Detection of artifacts in oscillometric pulsations signals

Havlík, Jan; Martinovská, Zuzana; Dvořák, Jan; Lhotská, Lenka

doi:10.1109/bhi.2014.6864462

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…Most of the existing artifact detection methods divide the pulse signal into multiple segments of 5∼10 seconds; extract statistical information such as mean, variance, and sample entropy of the segment; then analyze these statistical features; and use the threshold method to distinguish whether the pulse segment is segmented. [9,10,14,[15][16][17].…”

Section: Detection and Removal Of Artifactsmentioning

confidence: 99%

“…In 1994, Qian Weili and others of Lanzhou University proposed to use the Gaussian function to synthesize pulse waves and establish a pulse signal model [9]. Many scholars have demonstrated the Gaussian function synthetic pulse wave, and the results show that the three Gaussian functions can e ectively describe the pulse waveform morphology [10,11].…”

Section: Algorithm Simulation and Comparisonmentioning

confidence: 99%

“…A periodic pulse wave is shown in the sub-gure of Figure 8(a). As can be seen from the gure, the synthesized pulse signal contains the basic characteristics of the pulse signal, and the pulse signals of di erent pulses can be simulated by changing the parameters in (9). A single periodic pulse wave generated by ( 9) is extended to generate a clean signal that can simulate the actual pulse signal.…”

Section: Algorithm Simulation and Comparisonmentioning

confidence: 99%

“…For the detection of artifacts, the current existing method is built on the extraction of statistical information from the pulse signal. After extracting the statistical information such as the mean value, variance, and sample entropy from the pulse signal, a threshold is set on the statistical information to detect a signal containing artifacts [9,10]. Since the empirical mode decomposition (EMD) method can separate the highfrequency part of the signal, and the frequency of artifacts tends to be high, empirical mode decomposition is also often used for artifact detection [11][12][13].…”

Section: Introducementioning

confidence: 99%

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One Method for Preprocessing Pulse Signal Based on Deep Learning

Zhao

2022

Mathematical Problems in Engineering

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The collection of pulse signals is accompanied by considerable noise interference, and it is necessary to denoise the collected signals to eliminate the error brought about by the outside world and the instrument itself to the actual data to the greatest extent. Considering this, this article proposes a preprocessing scheme for noise reduction. Firstly, the saturation detection algorithm in signals is based on the gradient, and extreme is utilized to remove the saturation interference. On this basis, the artifact detection module based on complex network connectivity is proposed. Finally, the self-adjusting parameter integer coefficient filtering is utilized to include the baseline drift. The noise inside is filtered out. The experimental results demonstrate that the proposed method, in the case of a similar signal-to-noise ratio, has a mean square error of 15.7 and a shorter convergence time of 0.02s.

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Section: Detection and Removal Of Artifactsmentioning

confidence: 99%

Section: Algorithm Simulation and Comparisonmentioning

confidence: 99%

Section: Algorithm Simulation and Comparisonmentioning

confidence: 99%

Section: Introducementioning

confidence: 99%

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One Method for Preprocessing Pulse Signal Based on Deep Learning

Zhao

2022

Mathematical Problems in Engineering

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“…One way to measure the BP is through arterial tonometer [5,6]. The heart-to-beat waveform can be measured from arterial tonometer but is strongly subject to motion artifacts caused by high sensitivity, sensor placement and movement [7]. Cuff sphygmomanometer is the most standard non-invasive technique that is used to measure BP [8].…”

Section: Introductionmentioning

confidence: 99%

Pervasive blood pressure monitoring using Photoplethysmogram (PPG) sensor

Riaz¹,

Azad

Arshad

et al. 2019

Future Generation Computer Systems

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Preventive healthcare requires continuous monitoring of the blood pressure (BP) of patients, which is not feasible using conventional methods. Photoplethysmogram (PPG) signals can be effectively used for this purpose as there is a physiological relation between the pulse width and BP and can be easily acquired using a wearable PPG sensor. However, developing real-time algorithms for wearable technology is a significant challenge due to various conflicting requirements such as high accuracy, computationally constrained devices, and limited power supply. In this paper, we propose a novel feature set for continuous, real-time identification of abnormal BP. This feature set is obtained by identifying the peaks and valleys in a PPG signal (using a peak detection algorithm), followed by the calculation of rising time, falling time and peak-to-peak distance. The histograms of these times are calculated to form a feature set that can be used for classification of PPG signals into one of the two classes: normal or abnormal BP. No public dataset is available for such study and therefore a prototype is developed to collect PPG signals alongside BP measurements. The proposed feature set shows very good performance with an overall accuracy of approximately 95%. Although the proposed feature set is effective, the significance of individual features varies greatly (validated using significance testing) which led us to perform weighted voting of features for classification by performing autoregressive modeling. Our experiments show that the simplest linear classifiers produce very good results indicating the strength of the proposed feature set. The weighted voting improves the results significantly, producing an overall accuracy of about 98%. Conclusively, the PPG signals can be effectively used to identify BP, and the proposed feature set is efficient and computationally feasible for implementation on standalone devices.

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