A Method for the Automated Detection of Venous Gas Bubbles in Humans Using Empirical Mode Decomposition

Chappell, Michael A.; Payne, Stephen J.

doi:10.1007/s10439-005-6045-8

Cited by 19 publications

(17 citation statements)

References 20 publications

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“…Doppler [12] and in particular ultrasound Doppler [13,14] is employed both in medical and in industrial applications for estimating fluid velocity. Doppler systems are most commonly used [15] and ultrasonic detection of circulating VGE after diving is considered a useful index for safe decompression [16]. Doppler ultrasound (DU) monitoring provides an audio signal that, through aural analysis, allows a trained observer to detect the number of embolic event in divers.…”

Section: Introductionmentioning

confidence: 99%

“…This type of information is evident in a set of IMFs, but quite hidden in STFT or DWT analysis. EMD has been applied for the off-line detection of VGE in the precordial region [15]. Such an approach relies on the regular occurrence of artifacts in the background signal compared to the transient nature of the embolic signals.…”

Section: Introductionmentioning

confidence: 99%

“…EMD is more suitable for detecting single bubbles of big entity (i.e., greater than 25 microns) than other algorithms, but small or showers of bubbles may be undetectable. Moreover, although excellent sensitivity has been achieved with this method when run on precordial Doppler audio recordings, specificity (false positives) was reported as a problem [15]. To overcome such limitations, a new EMD-based approach for analyzing echo Doppler audio recorded in precordial region is presented.…”

Section: Introductionmentioning

confidence: 99%

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An EMD-Based Algorithm for Emboli Detection in Echo Doppler Audio Signals

et al. 2019

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Divers' health state after underwater activity can be assessed after the immersion using precordial echo Doppler examination. An audio analysis of the acquired signals is performed by specialist doctors to detect circulating gas bubbles in the vascular system and to evaluate the decompression sickness risk. Since on-site medical assistance cannot always be guaranteed, we propose a system for automatic emboli detection using a custom portable device connected to the echo Doppler instrument. The empirical mode decomposition method is used to develop a real-time algorithm able to automatically detect embolic events and, consequently, assess the decompression sickness risk according to the Spencer's scale. The proposed algorithm has been tested according to an experimental protocol approved by the Divers Alert Network. It involved 30 volunteer divers and produced 37 echo Doppler files useful for the algorithm's performances evaluation. The results obtained by the proposed emboli detection algorithm (83% sensitivity and 76% specificity) make the system particularly suitable for real-time evaluation of the decompression sickness risk level. Furthermore, the system could also be used in continuous monitoring of hospitalized patients with embolic risks such as post surgery ones.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An EMD-Based Algorithm for Emboli Detection in Echo Doppler Audio Signals

et al. 2019

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“…Hence linear methods may not be effective in analyzing these signals. EMD and EEMD were successfully used for noise cancellation and analysis of some biomedical signals [25][26][27][28][29][30]. For example, Velasco MB et al [31] utilized EMD to filter the high-frequency noise and baseline wander of ECG.…”

Section: Introductionmentioning

confidence: 99%

Noise Cancellation from Vibrocardiographic Signals Based on the Ensemble Empirical Mode Decomposition

TaebiA¹,

Ha²

2017

JABB

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Vibrocardiographic (VCG) signals are the cardiac vibration measured at the chest surface. These signals can contain useful information for diagnosing cardiac conditions but are often contaminated by noise. Although band-pass and adaptive filters were used for noise removal from similar signals, the utility of ensemble empirical mode decomposition (EEMD) for filtering VCG was not previously investigated. In this study, an EEMD-based filter was proposed and tested. The filtering scheme first decomposed the VCG waveform into a set of intrinsic mode functions (IMF) then utilized the partial sum of IMFs to remove white noise that was added to simulated VCG signals. To measure the filter effectiveness, the normalized root-mean-square error (NRMSE) between the clean (i.e., before adding noise) and filtered signals was calculated for signal-to-noise ratios ranging from 1 to 20 dB. The EEMD-based filter performance was also compared with traditional methods such as Wiener filter. This comparison suggested that EEMDbased filter outperformed the Wiener filter in noise removal from simulated VCG. These results also suggested that EEMD may be utilized for white noise removal from actual VCG signals. Further investigations are warranted to study the relation between IMFs and different types of noise, which can enhance the effectiveness of EEMD-based filters in removing these noise types from actual VCG signals.

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“…Evidently, the resolution of this imaging method should be improved in order to achieve sensitive imaging and assessment of small µm-sized bubbles. Also, new method has to be developed for detection of stationary bubbles in tissue that could be used for prediction or diagnosis of gas emboli [66].…”

Section: Introductionmentioning

confidence: 99%

Novel Method of Noninvasive Detection and Assessment of Gas Emboli and DCS

Larin¹

2010

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information SSOCT has been developed. The system has an axial resolution of 10 µm, phase sensitivity of 0.03 radians, imaging depth of up to 6 mm in air, and in-depth scanning speed of 20 kHz for a single A-line. The performance of the sensing system was carefully evaluated in optical phantoms containing gas microbubbles with different diameter and tissues in vivo. Obtained results demonstrate that bubbles with diameter greater than 10 µm could be detected by both structural imaging and phase response whereas bubbles with diameters less than 10 µm could be detected by the phase response of the SSOCT with high sensitivity.Noninvasive, optical, microbbules, decompression AbstractNoninvasive functional imaging, monitoring and quantification of microbubbles forming in blood and tissues upon rapid changes in barometric pressure are extremely important for effective therapy and diagnostics of several diseases as well as for imaging and drug delivery projects. However, current techniques are unable of imaging and efficient detection of bubbles with diameter less than 50 micrometers. The goal of this proposal was to develop novel PhaseSensitive Swept Source Optical Coherence Tomography (PhS-SSOCT) technique capable of real-time, sensitive, accurate, and noninvasive imaging, monitoring, and quantification of microbubbles in tissues. During these studies, a novel phase resolved system based on Swept Source Optical Coherence Tomography (SSOCT) has been developed. The system has an axial resolution of 10 µm, phase sensitivity of 0.03 radians, imaging depth of up to 6 mm in air, and in-depth scanning speed of 20 kHz for a single A-line. The performance of the sensing system was carefully evaluated in optical phantoms containing gas microbubbles with different diameter. Obtained results demonstrate that bubbles with diameter greater than 10 µm could be detected by both structural imaging and phase response whereas bubbles with diameters less than 10 µm could be detected by the phase response of the SSOCT with high sensitivity. The accuracy for measurement of the diameter of gas microbubbles is limited to 10 µm in structural imaging and 0.01 µm in phase-sensitive monitoring. Preliminary studies were also performed in animals in vivo for the rapid assessment of the circulating microbubbles. The in vivo results demonstrate capability of the developed instrument to image bubbles with diameter above 8 µm.The results from the study indicate that the developed SSOCT system could be used to detect fast-moving microbubbles and warr...

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A Method for the Automated Detection of Venous Gas Bubbles in Humans Using Empirical Mode Decomposition

Cited by 19 publications

References 20 publications

An EMD-Based Algorithm for Emboli Detection in Echo Doppler Audio Signals

An EMD-Based Algorithm for Emboli Detection in Echo Doppler Audio Signals

Noise Cancellation from Vibrocardiographic Signals Based on the Ensemble Empirical Mode Decomposition

Novel Method of Noninvasive Detection and Assessment of Gas Emboli and DCS

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