Guy A. Dumont

System identification and control using genetic algorithms

It is shown how genetic algorithms can be applied for system identification of both continuous and discrete time systems. It is shown that they are effective in both domains and are able to directly identify physical parameters or poles and zeros. This can be useful because changing one physical parameter might affect every parameter of a system transfer function. The estimates of poles and zeros are then used to design a discrete time pole placement adaptive controller. Simulations for minimum and nonminimum phase systems and a system with unmodeled dynamics are presented. >

The multimodulus blind equalization and its generalized algorithms

This paper presents a new blind equalization algorithm called multimodulus algorithm (MMA). This algorithm combines the benefits of the well-known reduced constellation algorithm (RCA) and constant modulus algorithm (CMA). In addition, MMA provides more flexibility than RCA and CMA, and is better suited to take advantage of the symbol statistics of certain types of signal constellations, such as nonsquare constellations, very dense constellations, and some wrong solutions.

Deterministic adaptive control based on Laguerre series representation

The behaviour of adaptive controllers in the presence of unmodelled dynamics, and the need for reduced a priori information have led us to abandon the usual ARMA transfer function representation for a representation by an orthonormal series. The appeal of our new approach is that it eliminates the need for assumptions about the plant order and the time delay. The plant is modelled by an orthonormal Laguerre network put in state-space form. A simple predictive control law is proposed. An explicit deterministic adaptive controller is then designed. Simulations show that it is easy to use, able to handle non-minimum phase plants, and more robust than the conventional model-based approach. Although we chose Laguerre functions, other orthonormal functions may be used. We have already tested some with success.

An optimum time scale for discrete Laguerre network

Any discrete-time stable transfer function can be expressed by a discrete-time Laguerre series with a chosen time scale. An optimum time scale such that an index is minimized is derived. This index ensures that the coefficients of higher-order Laguerre functions go toward zero quickly. The solution derived requires the knowledge of the impulse response of the discrete plant. Cases of first-order plants, second-order underdamped plants, and plants with multiunit delay are also discussed. >

Quantifying cortical activity during general anesthesia using wavelet analysis

This paper reports on a novel method for quantifying the cortical activity of a patient during general anesthesia as a surrogate measure of the patients level of consciousness. The proposed technique is based on the analysis of a single-channel (frontal) electroencephalogram (EEG) signal using stationary wavelet transform (SWT). The wavelet coefficients calculated from the EEG are pooled into a statistical representation, which is then compared to two well-defined states: the awake state with normal EEG activity, and the isoelectric state with maximal cortical depression. The resulting index, referred to as the wavelet-based anesthetic value for central nervous system monitoring (WAV/sub CNS/), quantifies the depth of consciousness between these two extremes. To validate the proposed technique, we present a clinical study which explores the advantages of the WAV/sub CNS/ in comparison with the BIS monitor (Aspect Medical Systems, MA), currently a reference in consciousness monitoring. Results show that the WAV/sub CNS/ and BIS are well correlated (r=0.969) during periods of steady-state despite fundamental algorithmic differences. However, in terms of dynamic behavior, the WAV/sub CNS/ offers faster tracking of transitory changes at induction and emergence, with an average lead of 15-30 s. Furthermore, and conversely to the BIS, the WAV/sub CNS/ regains its preinduction baseline value when patients are responding to verbal command after emergence from anesthesia. We conclude that the proposed analysis technique is an attractive alternative to BIS monitoring. In addition, we show that the WAV/sub CNS/ dynamics can be modeled as a linear time invariant transfer function. This index is, therefore, well suited for use as a feedback sensor in advisory systems, closed-loop control schemes, and for the identification of the pharmacodynamic models of anesthetic drugs.

Wavelet-based motion artifact removal for functional near-infrared spectroscopy

Functional near-infrared spectroscopy (fNIRS) is a powerful tool for monitoring brain functional activities. Due to its non-invasive and non-restraining nature, fNIRS has found broad applications in brain functional studies. However, for fNIRS to work well, it is important to reduce its sensitivity to motion artifacts. We propose a new wavelet-based method for removing motion artifacts from fNIRS signals. The method relies on differences between artifacts and fNIRS signal in terms of duration and amplitude and is specifically designed for spike artifacts. We assume a gaussian distribution for the wavelet coefficients corresponding to the underlying hemodynamic signal in detail levels and identify the artifact coefficients using this distribution. An input parameter controls the intensity of artifact attenuation in trade-off with the level of distortion introduced in the signal. The method only modifies wavelet coefficients in levels adaptively selected based on the degree of contamination with motion artifact. To demonstrate the feasibility of the method, we tested it on experimental fNIRS data collected from three infant subjects. Normalized mean-square error and artifact energy attenuation were used as criteria for performance evaluation. The results show 18.29 and 16.42 dB attenuation in motion artifacts energy for 700 and 830 nm wavelength signals in a total of 29 motion events with no more than -16.7 dB distortion in terms of normalized mean-square error in the artifact-free regions of the signal.

Automated Real-Time Epileptic Seizure Detection in Scalp EEG Recordings Using an Algorithm Based on Wavelet Packet Transform

A novel wavelet-based algorithm for real-time detection of epileptic seizures using scalp EEG is proposed. In a moving-window analysis, the EEG from each channel is decomposed by wavelet packet transform. Using wavelet coefficients from seizure and nonseizure references, a patient-specific measure is developed to quantify the separation between seizure and nonseizure states for the frequency range of 1-30 Hz. Utilizing this measure, a frequency band representing the maximum separation between the two states is determined and employed to develop a normalized index, called combined seizure index (CSI). CSI is derived for each epoch of every EEG channel based on both rhythmicity and relative energy of that epoch as well as consistency among different channels. Increasing significantly during ictal states, CSI is inspected using one-sided cumulative sum test to generate proper channel alarms. Analyzing alarms from all channels, a seizure alarm is finally generated. The algorithm was tested on scalp EEG recordings from 14 patients, totaling 75.8 h with 63 seizures. Results revealed a high sensitivity of 90.5% , a false detection rate of 0.51 h-1 and a median detection delay of 7 s. The algorithm could also lateralize the focus side for patients with temporal lobe epilepsy.

Continuous noninvasive blood pressure measurement by pulse transit time

Blood pressure measurement is performed either invasively by an intra arterial catheter or noninvasively by cuff sphygmomanometry. The invasive method is continuous and accurate but has increased risk; the cuff is safe but less reliable and infrequent. A reliable continuous noninvasive blood pressure measurement is highly desirable. While the possibility of using pulse transit time to monitor blood pressure has previously been investigated, most studies were limited to calculating the correlation of the pulse transit time and blood pressure under rather static conditions. The relationship between the pulse transit time and blood pressure is yet to be clearly identified. This paper focuses on the modeling between the two values and presents results on cases where dramatic variation in blood pressure of the patient was induced by drug administration or surgical stimulation.

On PID controller tuning using orthonormal series identification

Abstract The step response of a closed loop system is identified by means of a Laguerre expansion. This offers certain advantages over ARMA models, namely lack of bias in the estimates, structural flexibility and the ability to precompute the regressors. The Laguerre coefficients of the retuned system are calculated in terms of the tuning constants, and a quadratic performance index is optimized. Results from simulations and from industrial trials are given.

Multiparameter Respiratory Rate Estimation From the Photoplethysmogram

We present a novel method for estimating respiratory rate in real time from the photoplethysmogram (PPG) obtained from pulse oximetry. Three respiratory-induced variations (frequency, intensity, and amplitude) are extracted from the PPG using the Incremental-Merge Segmentation algorithm. Frequency content of each respiratory-induced variation is analyzed using fast Fourier transforms. The proposed Smart Fusion method then combines the results of the three respiratory-induced variations using a transparent mean calculation. It automatically eliminates estimations considered to be unreliable because of detected presence of artifacts in the PPG or disagreement between the different individual respiratory rate estimations. The algorithm has been tested on data obtained from 29 children and 13 adults. Results show that it is important to combine the three respiratory-induced variations for robust estimation of respiratory rate. The Smart Fusion showed trends of improved estimation (mean root mean square error 3.0 breaths/min) compared to the individual estimation methods (5.8, 6.2, and 3.9 breaths/min). The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas.