Current state of nonlinear-type time–frequency analysis and applications to high-frequency biomedical signals

Current Opinion in Systems Biology - Tập 23 - Trang 8-21 - 2020
Hau-Tieng Wu1,2,3
1Department of Mathematics, Duke University, Durham, NC, USA
2Department of Statistical Science, Duke University, Durham, NC, USA;
3Mathematics Division, National Center for Theoretical Sciences, Taipei, Taiwan

Tài liệu tham khảo

Adak, 1998, Time-dependent spectral analysis of nonstationary time series, J Am Stat Assoc, 93, 1488, 10.1080/01621459.1998.10473808 Ahrabian, 2015, Synchrosqueezing-based time-frequency analysis of multivariate data, Signal Process, 106, 331, 10.1016/j.sigpro.2014.08.010 Alagapan, 2019, Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography (iEEG), J Neural Eng, 16, 10.1088/1741-2552/aaf2ba Alian, 2014, Impact of lower body negative pressure induced hypovolemia on peripheral venous pressure waveform parameters in healthy volunteers, Physiol Meas, 35, 1509, 10.1088/0967-3334/35/7/1509 Andén, 2019, Joint time–frequency scattering, IEEE Trans Signal Process, 67, 3704, 10.1109/TSP.2019.2918992 Andén, 2014, Deep scattering spectrum, IEEE Trans Signal Process, 62, 4114, 10.1109/TSP.2014.2326991 Auger, 2012, Making reassignment adjustable: the levenberg-marquardt approach, in acoustics, speech and signal processing (ICASSP), 3889 Auger, 1995, Improving the readability of time-frequency and time-scale representations by the reassignment method, IEEE Trans Signal Process, 43, 1068, 10.1109/78.382394 Auger, 2013, Recent advances in time-frequency reassignment and synchrosqueezing, IEEE Trans Signal Process, 30, 32, 10.1109/MSP.2013.2265316 Babadi, 2014, A review of multitaper spectral analysis, IEEE Trans Biomed Eng, 61, 1555, 10.1109/TBME.2014.2311996 Baudin, 2014, Impact of ventilatory modes on the breathing variability in mechanically ventilated infants, Front Pediatr Neonatol, 2 Behar, 2019, Noninvasive fetal electrocardiography for the detection of fetal arrhythmias, Prenat Diagn, 39, 178, 10.1002/pd.5412 Benchetrit, 2000, Breathing pattern in humans: diversity and individuality, Respir Physiol, 122, 123, 10.1016/S0034-5687(00)00154-7 Berrian, 2017, Adaptive synchrosqueezing based on a quilted short-time fourier transform, vol. 10394, 1039420 Bickel, 2008, Event weighted tests for detecting periodicity in photon arrival times, Astrophys J, 685, 384, 10.1086/590399 Bien, 2011, Comparisons of predictive performance of breathing pattern variability measured during t-piece, automatic tube compensation, and pressure support ventilation for weaning intensive care unit patients from mechanical ventilation, Crit Care Med, 39, 2253, 10.1097/CCM.0b013e31822279ed Brockwell, 2002 Bruna, 2015, Intermittent process analysis with scattering moments, Ann Stat, 43, 323, 10.1214/14-AOS1276 Chassande-Mottin, 2003, Time-frequency/time-scale reassignment, 233 Chassande-Mottin, 1997, Differential reassignment, IEEE Signal Process Lett, 4, 293, 10.1109/97.633772 Chassande-Mottin, 1998, On the statistics of spectrogram reassignment vectors, Multidimens Syst Signal Process, 9, 355, 10.1023/A:1008485706244 Chen, 2014, Non-parametric and adaptive modelling of dynamic periodicity and trend with heteroscedastic and dependent errors, J Roy Stat Soc Ser B Stat Methodol, 76, 651, 10.1111/rssb.12039 Chen, 2020 Chiu, 1989, Detecting periodic components in a white Gaussian time series, J Roy Stat Soc B, 51, 249 Chudáček, 2013, Scattering transform for intrapartum fetal heart rate variability fractal analysis: a case-control study, IEEE Trans Biomed Eng, 61, 1100, 10.1109/TBME.2013.2294324 Chui, 2016, Signal decomposition and analysis via extraction of frequencies, Appl Comput Harmon Anal, 40, 97, 10.1016/j.acha.2015.01.003 Chui, 2016, Data-driven atomic decomposition via frequency extraction of intrinsic mode functions, GEM-Int J Geomath, 7, 117, 10.1007/s13137-015-0079-3 Cicone, 2020, Study of boundary conditions in the iterative filtering method for the decomposition of nonstationary signals, J Comput Appl Math, 373, 112248, 10.1016/j.cam.2019.04.028 Cicone, 2019, Spectral and convergence analysis of the discrete alif method, Lin Algebra Appl, 580, 62, 10.1016/j.laa.2019.06.021 Cicone, 2020 Cicone, 2016, Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis, Appl Comput Harmon Anal, 41, 384, 10.1016/j.acha.2016.03.001 Cicone, 2017, How nonlinear-type time-frequency analysis can help in sensing instantaneous heart rate and instantaneous respiratory rate from photoplethysmography in a reliable way, Front Physiol, 8, 701, 10.3389/fphys.2017.00701 Coifman, 2006, Diffusion maps, Appl Comput Harmon Anal, 21, 5, 10.1016/j.acha.2006.04.006 Coifman, 2019, Phase unwinding, or invariant subspace decompositions of hardy spaces, J Fourier Anal Appl, 25, 684, 10.1007/s00041-018-9623-5 Coifman, 2017, Nonlinear phase unwinding of functions, J Fourier Anal Appl, 23, 778, 10.1007/s00041-016-9489-3 Coifman, 2017, Carrier frequencies, holomorphy, and unwinding, SIAM J Math Anal, 49, 4838, 10.1137/16M1081087 Dahlhaus, 1997, Fitting time series models to nonstationary processes, Ann Stat, 25, 1, 10.1214/aos/1034276620 Daubechies, 1988, Time-frequency localization operators: a geometric phase space approach, IEEE Trans Inf Theor, 34, 605, 10.1109/18.9761 Daubechies, 2011, Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool, Appl Comput Harmon Anal, 30, 243, 10.1016/j.acha.2010.08.002 Daubechies, 1996 Daubechies, 2016, ConceFT: concentration of frequency and time via a multitapered synchrosqueezed transform, Philos Trans Roy Soc A-Math Phys Eng Sci, 374, 20150193, 10.1098/rsta.2015.0193 De Livera, 2011, Forecasting time series with complex seasonal patterns using exponential smoothing, J Am Stat Assoc, 106, 1513, 10.1198/jasa.2011.tm09771 Dehkordi, 2018, Extracting instantaneous respiratory rate from multiple photoplethysmogram respiratory-induced variations, Front Physiol, 9, 948, 10.3389/fphys.2018.00948 Delprat, 1992, Asymptotic wavelet and gabor analysis: extraction of instantaneous frequencies, IEEE Trans Inf Theor, 38, 644, 10.1109/18.119728 Eisner, 2014, Discrete orthogonality of the malmquist takenaka system of the upper half plane and rational interpolation, J Fourier Anal Appl, 20, 1, 10.1007/s00041-013-9285-2 Feichtinger, 2013, Hyperbolic wavelets and multiresolution in the hardy space of the upper half plane, 193 Fisher, 1929, Tests of significance in harmonic analysis, Proc Roy Stat Soc Ser, 125, 54 Flandrin, 1999, Time-frequency/time-scale analysis, vol. 10 Flandrin, 2013, A note on reassigned gabor spectrograms of hermite functions, J Fourier Anal Appl, 19, 285, 10.1007/s00041-012-9253-2 Flandrin, 2015, Time-frequency filtering based on spectrogram zeros, IEEE Signal Process Lett, 22, 2137, 10.1109/LSP.2015.2463093 Flandrin, 2004, Empirical mode decomposition as a filter bank, IEEE Signal Process Lett, 11, 112, 10.1109/LSP.2003.821662 Gabor, 1946, Theory of communication. part 1: the analysis of information, J Inst Elec Eng Part III, 93, 429 Galiano, 2014, On a non-local spectrogram for denoising one-dimensional signals, Appl Math Comput, 244, 1 Garnett, 1981, Bounded analytic functions, vol. 96 Genton, 2007, Statistical inference for evolving periodic functions, J Roy Stat Soc B, 69, 643, 10.1111/j.1467-9868.2007.00604.x Ghanbari, 2017, K-complex detection based on synchrosqueezing transform, AUT J Electr Eng, 49, 214 Hall, 2006, Using the periodogram to estimate period in nonparametric regression, Biometrika, 93, 411, 10.1093/biomet/93.2.411 Hannan, 1961, Testing for a jump in the spectral function, J Roy Stat Soc B, 23, 394 Hemakom, 2017, Quantifying team cooperation through intrinsic multi-scale measures: respiratory and cardiac synchronization in choir singers and surgical teams, Roy Soc Open Sci, 4, 170853, 10.1098/rsos.170853 Herry, 2017, Heart beat classification from single-lead ECG using the synchrosqueezing transform, Physiol Meas, 38, 171, 10.1088/1361-6579/aa5070 Hou, 2013, Data-driven time-frequency analysis, Appl Comput Harmon Anal, 35, 284, 10.1016/j.acha.2012.10.001 Hou, 2014, Convergence of a data-driven time-frequency analysis method, Appl Comput Harmon Anal, 37, 235, 10.1016/j.acha.2013.12.004 Hou, 2011, Adaptive data analysis via sparse time-frequency representation, Adv Adapt Data Anal, 3, 1, 10.1142/S1793536911000647 Hou, 2016, Extracting a shape function for a signal with intra-wave frequency modulation, Phil Trans R Soc A, 374, 20150194, 10.1098/rsta.2015.0194 Hou, 2013, Sparse time frequency representations and dynamical systems, Commun Math Sci Huang, 2009, Convergence of a convolution-filtering-based algorithm for empirical mode decomposition, Adv Adapt Data Anal, 1, 561, 10.1142/S1793536909000205 Huang, 1998, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc R Soc London Ser A-Math Phys Eng Sci, 454, 903, 10.1098/rspa.1998.0193 Huang, 2009, On instantaneous frequency, Adv Adapt Data Anal, 1, 177, 10.1142/S1793536909000096 Huang, 2020 Huang, 2015, Synchrosqueezing s-transform and its application in seismic spectral decomposition, IEEE Trans Geosci Rem Sens, 1 Iatsenko, 2013, Evolution of cardiorespiratory interactions with age, Phil Trans Math Phys Eng Sci, 371, 20110622 Iatsenko, 2015, Nonlinear mode decomposition: a noise-robust, adaptive decomposition method, Phys Rev, 92 Jaillet, 2007, Time-frequency Jigsaw puzzle: adaptive multiwindow and multilayered gabor expansions, Int J Wavelets, Multiresolut Inf Process, 5, 293, 10.1142/S0219691307001768 Jarchi, 2018, Validation of instantaneous respiratory rate using reflectance ppg from different body positions, Sensors, 18, 3705, 10.3390/s18113705 Kabir, 2015, Enhanced automated sleep spindle detection algorithm based on synchrosqueezing, Med Biol Eng Comput, 53, 635, 10.1007/s11517-015-1265-z Kabir, 2014, Development of analytical approach for an automated analysis of continuous long-term single lead ecg for diagnosis of paroxysmal atrioventricular block, 913 Kodera, 1976, A new method for the numerical analysis of non-stationary signals, Phys Earth Planet In, 12, 142, 10.1016/0031-9201(76)90044-3 Kodera, 1978, Analysis of time-varying signals with small bt values, IEEE Trans Acoust Speech Signal Process, 26, 64, 10.1109/TASSP.1978.1163047 Kowalski, 2018, Convex optimization approach to signals with fast varying instantaneous frequency, Appl Comput Harmon Anal, 44, 89, 10.1016/j.acha.2016.03.008 Li, 2017, Efficient fetal-maternal ECG signal separation from two channel maternal abdominal ECG via diffusion-based channel selection, Front Physiol, 8, 277, 10.3389/fphys.2017.00277 Lin, 2018, Wave-shape function analysis–when cepstrum meets time-frequency analysis, J Fourier Anal Appl, 24, 451, 10.1007/s00041-017-9523-0 Lin, 2009, Iterative filtering as an alternative for empirical mode decomposition, Adv Adapt Data Anal, 1, 543, 10.1142/S179353690900028X Lin, 2015 Lin, 2019, Unexpected sawtooth artifact in beat-to-beat pulse transit time measured from patient monitor data, PloS One, 14, 10.1371/journal.pone.0221319 Lin, 2019 Lin, 2016, ConceFT for time-varying heart rate variability analysis as a measure of noxious stimulation during general anesthesia, IEEE Trans Biomed Eng, 64, 145, 10.1109/TBME.2016.2549048 Lin, 2014, Time-varying spectral analysis revealing differential effects of sevoflurane anaesthesia: non-rhythmic-to-rhythmic ratio, Acta Anaesthesiol Scand, 58, 157, 10.1111/aas.12251 Lin, 2011, Analyzing autonomic activity in electrocardiography about general anesthesia by spectrogram with multitaper time-frequency reassignment, vol. 2, 630 Lin, 2017, Sleep apnea detection based on thoracic and abdominal movement signals of wearable piezo-electric bands, IEEE J Biomed Health, 21, 1533, 10.1109/JBHI.2016.2636778 Liu, 2020, Diffuse to fuse eeg spectra–intrinsic geometry of sleep dynamics for classification, Biomed Signal Process Contr, 55, 101576, 10.1016/j.bspc.2019.101576 Liu, 2019, Recent development of respiratory rate measurement technologies, Physiol Meas, 40, 10.1088/1361-6579/ab299e Lo, 2020, Hypoventilation patterns during bronchoscopic sedation and their clinical relevance based on capnographic and respiratory impedance analysis, J Clin Monit Comput, 34, 171, 10.1007/s10877-019-00269-0 Lobmaier, 2019, Fetal heart rate variability responsiveness to maternal stress, non-invasively detected from maternal transabdominal ECG, Arch Gynecol Obstet, 1 Lu, 2008, Can photoplethysmography variability serve as an alternative approach to obtain heart rate variability information?, J Clin Monit Comput, 22, 23, 10.1007/s10877-007-9103-y Lu, 2019, Recycling cardiogenic artifacts in impedance pneumography, Biomed Signal Process Contr, 51, 162, 10.1016/j.bspc.2019.02.027 Lukianchikov, 2019, Iterative variable-blaschke factorization, Complex Anal Operat Theory, 13, 3795, 10.1007/s11785-019-00931-0 Maes, 1995, The synchrosqueezed representation yields a new reading of the wavelet transform Malik, 2017, Single-lead f-wave extraction using diffusion geometry, Physiol Meas, 38, 1310, 10.1088/1361-6579/aa707c Mallat, 2012, Group invariant scattering, Pure Appl Math, 10, 1331, 10.1002/cpa.21413 Mert, 2018, Emotion recognition based on time–frequency distribution of EEG signals using multivariate synchrosqueezing transform, Digit Signal Process, 81, 106, 10.1016/j.dsp.2018.07.003 Meynard, 2018, Spectral analysis for nonstationary audio, IEEE/ACM Trans Audio, Speech, Lang Process, 26, 2371, 10.1109/TASLP.2018.2862353 Nahon, 2000 Nason, 2000, Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum, J Roy Stat Soc B, 62, 271, 10.1111/1467-9868.00231 Oberlin, 2015, Second-order synchrosqueezing transform or invertible reassignment? towards ideal time-frequency representations, IEEE Trans Signal Process, 63, 1335, 10.1109/TSP.2015.2391077 Oh, 2004, Period analysis of variable stars by robust smoothing, J Roy Stat Soc B, 53, 15 Oppenheim, 2004, From frequency to quefrency: a history of the cepstrum, IEEE Signal Process Mag, 21, 95, 10.1109/MSP.2004.1328092 Orini, 2012, Characterization of dynamic interactions between cardiovascular signals by time-frequency coherence, IEEE Trans Biomed Eng, 59, 663, 10.1109/TBME.2011.2171959 Ozel, 2019, Synchrosqueezing transform based feature extraction from eeg signals for emotional state prediction, Biomed Signal Process Contr, 52, 152, 10.1016/j.bspc.2019.04.023 Pahlevan, 2014, Intrinsic frequency for a systems approach to haemodynamic waveform analysis with clinical applications, J Roy Soc Interface Roy Soc, 11, 20140617, 10.1098/rsif.2014.0617 Pap, 2006, The voice transform on the blaschke group i, Pure Math. Appl, 17, 387 Park, 2011, Analysis of long period variable starts with nonparametric tests for trend detection, J Am Stat Assoc, 106, 832, 10.1198/jasa.2011.ap08689 Petrasek, 2015, Intrinsic frequency and the single wave biopsy: implications for insulin resistance, J Diabet Sci Technol, 9, 1246 Pham, 2017, High-order synchrosqueezing transform for multicomponent signals analysis–––with an application to gravitational-wave signal, IEEE Trans Signal Process, 65, 3168, 10.1109/TSP.2017.2686355 Picinbono, 1997, On instantaneous amplitude and phase of signals, IEEE Trans Signal Process, 45, 552, 10.1109/78.558469 Poupard, 2008, Use of thoracic impedance sensors to screen for sleep-disordered breathing in patients with cardiovascular disease, Physiol Meas, 29, 255, 10.1088/0967-3334/29/2/008 Priestley, 1996, Wavelets and time-dependent spectral analysis, J Time Anal, 17, 85 Priestley, 1965, Evolutionary spectra and non-stationary processes, J Roy Stat Soc B, 27, 204 Qian, 2010, Intrinsic mono-component decomposition of functions: an advance of fourier theory, Math Methods Appl Sci, 33, 880, 10.1002/mma.1214 Qian, 2011, Algorithm of adaptive fourier decomposition, IEEE Trans Signal Process, 59, 5899, 10.1109/TSP.2011.2168520 Ricaud, 2014, A survey of uncertainty principles and some signal processing applications, Adv Comput Math, 40, 629, 10.1007/s10444-013-9323-2 Rilling, 2007, One or two frequencies? the empirical mode decomposition answers, IEEE Trans Signal Process, 56, 85, 10.1109/TSP.2007.906771 Rutkowski, 2014, Multichannel EEG sonification with ambisonics spatial sound environment, 1 Sameni, 2010, A review of fetal ECG signal processing; issues and promising directions, Open Pacing Electrophysiol Ther J, 3, 4 Seppä, 2011, A method for suppressing cardiogenic oscillations in impedance pneumography, Physiol Meas, 32, 337, 10.1088/0967-3334/32/3/005 Sethares, 1999, Periodicity transforms, IEEE Trans Signal Process, 47, 2953, 10.1109/78.796431 Sharma, 2017, Automatic sleep stages classification based on iterative filtering of electroencephalogram signals, Neural Comput Appl, 28, 2959, 10.1007/s00521-017-2919-6 Sharma, 2016, QRS complex detection in ecg signals using the synchrosqueezed wavelet transform, IETE J Res, 62, 885, 10.1080/03772063.2016.1221744 Shelley, 2007, Photoplethysmography: beyond the calculation of arterial oxygen saturation and heart rate, Anesth Analg, 105, S31, 10.1213/01.ane.0000269512.82836.c9 Sheu, 2017, Entropy-based time-varying window width selection for nonlinear-type time–frequency analysis, Int J Data Sci Analytics, 3, 231, 10.1007/s41060-017-0053-2 Slapničar, 2019, Blood pressure estimation from photoplethysmogram using a spectro-temporal deep neural network, Sensors, 19, 3420, 10.3390/s19153420 Sourisseau, 2019 Steinerberger, 2019, On zeroes of random polynomials and an application to unwinding, Int Math Res Not, 10.1093/imrn/rnz096 Su, 2017, Extract fetal ECG from single-lead abdominal ECG by de-shape short time fourier transform and nonlocal median, Front Appl Math Stat, 2, 2 Su, 2019, Recovery of the fetal electrocardiogram for morphological analysis from two trans-abdominal channels via optimal shrinkage, Physiol Meas, 40, 115005, 10.1088/1361-6579/ab4b13 Takenaka, 1925, On the orthogonal functions and a new formula of interpolation, vol. 2, 129 Tan, 2018, A novel blaschke unwinding adaptive-fourier-decomposition-based signal compression algorithm with application on ecg signals, IEEE J Biomed Health Inf, 23, 672, 10.1109/JBHI.2018.2817192 1996, Heart rate variability : standards of measurement, physiological interpretation, and clinical use, Circulation, 93, 1043, 10.1161/01.CIR.93.5.1043 Tavallali, 2015, On the convergence and accuracy of the cardiovascular intrinsic frequency method, Roy Soc Open Sci, 2, 150475, 10.1098/rsos.150475 Tavallali, 2014, Extraction of intrawave signals using the sparse time-frequency representation method, Multiscale Model Simul, 12, 1458, 10.1137/140957767 Tenneti, 2015, Nested periodic matrices and dictionaries: new signal representations for period estimation, IEEE Trans Signal Process, 63, 3736, 10.1109/TSP.2015.2434318 Thakur, 2013, The synchrosqueezing algorithm for time-varying spectral analysis: robustness properties and new paleoclimate applications, Signal Process, 93, 1079, 10.1016/j.sigpro.2012.11.029 Thomson, 1982, Spectrum estimation and harmonic analysis, Proc IEEE, 70, 1055, 10.1109/PROC.1982.12433 van der Pol, 1946, The fundamental principles of frequency modulation, J Inst Electr Eng - Part III: Radio Commun Eng, 93, 153 Vatchev, 2008, Decomposition of functions into pairs of intrinsic mode functions, Proc Math Phys Eng Sci, 464, 2265 Wang, 2020, Novel imaging revealing inner dynamics for cardiovascular waveform analysis via unsupervised manifold learning, Anesth Analg, 130, 1244, 10.1213/ANE.0000000000004738 Wang, 2012, Iterative filtering decomposition based on local spectral evolution kernel, J Sci Comput, 50, 629, 10.1007/s10915-011-9496-0 Wang, 2012, Mode decomposition evolution equations, J Sci Comput, 50, 495, 10.1007/s10915-011-9509-z Wardhan, 2009, Peripheral venous pressure waveform, Curr Opin Anesthesiol, 22, 814, 10.1097/ACO.0b013e328332a343 Wu, 2011 Wu, 2013, Instantaneous frequency and wave shape functions (I), Appl Comput Harmon Anal, 35, 181, 10.1016/j.acha.2012.08.008 Wu, 2020, A new approach to complicated and noisy physiological waveforms analysis: peripheral venous pressure waveform as an example, J Clin Monit Comput, 1 Wu, 2014, Using synchrosqueezing transform to discover breathing dynamics from ECG signals, Appl Comput Harmon Anal, 36, 354, 10.1016/j.acha.2013.07.003 Wu, 2013, Evaluating physiological dynamics via synchrosqueezing: prediction of ventilator weaning, IEEE Trans Biomed Eng, 61, 736, 10.1109/TBME.2013.2288497 Wu, 2016, Optimizing estimates of instantaneous heart rate from pulse wave signals with the synchrosqueezing transform, Methods Inf Med, 55, 463, 10.3414/ME16-01-0026 Wu, 2018, Analyzing transient-evoked otoacoustic emissions by concentration of frequency and time, J Acoust Soc Am, 144, 448, 10.1121/1.5047749 Wu, 2018, A new approach for analysis of heart rate variability and qt variability in long-term ECG recording, Biomed Eng Online, 17, 54, 10.1186/s12938-018-0490-8 Wu, 2015, Assess sleep stage by modern signal processing techniques, IEEE Trans Biomed Eng, 62, 1159, 10.1109/TBME.2014.2375292 Wu, 2009, Ensemble empirical mode decomposition: a noise-assisted data analysis method, Adv Adapt Data Anal, 1, 1, 10.1142/S1793536909000047 Xiao, 2007, Multitaper time-frequency reassignment for nonstationary spectrum estimation and chirp enhancement, IEEE Trans Signal Process, 55, 2851, 10.1109/TSP.2007.893961 Xu, 2018, Recursive diffeomorphism-based regression for shape functions, SIAM J Math Anal, 50, 5, 10.1137/16M1097535 Yang, 2015, Synchrosqueezed wave packet transforms and diffeomorphism based spectral analysis for 1D general mode decompositions, Appl Comput Harmon Anal, 39, 33, 10.1016/j.acha.2014.08.004 Yang, 2018, Statistical analysis of synchrosqueezed transforms, Appl Comput Harmon Anal, 45, 526, 10.1016/j.acha.2017.01.001 Yavari, 2016, Synchrosqueezing an effective method for analyzing Doppler radar physiological signals, 263 Zhao, 2017, Noncontact physiological dynamics detection using low-power digital-if Doppler radar, IEEE Transactions on Instrumentation and Measurement, 66, 1780, 10.1109/TIM.2017.2669699 Zhou, 2013, Heteroscedasticity and autocorrelation robust structural change detection, J Am Stat Assoc, 108, 726, 10.1080/01621459.2013.787184 Zhou, 2014, Inference of weighted v-statistics for nonstationary time series and its applications, Ann Stat, 42, 87, 10.1214/13-AOS1184 Zhu, 2019, Multiple squeezes from adaptive chirplet transform, Signal Process, 163, 26, 10.1016/j.sigpro.2019.05.008