A new energy detector of micro-emboli using a time-varying threshold

Geryes, Maroun; Ménigot, Sébastien; Hassan, Walid; Almar, Marilys; Guibert, Benoît; Gautier, C; Charara, Jamal; Girault, Jean‐Marc

doi:10.1109/icabme.2015.7323258

Cited by 5 publications

(9 citation statements)

References 7 publications

(9 reference statements)

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“…The clinical study was approved by the ethical committee of the CHRU of Lille. The Holter Transcranial Doppler System-TCD-X (TCD-X, Atys Medical, Soucieu-en-Jarrest, France) along with the description of data collection can be found in our recently published articles [14,19].…”

Section: Methodsmentioning

confidence: 99%

“…The use of the TCD Holter version results in an increase in the number of signal artifacts. For this reason, a manual artifact rejection system was applied as in [14,19].…”

Section: Methodsmentioning

confidence: 99%

“…Notice that this study is an extension of a previous work we proposed [14], and includes higher number of tested signals. However, in the present study, we have omitted the rise rate calculation phase we used in [14] due to its high complexity. We have also added a training phase to optimize the detection thresholds.…”

Section: Introductionmentioning

confidence: 93%

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Enhanced weak Doppler micro-embolic signal detection using energy fluctuations

Geryes

Ménigot

Charara

et al. 2019

Biomedical Signal Processing and Control

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Ultrasound transcranial Holter offers the possibility of long-duration recordings with the micro-emboli detection process being performed offline. This offline detection allows developing much more robust micro-embolic detection procedures and applications. From a signal processing perspective, most commercial automatic detection systems, based on the short time Fourier transform, employ constant detection thresholds either on the whole band or on sub-bands. However, earlier studies highlighted many doubts about the accuracy and robustness of these systems for the detection of weak micro-embolic signatures. In this work, we present an original detection technique based on energy fluctuations as a strong tool for the detection of the weakest micro-embolic signal. Results, from a set of real signals, show a detection rate of 92% and a false alarm rate of 10%. These good performances lead us to consider the proposed technique as a good candidate to detect weak micro-embolic signals.

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Section: Methodsmentioning

confidence: 99%

“…The use of the TCD Holter version results in an increase in the number of signal artifacts. For this reason, a manual artifact rejection system was applied as in [14,19].…”

Section: Methodsmentioning

confidence: 99%

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Enhanced weak Doppler micro-embolic signal detection using energy fluctuations

Geryes

Ménigot

Charara

et al. 2019

Biomedical Signal Processing and Control

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“…To determine the detection threshold, we examine the standard deviation of prediction errors. (A similar strategy was previously proposed in [23].) In our method, the detection threshold is set to…”

Section: B Hits Detectionmentioning

confidence: 99%

A Time-Frequency Approach for Cerebral Embolic Load Monitoring

Imaduddin

LaRovere

Kussman

et al. 2020

IEEE Trans. Biomed. Eng.

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To enable reliable cerebral embolic load monitoring from high-intensity transient signals (HITS) recorded with single-channel transcranial Doppler (TCD) ultrasound. Methods: We propose a HITS detection and characterization method using a weighted-frequency Fourier linear combiner that estimates baseline Doppler signal power. An adaptive threshold is determined by examining the Doppler signal power variance about the baseline estimate, and HITS are extracted if their Doppler power exceeds this threshold. As signatures from multiple emboli may be superimposed, we analyze the detected HITS in the time-frequency (TF) domain to segment the signals into individual emboli. A logistic regression classification approach is employed to classify HITS into emboli or artifacts. Data were collected using a commercial TCD device with emboli-detection capabilities from twelve children undergoing mechanical circulatory support or cardiac catheterization. A subset of 696 HITS were reviewed, annotated, and split into training and testing sets for developing and evaluating the HITS classification algorithm. Results: The classifier yielded 98% and 96% sensitivity for 100% specificity on training and testing data, respectively. The TF approach decomposed 38% of candidate embolic signals into two or more embolic events that ultimately account for 69% of the overall embolic counts. Our processing pipeline resulted in highly accurate emboli identification and produced emboli counts that were lower (by a median of 64%) compared to the commercial ultrasound system's estimates. Significance: Using only single-channel, single-frequency Doppler ultrasound, the proposed method enables sensitive detection and segmentation of embolic signatures. Our approach paves the way towards accurate real-time cerebral emboli monitoring.

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“…Notice that instead of basing the detector on rapid change model, it is also possible to take into account heteroskedastic and cyclostationary properties in the energy random fluctuations [21,22]. In that case, the flucutation of the time-varying energy is obtained by removing the local trend (with a moving average process) from the energy Doppler signal (see Figure 3 (a)).…”

mentioning

confidence: 99%

An overview of nearly a half century of microembolic signal processing techniques

Girault¹,

Geryes

Charara

et al. 2019

2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME)

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Micro-emboli detection for patients with high risk of strokes has been performed with transcranial Doppler (TCD) systems since 1969. As a consequence, instrumentation of TCD systems progressed with the introduction of multigate systems, power mode systems and robotized probes, to name but a few. These new types of TCD have increase the chance of robust detections of quite big micro-emboli and at the same time increased the efficiency of artefact rejection.For a couple of years now, it is now possible to prevent cerebrovascular accidents (CVA) by detecting very small micro-emboli, the latter being precursor signs of strokes. For this sake, a new generation of transcranial Doppler (TCD) systems (holter) is used to record examinations of long duration. In an attempt to detect the smallest possible micro-emboli, offline softwares based on recent signal processing techniques complete advantageously these holter systems.In this communication, an overview of fifty years of research developments in embolic signal processing is proposed. What is interesting during this adventure of a half century is that detection methods were inspired as signal processing discoveries coming from speech processing to econometric. With the advent of the artificial intelligence, new challenges are being drawn up.

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A new energy detector of micro-emboli using a time-varying threshold

Cited by 5 publications

References 7 publications

Enhanced weak Doppler micro-embolic signal detection using energy fluctuations

Enhanced weak Doppler micro-embolic signal detection using energy fluctuations

A Time-Frequency Approach for Cerebral Embolic Load Monitoring

An overview of nearly a half century of microembolic signal processing techniques

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