Wavelet entropy as a measure of ventricular beat suppression from the electrocardiogram in atrial fibrillation

A novel method of quantifying the effectiveness of the suppression of ventricular activity from electrocardiograms (ECGs) in atrial fibrillation is proposed. The temporal distribution of the energy of wavelet coefficients is quantified by wavelet entropy at each ventricular beat. More effective ventricular activity suppression yields increased entropies at scales dominated by the ventricular and atrial components of the ECG. Two studies are undertaken to demonstrate the efficacy of the method: first, using synthesised ECGs with controlled levels of residual ventricular activity, and second, using patient recordings with ventricular activity suppressed by an average beat template subtraction algorithm. In both cases wavelet entropy is shown to be a good measure of the effectiveness of ventricular beat suppression

Extracting fetal heart beats from maternal abdominal recordings: Selection of the optimal principal components

This study presents a systematic comparison of different approaches to the automated selection of the principal components (PC) which optimise the detection of maternal and fetal heart beats from non-invasive maternal abdominal recordings. A public database of 75 4-channel non-invasive maternal abdominal recordings was used for training the algorithm. Four methods were developed and assessed to determine the optimal PC: (1) power spectral distribution, (2) root mean square, (3) sample entropy, and (4) QRS template. The sensitivity of the performance of the algorithm to large-amplitude noise removal (by wavelet de-noising) and maternal beat cancellation methods were also assessed. The accuracy of maternal and fetal beat detection was assessed against reference annotations and quantified using the detection accuracy score F1 [2*PPV*Se / (PPV + Se)], sensitivity (Se), and positive predictive value (PPV). The best performing implementation was assessed on a test dataset of 100 recordings and the agreement between the computed and the reference fetal heart rate (fHR) and fetal RR (fRR) time series quantified. The best performance for detecting maternal beats (F1 99.3%, Se 99.0%, PPV 99.7%) was obtained when using the QRS template method to select the optimal maternal PC and applying wavelet de-noising. The best performance for detecting fetal beats (F1 89.8%, Se 89.3%, PPV 90.5%) was obtained when the optimal fetal PC was selected using the sample entropy method and utilising a fixed-length time window for the cancellation of the maternal beats. The performance on the test dataset was 142.7 beats2/min2 for fHR and 19.9 ms for fRR, ranking respectively 14 and 17 (out of 29) when compared to the other algorithms presented at the Physionet Challenge 2013

Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG

[EN] Objective: This study proposes a reference database, composed of a large number of simulated ECG signals in atrial fibrillation (AF), for investigating the performance of methods for extraction of atrial fibrillatory waves (f -waves). Approach: The simulated signals are produced using a recently published and validated model of 12-lead ECGs in AF. The database is composed of eight signal sets together accounting for a wide range of characteristics known to represent major challenges in f -wave extraction, including high heart rates, high morphological QRST variability, and the presence of ventricular premature beats. Each set contains 30 5 min signals with different f -wave amplitudes. The database is used for the purpose of investigating the statistical association between different indices, designed for use with either real or simulated signals. Main results: Using the database, available at the PhysioNet repository of physiological signals, the performance indices unnormalized ventricular residue (uVR), designed for real signals, and the root mean square error, designed for simulated signals, were found to exhibit the strongest association, leading to the recommendation that uVR should be used when characterizing performance in real signals. Significance: The proposed database facilitates comparison of the performance of different f -wave extraction methods and makes it possible to express performance in terms of the error between simulated and extracted f -wave signals.This work was supported by project DPI2017-83952-C3 of the Spanish Ministry of Economy, Industry and Competitiveness, project SBPLY/17/180501/000411 of the Junta de Comunidades de Castilla-La Mancha, Grant 'Jose Castillejo' (CAS17/00436) from the Spanish Ministry of Education, Culture and Sport, Grant No. BEST/2017/028 from the Education, Research, Culture and Sports Department of Generalitat Valenciana, European Regional Development Fund, and Grant No. 03382/2016 from the Swedish Research Council.Alcaraz, R.; Sornmo, L.; Rieta, JJ. (2019). Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG. Physiological Measurement. 40(7):1-11. https://doi.org/10.1088/1361-6579/ab2b17S111407Chugh, S. 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L., Lecannelier, E. A., Pino, E. J., & Rojas, A. J. (2013). Atrial activity selection for atrial fibrillation ECG recordings. Computers in Biology and Medicine, 43(10), 1628-1636. doi:10.1016/j.compbiomed.2013.08.002Fauchier, L., Villejoubert, O., Clementy, N., Bernard, A., Pierre, B., Angoulvant, D., … Lip, G. Y. H. (2016). Causes of Death and Influencing Factors in Patients with Atrial Fibrillation. The American Journal of Medicine, 129(12), 1278-1287. doi:10.1016/j.amjmed.2016.06.045Fujiki, A., Sakabe, M., Nishida, K., Mizumaki, K., & Inoue, H. (2003). Role of Fibrillation Cycle Length in Spontaneous and Drug-Induced Termination of Human Atrial Fibrillation. Circulation Journal, 67(5), 391-395. doi:10.1253/circj.67.391Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., … Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet. Circulation, 101(23). doi:10.1161/01.cir.101.23.e215Roonizi, E. K., & Sassi, R. (2017). An Extended Bayesian Framework for Atrial and Ventricular Activity Separation in Atrial Fibrillation. IEEE Journal of Biomedical and Health Informatics, 21(6), 1573-1580. doi:10.1109/jbhi.2016.2625338Krijthe, B. P., Kunst, A., Benjamin, E. J., Lip, G. Y. H., Franco, O. H., Hofman, A., … Heeringa, J. (2013). Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. European Heart Journal, 34(35), 2746-2751. doi:10.1093/eurheartj/eht280Langley, P. (2015). Wavelet Entropy as a Measure of Ventricular Beat Suppression from the Electrocardiogram in Atrial Fibrillation. Entropy, 17(12), 6397-6411. doi:10.3390/e17096397Langley, P., Rieta, J. J., Stridh, M., Millet, J., Sornmo, L., & Murray, A. (2006). Comparison of Atrial Signal Extraction Algorithms in 12-Lead ECGs With Atrial Fibrillation. IEEE Transactions on Biomedical Engineering, 53(2), 343-346. doi:10.1109/tbme.2005.862567Lee, J., Song, M., Shin, D., & Lee, K. (2012). Event synchronous adaptive filter based atrial activity estimation in single-lead atrial fibrillation electrocardiograms. Medical & Biological Engineering & Computing, 50(8), 801-811. doi:10.1007/s11517-012-0931-7Lemay, M., Vesin, J.-M., van Oosterom, A., Jacquemet, V., & Kappenberger, L. (2007). Cancellation of Ventricular Activity in the ECG: Evaluation of Novel and Existing Methods. IEEE Transactions on Biomedical Engineering, 54(3), 542-546. doi:10.1109/tbme.2006.888835Llinares, R., Igual, J., & Miró-Borrás, J. (2010). A fixed point algorithm for extracting the atrial activity in the frequency domain. Computers in Biology and Medicine, 40(11-12), 943-949. doi:10.1016/j.compbiomed.2010.10.006Malik, J., Reed, N., Wang, C.-L., & Wu, H. (2017). Single-lead f-wave extraction using diffusion geometry. Physiological Measurement, 38(7), 1310-1334. doi:10.1088/1361-6579/aa707cMateo, J., & Joaquín Rieta, J. (2013). Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation. Computers in Biology and Medicine, 43(2), 154-163. doi:10.1016/j.compbiomed.2012.11.007McSharry, P. E., Clifford, G. D., Tarassenko, L., & Smith, L. A. (2003). A dynamical model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering, 50(3), 289-294. doi:10.1109/tbme.2003.808805Nault, I., Lellouche, N., Matsuo, S., Knecht, S., Wright, M., Lim, K.-T., … Haïssaguerre, M. (2009). Clinical value of fibrillatory wave amplitude on surface ECG in patients with persistent atrial fibrillation. Journal of Interventional Cardiac Electrophysiology, 26(1), 11-19. doi:10.1007/s10840-009-9398-3Petrenas, A., Marozas, V., Sološenko, A., Kubilius, R., Skibarkiene, J., Oster, J., & Sörnmo, L. (2017). Electrocardiogram modeling during paroxysmal atrial fibrillation: application to the detection of brief episodes. 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Characterization, Classification, and Genesis of Seismocardiographic Signals

Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG

Wearable Wireless Devices

No abstract available

Wearable Wireless Devices

No abstract available

A Noise-Adaptive Method for Detection of Brief Episodes of Paroxysmal Atrial Fibrillation

Abstract The aim of this work is to develop a method for detection of brief episode paroxysmal atrial fibrillation (PAF Introduction The detection of brief episode paroxysmal atrial fibrillation (PAF) is an important problem to solve since atrial fibrillation (AF) is a progressive disorder. If not treated, PAF usually becomes more frequent and longer until it becomes permanent Automatic AF detection can be done in different waysone is based on identification of P-wave absence and another on the analysis of RR interval irregularity. Since P-waves are not apparent during AF such knowledge can be combined with RR irregularity information in order to improve the performance of AF detection Recently, there has been a growing interest in developing algorithms for detection of brief AF episodes. A sample entropy based method was proposed that is capable of detecting AF using only 12 consecutive RR intervals A novel detector architecture was recently proposed, where information on P wave presence/absence, heart rate irregularity, and atrial activity analysis was combined, using an artificial neural network as classifier In this study, the proposed method is based on atrial activity extraction using an echo state network (ESN) recently introduced as a unified solution to the problem of QRST cancellation in the presence of substantial variation in beat morphology and/or occasional ectopic beats Methods The main processing steps of the proposed AF detector are illustrated i

Magnetocardiography in unshielded environment based on optical magnetometry and adaptive noise cancellation

This thesis proposes and demonstrates the concept of a magnetocardiographic system employing an array of optically-pumped quantum magnetometers and an adaptive noise cancellation for heart magnetic field measurement within a magnetically-unshielded environment. Optically-pumped quantum magnetometers are based on the use of the atomic-spin-dependent optical properties of an atomic medium. An Mxconfiguration- based optically-pumped quantum magnetometer employing two sensing cells containing caesium vapour is theoretically described and experimentally developed, and the dependence of its sensitivity and frequency bandwidth upon the light power and the alkali vapour temperature is experimentally demonstrated. Furthermore, the capability of the developed magnetometer of measuring very weak magnetic fields is experimentally demonstrated in a magnetically-unshielded environment. The adaptive noise canceller is based on standard Least-Mean-Squares (LMS) algorithms and on two heuristic optimization techniques, namely, Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The use of these algorithms is investigated for suppressing the power line generated 50Hz interference and recovering of the weak magnetic heart signals from a much higher electromagnetic environmental noise. Experimental results show that all the algorithms can extract a weak heart signal from a much-stronger magnetic noise, detect the P, QRS, and T heart features and highly suppress the common power line noise component at 50 Hz. Moreover, adaptive noise cancellation based on heuristic algorithms is shown to be more efficient than adaptive noise canceller based on standard or normalised LMS algorithm in heart features detection

Investigations of an On-body Reflectometer Probe

Radar can be utilized to detect the mechanical heart activity and is a potential alternative to today’s heartbeat monitoring techniques in medicine. It can detect details of the heart activity, such as filling and ejection of heart chambers and opening and closing of heart valves. This is due to the radars ability to detect movements and direction of motion. Compared to electrocardiogram and ultrasound it has the advantage that it is a contactless measurement. The objective of this thesis is the development of a proof-of-concept prototype of a novel microwave on-body sensor for heartbeat detection, which can be used inside an MRI system and which could provide prospective triggering information. The main idea is to use a microwave sensor (reflectometer) with an on-body antenna illuminating the heart and detecting the reflected signal. The measurement is based on the evaluation of the heart-related time-dependent reflection coefficient of the antenna, by minimizing the static and respiration-related components of the reflection coefficient. In a first step, this is done by minimizing the antenna mismatch with an automatic impedance matching circuit after the placement of the antenna on the chest of an individual; the antenna mismatch is dependent on the position and the individual body properties. In a second step the residual static and slow variation signal from respiration is suppressed by a canceller circuit (well-known from CW radar technology as reflected power canceller). With the reflectometer sensor system consisting of a CW signal generator (transmitter, Tx), on-body antenna, adaptive impedance matching circuit and demodulator circuit as part of the reflected signal canceller, the performance of each component influences the performance of the sensor system. Thus, the thesis concentrates on the design of the circuits and the antenna but also investigates the wave propagation scenario of the sensor applied to a human chest. The signals measured with the microwave sensor are compared with a standard measurement method for heart activity, a heart sound measurement. This is used in order to assess the obtained signal and relate the signal states to certain heart states. The measured radar signals are found to be sensitive to position of the sensor, the individual and the posture of the individual, making the interpretation of the signals challenging