The use of the wavelet transform to describe embolic signals

Aydin, Nizamettin; Padayachee, Soundrie; Markus, Hugh S.

doi:10.1016/s0301-5629(99)00052-6

Cited by 71 publications

(46 citation statements)

References 16 publications

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“…[13][14][15][16][17][18][19][20][21][22][23][24] In our study, the signals were reviewed by 2 observers, and for inclusion, both observers had to agree that the signal was a true embolic signal. The following definition for embolic signals was used: (1) typical visible, (2) short duration, (3) high-intensity signal, (4) within the Doppler spectrum, and (5) occurring at random within the cardiac cycle.…”

Section: Role Of Human Expertsmentioning

confidence: 99%

“…12 Previous attempts at producing an automated system for classifying HITS into artifacts or emboli have failed to achieve the same level of sensitivity (embolus detection) and specificity (artifact rejection) as the human expert, considered the current "gold standard." [13][14][15][16][17][18][19][20][21][22][23][24] Without an automated embolic signal detection system that is as effective as the human expert in terms of sensitivity and specificity, embolus detection remains a time-consuming process, restricted to research studies and unsuitable for clinical practice. 25 To separate artifacts from emboli, the best compromise is dual-gate TCD, which consists of tracing the embolus at 2 different depths in the same artery (time delay) ( Figure 1).…”

mentioning

confidence: 99%

“…24,25,28 Consequently, several authors have argued that new time-frequency techniques that avoid the limitations of FFT are needed, especially in the field of embolus detection and classification. 24,28,29 In addition, recent pilot studies have suggested that wavelet approaches 20,26,27 are particularly well suited to the analysis of short-duration transient signals, such as HITS, and that these new techniques are better than FFT in describing embolic signals. On the basis of our previous pilot study, 27 we have tested one such new approach on a large number of HITS in 2 different clinical situations, while a series of artifacts was produced in normal subjects.…”

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confidence: 99%

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Automatic Classification of HITS Into Artifacts or Solid or Gaseous Emboli by a Wavelet Representation Combined With Dual-Gate TCD

Devuyst

Darbellay

Vesin

et al. 2001

Stroke

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Background and Purpose-Transcranial Doppler (TCD) can detect high-intensity transient signals (HITS) in the cerebralcirculation. HITS may correspond to artifacts or solid or gaseous emboli. The aim of this study was to develop an offline automated Doppler system allowing the classification of HITS. Methods-We studied 600 HITS in vivo, including 200 artifacts from normal subjects, 200 solid emboli from patients with symptomatic internal carotid artery stenosis, and 200 gaseous emboli in stroke patients with patent foramen ovale. The study was 2-fold, each part involving 300 HITS (100 of each type). The first 300 HITS (learning set) were used to construct an automated classification algorithm. The remaining 300 HITS (validation set) were used to check the validity of this algorithm. To classify HITS, we combined dual-gate TCD with a wavelet representation and compared it with the current "gold standard," the human experts. Results-A combination of the peak frequency of HITS and the time delay makes it possible to separate artifacts from emboli. On the validation set, we achieved a sensitivity of 97%, a specificity of 98%, a positive predictive value (PPV) of 99%, and a negative predictive value (NPV) of 94%. To distinguish between solid and gaseous emboli, where positive refers now to the solid emboli, we used the peak frequency, the relative power, and the envelope symmetry of HITS.On the validation set, we achieved a sensitivity of 89%, a specificity of 86%, a conditional PPV of 89%, and a conditional NPV of 89%. Conclusions-An

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Section: Role Of Human Expertsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Automatic Classification of HITS Into Artifacts or Solid or Gaseous Emboli by a Wavelet Representation Combined With Dual-Gate TCD

Devuyst

Darbellay

Vesin

et al. 2001

Stroke

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“…One of the most widely used techniques for identifying non-repetitive and/or periodic patterns or distortions has been the wavelet transform [4][5].This technique adapts a wavelet pattern to the characteristics of the signal distortion to be identified.This has been used for identification of epileptic spikes in electroencephalography (EEG) signal [6][7][8][9], to identify emboli in the blood flow signal [10][11][12][13], to identify arrhythmias in the ECG signal [14][15][16], for identifying flaws in industrial materials (metals, concrete, etc. )in the ultrasound signal [17][18][19], and many other scenarios.…”

Section: Iirelated Workmentioning

confidence: 99%

Stochastic Method for Non-Homogeneous Cycles Identification in Quasi-Periodic Signals

Guzmán¹,

Carbajal²

2015

IJAREEIE

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ABSTRACT:Quasi-periodic signals can be contaminated with random distortions ("artifacts") not manifested periodically and homogenously,without affecting all signal cycles.These distortions cannot be characterized statistically or modelled with a known probability function. In this paper, a stochastic analysis method to detect the presence of such distortions is proposed. The aim of the method is identifying the affected cycles, which exhibit a different morphology compared to the unaffected cycles.The identification of the affected cycles (or non-homogeneous cycles) allows to estimate parameters and extract the useful information needed for a correct characterization of the signal.The method compares nearly periodic signal cycles through the mean square error and the estimated variance of the inherent noise affecting the signal. Expressions are derived to estimate this error and compared with experimental results.

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“…However, the presence of short duration embolic signals invalidates this assumption. New analysis methods may be more suited to embolic signal detection (Fan and Evans, 1994;Aydin et al, 1999) but until these are further developed and widely accepted, processing of embolic signals will continue to be based on the FFT. In view of the potential difficulties in applying the FFT to processing such signals, it is important that signal processing parameters are optimized for embolic signals (Markus et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Optimization of processing parameters for the analysis and detection of embolic signals

Aydin

Markus

2000

European Journal of Ultrasound

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The fast Fourier transform (FFT), which is employed by all commercially available ultrasonic systems, provides a time-frequency representation of Doppler ultrasonic signals obtained from blood flow. The FFT assumes that the signal is stationary within the analysis window. However, the presence of short duration embolic signals invalidates this assumption. For optimal detection of embolic signals if FFT is used for signal processing, it is important that the FFT parameters such as window size, window type, and required overlap ratio should be optimized. The effect of varying window type, window size and window overlap ratio were investigated for both simulated embolic signals, and recorded from patients with carotid artery stenosis. An optimal compromise is the use of a Hamming or Hanning window with a FFT size of 64 (8.9 ms) or 128 (17.9 ms). A high overlap ratio should also be employed in order not to miss embolic events occurring at the edges of analysis windows. The degree of overlap required will depend on the FFT size. The minimum overlap should be 65% for a 64-point window and 80% for a 128-point window.

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The use of the wavelet transform to describe embolic signals

Cited by 71 publications

References 16 publications

Automatic Classification of HITS Into Artifacts or Solid or Gaseous Emboli by a Wavelet Representation Combined With Dual-Gate TCD

Automatic Classification of HITS Into Artifacts or Solid or Gaseous Emboli by a Wavelet Representation Combined With Dual-Gate TCD

Stochastic Method for Non-Homogeneous Cycles Identification in Quasi-Periodic Signals

Optimization of processing parameters for the analysis and detection of embolic signals

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