Probability of detection of clinical seizures using heart rate changes.

PURPOSE: Heart rate-based seizure detection is a viable complement or alternative to ECoG/EEG. This study investigates the role of various biological factors on the probability of clinical seizure detection using heart rate. METHODS: Regression models were applied to 266 clinical seizures recorded from 72 subjects to investigate if factors such as age, gender, years with epilepsy, etiology, seizure site origin, seizure class, and data collection centers, among others, shape the probability of EKG-based seizure detection. RESULTS: Clinical seizure detection probability based on heart rate changes, is significantly (p<0.001) shaped by patients' age and gender, seizure class, and years with epilepsy. The probability of detecting clinical seizures (>0.8 in the majority of subjects) using heart rate is highest for complex partial seizures, increases with a patient's years with epilepsy, is lower for females than for males and is unrelated to the side of hemisphere origin. CONCLUSION: Clinical seizure detection probability using heart rate is multi-factorially dependent and sufficiently high (>0.8) in most cases to be clinically useful. Knowledge of the role that these factors play in shaping said probability will enhance its applicability and usefulness. Heart rate is a reliable and practical signal for extra-cerebral detection of clinical seizures originating from or spreading to central autonomic network structures.

Is seizure detection based on EKG clinically relevant?

OBJECTIVE: Real-time EKG-based automated seizure detection is emerging as a complement or supplement to that based on cortical signals, but its value is unproven. This study assesses the clinically relevance of EKG-based seizure detection by comparing the information content in EKG and ECoG. METHODS: ECoGs (6935h; 241 clinical and 4311 sub-clinical seizures) with simultaneous EKG from 81 subjects undergoing surgical evaluation were used in these analyses. Differences, if any, between clinical and sub-clinical seizures in variables such as intensity, duration and their product severity, were investigated with a multi-variate regression model. RESULTS: Highly statistically significant differences in severity between clinical and sub-clinical seizures were discerned with EKG and ECoG. Furthermore, EKG-based seizure severity was linearly correlated with that estimated using ECoG. CONCLUSIONS: These findings support the notion that EKG-based seizure detection is clinically relevant in certain localization-related epilepsies, providing similar information to that yielded by neuronal electrical signals. SIGNIFICANCE: The information content equivalence between EKG and ECoG would enable automated seizure detection, quantification and therapy delivery, without resorting to cortical monitoring. The considerably higher S/N and ease of acquisition and processing of EKG compared to ECoG/EEG may foster widespread clinical applications of this novel detection approach.

An introduction to contingent (closed-loop) brain electrical stimulation for seizure blockage, to ultra-short-term clinical trials, and to multidimensional statistical analysis of therapeutic efficacy.

Automated seizure blockage is a top research priority of the American Epilepsy Society. This delivery modality (referred to herein as contingent or closed loop) requires for implementation a seizure detection algorithm for control of delivery of therapy via a suitable device. The authors address the many potential advantages of this modality over conventional alternatives (periodic or continuous), and the challenges it poses in the design and analysis of trials to assess efficacy and safety-in the particular context of direct delivery of electrical stimulation to brain tissue. The experimental designs of closed-loop therapies are currently limited by ethical, technical, medical, and practical considerations. One type of design that has been used successfully in an in-hospital "closed-loop" trial using subjects undergoing epilepsy surgery evaluation as their own controls is discussed in detail. This design performs a two-way comparison of seizure intensity, duration, and extent of spread between the control (surgery evaluation) versus the experimental phase, and, within the experimental phase, between treated versus untreated seizures. The proposed statistical analysis is based on a linear model that accounts for possible circadian effects, changes in treatment protocols, and other important factors such as change in seizure probability. The analysis is illustrated using seizure intensity as one of several possible end points from one of the subjects who participated in this trial. In-hospital ultra-short-term trials to assess safety and efficacy of closed-loop delivery of electrical stimulation for seizure blockage are both feasible and valuable.

Interpreting comparative experiments on parasite burdens in animals using randomization and canonical variates.

Comparative experiments on parasite burdens present difficulties for modelling and interpretation: data tend to have highly skewed distributions, some standard methods of testing for effects have low power, and there is a need to make allowance for dependencies among the various measurements. Randomization testing and canonical variates analysis are advocated as a means of minimising these problems.

Analysis of polymorphic variation in different types of habitat.

A correlation between the distribution of an organism and features of its environment can be taken as indirect evidence of natural selection. Biologists may therefore collect samples from polymorphic populations at a number of locations, classify the locations into habitat types, and consider whether the distribution of morphs varies with the habitat. Statistical aspects of this type of study are discussed in this paper. A randomization test for habitat effects is proposed and a negative binomial model is suggested for the distribution of morphs from random locations within one type of habitat. Data on the distribution of Cepaea hortensis and C. nemoralis snails in southern England provide an example. For both species there is clear evidence of differences between habitats, although the morph distributions are rather variable within habitats. The negative binomial model suggests that, for the snail data, variation in morph proportions is mainly due to location differences. The binomial sampling error is relatively unimportant unless the sample size at a location is very small. Therefore it is reasonable to analyse morph proportions by standard methods without giving different weights to data from different locations. The snail data are analysed in this way. Discriminant function analyses are used to test for habitat effects. The relationships between C. hortensis and C. nemoralis morph frequencies within one habitat are examined by a canonical correlation analysis.

Some examples of a method for determining key factors from life table data.

The method of Manly (1977) for determining key factors in the life cycle of animal populations is illustrated on several sets of published data.

A new index for the intensity of natural selection.

A new index for the intensity of natural selection is proposed, based upon the double exponential model for fitness functions. This index is defined as a variance and is such that a value of zero indicates no selection while a value of one indicates quite strong selection. The use of the index is demonstrated using published data on the survival of human infants with different birth weights and gestation times.

The determination of key factors from life table data.

A new method is proposed for detecting key factors from life tables for animals whose life-cycle is divided into several stages. This method is based upon an equation that partitions the variance of the number alive at the end of the life-cycle into components associated with variation in the number entering the first stage of the cycle, variation in survival rates, and also density-dependent aspects of survival. It is illustrated by analysing Blank and Ash's (1962) census data for a population of partridges. The conclusions reached are somewhat different from those reached by Blank et al. (1967) when they applied Varley and Gradwell's (1960) type of key factor analysis to the same set of data.

A model for dispersion experiments.

A new model is proposed for the dispersion of animals and other organisms and its use is discussed for the analysis of the data from experiments on dispersion. The model is a generalisation of the random walk model, but because of its flexibility it should be much more widely applicable than the random walk model.The new model has been found to fit the results of many dispersion experiments and examples are given of its use with data for millipedes and Drosophila.

Some examples of double exponential fitness functions.

The advantages of using a double exponential fitness function are pointed out for the analysis of differential survival data. The uses of this type of function are illustrated on Bumpus' (1898) data on the survival of English sparrows after a severe storm and also on Cook and O'Donald's (1971) data on the over-winter survival of a large sample of snails.

A second look at some data on a cline.

The release-recapture data given in table 1 of Kettlewell et al.'s (1969) paper on a cline in the frequencies of the typica and edda morphs of the moth. Amathes glareosa have been re-analysed. Estimates of daily survival probabilities have been calculated and these have been "explained" in terms of differences between localities of release and the morph involved. The conclusions of Kettlewell et al. (1969) with regard to the survival of the moths have not been confirmed.

Estimation of stage-specific survival rates and other parameters for insect populations developing through several stages.

A method of analysis is suggested for data obtained by sampling an insect population while the individuals in the population are developing through several stages. The method allows the estimation of (i) the numbers entering each stage, (ii) the mean duration of each stage, and (iii) daily survival rates. A basic assumption made is that the time of entry to a stage follows a normal distribution.The method is illustrated on data from a field population of grasshoppers and a laboratory population of locusts.

A comparison of methods for the analysis of insect stage-frequency data.

An electronic computer has been used to simulate the development and sampling of a number of insect populations containing individuals passing through stages (instar I, instar II, etc.). This was done in order to compare five methods for analysing sample stage-frequency data (Richards and Waloff, 1954; Richards et al., 1960; Dempster, 1961; Kiritani and Nakasuji, 1967; Manly, 1974a) under a variety of conditions. The simulation results suggest (i) that the method of Kiritani and Nakasuji should be used to estimate stage-specific survival rates whenever populations are sampled at equal intervals of time until almost all insects are dead, and (ii) that the method of Manly should be used if the sample times do not permit the use of Kiritani and Nakasuji's method or if it is desired to estimate the actual number of insects entering stages.