Spectrogram analysis of arterial Doppler signals for off-line automated hits detection

Embolic Doppler ultrasound signal detection via fractional Fourier transform

Computerized analysis of Doppler ultrasound signals can aid early detection of asymptomatic circulating emboli. For analysis, physicians use informative features extracted from Doppler ultrasound signals. Time -frequency analysis methods are useful tools to exploit the transient like signals such as Embolic signals. Detection of discriminative features would be the first step toward automated analysis of embolic Doppler ultrasound signals. The most problematic part of setting up emboli detection system is to differentiate embolic signals from confusing similar wave-like patterns such as Doppler speckle and artifacts caused by tissue movement, probe tapping, speaking etc. In this study, discrete version of fractional Fourier transform is presented as a solution in the detection of emboli in digitized Doppler ultrasound signals. An accurate set of parameters are extracted using short time Fourier transform and fractional Fourier transform and the results are compared to reveal detection quality. Experimental results prove the efficiency of fractional Fourier transform in which discriminative features becomes more evident.

An automated feature extraction and emboli detection system based on the PCA and fuzzy sets

The Doppler ultrasound technique is commonly used to detect emboli in the cerebral circulation. Here an automated feature extraction and emboli detection system is proposed based on the principal components analysis (PCA) and fuzzy sets. In the system, two features, R(ry) and k, are extracted by the PCA method. Meanwhile, MMR and sigma(f min) are obtained with the traditional temporal processing and spectrogram analysis, respectively. Normal blood flow signals are firstly distinguished from abnormal signals by MMR. Then signals containing emboli and disturbance noises are further differentiated by other features based on fuzzy sets. From experiments with computer-simulated and clinical Doppler ultrasound signals, it is shown that features extracted from the PCA method achieve better classification performance than those of traditional methods. The fuzzy-based detection system not only obtains high classification accuracy but is more applicable in clinical diagnosis.

Multigate transcranial Doppler ultrasound system with real-time embolic signal identification and archival

An integrated system for acquisition and processing of intracranial and extracranial Doppler signals and automatic embolic signal detection has been developed. The hardware basis of the system is a purpose-built acquisition/processing board that includes a multigate Doppler unit controlled through a computer. The signal-processing engine of the system contains a fast Fourier transform (FFT)-based, spectral-analysis unit and an embolic signal-detection unit using expert system reasoning theory. The system is designed so that up to four receive gates from a single transducer can be used to provide useful reasoning information to the embolic signal-detection unit. Alternatively, two transducers can be used simultaneously, either for bilateral transcranial Doppler (TCD) investigations or for simultaneous intra- and extracranial investigation of different arteries. The structure of the software will allow the future implementation of embolus detection algorithms that use the information from all four channels when a single transducer is used, or of independent embolus detection in two sets of two channels when two transducers are used. The user-friendly system has been tested in-vitro, and it has demonstrated a 93.6% sensitivity for micro-embolic signal (MES) identification. Preliminary in-vivo results also are encouraging.

A method for the automated detection of venous gas bubbles in humans using empirical mode decomposition

Doppler ultrasound signals are widely used to grade the quantity of circulating venous bubbles in divers. Current techniques rely on trained observers, making the grading process both time-consuming and subjective. The automated detection of bubbles, however, is confounded by the presence of other signals, primarily those arising from blood motion. Empirical Mode Decomposition was used here to calculate the intrinsic mode functions (IMFs) of a number of Doppler ultrasound signals from recreational divers, post-decompression. The IMFs provide a basis set for signal decomposition, each IMF corresponding to a different timescale in the signal. Each signal was found to comprise approximately 20 IMFs: the precise number being dependent upon the nature of the signal. A method is presented to detect bubbles using the IMF; features are first identified in the individual heart cycles, these having been previously determined using a robust peak detection method, by examining deviations from the ensemble averaged IMF. Bubbles are then identified as features appearing in more than one IMF, with significant energy in the original signal. This method has been applied to a subset of the available database and appears to perform with good sensitivity even when the signal has variable signal strength.

Embolic Doppler Ultrasound Signal Detection Using Discrete Wavelet Transform

Asymptomatic circulating emboli can be detected by Doppler ultrasound. Embolic Doppler ultrasound signals are short duration transient like signals. The wavelet transform is an ideal method for analysis and detection of such signals by optimizing time-frequency resolution. We propose a detection system based on the discrete wavelet transform (DWT) and study some parameters, which might be useful for describing embolic signals (ES). We used a fast DWT algorithm based on the Daubechies eighth-order wavelet filters with eight scales. In order to evaluate feasibility of the DWT of ES, two independent data sets, each comprising of short segments containing an ES (N = 100), artifact (N = 100) or Doppler speckle (DS) (N = 100), were used. After applying the DWT to the data, several parameters were evaluated. The threshold values used for both data sets were optimized using the first data set. While the DWT coefficients resulting from artifacts dominantly appear at the higher scales (five, six, seven, and eight), the DWT coefficients at the lower scales (one, two, three, and four) are mainly dominated by ES and DS. The DWT is able to filter out most of the artifacts inherently during the transform process. For the first data set, 98 out of 100 ES were detected as ES. For the second data set, 95 out of 100 ES were detected as ES when the same threshold values were used. The algorithm was also tested with a third data set comprising 202 normal ES; 198 signals were detected as ES.

Comparison of time-frequency estimators for peripheral embolus detection

Recently, a time-frequency processing of peripheral arterial Doppler signals, based on the spectrogram, was proposed to detect automatically high-intensity transient signal. Three time-frequency representations, the smooth-pseudo-Wigner-Ville, the Choï-Williams and the cone-kernel distributions were compared with the spectrogram, following the detection scheme previously reported. The results showed that the spectrogram provided the best compromises between false-detection and no-detection compared with the other time-frequency representations.