Guy Carrault

Fuzhi Wu, Jiasong Wu, Youyong Kong et al.

Deep learning and Convolutional Neural Networks (CNNs) have driven major transformations in diverse research areas. However, their limitations in handling low-frequency information present obstacles in certain tasks like interpreting global structures or managing smooth transition images. Despite the promising performance of transformer structures in numerous tasks, their intricate optimization complexities highlight the persistent need for refined CNN enhancements using limited resources. Responding to these complexities, we introduce a novel framework, the Multiscale Low-Frequency Memory (MLFM) Network, with the goal to harness the full potential of CNNs while keeping their complexity unchanged. The MLFM efficiently preserves low-frequency information, enhancing performance in targeted computer vision tasks. Central to our MLFM is the Low-Frequency Memory Unit (LFMU), which stores various low-frequency data and forms a parallel channel to the core network. A key advantage of MLFM is its seamless compatibility with various prevalent networks, requiring no alterations to their original core structure. Testing on ImageNet demonstrated substantial accuracy improvements in multiple 2D CNNs, including ResNet, MobileNet, EfficientNet, and ConvNeXt. Furthermore, we showcase MLFM's versatility beyond traditional image classification by successfully integrating it into image-to-image translation tasks, specifically in semantic segmentation networks like FCN and U-Net. In conclusion, our work signifies a pivotal stride in the journey of optimizing the efficacy and efficiency of CNNs with limited resources. This research builds upon the existing CNN foundations and paves the way for future advancements in computer vision. Our codes are available at https://github.com/AlphaWuSeu/ MLFM.

X. Navarro, F. Porée, M. Kuchenbuch et al.

The study of electroencephalographic (EEG) bursts in preterm infants provides valuable information about maturation or prognostication after perinatal asphyxia. Over the last two decades, a number of works proposed algorithms to automatically detect EEG bursts in preterm infants, but they were designed for populations under 35 weeks of post menstrual age (PMA). However, as the brain activity evolves rapidly during postnatal life, these solutions might be under-performing with increasing PMA. In this work we focused on preterm infants reaching term ages (PMA $\geq$ 36 weeks) using multi-feature classification on a single EEG channel. Five EEG burst detectors relying on different machine learning approaches were compared: Logistic regression (LR), linear discriminant analysis (LDA), k-nearest neighbors (kNN), support vector machines (SVM) and thresholding (Th). Classifiers were trained by visually labeled EEG recordings from 14 very preterm infants (born after 28 weeks of gestation) with 36 - 41 weeks PMA. The most performing classifiers reached about 95\% accuracy (kNN, SVM and LR) whereas Th obtained 84\%. Compared to human-automatic agreements, LR provided the highest scores (Cohen's kappa = 0.71) and the best computational efficiency using only three EEG features. Applying this classifier in a test database of 21 infants $\geq$ 36 weeks PMA, we show that long EEG bursts and short inter-bust periods are characteristic of infants with the highest PMA and weights. In view of these results, LR-based burst detection could be a suitable tool to study maturation in monitoring or portable devices using a single EEG channel.

Yuan Wang, Guy Carrault, Alain Beuchee et al.

Apnea-bradycardia is one of the major clinical early indicators of late-onset sepsis occurring in approximately 7% to 10% of all neonates and in more than 25% of very low birth weight infants in NICU. The objective of this paper was to determine if HRV, respiration and their relationships help to diagnose infection in premature infants via non-invasive ways in NICU. Therefore, we implement Mono-Channel (MC) and Bi-Channel (BC) Analysis in two groups: sepsis (S) vs. non-sepsis (NS). Firstly, we studied RR series not only by linear methods: time domain and frequency domain, but also by non-linear methods: chaos theory and information theory. The results show that alpha Slow, alpha Fast and Sample Entropy are significant parameters to distinguish S from NS. Secondly, the question about the functional coupling of HRV and nasal respiration is addressed. Local linear correlation coefficient r2t,f has been explored, while non-linear regression coefficient h2 was calculated in two directions. It is obvious that r2t,f within the third frequency band (0.2<f<0.4 Hz) and h2 in two directions were complementary approaches to diagnose sepsis. Thirdly, feasibility study is carried out on the candidate parameters selected from MC and BC respectively. We discovered that the proposed test based on optimal fusion of 6 features shows good performance with the largest AUC and a reduced probability of false alarm (PFA).