23 research outputs found

    A comparison of statistical machine learning methods in heartbeat detection and classification

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    In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

    A study of automatic contingency selection algorithms for steady-state security assessment of power systems and the application of parallel processing

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    The performance of various Contingency Selection methods has been investigated within the framework of accuracy for application to steady-state power system security assessment and suitability for execution in a real-time environment. In the study the following requirements have been considered: (a) Effectiveness: in identifying contingencies which may cause limit violations and discarding all others; (b) Adaptability: to model both permanent and temporary changes in the system; (c) Flexibility: to model any number and type of contingencies; (d) Computational efficiency: in terms of speed in selecting the sub-set of contingencies as well as in terms of storage requirements; (e) Ability: to update and augment on-line the list of contingencies given the actual system operating data. [Continues.

    36th International Symposium on Theoretical Aspects of Computer Science: STACS 2019, March 13-16, 2019, Berlin, Germany

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