Atrial fibrillation subtypes classification using the General Fourier-family Transform

Atrial fibrillation patients can be classified into paroxysmal, persistent and permanent attending to the temporal pattern of this arrhythmia. The surface electrocardiogram hides this differentiation. A classification method to discriminate between the different subtypes of atrial fibrillation by using short segments of electrocardiograms recordings is presented. We will process the electrocardiograms (ECGs) using time-frequency techniques with a global accuracy of 80%. Real cases are evaluated showing promising results for an implementation in a semiautomated diagnostic system.This work was supported by grants MTM2010-15200, PrometeoII/2013/013 and UPV-IIS La Fe, 2012/0468.Ortigosa, N.; Cano, O.; Ayala Gallego, G.; Galbis Verdu, A.; Fernandez Rosell, C. (2014). Atrial fibrillation subtypes classification using the General Fourier-family Transform. Medical Engineering and Physics. 36(4):554-560. https://doi.org/10.1016/j.medengphy.2013.12.005S55456036

Early prediction of cardiac resynchronization therapy response by non-invasive electrocardiogram markers

[EN] Cardiac resynchronization therapy (CRT) is an effective treatment for those patients with severe heart failure. Regrettably, there are about one third of CRT "non-responders", i.e. patients who have undergone this form of device therapy but do not respond to it, which adversely affects the utility and cost-effectiveness of CRT. In this paper, we assess the ability of a novel surface ECG marker to predict CRT response. We performed a retrospective exploratory study of the ECG previous to CRT implantation in 43 consecutive patients with ischemic (17) or non-ischemic (26) cardiomyopathy. We extracted the QRST complexes (consisting of the QRS complex, the S-T segment, and the T wave) and obtained a measure of their energy by means of spectral analysis. This ECG marker showed statistically significant lower values for non-responder patients and, joint with the duration of QRS complexes (the current gold-standard to predict CRT response), the following performances: 86% accuracy, 88% sensitivity, and 80% specificity. In this manner, the proposed ECG marker may help clinicians to predict positive response to CRT in a non-invasive way, in order to minimize unsuccessful procedures.This work was supported by MINECO under grants MTM2013-43540-P and MTM2016-76647-P.Ortigosa, N.; Pérez-Roselló, V.; Donoso, V.; Osca Asensi, J.; Martínez-Dolz, L.; Fernández Rosell, C.; Galbis Verdu, A. (2018). Early prediction of cardiac resynchronization therapy response by non-invasive electrocardiogram markers. Medical & Biological Engineering & Computing. 56(4):611-621. https://doi.org/10.1007/s11517-017-1711-1S611621564Boggiatto P, Fernández C, Galbis A (2009) A group representation related to the stockwell transform. Indiana University Mathematics Journal 58(5):2277–2296Brignole M, Auricchio A, Baron-Esquivias G, Bordachar P, Boriani G et al (2013) 2013 ESC guidelines on cardiac pacing and cardiac resynchronization therapy. Europace 15:1070–1118Brown RA, Lauzon ML, Frayne R (2010) A general description of linear time-frequency transforms and formulation of a fast, invertible transform that samples the continuous s-transform spectrum nonredundantly. IEEE Trans Signal Process 58(1): 281–290Carità P, Corrado E, Pontone G, Curnis A, Bontempi L et al (2016) Non-responders to cardiac resynchronization therapy: insights from multimodality imaging and electrocardiography. A brief review. Int J Cardiol 225:402–407Cazeau S, Leclercq C, Lavergne T, Walker S, Varma C, Linde C et al (2001) Effects of multisite biventricular pacing in patients with heart failure and intraventricular conduction delay. N Engl J Med 344:873–880Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27:1–27:27Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16(1):321–357Cleland JGF, Abraham WT, Linde C, Gold MR, Young J et al (2013) An individual patient meta-analysis of five randomized trials assessing the effects of cardiac resyn- chronization therapy on morbidity and mortality in patients with symptomatic heart failure. Eur Heart Journal 34(46):3547–3556Cleland JGF, Calvert MJ, Verboven Y, Freemantle N (2009) Effects of cardiac resynchronization therapy on long-term quality of life: an analysis from the Cardiac Resynchronisation-Heart Failure (CARE-HF) study. Am Heart J 157:457–466Cleland JGF, Freemantle N, Erdmann E, Gras D, Kappenberger L et al (2012) Long-term mortality with cardiac resynchronization therapy in the Cardiac Resynchronization-Heart Failure (CARE-HF) trial. Eur J Heart Fail 14:628–634Egoavil CA, Ho RT, Greenspon AJ, Pavri BB (2005) Cardiac resynchronization therapy in patients with right bundle branch block: analysis of pooled data from the MIRACLE and Contak CD trials. Heart Rhythm 2(6):611–615Engels EB, Mafi-Rad M, van Stipdonk AM, Vernooy K, Prinzen FW (2016) Why QRS duration should be replaced by better measures of electrical activation to improve patient selection for cardiac resynchronization therapy. J Cardiovasc Transl Res 9(4):257–265Engels EB, Végh EM, Van Deursen CJ, Vernooy K, Singh JP, Prinzen FW (2015) T-wave area predicts response to cardiac resynchronization therapy in patients with left bundle branch block. J Cardiovasc Electrophysiol 26(2):176–183Eschalier R, Ploux S, Ritter P, Haïssaguerre M, Ellenbogen K, Bordachar P (2015) Nonspecific intraventricular conduction delay: definitions, prognosis, and implications for cardiac resynchronization therapy. Heart Rhythm 12(5):1071–1079Goldenberg I, Kutyifa V, Klein HU, Cannom DS, Brown MW et al (2014) Survival with cardiac-resynchronization therapy in mild heart failure. N Engl J Med 370:1694–1701He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: International joint conference on neural networks, pp 1322–1328Jacobsson J, Borgguist R, Reitan C, Ghafoori E, Chatterjee NA et al (2016) Usefulness of the sum absolute QRST integral to predict outcomes in patients receiving cardiac resynchronization therapy. J Cardiovasc Electrophysiol 118(3):389–395McMurray JJ (2010) Clinical practice. Systolic heart failure. N Engl J Med 3623:228–238Meyer CR, Keiser HN (1977) Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques. Comput Biomed Res 10:459–470Ortigosa N, Giménez VM (2014) Raw data extraction from electrocardiograms with portable document format. Comput Meth Programs Biomed 113(1):284–289Ortigosa N, Osca J, Jiménez R, Rodríguez Y, Fernández C, Galbis A (2016) Predictive analysis of cardiac resynchronization therapy response by means of the ECG. 2016 Comput Cardio 43:753–756. https://doi.org/10.22489/CinC.2016.218-415Ponikowski P, Voors AA, Anker S, Bueno H, Cleland JG, Coats AJ et al (2016) 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail 18(8):891–975Rad MM, Wijntjens GW, Engels EB, Blaauw Y, Luermans JG et al (2016) Vectorcardiographic QRS area identifies delayed left ventricular lateral wall activation determined by electroanatomic mapping in candidates for cardiac resynchronization therapy. Heart Rhythm 13(1):217–225Shanks M, Delgado V, Bax JJ (2016) Cardiac resynchronization therapy in non-ischemic cardiomyopathy. Journal of Atrial Fibrillation 8(5):47–52Singh JP, Fan D, Heist EK, Alabiad CR, Taub C et al (2006) Left ventricular lead electrical delay predicts response to cardiac resynchronization therapy. Heart Rhythm 3(11):1285–1292Sohaib SM, Finegold JA, Nijjer SS, Hossain R, Linde C et al (2015) Opportunity to increase life span in narrow QRS cardiac resynchronization therapy recipients by deactivating ventricular pacing: evidence from randomized controlled trials. JACC Heart Fail 3:327–336Stockwell RG, Mansinha L, Lowe RP (1996) Localization of the complex spectrum: the S transform. IEEE Trans Signal Process 44(4):998–1001Tang ASL, Wells GA, Talajic M, Arnold MO, Sheldon R et al (2010) Cardiac-resynchronization therapy for mild-to-moderate heart failure. N Engl J Med 363:2385–2395Tereshchenko LG, Cheng A, Park J, Wold N, Meyer TE, Gold MR et al (2015) Novel measure of electrical dyssynchrony predicts response in cardiac resynchronization therapy: results from the SMART-AV trial. Heart Rhythm 12(2):2402–2410van Deursen CJ, Vernooy K, Dudink E, Bergfeldt L, Crijns HJ et al (2015) Vectorcardiographic QRS area as a novel predictor of response to cardiac resynchronization therapy. J Electrocardiol 48(1):45–52Wang TJ (2003) Natural history of asymptomatic left ventricular systolic dysfunction in the community. Circulation 108:977–982Woods B, Hawkins N, Mealing S, Sutton A, Abraham WT et al (2015) Individual patient data network meta-analysis of mortality effects of implantable cardiac devices. Heart 101:1800–1806Ypenburg C, van Bommel RJ, Borleffs CJ, Bleeker GB, Boersma E et al (2009) Long-term prognosis after cardiac resynchronization therapy is related to the extent of left ventricular reverse remodeling at midterm follow-up. 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Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms

Atrial fibrillation, which is the most common cardiac arrhythmia, is typically classified into four clinical subtypes: paroxysmal, persistent, long-standing persistent and permanent. The ability to distinguish between them is of crucial significance in choosing the most suitable therapy for each patient. Nevertheless, classification is currently established once the natural history of the arrhythmia has been disclosed as it is not possible to make an early differentiation. This paper presents a novel method to discriminate persistent and long-standing atrial fibrillation patients by means of a time-frequency analysis of the surface electrocardiogram. Classification results provide approximately 75% accuracy when evaluating ECGs of consecutive unselected patients from a tertiary center and higher than 80% when patients are not under antiarrhythmic treatment or do not have structural heart disease (76% sensitivity and 88% specificity). Moreover, to our knowledge, this is the first study that discriminates between persistent and long-standing persistent subtypes in a heterogeneous population sample and without discontinuing antiarrhythmic therapy to patients. Thus, it can help clinicians to address the most suitable therapeutic approach for each patient.This work was supported by Generalitat Valenciana under grant PrometeoII/2013/013 and by MINECO under grants MTM2010-15200, MTM2013-43540-P.Ortigosa, N.; Fernández, C.; Galbis, A.; Cano, O. (2016). Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms. Biomedical Engineering / Biomedizinische Technik. 61(1):19-27. https://doi.org/10.1515/bmt-2014-0154S192761

An Analysis of Stockwell Transforms, with Applications to Image Processing

Time-frequency analysis is a powerful tool for signal analysis and processing. The Fourier transform and wavelet transforms are used extensively as is the Short-Time Fourier Transform (or Gabor transform). In 1996 the Stockwell transform was introduced to maintain the phase of the Fourier transform, while also providing the progressive resolution of the wavelet transform. The discrete orthonormal Stockwell transform is a more efficient, less redundant transform with the same properties. There has been little work on mathematical properties of the Stockwell transform, particularly how it behaves under operations such as translation and modulation. Previous results do discuss a resolution of the identity, as well as some of the function spaces that may be associated with it [2]. We extend the resolution of the identity results, and behaviour under translation, modulation, convolution and differentiation. boundedness and continuity properties are also developed, but the function spaces associated with the transform are unrelated to the focus of this thesis. There has been some work on image processing using the Stockwell transform and discrete orthonormal Stockwell transform. The tests were quite preliminary. In this thesis, we explore some of the mathematics of the Stockwell transform, examining properties, and applying it to various continuous examples. The discrete orthonormal Stockwell transform is compared directly with Newland’s harmonic wavelet transform, and we extend the definition to include varitions, as well as develop the discrete cosine based Stockwell transform. All of these discrete transforms are tested against current methods for image compression

A Comparative Study On Spectrogram And S-Transform For Batteries Parameters Estimation

This research presents the analysis of battery charging and discharging signals using spectrogram, and S-transform techniques. The analysed batteries are lead acid (LA), nickel-metal hydride (Ni-MH), and lithium-ion (Li-ion). From the equivalent circuit model (ECM) simulated using MATLAB, the constant charging and discharging signals are presented, jointly, in time-frequency representation (TFR). From the TFR, the battery signal characteristics are determined from the estimated parameters of instantaneous means square voltage (V RMS (t)), instantaneous direct current voltage (V DC (t)), and instantaneous alternating current voltage (V AC (t)). Hence, an equation for battery remaining capacity as a function of estimated parameter of V AC (t) using curve fitting tool is presented. In developing a real-time automated battery parameters estimation system, the best time-frequency distribution (TFD) is chosen in terms of accuracy of the battery parameters, computational complexity in signal processing, and memory size. The advantages in high accuracy for battery parameters estimation, and low in memory size requirement makes the S-transform technique is selected to be the best TFD. Then, field testing is conducted for different cases, and the results show that the average mean absolute percentage error (MAPE) calculated is around 4%

Fault Management in DC Microgrids:A Review of Challenges, Countermeasures, and Future Research Trends

The significant benefits of DC microgrids have instigated extensive efforts to be an alternative network as compared to conventional AC power networks. Although their deployment is ever-growing, multiple challenges still occurred for the protection of DC microgrids to efficiently design, control, and operate the system for the islanded mode and grid-tied mode. Therefore, there are extensive research activities underway to tackle these issues. The challenge arises from the sudden exponential increase in DC fault current, which must be extinguished in the absence of the naturally occurring zero crossings, potentially leading to sustained arcs. This paper presents cut-age and state-of-the-art issues concerning the fault management of DC microgrids. It provides an account of research in areas related to fault management of DC microgrids, including fault detection, location, identification, isolation, and reconfiguration. In each area, a comprehensive review has been carried out to identify the fault management of DC microgrids. Finally, future trends and challenges regarding fault management in DC-microgrids are also discussed

New Model And Simulation Algorithm Of Nonstationary Non-gaussian Ground Motions Based On S-transform

The seismic ground motions are nonstationary stochastic processes and vary from site to site. The time histories of synthetic ground motions are used for nonlinear inelastic structural dynamic analysis since the historical records are limit or unavailable for a particular scenario seismic event. This is especially the case for structures with multiple supports. The characteristics of the nonstationary stochastic ground motions depend on the earthquake magnitude, fault mechanism, source-to-site distance, and local site conditions. The characteristics could be represented by time-frequency (dependent) power spectral density (TFPSD) and coherence functions. The assessment of such power spectral density and coherence functions are presented by using historical records and the S-transform – a Fourier transform with time localized and frequency-dependent windows – is carried out. New models of the TFPSD function and coherence function are presented. Also, new time-frequency spectral representation methods (TFSRMs) to simulate nonstationary stochastic processes are proposed. The TFSRM is developed by taking the advantages of the orthonormal basis functions in the discrete orthogonal S-transform (DOST) and the refined time-frequency representation obtained by using the S-transform. TFSRM can be used to simulate ground motions at a single site or multiple sites. They can also be used to simulate seismic ground motions conditioned on observed ground motions. TFSRM can cope with the time-varying lagged coherence function; this is not the case with the well-known spectral representation method (SRM). Similar to the SRM, the direct use of TFSRM leads to Gaussian processes (stationary or nonstationary). However, there is indicates that the seismic ground motions may not be Gaussian. A new iterative power and amplitude correction algorithm is proposed to simulate nonstationary non-Gaussian stochastic processes. This procedure is successfully implemented and illustrated by numerical examples

Interference Mitigation and Localization Based on Time-Frequency Analysis for Navigation Satellite Systems

Interference Mitigation and Localization Based on Time-Frequency Analysis for Navigation Satellite SystemsNowadays, the operation of global navigation satellite systems (GNSS) is imperative across a multitude of applications worldwide. The increasing reliance on accurate positioning and timing information has made more serious than ever the consequences of possible service outages in the satellite navigation systems. Among others, interference is regarded as the primary threat to their operation. Due the recent proliferation of portable interferers, notably jammers, it has now become common for GNSS receivers to endure simultaneous attacks from multiple sources of interference, which are likely spatially distributed and transmit different modulations. To the best knowledge of the author, the present dissertation is the first publication to investigate the use of the S-transform (ST) to devise countermeasures to interference. The original contributions in this context are mainly: • the formulation of a complexity-scalable ST implementable in real time as a bank of filters; • a method for characterizing and localizing multiple in-car jammers through interference snapshots that are collected by separate receivers and analysed with a clever use of the ST; • a preliminary assessment of novel methods for mitigating generic interference at the receiver end by means the ST and more computationally efficient variants of the transform. Besides GNSSs, the countermeasures to interference proposed are equivalently applicable to protect any direct-sequence spread spectrum (DS-SS) communication