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Background: Classification of the electrocardiogram using Neural Networks has become a widely used method in recent years. The efficiency of these classifiers depends upon a number of factors including network training. Unfortunately, there is a shortage of evidence available to enable specific design choices to be made and as a consequence, many designs are made on the basis of trial and error. In this study we develop prediction models to indicate the point at which training should stop for Neural Network based Electrocardiogram classifiers in order to ensure maximum generalisation.

A well‐differentiated mucoepidermoid carcinoma that was confined to, and apparently arose within, an intraparotid lymph node is reported. Salivary gland ducts and acini often are found within intraparotid lymph nodes, and occasionally within extraparotid nodes. Salivary gland tumors, both benign and malignant, can develop within this ectopic salivary tissue. When a malignant salivary‐gland‐type neoplasm is found within an intraparotid or periparotid lymph node, the possibility exists that the tumor has arisen within the node and does not necessarily represent a metastasis from some other occult site.