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

Recently, a time processing of arterial Doppler signals was proposed to detect automatically high-intensity transient signals (HITS). This technique provided satisfactory detection results, but was not always constantly accurate, particularly with high-resistance blood velocity profiles. A time-frequency processing, based on the spectrogram, is presented to detect the presence of emboli in the arterial Doppler signals. The method uses the narrow-band hypothesis and extracts the detection criterion from the time-frequency representation (TFR). A first database of 560 peripheral arterial Doppler HITS was created to study microemboli and to define the normal limits to be used in our method. A threshold was experimentally defined using this database, and then applied to 38 recordings from 12 patients. Using another database, 6 human expert Doppler users reported 140, 176, 155, 161, 161 and 146 HITS, corresponding to a total of 197 different observed HITS. When an event was detected by at least 6, 5, 4, 3, 2 and 1 of the observers, sensitivity of the automatic detection was 93.9, 91.7, 89.6, 88.7, 84.7 and 73.1%, respectively. The sensitivity of our automatic detection is, thus, highly associated with the number of observers in agreement. A preliminary experiment has been performed to test the method in the case of long recording duration. In 15 patients, 6 h 24 min of recordings have been analyzed. The proposed automated processing provided an overall sensibility of 91.5%. The present work shows that this detection scheme preserves good sensibility and improves the positive predictive value compared with the time-processing recently proposed.

[1]  P. Fish,et al.  Comparison of Doppler signal analysis techniques for velocity waveform, turbulence and vortex measurement: a simulation study. , 1996, Ultrasound in medicine & biology.

[2]  S. Nicholls,et al.  Venous thromboembolism: detection by duplex scanning. , 1996, Journal of vascular surgery.

[3]  Edward J. Wegman,et al.  Statistical Signal Processing , 1985 .

[4]  R. Cobbold,et al.  Comparison of four digital maximum frequency estimators for Doppler ultrasound. , 1988, Ultrasound in medicine & biology.

[5]  M.A. Moehring,et al.  Pulse Doppler ultrasound detection, characterization and size estimation of emboli in flowing blood , 1994, IEEE Transactions on Biomedical Engineering.

[6]  M. Ruano,et al.  Nonstationarity broadening reduction in pulsed Doppler spectrum measurements using time-frequency estimators , 1996, IEEE Transactions on Biomedical Engineering.

[7]  M. Portnoff Short-time Fourier analysis of sampled speech , 1981 .

[8]  A. Ishimaru,et al.  Sizing emboli in blood using pulse Doppler ultrasound. II. Effects of beam refraction , 1996, IEEE Transactions on Biomedical Engineering.

[9]  C. Tegeler High-Intensity Transient Signals Detected by Doppler Ultrasonography: Searching for Answers , 1994 .

[10]  R. Redner,et al.  Mixture densities, maximum likelihood, and the EM algorithm , 1984 .

[11]  M P Spencer,et al.  Experiments on decompression bubbles in the circulation using ultrasonic and electromagnetic flowmeters. , 1969, Journal of occupational medicine. : official publication of the Industrial Medical Association.

[12]  L. Scharf,et al.  Statistical Signal Processing: Detection, Estimation, and Time Series Analysis , 1991 .

[13]  J L Saumet,et al.  The narrow band hypothesis: an interesting approach for high-intensity transient signals (HITS) detection. , 1998, Ultrasound in medicine & biology.

[14]  Carl W. Helstrom,et al.  Elements of signal detection and estimation , 1994 .

[15]  A. Nowicki,et al.  Comparison of the performance of three maximum Doppler frequency estimators coupled with different spectral estimation methods. , 1994, Ultrasound in medicine & biology.

[16]  L.-G. Durand,et al.  Comparison of time-frequency distribution techniques for analysis of simulated Doppler ultrasound signals of the femoral artery , 1994, IEEE Transactions on Biomedical Engineering.

[17]  M. Brown,et al.  Computerized Detection of Cerebral Emboli and Discrimination From Artifact Using Doppler Ultrasound , 1993, Stroke.

[18]  Helmuth Steinmetz,et al.  Detection of Intracranial Emboli in Patients With Symptomatic Extracranial Carotid Artery Disease , 1992, Stroke.

[19]  P R Bell,et al.  Differentiation between emboli and artefacts using dual-gated transcranial Doppler ultrasound. , 1996, Ultrasound in medicine & biology.

[20]  F. Hlawatsch,et al.  Linear and quadratic time-frequency signal representations , 1992, IEEE Signal Processing Magazine.

[21]  W. Mess,et al.  Automatic embolus detection compared with human experts. A Doppler ultrasound study. , 1996, Stroke.

[22]  J. Ritcey,et al.  Sizing emboli in blood using pulse Doppler ultrasound. I. Verification of the EBR model , 1996, IEEE Transactions on Biomedical Engineering.

[23]  M. Spencer,et al.  Transcranial Doppler monitoring and causes of stroke from carotid endarterectomy. , 1997, Stroke.

[24]  M. Spencer Detection of Embolism with Doppler Ultrasound , 1996, Echocardiography.

[25]  J. Jensen Estimation of Blood Velocities Using Ultrasound: A Signal Processing Approach , 1996 .

[26]  J.B. Allen,et al.  A unified approach to short-time Fourier analysis and synthesis , 1977, Proceedings of the IEEE.

[27]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[28]  A. Naylor,et al.  Processing Doppler ultrasound signals from blood-borne emboli , 1994 .

[29]  G Rose,et al.  Real-time identification of cerebral microemboli with US feature detection by a neural network. , 1994, Radiology.