Multidimensional Ultrasound Doppler Signal Analysis for Fetal Activity Monitoring
| Autor: | Franck Perrotin, Sophie Ribes, Denis Kouame, Jean-Marc Girault |
|---|---|
| Přispěvatelé: | Traitement et Compréhension d’Images (IRIT-TCI), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université de Tours, Institut National de la Santé et de la Recherche Médicale (INSERM), CHRU Tours, Hôpital Bretonneau, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National de la Santé et de la Recherche Médicale - INSERM (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Centre Hospitalier Régional Universitaire de Tours - CHU Tours (FRANCE), Université de Tours (FRANCE), Hôpital Bretonneau (Tours, France), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université Toulouse Capitole (UT Capitole), Université Fédérale Toulouse Midi-Pyrénées-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse Capitole (UT Capitole), Université de Tours (UT), (OATAO), Open Archive Toulouse Archive Ouverte |
| Rok vydání: | 2014 |
| Předmět: |
Support vector machine
Support Vector Machine Acoustics and Ultrasonics Multidimensional signals [INFO.INFO-GR] Computer Science [cs]/Graphics [cs.GR] 02 engineering and technology Doppler echocardiography [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Traitement des images [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 0302 clinical medicine [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Pregnancy Image Processing Computer-Assisted Traitement du signal et de l'image Fetal Monitoring Fetal Movement Mathematics Signal processing Radiological and Ultrasound Technology medicine.diagnostic_test Ultrasound Signal Processing Computer-Assisted Vision par ordinateur et reconnaissance de formes Heart Rate Fetal [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR] Echocardiography Doppler 3. Good health [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] Fetal movement symbols Female Doppler effect [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI] Adult medicine.medical_specialty [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing 0206 medical engineering Biophysics Fetal monitoring Sensitivity and Specificity Ultrasonography Prenatal 03 medical and health sciences symbols.namesake Young Adult medicine Humans Radiology Nuclear Medicine and imaging Sensitivity (control systems) Synthèse d'image et réalité virtuelle business.industry [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Pattern recognition Ultrasonography Doppler Intelligence artificielle 020601 biomedical engineering Surgery Data set Fetal heart rhythm Feasibility Studies Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | Ultrasound in Medicine & Biology Ultrasound in Medicine & Biology, Elsevier, 2015, vol. 41 (n° 12), pp. 3172-3181. ⟨10.1016/j.ultrasmedbio.2015.07.026⟩ Ultrasound in Medicine & Biology, 2015, vol. 41 (n° 12), pp. 3172-3181. ⟨10.1016/j.ultrasmedbio.2015.07.026⟩ |
| ISSN: | 1879-291X 0301-5629 |
| Popis: | International audience; Fetal activity parameters such as movements, heart rate and the related parameters are essential indicators of fetal wellbeing, and no device provides simultaneous access to and sufficient estimation of all of these parameters to evaluate fetal health. This work was aimed at collecting these parameters to automatically separate healthy from compromised fetuses. To achieve this goal, we first developed a multi-sensor-multi-gate Doppler system. Then we recorded multidimensional Doppler signals and estimated the fetal activity parameters via dedicated signal processing techniques. Finally, we combined these parameters into four sets of parameters (or four hyper-parameters) to determine the set of parameters that is able to separate healthy from other fetuses. To validate our system, a data set consisting of two groups of fetal signals (normal and compromised) was established and provided by physicians. From the estimated parameters, an instantaneous Manning-like score, referred to as the ultrasonic score, was calculated and was used together with movements, heart rate and the associated parameters in a classification process employing the support vector machine method. We investigated the influence of the sets of parameters and evaluated the performance of the support vector machine using the computation of sensibility, specificity, percentage of support vectors and total classification error. The sensitivity of the four sets ranged from 79% to 100%. Specificity was 100% for all sets. The total classification error ranged from 0% to 20%. The percentage of support vectors ranged from 33% to 49%. Overall, the best results were obtained with the set of parameters consisting of fetal movement, short-term variability, long-term variability, deceleration and ultrasound score. The sensitivity, specificity, percentage of support vectors and total classification error of this set were respectively 100%, 100%, 35% and 0%. This indicated our ability to separate the data into two sets (normal fetuses and pathologic fetuses), and the results highlight the excellent match with the clinical classification performed by the physicians. This work indicates the feasibility of detecting compromised fetuses and also represents an interesting method of close fetal monitoring during the entire pregnancy. |
| Databáze: | OpenAIRE |
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