Spectral analysis of electroencephalogram during sleep-related apneas in pre-term and term born infants in the first weeks of life.

OBJECTIVES: A correlation between sudden infant death syndrome (SIDS) and apnea has been discussed in a variety of studies. We analyzed polysomnographic data in 51 infants and investigated correlations between the manifestation of apneas and changes in EEG pattern. METHODS: Measurements were performed by means of the commercial polysomnographic device 'ALICE III'. Spectral analysis of certain parts of the registered EEG was performed. RESULTS: During apnea a reduction of the EEG amplitude was seen. The extent of the decrease differed slightly between the frequency bands. CONCLUSIONS: According to the widespread agreement that a lower voltage of electrical brain activity is a fundamental sign of waking up, we hypothesize that the observed change might be an immature type of arousal reaction.

Brain dysmaturity index for automatic detection of high-risk infants.

The definition of an electroencephalographic (EEG)-based brain dysmaturity index that could allow automatic detection of neonates who deviate from expected ontogenetic patterns is proposed. The investigation was performed in a group of 94 term and preterm infants (28-112 weeks postconceptional age). For each neonate, one continuous two-channel EEG of 1-6 hours was recorded. The cluster analysis of different age groups was performed with a self-referential neural network. The network performed a nonlinear discriminant analysis; the synaptic strength of input nodes indicates the relevance of an individual EEG feature. The most relevant EEG features are given by the average amplitude in the delta and theta bands and by the relative amplitudes of beta-1/theta and beta-1/delta, respectively. The correlation between the frequency shifts and the postconceptional age agreed with measures of brain dysmaturity in healthy preterm neonates. Thus the presented trend in early EEG development demonstrates that it is possible to establish clinically relevant age dysmaturity scores.

A general probability estimation approach for neural comp.

We describe an analytical framework for the adaptations of neural systems that adapt its internal structure on the basis of subjective probabilities constructed by computation of randomly received input signals. A principled approach is provided with the key property that it defines a probability density model that allows studying the convergence of the adaptation process. In particular, the derived algorithm can be applied for approximation problems such as the estimation of probability densities or the recognition of regression functions. These approximation algorithms can be easily extended to higher-dimensional cases. Certain neural network models can be derived from our approach (e.g., topological feature maps and associative networks).

Analytical description of the evolution of neural networks: learning rules and complexity.

We present the foundation of a physical formalism that allows us to characterize the dynamics of the evolution of neural networks both in regard to the network configuration and to network performance. Model runs were performed on a simple network consisting of six neurons, allowing complete analytical description of the network's behaviour. Order parameters are characterized that allow an analytical description of critical periods in network evolution. Thus, correlations of the local dynamics and the system's global behaviour could be computed. It is shown that local learning rules are sufficient to model complex dynamical aspects of the evolution of networks. It is demonstrated in how far novel statistical formalisms, e.g. neural complexity, can be employed to evaluate the system's dynamics. The introduction of order parameters allows an analytical characterization of transient phases in the network's behaviour, correlating network connectivity with neuronal firing patterns. The relevance of this approach for the interpretation of physiological data is discussed.

Clinical relevance of age-dependent EEG signatures in the detection of neonates at high risk for apnea.

Age dependent EEG signatures were detected in the EEG of 71 neonates of 28-100 weeks of conceptional age (CA). Using the new method presented, neonates were automatically classified in three age groups (28-35 weeks CA), (36-40 weeks CA) and (41-100 weeks CA). Analysis was performed employing relative distance functions for the complete frequency spectra of the EEG registered by electrodes in C3 and C4 positions. The analysis was successful in automatic identification of individuals showing EEG anomalies. Polysomnographic analysis demonstrated these to be apnea risk patients. Accordingly, a purely EEG based detection of neonates that are medically at risk becomes feasible.