Abstract
The detection of changes in the signals used to evaluate the depth of anesthesia of patients undergoing surgery is of foremost importance. This detection allows to decide how to adapt the doses of hypnotics and analgesics to be administered to patients for minimally invasive diagnostics and therapeutic procedures. This paper presents an algorithm based on the Page-Hinkley test to automatically detect changes in the referred depth of anesthesia signals of patients undergoing general anesthesia. The performance of the proposed method is evaluated online using data from patients subject to surgery. The results show that most of the detected changes are in accordance with the actions of the clinicians in terms of times where a change in the hypnotic or analgesic rates had occurred. This detection was performed under the presence of noise and sensor faults. The results encourage the inclusion of the proposed algorithm in a decision support system based on depth of anesthesia signals. © 2012 IFAC.

Abstract
In this work we present XMuSer, a multi-relational framework suitable to explore temporal patterns available in multi-relational databases. XMuSer's main idea consists of exploiting frequent sequence mining, using an efficient and direct method to learn temporal patterns in the form of sequences. Grounded on a coding methodology and on the efficiency of sequential miners, we find the most interesting sequential patterns available and then map these findings into a new table, which encodes the multi-relational timed data using sequential patterns. In the last step of our framework, we use an ILP algorithm to learn a theory on the enlarged relational database that consists on the original multi-relational database and the new sequence relation. We evaluate our framework by addressing three classification problems. Moreover, we map each one of three different types of sequential patterns: frequent sequences, closed sequences or maximal sequences.

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This paper presents an hybrid adaptive system for induction of forest of trees from data streams. The Ultra Fast Forest Tree system (UFFT) is an incremental algorithm, with constant time for processing each example, works online, and uses the Hoeffding bound to decide when to install a splitting test in a leaf leading to a decision node. Our system has been designed for continuous data. It uses analytical techniques to choose the splitting criteria, and the information gain to estimate the merit of each possible splitting-test. The number of examples required to evaluate the splitting criteria is sound, based on the Hoeffding bound. For multiclass problems,the algorithm builds a binary tree for each possible pair of classes, leading to a forest of trees. During the training phase the algorithm maintains a short term memory. Given a data stream, a fixed number of the most recent examples are maintained in a data-structure that supports constant time insertion and deletion. When a test is installed, a leaf is transformed into a decision node with two descendant leaves. The sufficient statistics of these leaves are initialized with the examples in the short term memory that will fall at these leaves. We study the behavior of UFFT in different problems. The experimental results shows that UFFT is competitive against a batch decision tree learner in large and medium datasets.

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