Using Hidden Markov Models and Spark to Mine ECG Data
نویسنده
چکیده
New potential risk factors for cardioembolic strokes are being considered in the medical community. The presence of these factors can be determined by reading an electrocradiogram (ECG). Manual ECG analysis can take hours. We propose combining accurate Hidden Markov Model (HMM) techniques with Apache Spark to improve the speed of ECG analysis. The potential exists for developing a fast classifer for these risk factors.
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