Electrocardiogram (ECG) signal is the most commonly used non-invasive tool in the assessment of cardiovascular diseases. Segmentation of the ECG signal to locate its constitutive waves, in particular the R-peaks, is a key step in ECG processing and analysis. Over the years, several segmentation and QRS complex detection algorithms have been proposed with different features; however, their performance highly depends on applying preprocessing steps which makes them unreliable in real-time data analysis of ambulatory care settings and remote monitoring systems, where the collected data is highly noisy. Moreover, some issues still remain with the current algorithms in regard to the diverse morphological categories for the ECG signal and their high computation cost. In this paper, we introduce a novel graph-based optimal changepoint detection (GCCD) method for reliable detection of R-peak positions without employing any preprocessing step. The proposed model guarantees to compute the globally optimal changepoint detection solution. It is also generic in nature and can be applied to other time-series biomedical signals. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed method achieves overall sensitivity Sen = 99.76, positive predictivity PPR = 99.68, and detection error rate DER = 0.55 which are comparable to other state-of-the-art approaches.
Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholding
University of California San Diego（加州大学圣地亚哥分校）；Fred Hutchinson Cancer Research Center（弗雷德·哈钦森癌症研究中心）；Beckman Research Institute, City of Hope（希望之城贝克曼研究所）；Center for Children’s Healthy Lifestyles and Nutrition, Children’s Mercy（儿童慈善医院儿童健康生活方式与营养中心）；Kaiser Permanente Washington Health Research Institute（凯撒永久华盛顿健康研究所）
ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks
Universitat Pompeu Fabra（庞培法布拉大学）；Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物工程、生物材料与纳米医学生物医学研究网络中心（CIBER-BBN））；Aragón Institute of Engineering Research（阿拉贡工程研究所）；University of Zaragoza（萨拉戈萨大学）
Deep learning based ECG segmentation for delineation of diverse arrhythmias
Seoul National University（首尔大学）；Asan Medical Center, University of Ulsan College of Medicine（蔚山大学医学院附属峨山医院）；Pusan National University Hospital, Pusan National University School of Medicine（釜山大学医学院附属釜山国立大学医院）；AI Institute, Seoul National University（首尔大学人工智能研究院）；Seoul National University Hospital（首尔大学医院）；Medifarmsoft Co., Ltd.（Medifarmsoft有限公司）
本文提出一种类 U-Net 的心电图分割模型及后处理算法，用于在多种心律失常条件下准确 delineation P、QRS 和 T 波，并针对标准基准中代表性不足的心律失常提出改进策略。
HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
Universiti Malaysia Pahang（马来西亚彭亨大学）；Queensland University of Technology（昆士兰科技大学）；Islamic University（伊斯兰大学）；The University of Sydney（悉尼大学）；University of Cambridge（剑桥大学）；The Garvan Institute of Medical Research（加文医学研究所）；The University of Queensland（昆士兰大学）
ECG-Based Driver Stress Levels Detection System Using Hyperparameter Optimization
School of Electrical Engineering and Robotics, Queensland University of Technology（昆士兰科技大学电气工程与机器人学院）；Centre for Accident Research & Road Safety Queensland, Queensland University of Technology（昆士兰科技大学昆士兰事故研究与道路安全中心）；School of Information Technology, Deakin University（迪肯大学信息技术学院）
