure’s outcome. We investigate here photoacoustic contrast between ablated and healthy atrial-wall tissue in vitro in wavelengths spanning from 410 nm to 1000 nm. We studied single-and multi-wavelength imaging of ablation lesions and we demonstrate that a two-wavelength technique yields precise detection of lesions, achieving a diagnostic accuracy of 97%. We compare this with a best single-wavelength (640 nm) analysis that correctly identifies 82% of lesions. We discuss the origin of relevant spectroscopic features and perspectives for translation to clinical imaging

    Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems

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    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, Björn Eskofier, Socrates Dokos, Derek Abbot

    Bottom-up design of artificial neural network for single-lead electrocardiogram beat and rhythm classification

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    Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined in terms of perfecting individual sub-systems to improve the over all system performance is used. Sub-systems include pre-processing, QRS detection and fiducial point estimations, feature calculations, and pattern classification. Inaccuracies in time-domain fiducial point estimations are overcome with the derivation of features in the frequency domain. Feature extraction in frequency domain is based on a spectral estimation technique (combination of simulation and subtraction of a normal beat). Auto-regressive spectral estimation methods yield a highly sensitive spectrum, providing several local features with information on beat classes like flutter, fibrillation, and noise. A total of 27 features, including 16 in time domain and 11 in frequency domain are calculated. The entire data and problem are divided into four major groups, each group with inter-related beat classes. Classification of each group into related sub-classes is performed using smaller feed-forward neural networks. Input feature sub-set and the structure of each network are optimized using an iterative process. Optimal implementations of feed-forward neural networks provide high accuracy in beat classification. Associated neural networks are used for the more deterministic rhythm-classification task. An accuracy of more than 85% is achieved for all 13 classes included in this study. The system shows a graceful degradation in performance with increasing noise, as a result of the noise consideration in the design of every sub-system. Results indicate a neural network-based bottom-up design of single-lead ECG classification is able to provide very high accuracy, even in the presence of noise, flutter, and fibrillation

    Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

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    Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien kĂ€yttö sydĂ€nkardiografiassa sekĂ€ lÀÀketieteellisessĂ€ 4D-kuvantamisessa Tausta: SydĂ€n- ja verisuonitaudit ovat yleisin kuolinsyy. NĂ€istĂ€ kuolemantapauksista lĂ€hes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron hĂ€iriöistĂ€. Moniulotteiset mikroelektromekaaniset jĂ€rjestelmĂ€t (MEMS) mahdollistavat sydĂ€nlihaksen mekaanisen liikkeen mittaamisen, mikĂ€ puolestaan tarjoaa tĂ€ysin uudenlaisen ja innovatiivisen ratkaisun sydĂ€men rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjĂ€rjestelmien kĂ€yttĂ€misen sydĂ€men toiminnan tutkimuksessa sekĂ€ lÀÀketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. MenetelmĂ€t: TĂ€mĂ€ vĂ€itöskirjatyö esittelee uuden sydĂ€men kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien kĂ€yttöön. Uudet laskennalliset lĂ€hestymistavat, jotka perustuvat signaalinkĂ€sittelyyn ja koneoppimiseen, mahdollistavat sydĂ€men patologisten hĂ€iriöiden havaitsemisen MEMS-antureista saatavista signaaleista. TĂ€ssĂ€ tutkimuksessa keskitytÀÀn erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). NĂ€iden tekniikoiden avulla voidaan mitata kardiorespiratorisen jĂ€rjestelmĂ€n mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, ettĂ€ integroimalla usean sensorin dataa voidaan mitata syketiheyttĂ€ 99% (terveillĂ€ n=29) tarkkuudella, havaita sydĂ€men rytmihĂ€iriöt (n=435) 95-97%, tarkkuudella, sekĂ€ havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). LisĂ€ksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydĂ€men 4D PET-kuvan laatua, kun liikeepĂ€tarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillĂ€, n=9) osoitti lupaavia tuloksia sydĂ€nsykkeen ajoituksen ja intervallien sekĂ€ sydĂ€nlihasmuutosten mittaamisessa. PÀÀtelmĂ€: TĂ€mĂ€n tutkimuksen tulokset osoittavat, ettĂ€ kardiologisilla MEMS-liikeantureilla on kliinistĂ€ potentiaalia sydĂ€men toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistÀÀ eteisvĂ€rinĂ€n (AFib), sydĂ€ninfarktin (MI) ja CAD:n havaitsemista. LisĂ€ksi MEMS-liiketunnistus parantaa sydĂ€men PET-kuvantamisen luotettavuutta ja laatua
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