Baseline Drift Tolerant Signal Encoding for ECG Classification with Deep Learning

arXiv:2405.00724v1 Announce Type: cross
Abstract: Common artefacts such as baseline drift, rescaling, and noise critically limit the performance of machine learningbased automated ECG analysis and interpretation. This study proposes Derived Peak (DP) encoding, a non-parametric method that generates signed spikes corresponding to zero crossings of the signals first and second-order time derivatives. Notably, DP encoding is invariant to shift and scaling artefacts, and its implementation is further simplified by the absence of userdefined parameters. DP encoding was used to encode …

abstract analysis arxiv automated classification cs.lg deep learning drift eess.sp encoding interpretation machine noise non-parametric parametric peak performance signal study type