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

Pierleoni, Paola; Palma, Lorenzo; Belli, Alberto; Pieri, Massimo; Maurizi, Lorenzo; Pellegrini, Maurizio; Marroni, Alessandro

doi:10.3390/electronics8080824

Cited by 4 publications

(3 citation statements)

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“…Huang and Flandrin proposed the EEMD algorithm for the mode mixing in the EMD [9,10] method and the characteristics of the white noise decomposition process [3]. The specific decomposition steps are as follows.…”

Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

Research on Power Quality Disturbance Detection Method Based on Improved Ensemble Empirical Mode Decomposition

et al. 2020

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With the increasing proportion of various unbalanced loads in the power grid, power quality is seriously challenged. It is of great significance to effectively detect, analyze, and evaluate the power quality problems. First, this paper introduces the current situation of power quality (PQ) disturbance detection methods. It summarizes that the current PQ disturbance detection methods include Wavelet Transform (WT), Hilbert–Huang Transform (HHT), and Ensemble Empirical Mode Decomposition (EEMD). EEMD has a better detection accuracy, but its running time is longer. Therefore, to reduce the running time of the EEMD algorithm, this paper proposed two improvements: increasing the screening threshold and selecting piecewise cubic Hermite interpolation polynomial fitting. At the same time, the mathematical models of transient power quality disturbance and harmonic were established for comparative verification. The experimental results showed that the improved Ensemble Empirical Mode Decomposition (IEEMD) algorithm greatly reduced the running time of the algorithm on the premise of ensuring the detection accuracy. Hence, the improvement of this paper is of great significance for the industrial application of the EEMD algorithm.

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Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

Research on Power Quality Disturbance Detection Method Based on Improved Ensemble Empirical Mode Decomposition

et al. 2020

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“…Recently, a dataset of post-dive Doppler recordings was released for the purposes of automated VGE extraction and grading algorithm development [ 14 ]. Several signal-separation algorithms have been explored using this data such as adaptive empirical mode decomposition and complete ensemble empirical mode decomposition [ 15 , 16 ]. Even with its clear usefulness for the development of new VGE analysis methodologies, this real-world dataset from Pierleoni et al (2019) still exhibits certain limitations, such as limited dataset size (30 recordings) and use of a fetal Doppler system (FD1 2-MHz Doppler probe, Huntleigh Ltd, Cardiff, UK) which is not standard for all decompression research.…”

Section: Introductionmentioning

confidence: 99%

An open-source framework for synthetic post-dive Doppler ultrasound audio generation

Hoang

Azarang

et al. 2023

PLoS ONE

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Doppler ultrasound (DU) measurements are used to detect and evaluate venous gas emboli (VGE) formed after decompression. Automated methodologies for assessing VGE presence using signal processing have been developed on varying real-world datasets of limited size and without ground truth values preventing objective evaluation. We develop and report a method to generate synthetic post-dive data using DU signals collected in both precordium and subclavian vein with varying degrees of bubbling matching field-standard grading metrics. This method is adaptable, modifiable, and reproducible, allowing for researchers to tune the produced dataset for their desired purpose. We provide the baseline Doppler recordings and code required to generate synthetic data for researchers to reproduce our work and improve upon it. We also provide a set of pre-made synthetic post-dive DU data spanning six scenarios representing the Spencer and Kisman-Masurel (KM) grading scales as well as precordial and subclavian DU recordings. By providing a method for synthetic post-dive DU data generation, we aim to improve and accelerate the development of signal processing techniques for VGE analysis in Doppler ultrasound.

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“…The audio tracks once acquired have been filtered eliminating a few seconds at the beginning and at the end of the entire recording and the interval between the two measurements in order to reduce the unwanted noise due to doppler probe positioning. For each acquisition an assessment of the circulating bubbles according to the extended Spencer scale has been provided by experts.Data source locationDepartment of Information Engineering, Università Politecnica delle Marche, Ancona, ItalyData accessibilityWith the articleRelated research articlePaola Pierleoni, Lorenzo Palma, Alberto Belli, Massimo Pieri, Lorenzo Maurizi, Marco Pellegrini and Alessandro Marroni“An EMD-Based Algorithm for Emboli Detection in Echo Doppler Audio Signals”Electronics https://doi.org/10.3390/electronics8080824 [1]…”

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confidence: 99%

Doppler ultrasound dataset for the development of automatic emboli detection algorithms

et al. 2019

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The article describes a dataset of doppler ultrasound audio tracks taken on a sample of 30 divers according to the acquisition protocol defined by the Divers Alert Network. The audio tracks are accompanied by a medical evaluation for the decompression sickness risk according to the Spencer's scale levels.During the acquisition campaign, each diver in the post-dive phase was subjected to a double doppler ultrasound examination of approximately 45 seconds each one in the precordial area using a Huntleigh FD1 Fetal doppler probe. The two measurements were separated by a time of 8–10 seconds necessary for carrying out specific physical exercises designed to free the bubbles trapped in the tissues. The audio tracks were stored without compression via the TASCAM DP-004 recorder and processed in order to eliminate the noise generated by the positioning of the probe and the time interval between the two measurements.The audio tracks recorded during the acquisition campaign have been evaluated by experts belonging to three independent blind teams in order to provide an assessment of the decompression sickness risk according to Extended Spencer's scale. The specific typology of doppler ultrasound audio tracks and the associated medical evaluation according to the Spencer's scale levels make this dataset useful for the development, testing, and performance evaluation of new audio processing algorithms capable of automatically detecting bubbles in the blood vessels.

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

Cited by 4 publications

References 31 publications

Research on Power Quality Disturbance Detection Method Based on Improved Ensemble Empirical Mode Decomposition

Research on Power Quality Disturbance Detection Method Based on Improved Ensemble Empirical Mode Decomposition

An open-source framework for synthetic post-dive Doppler ultrasound audio generation

Doppler ultrasound dataset for the development of automatic emboli detection algorithms

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