使用深度神经网络和组合优化进行生理信号的分割与最优区域选择

U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging

Neural networks are becoming more and more popular for the analysis of physiological time-series. The most successful deep learning systems in this domain combine convolutional and recurrent layers to extract useful features to model temporal relations. Unfortunately, these recurrent models are difficult to tune and optimize. In our experience, they often require task-specific modifications, which makes them challenging to use for non-experts. We propose U-Time, a fully feed-forward deep learning approach to physiological time series segmentation developed for the analysis of sleep data. U-Tim…

使用可穿戴传感器预测患有认知障碍的老年人的认知评估分数

Predicting Cognitive Assessment Scores in Older Adults with Cognitive Impairment Using Wearable Sensors

本研究利用可穿戴设备采集23名认知障碍老年人的生理信号，通过监督学习预测NIH认知测试分数，模型Spearman's ρ达0.73-0.82，证明AI结合可穿戴传感器可实现非侵入式持续认知监测。

DENS-ECG: A Deep Learning Approach for ECG Signal Delineation

Objectives: With the technological advancements in the field of tele-health monitoring, it is now possible to gather huge amounts of electro-physiological signals such as electrocardiogram (ECG). It is therefore necessary to develop models/algorithms that are capable of analysing these massive amounts of data in real-time. This paper proposes a deep learning model for real-time segmentation of heartbeats. Methods: The proposed algorithm, named as the DENS-ECG algorithm, combines convolutional neural network (CNN) and long short-term memory (LSTM) model to detect onset, peak, and offset of diff…

gcDLSeg：将 Graph-cut 集成到深度学习中用于二值语义分割

gcDLSeg: Integrating Graph-cut into Deep Learning for Binary Semantic Segmentation

本文提出 gcDLSeg，通过残差 graph-cut 损失与准残差连接将 graph-cut 嵌入深度学习端到端框架，实现全局最优二值分割，并在医学数据集上提升精度与对抗鲁棒性。

Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients

Glioblastoma is profoundly heterogeneous in microstructure and vasculature, which may lead to tumor regional diversity and distinct treatment response. Although successful in tumor sub-region segmentation and survival prediction, radiomics based on machine learning algorithms, is challenged by its robustness, due to the vague intermediate process and track changes. Also, the weak interpretability of the model poses challenges to clinical application. Here we proposed a machine learning framework to semi-automatically fine-tune the clustering algorithms and quantitatively identify stable sub-re…

Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network

Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic and robust system for R-peak detection in Holter ECG signals. While many proposed algorithms have successfully addressed the problem of ECG R-peak detection, there is still a notable gap in the performance of these detectors on such low-quality ECG records. Therefore, in this study, a novel implementation of the 1D Convolutional Neural Network (CNN) is used integrated with a verification model to reduce the number of…