We present a robust graphical procedure for routine detection of isolated and patchy outliers in univariate time series. This procedure is suitable for retrospective as well as for online identification of outliers. It is based on a phase space reconstruction of the time series which allows to...

As high dimensional data occur as a rule rather than an exception in critical care today, it is of utmost importance to improve acquisition, storage, modelling and analysis of medical data, which appears feasible only with the help of bedside computers. The use of clinical information systems...

As high dimensional data occur as a rule rather than an exception in critical care today, it is of utmost importance to improve acquisition, storage, modelling and analysis of medical data, which appears feasible only with the help of bedside computers. The use of clinical information systems...

We present a robust graphical procedure for routine detection of isolated and patchy outliers in univariate time series. This procedure is suitable for retrospective as well as for online identification of outliers. It is based on a phase space reconstruction of the time series which allows to...

As high dimensional data occur as a rule rather than an exception in critical care today, it is of utmost importance to improve acquisition, storage, modelling and analysis of medical data, which appears feasible only with the help of bedside computers. The use of clinical information systems...

We present a robust graphical procedure for routine detection of isolated and patchy outliers in univariate time series. This procedure is suitable for retrospective as well as for online identification of outliers. It is based on a phase space reconstruction of the time series which allows to...