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In this paper, we introduce a new system for ECG beat classification using support vector machines classifier with a double hinge loss. The proposed classifier rejects samples that cannot be classified with enough confidence. Specifically in medical diagnoses, the consequence of a wrong classification can be so harmful that it is convenient to reject such sample. After ECG preprocessing, feature selection and extraction, our decision rule uses dynamic reject thresholds according to the cost of rejecting or misclassifying a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrythmia database. The achieved results are represented by the error reject tradeoff. We obtained 98.2% of sensitivity with no rejection and more than 99% of sensitivity for the optimal classification cost being competitive to other published studies. [ABSTRACT FROM AUTHOR]

The Arctangent function is used in several signal processing applications and it is widely needed for hardware implementations. However, it often constitutes the performance bottleneck because of its inaccuracy and/or its poor performances. We present a generic VLSI design to respond to some applications' need of fast Arctangent functions handling high precision data. The method presented here contrasts with the usual Look Up table based solutions which allow the output customization but limit the precision of the corresponding tangent input. Detailed logical design and FPGA implementation results are provided to show our solution as the best candidate to comply with fast execution as well as high precision for both input and output data. [ABSTRACT FROM AUTHOR]