This study tackles the ECG classification problem by means of a methodology, which is able to enhance classification performance while simultaneously reducing the computational resources, making it specially adequate for its application in the improvement of ambulatory settings. For this purpose, the sequential forward floating search (SFFS) algorithm is applied with a new criterion function index based on linear discriminants. This criterion has been devised specifically to be a quality indicator in ECG arrhythmia classification. Based on this measure, a comprehensive feature set is analyzed with the SFFS algorithm, and the most suitable subset returned is additionally evaluated with a multilayer perceptron (MLP) to assess the robustness of the model. Aiming at obtaining meaningful estimates of the real-world performance and facilitating comparison with similar studies, the present contribution follows the Association for the Advancement of Medical Instrumentation standard EC57:1998 and the same interpatient division scheme used in several previous studies. Results show that by applying the proposed methods, the performance obtained in similar studies under the same constraints can be exceeded, while keeping the requirements suitable for ambulatory monitoring
SummaryOrange essence oils of the Navel and Valencia variety were analyzed by GC and G U M S using two parallel capillarycolumns of different selectivity. Data were normalized, converted into a computer compatible format, and transferred via an asynchronous communications interface to a mainframe computer for statistical treatment, using the chemometrics software package ARTHUR. After suitable preprocessing of the raw data, reliable classificationwas possible. It appears that foursubstances which are present in both varieties can be used for that purpose.
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