مشاهده مشخصات مقاله
Threshold-Based Hidden Markov Model for Anomaly Detection in SAX-Represented ECG Signals
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نویسنده (ها) |
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Milad Zandi-Goharrizy
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Mohammad-Reza Zare-Mirakabad
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Fatemeh Kaveh-Yazdy
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مربوط به کنفرانس |
سمپوزیوم هوش مصنوعی و پردازش سیگنال 2013 |
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چکیده |
Electrocardiogram (ECG) signals are widely used in healthcare systems for monitoring vital status of patients. Anomalous patterns in ECG of a patient might trigger an alarm for an emergency case; therefore anomaly detec-tion is a basic problem in health monitoring systems. In this paper, we propose a hidden Markov model (HMM) based novel anomaly detection framework, which uses SAX1-represented ECGs. According to basic investigations, typical HMM and SAX are not good candidates for anomaly detection, because of low resolu-tion of SAX. However, we contribute a threshold-based hidden Markov model which compensates for the SAX low-resolution problem. Furthermore, our pro-posed threshold reduces the dependency of the model to the distribution of hidden state by taking into account the likelihood probability of anomalous patterns. Re-sults of experiments demonstrate that the threshold based HMM labels samples with the accuracy of 96% and 99% in two datasets |
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قیمت |
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