Fig 4 - uploaded by Anatolii A. Pulavskyi
Content may be subject to copyright.
Scatterplot of maxDiffn and CRn for different types of noise: rem(a), rma(b), white(c), pink(d)

Scatterplot of maxDiffn and CRn for different types of noise: rem(a), rma(b), white(c), pink(d)

Contexts in source publication

Context 1
... Fs = 2000 Hz. At the same time, CRc tends to decrease with increasing HR. The coefficient of kurtosis is in the range from 7 to 10 and almost does not depend on the sampling frequency, but it also tends to decrease with increasing HR. The dependence of the metric maxDiffn on CRn for different types of the noise and Fs= 250 Hz is represented in Fig. 4. The results of the study are represented in Tables 1-3. The minimum values of CRn and kurtn, starting from which the metric maxDiffn occurs to be zero, are ...
Context 2
... the data obtained it follows that the minimum value of the coefficient of kurtosis, at which maxDiffn = 0, varies significantly for different types of noise. At the same time, for some types of the noise, this coefficient is higher than 7 (the minimum value for a noise-free ECG, Fig. 3-4). Therefore, the coefficient of kurtosis is inapplicable as a simple metric of ECG quality without a priori information about the type of dominant noise present for a given fragment. Analysis of the minimum value of the compression ratio, at which maxDiffn=0, demonstrates another behavior. ECG compression ratio, distorted by the ...

Citations

... Conventionally, except for the sudden abrupt interference, most noises can be assumed to be Gaussian (white) with flat frequency spectra [20]. However, many real-world noises are non-stationary, and they have non-flat spectral density functions, e.g., coloured noise [173], [174]. ...
... Other techniques, such as auto-zeroing and correlated double sampling, can also be used to reduce pink noise [177]. Other than the pink noise, the Brown noise relates to the baseline wander and electrode motion artifacts [173], [178], and the removal of MA has been extensively discussed in Section III-A. ...
Article
Full-text available
Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.
... For these purposes, post-filtering signal smoothing may be suitable, which is applied (or not applied) depending on some quickly-calculated real-time metric. The ECG compression coefficient can be used as such a metric [16]. The objective of this work is to determine the criteria for the necessity of using post-filtering smoothing based on ECG lossless compression for signals distorted by high-intensity noise. ...
... III. QUALITY METRICS At each of the four stages, several indicators were calculated for the corresponding signals. The first indicator is the degree of ECG compression, compression rate (CR), determined by the recently proposed algorithm [16]. The difference is that present CR is an averaged compression ratio obtained from 10-second non-overlapping fragments taken from the entire ECG. ...
Preprint
Full-text available
The electrocardiogram (ECG) of the first lead, obtained using portable signal collection tools is noise sensitive. One of the most common and poorly filterable types of noise is muscle artefact. As a result of the simulation, a criterion was found that identifies situations in which the use of kernel-based ECG smoothing after the filtering stage is relevant. This criterion is based on the difference in compression coefficients of the ECG smoothed after filtering and filtered ECG. The use of post-filtering smoothing is recommended if this difference is greater than zero. In this case, post-filtering smoothing improves signal quality in terms of mean-square error. The correctness of the criterion was confirmed on real ECG results (MIT-BIH Arrhythmia Database), distorted by real muscle artefact (MIT-BIH Noise Stress Test Database).