Use of Deep Learning to Detect the Maternal Heart Rate and False Signals on Fetal Heart Rate Recordings

We have developed deep learning models for automatic identification of the maternal heart rate (MHR) and, more generally, false signals (FSs) on fetal heart rate (FHR) recordings. The models can be used to preprocess FHR data prior to automated analysis or as a clinical alert system to assist the practitioner. Three models were developed and used to detect (i) FSs on the MHR channel (the FSMHR model), (ii) the MHR and FSs on the Doppler FHR sensor (the FSDop model), and (iii) FSs on the scalp ECG channel (the FSScalp model). The FSDop model was the most useful because FSs are far more frequent on the Doppler FHR channel. All three models were based on a multilayer, symmetric, GRU, and were trained on data recorded during the first and second stages of delivery. The FSMHR and FSDop models were also trained on antepartum recordings. The training dataset contained 1030 expert-annotated periods (mean duration: 36 min) from 635 recordings. In an initial evaluation of routine clinical practice, 30 fully annotated recordings for each sensor type (mean duration: 5 h for MHR and Doppler sensors, and 3 h for the scalp ECG sensor) were analyzed. The sensitivity, positive predictive value (PPV) and accuracy were respectively 62.20%, 87.1% and 99.90% for the FSMHR model, 93.1%, 95.6% and 99.68% for the FSDop model, and 44.6%, 87.2% and 99.93% for the FSScalp model. We built a second test dataset with a more solid ground truth by selecting 45 periods (lasting 20 min, on average) on which the Doppler FHR and scalp ECG signals were recorded simultaneously. Using scalp ECG data, the experts estimated the true FHR value more reliably and thus annotated the Doppler FHR channel more precisely. The models achieved a sensitivity of 53.3%, a PPV of 62.4%, and an accuracy of 97.29%. In comparison, two experts (blinded to the scalp ECG data) respectively achieved a sensitivity of 15.7%, a PPV of 74.3%, and an accuracy of 96.91% and a sensitivity of 60.7%, a PPV of 83.5% and an accuracy of 98.24%. Hence, the models performed at expert level (better than one expert and worse than the other), although a well-trained expert with good knowledge of FSs could probably do better in some cases. The models and datasets have been included in the Fetal Heart Rate Morphological Analysis open-source MATLAB toolbox and can be used freely for research purposes

Fetal heart rate baseline computation with a weighted median filter

International audienceBackgroundAutomated fetal heart rate (FHR) analysis removes inter- and intra-expert variability, and is a promising solution for reducing the occurrence of fetal acidosis and the implementation of unnecessary medical procedures. The first steps in automated FHR analysis are determination of the baseline, and detection of accelerations and decelerations (A/D). We describe a new method in which a weighted median filter baseline (WMFB) is computed and A/Ds are then detected.MethodThe filter weightings are based on the prior probability that the sampled FHR is in the baseline state or in an A/D state. This probability is computed by estimating the signal’s stability at low frequencies and by progressively trimming the signal. Using a competition dataset of 90 previously annotated FHR recordings, we evaluated the WMFB method and 11 recently published literature methods against the ground truth of an expert consensus. The level of agreement between the WMFB method and the expert consensus was estimated by calculating several indices (primarily the morphological analysis discordance index, MADI). The agreement indices were then compared with the values for eleven other methods. We also compared the level of method-expert agreement with the level of interrater agreement.ResultsFor the WMFB method, the MADI indicated a disagreement of 4.02% vs. the consensus; this value is significantly lower () than that calculated for the best of the 11 literature methods (7.27%, for Lu and Wei’s empirical mode decomposition method). The level of inter-expert agreement (according to the MADI) and the level of WMFB-expert agreement did not differ significantly (p=0.22).ConclusionThe WMFB method reproduced the expert consensus analysis better than 11 other methods. No differences in performance between the WMFB method and individual experts were observed. The method Matlab source code is available under General Public Licence at http://utsb.univ-catholille.fr/fhr-wmfb

Automated fetal heart rate analysis for baseline determination and acceleration/deceleration detection: A comparison of 11 methods versus expert consensus

International audienceBackgroundThe fetal heart rate (FHR) serves as a guide to fetal well-being during the first stage of delivery. The visual morphological analysis of the FHR during labor is subject to inter- and intra-observer variability – particularly when the FHR is abnormal. It has been suggested that automatic analysis of the FHR can reduce this variability.ObjectivesTo compare 11 morphological FHR analyses (baseline computation, and detection of FHR decelerations and accelerations) produced by automatic analysis methods (AAMs) with an expert consensus.Materials and methodsEleven AAMs were reprogrammed (using the description published in the literature) and applied to 90 FHR recordings collected during the early phase of labor. Furthermore, the recordings were divided into three tertiles, according to the difficulty of analysis. The results of the morphological FHR analyses produced by the AAMs were compared with a consensus morphological analysis performed by four experts. In addition to standard discriminant criteria, a new morphological analysis discriminant index (MADI) was introduced; it provides an overall evaluation that collates all the individual criteria.ResultsThe AAM developed by Lu and Wei's gave better results than the other AAMs for baseline computation. Regarding this method's detection of FHR decelerations and accelerations, the F-measure [95% confidence interval] was respectively 0.73 [0.67; 0.76] and 0.70 [0.64; 0.76]. The MADI indicated that Lu and Wei's AAM agreed best with the expert consensus (discordance: 7.3% [6.10; 8.60]).ConclusionOur study demonstrated the superiority of Lu and Wei's method for baseline computation and deceleration/acceleration detection, although there was still a significant degree of discordance versus expert consensus. The MADI appears to be a good overall index for evaluating AAMs with regard to the quality of baseline computation and acceleration/deceleration detection. The application of precise criteria and the methodology and software tools developed here should facilitate the evaluation of new AAMs and their comparison with other methods

Solar-Reflective “Cool” Walls: Benefits, Technologies, and Implementation

Raising the albedo (solar reflectance) of a building’s walls reduces unwanted solar heat gain in the cooling season. This saves electricity and lowers peak power demand by decreasing the need for air conditioning. It can also cool the outside air, which can mitigate the urban heat island effect and also improve air quality by slowing the reactions that produce smog. This project quantified the energy savings, peak demand reduction, urban cooling, and air quality improvements attainable from solar-reflective “cool” walls in California; collaborated with industry to assess the performance of existing cool-wall technologies, and to develop innovative cool-wall solutions; and worked with state and federal government agencies, utilities, and industry to create a cool-wall infrastructure, including application guidelines, a product rating program, incentives, and building code credits. Simulations indicate that cool walls provide annual energy savings, peak demand reduction, annual emission reduction, and summer heat island mitigation benefits comparable to those yielded by cool roofs, and are helpful across California and in most of the southern half of the United States (that is, in U.S. climate zones 1—4). Natural exposure trials conducted at three sites in California and another three sites across the United States indicate that cool-wall materials tend to stay clean and reflective. Significant advances were made in novel cool-wall technologies, such as fluorescent cool pigments that expand the color palette for cool-wall products. We prepared guidelines for the climate- and building-appropriate use of cool walls, convened a stakeholder workshop, and created a working group. Ongoing efforts seek to introduce or expand cool-wall provisions in building energy standards, green building programs, and energy efficiency incentive programs, and to develop a cool-wall product rating system