Highly Efficient Low Noise Solutions in ECG Signals

Liu, Fengru; Xu, Youren; Yuan, Y.

doi:10.1088/1742-6596/2246/1/012030

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…This approach assumes that biological signal content is commonly in a limited low band of frequencies (i.e. 0 to 30 Hz for EEG [52], and 0.05 to 100 Hz for ECG [53], with dominant components in the range < 30 Hz [54]), while noise commonly is present in the whole band of frequencies, exhibiting elevated levels of both frequency and energy [53].…”

Section: Standard Deviation Of the Upper And Lower Envelopementioning

confidence: 99%

Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis

Idrobo-Ávila,

Bognár,

Krefting

et al. 2024

IEEE Open J. Eng. Med. Biol.

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Goal: Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysiswhich involves their joint analysis -can be conducted and could provide deeper insights than unimodal analysis based on a single signal. However, it is unclear what percentage of intraoperatively acquired data is suitable for multimodal analysis. Due to the large amount of data, manual inspection and labelling into suitable and unsuitable segments are not feasible. Nevertheless, multimodal analysis is performed successfully in sleep studies since many years as their signals have proven suitable. Hence, this study evaluates the suitability to perform multimodal analysis on a surgery dataset (VitalDB) using a multi-center sleep dataset (SIESTA) as reference. Methods: We applied widely known algorithms entitled "signal quality indicators" to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10s duration. As there are no multimodal methods available, we used only unimodal signal quality indicators. In case, all three signals were determined as being adequate by the indicators, we assumed that the whole signal segment was suitable for multimodal analysis. Results: 82% of SIESTA and 72% of VitalDB are suitable for multimodal analysis. Unsuitable signal segments exhibit constant or physiologically unreasonable values. Histogram examination indicated similar signal quality distributions between the datasets, albeit with potential statistical biases due to different measurement setups. A correlation was found between entropy outliers and non-suitable electrocardiographic signals. Conclusions: The majority of data within the surgery dataset VitalDB demonstrated suitability for multimodal analysis.

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