Evaluating the Performance of State of the Art Algorithms for Enhancement of Seismocardiogram Signals

Sundar, Aditya; Pahwa, Vivek

doi:10.1007/978-981-10-2035-3_5

Cited by 2 publications

(1 citation statement)

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“…Reference [44] proposes several machine learning algorithms to discriminate between normal, sinus rhythm and AFib in BCG traces. In order to reduce artifacts in the SCG waveforms, authors in [45] compare different denoising techniques. Overall, wavelet thresholding achieved the best results in terms of signal enhancement accuracy and computational efficiency.…”

Section: Related Workmentioning

confidence: 99%

A Unified Methodology for Heartbeats Detection in Seismocardiogram and Ballistocardiogram Signals

et al. 2020

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This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets ( p > 0.05 from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found ( p < 0.01 ) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors.

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Section: Related Workmentioning

confidence: 99%