Agnese Sbrollini1, Angela Agostinelli1, Ilaria Marcantoni1, Micaela Morettini1, Luca Burattini2, Francesco Di Nardo1, Sandro Fioretti1, Laura Burattini3. 1. Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche 12, Ancona 60131, Italy. 2. Department of Maternal and Infantile Sciences (Salesi Hospital), Università Politecnica delle Marche, via F. Corridoni 11, Ancona 60123, Italy. 3. Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche 12, Ancona 60131, Italy. Electronic address: l.burattini@univpm.it.
Abstract
BACKGROUND AND OBJECTIVE: Cardiotocography (CTG), consisting in the simultaneous recording of fetal heart rate (FHR) and maternal uterine contractions (UC), is a popular clinical test to assess fetal health status. Typically, CTG machines provide paper reports that are visually interpreted by clinicians. Consequently, visual CTG interpretation depends on clinician's experience and has a poor reproducibility. The lack of databases containing digital CTG signals has limited number and importance of retrospective studies finalized to set up procedures for automatic CTG analysis that could contrast visual CTG interpretation subjectivity. In order to help overcoming this problem, this study proposes an electronic procedure, termed eCTG, to extract digital CTG signals from digital CTG images, possibly obtainable by scanning paper CTG reports. METHODS: eCTG was specifically designed to extract digital CTG signals from digital CTG images. It includes four main steps: pre-processing, Otsu's global thresholding, signal extraction and signal calibration. Its validation was performed by means of the "CTU-UHB Intrapartum Cardiotocography Database" by Physionet, that contains digital signals of 552 CTG recordings. Using MATLAB, each signal was plotted and saved as a digital image that was then submitted to eCTG. Digital CTG signals extracted by eCTG were eventually compared to corresponding signals directly available in the database. Comparison occurred in terms of signal similarity (evaluated by the correlation coefficient ρ, and the mean signal error MSE) and clinical features (including FHR baseline and variability; number, amplitude and duration of tachycardia, bradycardia, acceleration and deceleration episodes; number of early, variable, late and prolonged decelerations; and UC number, amplitude, duration and period). RESULTS: The value of ρ between eCTG and reference signals was 0.85 (P < 10-560) for FHR and 0.97 (P < 10-560) for UC. On average, MSE value was 0.00 for both FHR and UC. No CTG feature was found significantly different when measured in eCTG vs. reference signals. CONCLUSIONS: eCTG procedure is a promising useful tool to accurately extract digital FHR and UC signals from digital CTG images.
BACKGROUND AND OBJECTIVE: Cardiotocography (CTG), consisting in the simultaneous recording of fetal heart rate (FHR) and maternal uterine contractions (UC), is a popular clinical test to assess fetal health status. Typically, CTG machines provide paper reports that are visually interpreted by clinicians. Consequently, visual CTG interpretation depends on clinician's experience and has a poor reproducibility. The lack of databases containing digital CTG signals has limited number and importance of retrospective studies finalized to set up procedures for automatic CTG analysis that could contrast visual CTG interpretation subjectivity. In order to help overcoming this problem, this study proposes an electronic procedure, termed eCTG, to extract digital CTG signals from digital CTG images, possibly obtainable by scanning paper CTG reports. METHODS: eCTG was specifically designed to extract digital CTG signals from digital CTG images. It includes four main steps: pre-processing, Otsu's global thresholding, signal extraction and signal calibration. Its validation was performed by means of the "CTU-UHB Intrapartum Cardiotocography Database" by Physionet, that contains digital signals of 552 CTG recordings. Using MATLAB, each signal was plotted and saved as a digital image that was then submitted to eCTG. Digital CTG signals extracted by eCTG were eventually compared to corresponding signals directly available in the database. Comparison occurred in terms of signal similarity (evaluated by the correlation coefficient ρ, and the mean signal error MSE) and clinical features (including FHR baseline and variability; number, amplitude and duration of tachycardia, bradycardia, acceleration and deceleration episodes; number of early, variable, late and prolonged decelerations; and UC number, amplitude, duration and period). RESULTS: The value of ρ between eCTG and reference signals was 0.85 (P < 10-560) for FHR and 0.97 (P < 10-560) for UC. On average, MSE value was 0.00 for both FHR and UC. No CTG feature was found significantly different when measured in eCTG vs. reference signals. CONCLUSIONS: eCTG procedure is a promising useful tool to accurately extract digital FHR and UC signals from digital CTG images.
Authors: Julian D Fortune; Natalie E Coppa; Kazi T Haq; Hetal Patel; Larisa G Tereshchenko Journal: Comput Methods Programs Biomed Date: 2022-05-14 Impact factor: 7.027