Novel hybrid extraction systems for fetal heart rate variability monitoring based on non-invasive fetal electrocardiogram

This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.Web of Science713178413175

Reduced Cycle Spinning Method for the Undecimated Wavelet Transform

[EN] The Undecimated Wavelet Transform is commonly used for signal processing due to its advantages over other wavelet techniques, but it is limited for some applications because of its computational cost. One of the methods utilized for the implementation of the Undecimated Wavelet Transform is the one known as Cycle Spinning. This paper introduces an alternative Cycle Spinning implementation method that divides the computational cost by a factor close to 2. This work develops the mathematical background of the proposed method, shows the block diagrams for its implementation and validates the method by applying it to the denoising of ultrasonic signals. The evaluation of the denoising results shows that the new method produces similar denoising qualities than other Cycle Spinning implementations, with a reduced computational cost.This research was funded by grants number PGC2018-09415-B-I00 (MCIU/AEI/FEDER, UE) and TEC2015-71932-REDT.Rodríguez-Hernández, MA. (2019). Reduced Cycle Spinning Method for the Undecimated Wavelet Transform. Sensors. 19(12):1-16. https://doi.org/10.3390/s19122777S1161912Signal Processing Fourier and Wavelet Representationshttp://www.fourierandwavelets.org/SPFWR_a3.1_2012.pdfZhao, H., Zuo, S., Hou, M., Liu, W., Yu, L., Yang, X., & Deng, W. (2018). A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology. Sensors, 18(10), 3323. doi:10.3390/s18103323Gradolewski, D., Magenes, G., Johansson, S., & Kulesza, W. (2019). A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography. Sensors, 19(4), 957. doi:10.3390/s19040957Shikhsarmast, F., Lyu, T., Liang, X., Zhang, H., & Gulliver, T. (2018). Random-Noise Denoising and Clutter Elimination of Human Respiration Movements Based on an Improved Time Window Selection Algorithm Using Wavelet Transform. Sensors, 19(1), 95. doi:10.3390/s19010095Shensa, M. J. (1992). The discrete wavelet transform: wedding the a trous and Mallat algorithms. IEEE Transactions on Signal Processing, 40(10), 2464-2482. doi:10.1109/78.157290Li, M., & Ghosal, S. (2015). Fast Translation Invariant Multiscale Image Denoising. IEEE Transactions on Image Processing, 24(12), 4876-4887. doi:10.1109/tip.2015.2470601Hazarika, D., Nath, V. K., & Bhuyan, M. (2016). SAR Image Despeckling Based on a Mixture of Gaussian Distributions with Local Parameters and Multiscale Edge Detection in Lapped Transform Domain. Sensing and Imaging, 17(1). doi:10.1007/s11220-016-0141-8Sakhaee, E., & Entezari, A. (2017). Joint Inverse Problems for Signal Reconstruction via Dictionary Splitting. IEEE Signal Processing Letters, 24(8), 1203-1207. doi:10.1109/lsp.2017.2701815Ong, F., Uecker, M., Tariq, U., Hsiao, A., Alley, M. T., Vasanawala, S. S., & Lustig, M. (2014). Robust 4D flow denoising using divergence-free wavelet transform. Magnetic Resonance in Medicine, 73(2), 828-842. doi:10.1002/mrm.25176Rehman, N. ur, Abbas, S. Z., Asif, A., Javed, A., Naveed, K., & Mandic, D. P. (2017). Translation invariant multi-scale signal denoising based on goodness-of-fit tests. Signal Processing, 131, 220-234. doi:10.1016/j.sigpro.2016.08.019Mota, H. de O., Vasconcelos, F. H., & de Castro, C. L. (2016). A comparison of cycle spinning versus stationary wavelet transform for the extraction of features of partial discharge signals. IEEE Transactions on Dielectrics and Electrical Insulation, 23(2), 1106-1118. doi:10.1109/tdei.2015.005300Li, D., Wang, Y., Lin, J., Yu, S., & Ji, Y. (2016). Electromagnetic noise reduction in grounded electrical‐source airborne transient electromagnetic signal using a stationarywavelet‐based denoising algorithm. Near Surface Geophysics, 15(2), 163-173. doi:10.3997/1873-0604.2017003San Emeterio, J. L., & Rodriguez-Hernandez, M. A. (2014). Wavelet Cycle Spinning Denoising of NDE Ultrasonic Signals Using a Random Selection of Shifts. Journal of Nondestructive Evaluation, 34(1). doi:10.1007/s10921-014-0270-8Rodriguez-Hernandez, M. A., & Emeterio, J. L. S. (2015). Noise reduction using wavelet cycle spinning: analysis of useful periodicities in the z-transform domain. Signal, Image and Video Processing, 10(3), 519-526. doi:10.1007/s11760-015-0762-8Rodriguez-Hernandez, M. A. (2016). Shift selection influence in partial cycle spinning denoising of biomedical signals. Biomedical Signal Processing and Control, 26, 64-68. doi:10.1016/j.bspc.2015.12.002Beylkin, G., Coifman, R., & Rokhlin, V. (1991). Fast wavelet transforms and numerical algorithms I. Communications on Pure and Applied Mathematics, 44(2), 141-183. doi:10.1002/cpa.3160440202Beylkin, G. (1992). On the Representation of Operators in Bases of Compactly Supported Wavelets. SIAM Journal on Numerical Analysis, 29(6), 1716-1740. doi:10.1137/0729097Donoho, D. L., & Johnstone, I. M. (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81(3), 425-455. doi:10.1093/biomet/81.3.425Donoho, D. L., & Johnstone, I. M. (1995). Adapting to Unknown Smoothness via Wavelet Shrinkage. Journal of the American Statistical Association, 90(432), 1200-1224. doi:10.1080/01621459.1995.10476626Johnstone, I. M., & Silverman, B. W. (1997). Wavelet Threshold Estimators for Data with Correlated Noise. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 59(2), 319-351. doi:10.1111/1467-9868.00071Pardo, E., San Emeterio, J. L., Rodriguez, M. A., & Ramos, A. (2006). Noise reduction in ultrasonic NDT using undecimated wavelet transforms. Ultrasonics, 44, e1063-e1067. doi:10.1016/j.ultras.2006.05.101Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613-627. doi:10.1109/18.382009Lázaro, J. C., San Emeterio, J. L., Ramos, A., & Fernández-Marrón, J. L. (2002). Influence of thresholding procedures in ultrasonic grain noise reduction using wavelets. Ultrasonics, 40(1-8), 263-267. doi:10.1016/s0041-624x(02)00149-xKarpur, P., Shankar, P. M., Rose, J. L., & Newhouse, V. L. (1987). Split spectrum processing: optimizing the processing parameters using minimization. Ultrasonics, 25(4), 204-208. doi:10.1016/0041-624x(87)90034-5Pardo, E., Emeterio, S. J. L., Rodriguez, M. A., & Ramos, A. (2008). Shift Invariant Wavelet Denoising of Ultrasonic Traces. Acta Acustica united with Acustica, 94(5), 685-693. doi:10.3813/aaa.91808

Hybrid methods based on empirical mode decomposition for non-invasive fetal heart rate monitoring

This study focuses on fetal electrocardiogram (fECG) processing using hybrid methods that combine two or more individual methods. Combinations of independent component analysis (ICA), wavelet transform (WT), recursive least squares (RLS), and empirical mode decomposition (EMD) were used to create the individual hybrid methods. Following four hybrid methods were compared and evaluated in this study: ICA-EMD, ICA-EMD-WT, EMD-WT, and ICA-RLS-EMD. The methods were tested on two databases, the ADFECGDB database and the PhysioNet Challenge 2013 database. Extraction evaluation is based on fetal heart rate (fHR) determination. Statistical evaluation is based on determination of correct detection (ACC), sensitivity (Se), positive predictive value (PPV), and harmonic mean between Se and PPV (F1). In this study, the best results were achieved by means of the ICA-RLS-EMD hybrid method, which achieved accuracy(ACC) > 80% at 9 out of 12 recordings when tested on the ADFECGDB database, reaching an average value of ACC > 84%, Se > 87%, PPV > 92%, and F1 > 90%. When tested on the Physionet Challenge 2013 database, ACC > 80% was achieved at 12 out of 25 recordings with an average value of ACC > 64%, Se > 69%, PPV > 79%, and F1 > 72%.Web of Science8512185120

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Data-driven multivariate and multiscale methods for brain computer interface

This thesis focuses on the development of data-driven multivariate and multiscale methods for brain computer interface (BCI) systems. The electroencephalogram (EEG), the most convenient means to measure neurophysiological activity due to its noninvasive nature, is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its multichannel recording nature require a new set of data-driven multivariate techniques to estimate more accurately features for enhanced BCI operation. Also, a long term goal is to enable an alternative EEG recording strategy for achieving long-term and portable monitoring. Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary EEG signal into a set of components which are highly localised in time and frequency. It is shown that the complex and multivariate extensions of EMD, which can exploit common oscillatory modes within multivariate (multichannel) data, can be used to accurately estimate and compare the amplitude and phase information among multiple sources, a key for the feature extraction of BCI system. A complex extension of local mean decomposition is also introduced and its operation is illustrated on two channel neuronal spike streams. Common spatial pattern (CSP), a standard feature extraction technique for BCI application, is also extended to complex domain using the augmented complex statistics. Depending on the circularity/noncircularity of a complex signal, one of the complex CSP algorithms can be chosen to produce the best classification performance between two different EEG classes. Using these complex and multivariate algorithms, two cognitive brain studies are investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user attention to a sound source among a mixture of sound stimuli, which is aimed at improving the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments elicited by taste and taste recall are examined to determine the pleasure and displeasure of a food for the implementation of affective computing. The separation between two emotional responses is examined using real and complex-valued common spatial pattern methods. Finally, we introduce a novel approach to brain monitoring based on EEG recordings from within the ear canal, embedded on a custom made hearing aid earplug. The new platform promises the possibility of both short- and long-term continuous use for standard brain monitoring and interfacing applications