scholarly journals Recognition of dicrotic notch in arterial blood pressure pulses using signal processing techniques

2021 ◽  
Vol 1937 (1) ◽  
pp. 012034
Author(s):  
J Bethanney Janney ◽  
G Umashankar ◽  
S Krishnakumar ◽  
H Chandana ◽  
L Caroline Chriselda
Keyword(s):  
Blood Pressure ◽  
Signal Processing ◽  
Arterial Blood Pressure ◽  
Dicrotic Notch ◽  
Arterial Blood ◽  
Pressure Pulses ◽  
Processing Techniques ◽  
Signal Processing Techniques

scholarly journals Detection of Onset, Systolic Peak and Dicrotic Notch in Arterial Blood Pressure Pulses

2017 ◽  
Vol 50 (7-8) ◽  
pp. 170-176 ◽  
Author(s):  
Omkar Singh ◽  
Ramesh Kumar Sunkaria
Keyword(s):  
Blood Pressure ◽  
Wavelet Transform ◽  
Arterial Blood Pressure ◽  
Pressure Pulse ◽  
Data Set ◽  
Dicrotic Notch ◽  
Arterial Blood ◽  
Comparison Results ◽  
Empirical Wavelet Transform ◽  
Pressure Pulses

In this paper, we proposed an effective method for detecting fiducial points in arterial blood pressure pulses. An arterial blood pressure pulse normally consists of onset, systolic peak and dicrotic notch. Detection of fiducial points in blood pressure pulses is a critical task and has many potential applications. The proposed method employs empirical wavelet transform for locating the systolic peak and onset of blood pressure pulse. The proposed method first estimates the fundamental frequency of blood pressure pulse using empirical wavelet transform and utilizes the combination of the blood pressure pulse and the estimated frequency for locating onset and systolic peak. For dicrotic notch detection, it utilizes the first-order difference of blood pressure pulse. The algorithm was validated on various open-source databases and was tested on a data set containing 12,230 beats. Two benchmark parameters such as sensitivity and positive predictivity were used for the performance evaluation. The comparison results for accuracy of the detection of systolic peak, onset and dicrotic notch are reported. The proposed method attained a sensitivity and positive predictivity of 99.95% and 99.97%, respectively, for systolic peaks. For onsets, it attained a sensitivity and predictivity of 99.88% and 99.92%, respectively. For dicrotic notches, a sensitivity and positive predictivity of 98.98% and 98.81% were achieved, respectively.

Dicrotic Notch Identification: a Generalizable Hybrid Approach under Arterial Blood Pressure (ABP) Curve Deformations

2021 ◽  
Author(s):  
Mahya Saffarpour ◽  
Debraj Basu ◽  
Fatemeh Radaei ◽  
Kourosh Vali ◽  
Jason Y. Adams ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Arterial Blood Pressure ◽  
Hybrid Approach ◽  
Dicrotic Notch ◽  
Arterial Blood

A Unified Approach for Heart Rate Estimation from Electrocardiogram and Arterial Blood Pressure Pulses

2020 ◽  
Vol 12 (5) ◽  
pp. 588-592
Author(s):  
Omkar Singh ◽  
Ramesh Kumar Sunkaria
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Arterial Blood Pressure ◽  
Fundamental Frequency ◽  
Data Set ◽  
Rate Estimation ◽  
Arterial Blood ◽  
Empirical Wavelet Transform ◽  
Pressure Pulses ◽  
Heart Rate Estimation

The objective of this manuscript is to propose a unique methodology for heart rate estimation derived from Electrocardiogram (ECG) or arterial blood pressure (abp) signal. This methodology relies on the identification of a signal's fundamental frequency by use of empirical wavelet analysis, followed by peak identification within windows based on pseudo-periodic assumption. The proposed methodology is based on the concept that the most of the cardiovascular signals are quasi-periodic in nature. The proposed technique estimates the fundamental frequency of the signal from its corresponding Fourier spectrum using empirical wavelet transform and then utilizes a search window for locating the peaks in the corresponding signal which identifies the R peaks in ECG or Systolic peaks in blood pressure pulses. This approach was validated on 100 recordings of the computing in cardiology challenge 2014 training data set and performance parameters were compared with methods running only on ECG or ABP signals independently.

APPLICATION OF THE DISCRETE WAVELET TRANSFORM FOR THE ROBUST DETECTION OF THE IMPULSIVE INCIDENCES: APPLICATION TO ARTERIAL BLOOD PRESSURE CHARACTERISTIC EVENTS DETECTION–DELINEATION

2011 ◽  
Vol 09 (05) ◽  
pp. 813-842 ◽  
Author(s):  
MOHAMMAD R. HOMAEINEZHAD ◽  
MOHAMMAD AGHAEE ◽  
HAMID NAJJARAN TOOSI ◽  
ALI GHAFFARI ◽  
REZA RAHMANI
Keyword(s):  
Blood Pressure ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Arterial Blood Pressure ◽  
False Alarm Probability ◽  
Discrete Wavelet ◽  
Robust Detection ◽  
Dicrotic Notch ◽  
Arterial Blood ◽  
Events Detection

The major focus of this study is to describe the structure of a solution designed for robustly detecting and delineating the arterial blood pressure (ABP) signal events. To meet this end, first, the original ABP signal is pre-processed by application of à trous discrete wavelet transform (DWT) for extracting several dyadic scales. Then, a fixed sample size sliding window is moved on the appropriately selected scale and in each slid, six features namely as summation of the nonlinearly amplified Hilbert transform, summation of absolute first-order differentiation, summation of absolute second-order differentiation, curve length, area and variance of the excerpted segment are calculated. Then, all feature trends are normalized and utilized to construct a newly proposed principal components analyzed geometric index (PCAGI) (to be used as the segmentation decision statistic (DS)) by application of a linear orthonormal projection. After application of an adaptive-nonlinear transformation for making the DS baseline stationary, the histogram parameters of the enhanced DS are used to regulate the α-level Neyman–Pearson classifier for false alarm probability (FAP)-bounded delineation of the ABP events. In order to illustrate the capabilities of the presented algorithm, it was applied to all 18 subjects of the MIT-BIH Polysomnographic Database (359,000 beats) and the end-systolic and end-diastolic locations of the ABP signal as well as dicrotic notch pressure were extracted and values of sensitivity and positive predictivity Se = 99.86% and P+ = 99.95% were obtained for the detection of all ABP events. High robustness against measurement noises, acceptable detection-delineation accuracy of the ABP events in the presence of severe heart valvular and arrhythmic dysfunctions within a tolerable computational burden (processing time) and having no parameters dependency to the acquisition sampling frequency can be mentioned as the important merits and capabilities of the proposed PCAGI-based ABP events detection-segmentation algorithm.

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