The Accuracy Comparison of Time Series Classification in Vector Space between SAX and BOSS Methods: A Case Study of Electrocardiogram
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Abstract
The electrocardiogram (ECG) is an important procedure used to diagnose heart disorders. However, the ECG may contain different types of noise due to various of factors, potentially resulting in diagnostic errors. This research compares Symbolic Aggregate Approximation in Vector Space (SAXVSM) and Bag of Symbolic Fourier Approximation Symbols in Vector Space (BOSSVS) methods for classifying ECG data with noise. To choose a suitable classification algorithm for ECG5000 dataset, which is available in the Physionet database. Four types of ECG noises were simulated and then added to the data as follow: 1) Electromyography (EMG) 2) Powerline Interference 3) Baseline Wander and 4) Composite at 25%, 50% and 100% levels for the performance comparison of the ECG classification between normal and abnormal heart rhythms with SAXVSM and BOSSVS. The results show that both algorithms have similar high performance for all 13 datasets: accuracy and F1 score are 97-99%, precision is 95-99%, and recall is 97-100%, but BOSSVS has a longer running time than SAXVSM.
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