A Method for Extracting Characteristic Frequency Components of Blood Flow Signals Based on Wavelet Transform

2013 ◽  
Vol 313-314 ◽  
pp. 1221-1224 ◽  
Author(s):  
Ruo Fei Cui ◽  
Si Te Luo ◽  
Li Qian Lu ◽  
Wei Wei Zhou ◽  
Zeng Yong Li
Keyword(s):  
Blood Flow ◽  
Wavelet Transform ◽  
Characteristic Frequency ◽  
Frequency Resolution ◽  
Peripheral Blood Flow ◽  
Frequency Interval ◽  
Frequency Method ◽  
Doppler Flowmetry ◽  
Time Frequency ◽  
Frequency Components

The objective of this paper is to propose a method for exacting the characteristic frequency components of blood flow signals based on wavelet transform. The wavelet transform technique, a time-frequency method with logarithmic frequency resolution, was used to analyze oscillations in human peripheral blood flow measured by laser Doppler flowmetry (LDF). In the frequency interval from 0.008 to 2.0 Hz, the LDF signal consists of components with five different characteristic frequenciesmetabolic (0.008-0.02Hz), neurogenic (0.02-0.06Hz), myogenic (0.06-0.15Hz), respiratory (0.15-0.4Hz) and cardiac (0.4-2.0Hz). The five frequency components were extracted in time domain and reconstructed using cubic spline interpolation in this study. The results showed that it was an effective way to extract each component of blood flow signals.

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

Microcirculation and tissue oxygenation in the head and limbs during hyperbaric oxygen treatment

2021 ◽  
pp. 338-344
Author(s):  
Naoki Yamamoto ◽  
◽  
Ryohei Takada ◽  
Takuma Maeda ◽  
Toshitaka Yoshii ◽  
...  
Keyword(s):  
Blood Flow ◽  
Hyperbaric Oxygen ◽  
Peripheral Blood ◽  
Body Part ◽  
Peripheral Blood Flow ◽  
Therapeutic Effects ◽  
Transcutaneous Oxygen ◽  
Doppler Flowmetry ◽  
Post Exposure ◽  
Circulatory Disorders

Introduction: Hyperbaric oxygen (HBO) exposure for 10−15 min has been shown to reduce peripheral blood flow due to vasoconstriction. However, the relationship between decreased peripheral blood flow and the therapeutic effects of HBO treatment on peripheral circulatory disorders remain unknown. Longer exposures have been reported to have vasodilatory effects and increase peripheral blood flow. This study investigated the effect of HBO treatment on blood flow and transcutaneous oxygen pressure (TcPO2). Methods: Twenty healthy volunteers aged 20-65 years (nine males) participated in this study. All participants breathed oxygen for 60 min at 253.3 kPa. Peripheral blood flow using laser Doppler flowmetry and TcPO2 on the ear, hand, and foot were continuously measured from pre-HBO exposure to 10 min post-exposure. Results: Peripheral blood flow in each body part decreased by 7-23% at the beginning of the HBO exposure, followed by a slow increase. Post-exposure, peripheral blood flow increased 4-76% in each body part. TcPO2 increased by 840-1,513% during the exposure period, and remained elevated for at least 10 min after the exposure. Conclusions: The findings of the current study suggest vasoconstriction during HBO treatment is transient, and even when present does not inhibit the development of increased tissue oxygen partial pressure. These findings are relevant to studies investigating changes in peripheral blood flow during HBO treatment in patients with circulatory disorders.

Decomposition of Event-Related Brain Potentials into Multiple Functional Components Using Wavelet Transform

2001 ◽  
Vol 32 (3) ◽  
pp. 122-138 ◽  
Author(s):  
Tamer Demiralp ◽  
Ahmet Ademoglu
Keyword(s):  
Wavelet Transform ◽  
Three Dimensional ◽  
Experimental Session ◽  
Brain Potentials ◽  
Time Frequency ◽  
Similar Characterization ◽  
Component Identification ◽  
Frequency Components ◽  
Neurophysiological Mechanisms ◽  
Functional Components

Event related brain potential (ERP) waveforms consist of several components extending in time, frequency and topographical space. Therefore, an efficient processing of data which involves the time, frequency and space features of the signal, may facilitate understanding the plausible connections among the functions, the anatomical structures and neurophysiological mechanisms of the brain. Wavelet transform (WT) is a powerful signal processing tool for extracting the ERP components occurring at different time and frequency spots. A technical explanation of WT in ERP processing and its four distinct applications are presented here. The first two applications aim to identify and localize the functional oddball ERP components in terms of certain wavelet coefficients in delta, theta and alpha bands in a topographical recording. The third application performs a similar characterization that involves a three stimulus paradigm. The fourth application is a single sweep ERP processing to detect the P300 in single trials. The last case is an extension of ERP component identification by combining the WT with a source localization technique. The aim is to localize the time-frequency components in three dimensional brain structure instead of the scalp surface. The time-frequency analysis using WT helps isolate and describe sequential and/or overlapping functional processes during ERP generation, and provides a possibility for studying these cognitive processes and following their dynamics in single trials during an experimental session.

PECULIARITIES OF MICROCIRCULATION IN ATHLETES VERIFIED BY LASER DOPPLER FLOWMETRY

2017 ◽  
Author(s):  
В. Павлов ◽  
Н. Александрова ◽  
В. Карандашов ◽  
А. Резепов
Keyword(s):  
Blood Flow ◽  
Statistical Difference ◽  
Capillary Blood ◽  
Laser Doppler ◽  
Football Players ◽  
Peripheral Blood Flow ◽  
Doppler Flowmetry ◽  
High Class ◽  
High Baseline ◽  
Physically Inactive

Введение. Одной из физиологических особенностей спортсмена, отличающей его от физически неактивных лиц, является уровень развития у него микроцикуляторного русла. Цель работы: оценить возможности метода лазерной допплеровской флоуметрии (ЛДФ) для верификации особенностей микроциркуляции (МЦ) у спортсменов высокого класса. Материалы и методы. Обследовано 24 футболиста (средний стаж занятий спортом — 15,80 ± 3,24 лет) и 24 человека, не занимающихся спортом. Определяли показатель МЦ при помощи метода ЛДФ и рассчитывали резерв капиллярного кровотока (РКК). Результаты. У спортсменов высокого класса установлен высокий исходный показатель МЦ и умеренно повышенный РКК. Выявлено достоверно значимое различие в структуре типов МЦ у профессиональных спортсменов высокого класса в сравнении с физически неактивным населением. Заключение. Спортсменов от физически неактивных лиц отличают высокий исходный показатель МЦ, больший разброс ее параметров и лишь небольшое превалирование РКК. Характерными маркерами периферического кровотока спортсмена являются гиперемический и спастический типы МЦ. Introduction. Microcirculation (MC) state is one of physiological characteristics of athlete that differ him from physically inactive persons. The aim of study: to assess the possibilities of laser Doppler fl owmetry (LDF) for verifi cation of MC peculiarities in high-class athletes. Materials and methods. We examined 24 football players (average sports experience — 15,80 ± 3,24 years) and 24 non-athletes. By LDF method we determined MC index and calculated reserve of capillary blood fl ow (RCC). Results. In high-class athletes we found high baseline MC index and moderately increased RCC. A significantly statistical difference in structure of MC types among professional high-class athletes was revealed in comparison with physically inactive population. Conclusion. Athletes are diff ered from physically inactive persons by high baseline MC index, greater variation of MC parameters and slight prevalence of RCC. Hyperemic and spastic MC types are characteristic markers of peripheral blood flow in high-class athletes.

scholarly journals Adapted Wavelet Transform for Twisted Blade Diagnosis in Multi Stage Rotor

2019 ◽  
Vol 255 ◽  
pp. 02011
Author(s):  
Ahmed M. Abdelrhman ◽  
M. Salman Leong ◽  
Y.H. Ali ◽  
Iftikhar Ahmad ◽  
Christina G. Georgantopoulou ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Rotor System ◽  
Fault Detection And Diagnosis ◽  
Frequency Resolution ◽  
Time Frequency ◽  
Multi Stage ◽  
The Common ◽  
Detection And Diagnosis ◽  
Twisted Blade

This paper studies the diagnosis of twisted blade in a multi stages rotor system using adapted wavelet transform and casing vibration. The common detection method (FFT) is effective only if sever blade faults occurred while the minor faults usually remain undetected. Wavelet analysis as alternative technique is still unable to fulfill the fault detection and diagnosis accurately due to its inadequate time-frequency resolution. In this paper, wavelet is adapted and its time-frequency is improved. Experimental study was undertaken to simulate multi stages rotor system. Results showed that the adapted wavelet analysis is effective in twisted blade diagnosis compared to the conventional one.

scholarly journals Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170254 ◽  
Author(s):  
Jean Baptiste Tary ◽  
Roberto Henry Herrera ◽  
Mirko van der Baan
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Signal ◽  
American Football ◽  
Frequency Resolution ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Frequency Information ◽  
Synchrosqueezing Transform

The continuous wavelet transform (CWT) has played a key role in the analysis of time-frequency information in many different fields of science and engineering. It builds on the classical short-time Fourier transform but allows for variable time-frequency resolution. Yet, interpretation of the resulting spectral decomposition is often hindered by smearing and leakage of individual frequency components. Computation of instantaneous frequencies, combined by frequency reassignment, may then be applied by highly localized techniques, such as the synchrosqueezing transform and ConceFT, in order to reduce these effects. In this paper, we present the synchrosqueezing transform together with the CWT and illustrate their relative performances using four signals from different fields, namely the LIGO signal showing gravitational waves, a ‘FanQuake’ signal displaying observed vibrations during an American football game, a seismic recording of the M w 8.2 Chiapas earthquake, Mexico, of 8 September 2017, followed by the Irma hurricane, and a volcano-seismic signal recorded at the Popocatépetl volcano showing a tremor followed by harmonic resonances. These examples illustrate how high-localization techniques improve analysis of the time-frequency information of time-varying signals. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

scholarly journals Improvement of Accuracy in Damage Localization Using Frequency Slice Wavelet Transform

2012 ◽  
Vol 19 (4) ◽  
pp. 585-596 ◽  
Author(s):  
Xinglong Liu ◽  
Zhongwei Jiang ◽  
Zhonghong Yan
Keyword(s):  
Wavelet Transform ◽  
Damage Identification ◽  
Lamb Waves ◽  
Group Velocity Dispersion ◽  
Propagation Distance ◽  
Primary Objective ◽  
Frequency Resolution ◽  
Damage Localization ◽  
Arrival Times ◽  
Time Frequency

Damage localization is a primary objective of damage identification. This paper presents damage localization in beam structure using impact-induced Lamb wave and Frequency Slice Wavelet Transform (FSWT). FSWT is a new time-frequency analysis method and has the adaptive resolution feature. The time-frequency resolution is a vital factor affecting the accuracy of damage localization. In FSWT there is a unique parameter controlling the time-frequency resolution. To improve the accuracy of damage localization, a generalized criterion is proposed to determine the parameter value for achieving a suitable time-frequency resolution. For damage localization, the group velocity dispersion curve (GVDC) of A0Lamb waves in beam is first accurately estimated using FSWT, and then the arrival times of reflection wave from the crack for some individual frequency components are determined. An average operation on the calculated propagation distance is then performed to further improve the accuracy of damage localization.

MECHANICAL WATCH SIGNATURE ANALYSIS BASED ON WAVELET DECOMPOSITION

2009 ◽  
Vol 07 (04) ◽  
pp. 491-512 ◽  
Author(s):  
QINGBO HE ◽  
RUXU DU
Keyword(s):  
Acoustic Signal ◽  
Wavelet Decomposition ◽  
Wavelet Packet ◽  
Wide Frequency Range ◽  
Frequency Resolution ◽  
Signature Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Wavelet Techniques

The acoustic signal of mechanical watch is a distinct multi-component signal. It contains many frequency components corresponding to specific escapement motion sources with a very wide frequency range. Therefore, it is challenging for signature analysis of mechanical watch by the acoustic signal. This paper studies the time-frequency signatures of the mechanical watch based on wavelet decomposition. Two methods are proposed to improve the frequency resolution of traditional wavelet techniques by combining other beneficial techniques in the sense of decomposing specific mono- or independent components. The empirical mode decomposition (EMD) is presented to advance the wavelet packet decomposition (WPD) to decompose the mono-component signals. And the blind source separation (BSS) makes the redundancy of continuous wavelet transform (CWT) further gain good frequency resolution in the independent meaning. The decomposed signals by the two methods reveal the insight of mechanical watch movement and can contribute much simpler and clearer time-frequency signatures. Experimental results indicated the effectiveness of the two methods and the value of the time-frequency signatures in analyzing and diagnosing mechanical watch movements.

A NOVEL R-PEAK DETECTION METHOD COMBINING ENERGY AND WAVELET TRANSFORM IN ELECTROCARDIOGRAM SIGNAL

2014 ◽  
Vol 26 (01) ◽  
pp. 1450007 ◽  
Author(s):  
Xiuling Liu ◽  
Jianli Yang ◽  
Xiaoyu Zhu ◽  
Suiping Zhou ◽  
Hongrui Wang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Detection Method ◽  
Peak Detection ◽  
Frequency Resolution ◽  
Detection Accuracy ◽  
Energy Window ◽  
Qrs Complex ◽  
Time Frequency ◽  
Qrs Morphology ◽  
Morphology Changes

QRS complex is the most important part in electrocardiogram (ECG) as it contains the most important information of heart activities. R-peak detection is the first, yet crucial, step in most ECG automatic diagnose methods. Due to the existence of noise in ECG signals and changes in QRS morphology, most existing methods are not robust in different conditions. In the field of intelligent remote health caring, in addition to the detection accuracy, timeliness is also an important research issue. In this paper, wavelet transform and energy window transform are introduced, which form the basis of a novel R-peak detection method. Wavelet transform is used to efficiently reduce noise and highlight useful ECG signal for it has good time-frequency resolution characters, and energy window transform converts time domain signal to energy domain, which makes it easier to isolate QRS complex from other signals. As a result, influence from QRS morphology changes can be effectively alleviated. To validate the effectiveness of this new method, ECG records of MIT-BIH arrhythmia database are used in the experiments. The experiment results show that the proposed method is efficient and robust to noise and QRS morphology changes. The computational cost of the proposed method has also been evaluated.

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