Application of Signal Advance Technology to Electrophysiology

Medical instrumentation used in diagnosis and treatment relies on the accurate detection and processing of various physiological events and signals. While signal detection technology has improved greatly in recent years, there remain inherent delays in signal detection/ processing. These delays may have significant negative clinical consequences during various pathophysiological events. Reducing or eliminating such delays would increase the ability to provide successful early intervention in certain disorders thereby increasing the efficacy of treatment. In recent years, a physical phenomenon referred to as Negative Group Delay (NGD), demonstrated in simple electronic circuits, has been shown to temporally advance the detection of analog waveforms. Specifically, the output is temporally advanced relative to the input, as the time delay through the circuit is negative. The circuit output precedes the complete detection of the input signal. This process is referred to as signal advance (SA) detection. An SA circuit model incorporating NGD was designed, developed and tested. It imparts a constant temporal signal advance over a pre-specified spectral range in which the output is almost identical to the input signal (i.e., it has minimal distortion). Certain human patho-electrophysiological events are good candidates for the application of temporally-advanced waveform detection. SA technology has potential in early arrhythmia and epileptic seizure detection and intervention. Demonstrating reliable and consistent temporally advanced detection of electrophysiological waveforms may enable intervention with a pathological event (much) earlier than previously possible. SA detection could also be used to improve the performance of neural computer interfaces, neurotherapy applications, radiation therapy and imaging. In this study, the performance of a single-stage SA circuit model on a variety of constructed input signals, and human ECGs is investigated. The data obtained is used to quantify and characterize the temporal advances and circuit gain, as well as distortions in the output waveforms relative to their inputs. This project combines elements of physics, engineering, signal processing, statistics and electrophysiology. Its success has important consequences for the development of novel interventional methodologies in cardiology and neurophysiology as well as significant potential in a broader range of both biomedical and non-biomedical areas of application.

An exploratory analysis of variance and volatility in epileptic electroencephalograms

The electroencephalogram (EEG) is a physiological time series that measures electrical activity at different locations in the brain, and plays an important role in epilepsy research. Exploring the variance and/or volatility may yield insights for seizure prediction, seizure detection and seizure propagation/dynamics.^ Maximal Overlap Discrete Wavelet Transforms (MODWTs) and ARMA-GARCH models were used to determine variance and volatility characteristics of 66 channels for different states of an epileptic EEG – sleep, awake, sleep-to-awake and seizure. The wavelet variances, changes in wavelet variances and volatility half-lives for the four states were compared for possible differences between seizure and non-seizure channels.^ The half-lives of two of the three seizure channels were found to be shorter than all of the non-seizure channels, based on 95% CIs for the pre-seizure and awake signals. No discernible patterns were found the wavelet variances of the change points for the different signals. ^

Incidence, etiology and risk factors of neonatal seizures Harris County, Texas, 1992--1994

This study was conducted to determine the incidence and etiology of neonatal seizures, and evaluate risk factors for this condition in Harris County, Texas, between 1992 and 1994. Potential cases were ascertained from four sources: discharge diagnoses at local hospitals, birth certificates, death certificates, and a clinical study of neonatal seizures conducted concurrent with this study at a large tertiary care center in Houston, Texas. The neonatal period was defined as the first 28 days of life for term infants, and up to 44 weeks gestation for preterm infants.^ There were 207 cases of neonatal seizures ascertained among 116,048 live births, yielding and incidence of 1.8 per 1000. Half of the seizures occurred by the third day of life, 70% within the first week, and 93% within the first 28 days of life. Among 48 preterm infants with seizures 15 had their initial seizure after the 28th day of life. About 25% of all seizures occurred after discharge from the hospital of birth.^ Idiopathic seizures occurred most frequently (0.5/1000 births), followed by seizures attributed to perinatal hypoxia/ischemia (0.4/1000 births), intracranial hemorrhage (0.2/1000 births), infection of the central nervous system (0.2/1000 births), and metabolic abnormalities (0.1/1000 births).^ Risk factors were evaluated based on birth certificate information, using univariate and multivariate analysis (logistic regression). Factors considered included birth weight, gender, ethnicity, place of birth, mother's age, method of delivery, parity, multiple birth and, among term infants, small birth weight for gestational age (SGA). Among preterm infants, very low birth weight (VLBW, $<$1500 grams) was the strongest risk factor, followed by birth in private/university hospitals with a Level III nursery compared with hospitals with a Level II nursery (RR = 2.9), and male sex (RR = 1.8). The effect of very low birth weight varied according to ethnicity. Compared to preterm infants weighing 2000-2999 grams, non-white VLBW infants were 12.0 times as likely to have seizures; whereas white VLBW infants were 2.5 times as likely. Among term infants, significant risk factors included SGA (RR = 1.8), birth in Level III nursery private/university hospitals versus hospitals with Level II nursery (RR = 2.0), and birth by cesarean section (RR = 2.2). ^