PROJECT TITLE :
Bufferless Compression of Asynchronously Sampled ECG Signals in Cubic Hermitian Vector Space
Asynchronous level crossing sampling analog-to-digital converters (ADCs) are known to be additional energy efficient and produce fewer samples than their equidistantly sampling counterparts. However, as the required threshold voltage is lowered, the amount of samples and, in turn, the information rate and the energy consumed by the overall system increases. In this paper, we tend to present a cubic Hermitian vector-based mostly technique for on-line compression of asynchronously sampled electrocardiogram signals. The proposed methodology is computationally economical knowledge compression. The algorithm has complexity $O(n)$, so well fitted to asynchronous ADCs. Our algorithm requires no data buffering, maintaining the energy advantage of asynchronous ADCs. The proposed method of compression contains a compression ratio of up to ninety% with achievable share root-mean-square difference ratios as a low as 0.97. The algorithm preserves the superior feature-to-feature timing accuracy of asynchronously sampled signals. These benefits are achieved in a computationally efficient manner since algorithm boundary parameters for the signals are extracted a priori.
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