Browsing by Author "Torres, Frank Sill"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Embedded real-time feature extraction for electrode inversion detection in telemedicine electrocardiograms.
    (2020) Torres, Vitor Angelo Maria Ferreira; Silva, D. A.C.; Torres, Luiz Carlos Bambirra; Braga, Mateus T.; Cardoso, M. B. R.; Lino, Vinicius Terra; Torres, Frank Sill; Braga, Antônio de Pádua
    Early detection of technical errors in medical examinations, especially in remote locations, is of utmost importance in order to avoid invalid measurements that would require costly and time consuming repeti- tions. This paper proposes a highly efficient method for the identification of an erroneous inversion of the measuring electrodes during a multichannel electrocardiogram. Therefore, a widely applied approach for heart beat detection is modified and approximated feature extraction techniques are employed. In con- trast to existing works, the improved heart beat identification requires no removal of baseline wandering and no amplitude related thresholds. Furthermore, a piecewise linear approximation of the baseline and basic calculations are sufficient for extracting the cardiac axis, which allows the construction of a clas- sifier capable of quickly detecting electrode reversals. Our implementation indicates that the proposed method has minimal hardware costs and is able to operate in real-time on a simple micro-controller.
  • No Thumbnail Available
    Item
    Multi-objective decision in machine learning.
    (2016) Medeiros, Talles Henrique de; Rocha, Honovan Paz; Torres, Frank Sill; Takahashi, Ricardo Hiroshi Caldeira; Braga, Antônio de Pádua
    Thiswork presents a novel approach for decisionmaking for multi-objective binary classification problems. The purpose of the decision process is to select within a set of Pareto-optimal solutions, one model that minimizes the structural risk (generalization error). This new approach utilizes a kind of prior knowledge that, if available, allows the selection of a model that better represents the problem in question. Prior knowledge about the imprecisions of the collected data enables the identification of the region of equivalent solutions within the set of Pareto-optimal solutions. Results for binary classification problems with sets of synthetic and real data indicate equal or better performance in terms of decision efficiency compared to similar approaches.