Abstract
This chapter introduces feature extraction techniques that extract relevant features of interest from compressively sampled biosignals directly from compressed data circumventing the computationally complex reconstruction process. State-of-the-art compressed domain feature extraction process for that leverages on Johnson–Lindenstrauss lemma is presented. It is also shown that such approach is inadequate in the context of feature extraction for compressively sampled photoplethysmogram (PPG) signals. Lomb-Scargle periodogram (LSP) is introduced as an alternate approach for extracting the spectral features from compressively sampled PPG signals, which is then used to estimate the average heart rate (HR) and heart rate variability (HRV). The efficacy of the proposed approach is demonstrated through simulations which indicate that the average HR estimated is accurate within ±5 beats per minute (bpm) while HRV exhibits a correlation coefficient of > 0.90 at 30 × compression ratio (CR) compared to time domain HR and HRV estimation performed on Nyquist sampled PPG signals.
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- 1.
This is generally the case as the analysis and parameter extraction is performed on the reconstructed signal.
- 2.
A particular observation obscured by clouds on a given day, for example, can lead to irregularly spaced observations.
References
N. Verma, A. Shoeb, J. Bohorquez, J. Dawson, J. Guttag, A.P. Chandrakasan, A micro-power EEG acquisition SoC with integrated feature extraction processor for a chronic seizure detection system. IEEE J. Solid State Circuits 45(4), 804–816 (2010)
J. Allen, Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 28(3), R1 (2007)
G.B. Moody, R.G. Mark, MIT-BIH arrhythmia database (1992) [Online]. Available: https://www.physionet.org/physiobank/database/mitdb/
F. Ren, D. Marković, 18.5 A configurable 12-to-237kS/s 12.8 mW sparse-approximation engine for mobile ExG data aggregation, in 2015 IEEE International Solid-State Circuits Conference-(ISSCC) Digest of Technical Papers (IEEE, Piscataway, 2015), pp. 1–3
P.V. Rajesh, J.M. Valero-Sarmiento, L. Yan, A. Bozkurt, C. Van Hoof, N. Van Helleputte, R.F. Yazicioglu, M. Verhelst, A 172μW compressive sampling photoplethysmographic readout with embedded direct heart-rate and variability extraction from compressively sampled data, in 2016 IEEE International Solid-State Circuits Conference (ISSCC) (IEEE, Piscataway, 2016), pp. 386–387
T. Moy, L. Huang, W. Rieutort-Louis, C. Wu, P. Cuff, S. Wagner, J.C. Sturm, N. Verma, An EEG acquisition and biomarker-extraction system using low-noise-amplifier and compressive-sensing circuits based on flexible, thin-film electronics. IEEE J. Solid State Circuits 52(1), 309–321 (2017)
M. Shoaib, Design of energy-efficient sensing systems with direct computations on compressively-sensed data, Ph.D. dissertation, Princeton University, 2013
A. Csavoy, G. Molnar, T. Denison, Creating support circuits for the nervous system: considerations for brain-machine interfacing, in 2009 Symposium on VLSI Circuits (Jun 2009)
J. Yoo, C. Turnes, E.B. Nakamura, C.K. Le, S. Becker, E.A. Sovero, M.B. Wakin, M.C. Grant, J. Romberg, A. Emami-Neyestanak, E. Candes, A compressed sensing parameter extraction platform for radar pulse signal acquisition. IEEE J. Emerging Sel. Top. Circuits Syst. 2(3), 626–638 (2012)
M. Shoaib, N.K. Jha, N. Verma, A compressed-domain processor for seizure detection to simultaneously reduce computation and communication energy, in Proceedings of the IEEE 2012 Custom Integrated Circuits Conference (Sep 2012), pp. 1–4
M. Shoaran, C. Pollo, K. Schindler, A. Schmid, A fully integrated IC with 0.85-μW/channel consumption for epileptic iEEG detection. IEEE Trans. Circuits Syst. Express Briefs 62(2), 114–118 (2015)
A. Jafari, A. Page, C. Sagedy, E. Smith, T. Mohsenin, A low power seizure detection processor based on direct use of compressively-sensed data and employing a deterministic random matrix, in 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS) (Oct 2015), pp. 1–4
V.R. Pamula, M. Verhelst, C. Van Hoof, R.F. Yazicioglu, A novel feature extraction algorithm for on the sensor node processing of compressive sampled photoplethysmography signals, in SENSORS, 2015 IEEE (IEEE, Piscataway, 2015), pp. 1–4
V.R. Pamula, System and method for heart rate detection, Jun. 2 2016. US Patent App. 14/938,102
S. Dasgupta, A. Gupta, An elementary proof of a theorem of Johnson and Lindenstrauss. Random Struct. Algoritm. 22(1), 60–65 (2003)
J.H. Horne, S.L. Baliunas, A prescription for period analysis of unevenly sampled time series. Astrophys. J. 302, 757–763 (1986)
N.R. Lomb, Least-squares frequency analysis of unequally spaced data. Astrophys. Space Sci. 39(2), 447–462 (1976)
P. Laguna, G. Moody, R. Mark, Power spectral density of unevenly sampled heart rate data, in Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society (1995), pp. 157–158
P. Laguna, G. Moody, R. Mark, Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals. IEEE Trans. Biomed. Eng. 45(6), 698–715 (1998)
W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Numerical Recipes in C, vol. 2 (Cambridge University Press, Cambridge, 2002)
W. Zong, T. Heldt, G. Moody, R. Mark, An open-source algorithm to detect onset of arterial blood pressure pulses, in Computers in Cardiology, 2003 (IEEE, Piscataway, 2003), pp. 259–262
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Pamula, V.R., Van Hoof, C., Verhelst, M. (2019). Compressed Domain Feature Extraction. In: Analog-and-Algorithm-Assisted Ultra-low Power Biosignal Acquisition Systems. Analog Circuits and Signal Processing. Springer, Cham. https://doi.org/10.1007/978-3-030-05870-8_4
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