Key result
DWT-based features outperform FFT and DCT for arrhythmia detection after ~85% dimensionality reduction.
Why the study?
Reliable automatic cardiac arrhythmia detection requires evaluation of different data transformation techniques to improve classification performance in ECG analysis.
DWT features may aid efficient arrhythmia detection algorithms; leaves open prospective clinical validation before practice adoption.
Computer-aided ECG classification is an important tool for timely diagnosis of abnormal heart conditions. This paper proposes a novel framework that combines the theory of compressive sensing with random forests to achieve reliable automatic cardiac arrhythmia detection. Furthermore, the paper evaluates the characterization power of FFT, DCT and DWT data transformations in order to extract significant features that will bring the additional boost to the classification performance. The experiments – carried out over MIT-BIH benchmark arrhythmia database, following the standards and recommended practices provided by AAMI – demonstrate that DWT based features exhibit better performances compared to other two feature extraction techniques for a relatively small number of random projected coefficients, i.e. after considerable (approx. 85%) dimensionality reduction of the input signal. The results are very promising, suggesting that the proposed model could be implemented for practical applications of real-time ECG monitoring, due to its low-complexity.
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Marasović et al. (2017) studied Cardiac arrhythmia. DWT data transformation vs. FFT and DCT data transformations was evaluated on Classification performance. DWT-based features exhibited better classification performance for cardiac arrhythmia detection compared to FFT and DCT techniques after approximately 85% dimensionality reduction.
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