Biomedical subjects

Joachim Schult

Publications and source records attributed to Joachim Schult.

2 recordsLinked to original sources

PubMed2005

A neural-network technique to learn concepts from electroencephalograms.

A new technique is presented developed to learn multi-class concepts from clinical electroencephalograms (EEGs). A desired concept is represented as a neuronal computational model consisting of the input, hidden, and output neurons. In this model the hidden neurons learn independently to classify the EEG segments presented by spectral and statistical features. This technique has been applied to the EEG data recorded from 65 sleeping healthy newborns in order to learn a brain maturation concept of newborns aged between 35 and 51 weeks. The 39,399 and 19,670 segments from these data have been used for learning and testing the concept, respectively. As a result, the concept has correctly classified 80.1% of the testing segments or 87.7% of the 65 records.

Electroencephalography↗

PubMed2004

The combined technique for detection of artifacts in clinical electroencephalograms of sleeping newborns.

In this paper, we describe a new method combining the polynomial neural network and decision tree techniques in order to derive comprehensible classification rules from clinical electroencephalograms (EEGs) recorded from sleeping newborns. These EEGs are heavily corrupted by cardiac, eye movement, muscle, and noise artifacts and, as a consequence, some EEG features are irrelevant to classification problems. Combining the polynomial network and decision tree techniques, we discover comprehensible classification rules while also attempting to keep their classification error down. This technique is shown to out-perform a number of commonly used machine learning technique applied to automatically recognize artifacts in the sleep EEGs.

Algorithms↗