Key result
The proposed coarse-to-fine arrhythmia classification technique achieved a computational speed-up factor of 2.2:1 compared to standard techniques, with marginal loss (<1%) in classification accuracy.
Why the study?
Does a coarse-to-fine arrhythmia classification technique using MSVQ improve computational speed without compromising accuracy in processing large ECG records?
Does a coarse-to-fine arrhythmia classification technique using MSVQ improve computational speed without compromising accuracy in processing large ECG records?
Effect estimate: speed-up factor of 2.2:1
The proposed MSVQ-based arrhythmia classification technique significantly speeds up the processing of large ECG records (e.g., Holter data) by a factor of 2.2x with minimal loss in accuracy.
May accelerate large-scale ECG processing; leaves open prospective validation before clinical adoption.
In this paper we introduce a coarse-to-fine arrhythmia classification technique that can be used for efficient processing of large Electrocardiogram (ECG) records. This technique reduces time-complexity of arrhythmia classification by reducing size of the beats as well as by quantizing the number of beats using Multi-Section Vector Quantization (MSVQ) without compromising on the accuracy of the classification. The proposed solution is tested on MIT-BIH arrhythmia database. This work achieves a highest computational speed-up factor of 2.2:1 in comparison with standard arrhythmia classification technique with marginal loss (<;1%) in classification accuracy. The clinical application of this technique enhances physician's throughput by factor of 2x while processing large ECG records from Holter system.
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Chakroborty et al. (2014) studied Arrhythmia. Coarse-to-fine arrhythmia classification technique using Multi-Section Vector Quantization (MSVQ) vs. Standard arrhythmia classification technique was evaluated on Computational speed-up and classification accuracy (speed-up factor of 2.2:1). The proposed coarse-to-fine arrhythmia classification technique achieved a computational speed-up factor of 2.2:1 compared to standard techniques, with marginal loss (<1%) in classification accuracy.
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