Richard C. Watt

Information processing in microtubules.

Abstract Biological information processing, storage, and transduction are theorized to occur by “computer-like” transfer and resonance among subunits of polymerized cytoskeletal proteins: microtubules. Biological information functions (ciliary and flagellar control, axoplasmic transport, conscious awareness) could be explained by comparing microtubule structure and activities to Boolean switching matrices, parallel computers, and such technologies as transistor circuits, magnetic bubble memory, charge transfer devices, surface acoustic wave resonators, and/or holography.

Phase space electroencephalography (EEG): A new mode of intraoperative EEG analysis

Intraoperative monitoring of electroencephalography (EEG) data can help assess brain integrity and/or depth of anesthesia. We demonstrate a computer generated technique which provides a visually robust display of EEG data plotted as ‘phase space trajectories’ and a mathematically derived parameter (‘dimensionality’) which may correlate with depth of anesthesia. Application of nonlinear mathematical analysis, used to describe complex dynamical systems, can characterize ‘phase space’ EEG patterns by identifying attractors (geometrical patterns in phase space corresponding to specific ordered EEG data subjects) and by quantifying the degree of order and chaos (calculation of dimensionality). Dimensionality calculations describe the degree of complexity in a signal and may generate a clinically useful univariate EEG descriptor of anesthetic depth.In this paper we describe and demonstrate phase space trajectories generated for sine waves, mixtures of sine waves, and white noise (random chaotic events). We also present EEG phase space trajectories and dimensionality calculations from a patient undergoing surgery and general anesthesia in 3 recognizable states: awake, anesthetized, and burst suppression. Phase space trajectories of the three states are visually distinguishable, and dimensionality calculations indicate that EEG progresses from ‘chaos’ (awake) to progressively more ‘ordered’ attractors (anesthetized and burst suppression).

Cellular automata in cytoskeletal lattices

Cellular automata (CA) activities could mediate biological regulation and information processing via nonlinear electrodynamic effects in cytoskeletal lattice arrays. Frohlich coherent oscillations and other nonlinear mechanisms may effect discrete 10−10 to 10−11 s interval events which result in dynamic patterns in biolattices such as cylindrical protein polymers: microtubules (MT). Structural geometry and electrostatic forces of MT subunit dipole oscillations suggest neighbor rules among the hexagonally packed protein subunits. Computer simulations using these suggested rules and MT structural geometry demonstrate CA activities including dynamical and stable self-organizing patterns, oscillators, and traveling “gliders”. CA activities in MT and other cytoskeletal lattices may have important biological regulatory functions.

Alarms and anesthesia: challenges in design of intelligent systems for patient monitoring

The limitations of current anesthesia monitor alarm technology are first discussed. The challenges of applying technology to improve patient monitoring are then considered, with attention given to integrating stand-alone devices and functions that are part of the anesthesia workstation and to the standardization of anesthetic practices. Design strategies for intelligent alarms are addressed. The process of generating alarms is considered as compromising three distinct tasks: sensing, signal processing, and annunciation.<<ETX>>

Nonlinear Electrodynamics in Cytoskeletal Protein Lattices

Nonlinear electrodynamic theories predict dynamic organization of biomolecular activities at all cellular levels: DNA, membranes, extracelluular glycoproteins, and the interconnecting cytoskeletal lattice. 1–5 Cytoskeletal lattice proteins including microtubules are particularly involved in dynamic regulation of intracellular movements and activities.6, 7This paper considers possibilities and implications of biological information processing due to coupling of Davydov solitons, Frohlich coherent oscillations and other nonlinear electrodynamic phenomena to conformational states of the grid-like polymer subunits of cytoskeletal microtubules.

Phase Space Analysis of Human EEG during General Anesthesia

Electroencephalography (EEG) is an important derivative of brain function and can be a useful intraoperative monitor of brain integrity and/or anesthetic depth during surgical procedures. Anesthetic depth is a convenient paradigm to observe (inversely) the level of wakefulness and consciousness. Intraoperative EEG is commonly displayed and analyzed in a “power spectrum” frequency distribution mode where, in general, power at higher frequency decreases as anesthetic depth increases. Accordingly, univariate descriptors such as mean frequency and spectral edge (the frequency below which 95% of the EEG power occurs) are purported to be clinical indices of anesthetic depth. However, these univariate descriptors are clinically useful only with unimodal frequency distributions (virtually nonexistent in EEG) and change inconsistently among various anesthetics. Thus EEG monitoring of anesthetic depth may be aided by geometric and qualitative techniques that have evolved from mathematical theories on deterministic chaos to describe dynamic behavior of complex nonlinear systems. Prior to geometric nonlinear dynamic theory, such complex systems seemed analytically intractable.’ Characterization of a dynamic system on the order and chaos spectrum involves considering an n dimensional phase space into which the variable set [ x ( t ) , x ( t + T) ] can be mapped. In the case of EEG, x is the voltage amplitude, t is time, and T is a fixed-time interval (phase lag). An instantaneous state of such a system becomes a point in the phase space, whereas a sequence of such states followed in time defines a curve: the phase-space trajectory. As time grows, a system whose dynamics are reducible to a set of deterministic laws reaches a permanent state indicated by the convergence of families of phase-space trajectories towards an attractor (a subset of the phase space). Nonlinear dynamic theory and phase-space analysis of EEG can attempt to answer the following questions: (1) Do phase space trajectories demonstrate easily recognizable differences among EEG states? (Can an attractor be identified for a given EEG pattern?) (2) If an attractor exists, what is its dimensionality ( d ) and does d (or some other index) usefully correlate with anesthetic depth?

Integrated monitoring can detect critical events and improve alarm accuracy.

A computer-based, integrated monitor system was designed and utilized to collect and interactively manage physiologic data (13 variables and 3 waveforms) from six routinely used operating room monitors. Various approaches were developed to reduce false alarms, classify waveforms, and recognize events. False alarms: false alarms in ECG heart rate detection were reduced from 37.3% to 2.6% (p = 0.005) of total alarms using multi-variable analysis and rate-of-change limits. Waveform classification: using artificial neural networks (ANN), CO2 waveforms were classified into (a) spontaneous, (b) mechanical, and (c) mechanical/with spontaneous breathing attempts. The system properly classified 47 of 71 spontaneous, 65 of 67 mechanical, and 37 of 44 mechanical breaths/with spontaneous breathing attempts. Another ANN was used for detection of elevated and depressed ST segments in the ECG signal. All ST segment elevations and depressions of 0.1 mV were correctly identified. Event recognition: an algorithm developed to identify endotracheal intubation correctly recognized 13 of 17 intubations. This resulted in a 42% reduction in low end-tidal-CO2 false alarms.

Detection of false alarms using an integrated anesthesia monitor

A computer-based system was used to collect and analyze data from five routinely used operating room monitors in order to identify meaningful alarm conditions and reduce the number of false positive alarms. Several methods for reduction of false alarms were implemented (patient-dependent limits, multivariable analysis and rate of change). The system was tested using operating room data from 21 surgical cases. The integrated monitor was able to correctly identify 10 of 11 intubations. Recognition of intubation was used in detection of false end-tidal-CO/sub 2/ alarms, reducing them by 70%. False heart-rate alarms were reduced by 68% using multivariable analysis and rate-of-change limits.<<ETX>>

Artificial neural networks used with bispectral analysis for intra-operative EEG monitoring

The brain is the target organ of anesthesia yet the electroencephalogram (EEG) is not routinely monitored during anesthetic procedures. This is partly due to the difficulty of interpreting complex changes in the EEG waveform with respect to anesthetic conditions. Most attempts at developing EEG derived variables and display techniques have been based on spectral analysis. Bispectral analysis is a signal processing technique capable of detecting phase-coupling within a signal (which is lost using conventional power spectral analysis). Artificial neural networks (ANN) which excel at pattern classification were used in this study to interpret results of bispectral analysis. Six human subjects were studied at three anesthetic levels (light, nominal, and deep anesthesia). ANNs offer an efficient approach for extracting and using the additional signal information provided by bispectral analysis.

Human EEG dimensionality and depth of anesthesia

Although the brain is the target organ of anesthesia, the electroencephalogram (EEG) is not routinely monitored during anesthetic procedures. This is due primarily to the difficulty of interpreting changes in the complex EEG waveform with respect to anesthetic conditions. Most attempts at developing EEG derived variables have been based on spectral analysis. In this study the EEG is examined as a non-linear dynamic system that may exhibit chaotic behavior. Using this approach the EEG signal can be characterized by its fractal dimension based on a phase space geometric reconstruction of the EEG time series. Eight human subjects were studied at three anesthetic levels (light, nominal, and deep anesthesia). The dimensionality of EEG samples from these subjects is shown to decrease with increasing anesthetic depth. This property may prove important in the classification of brain activity and may have clinical utility as a diagnostic tool.<<ETX>>