Wiens Jenna, Guttag John V
Department of Electrical Engineering and Computer Science at MIT.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:5876-9. doi: 10.1109/IEMBS.2011.6091453.
We present an adaptive binary classification algorithm, based on transductive transfer learning. We illustrate the method in the context of electrocardiogram (ECG) analysis. Knowledge gained from a population of patients is automatically adapted to patients' records to accurately detect ectopic beats. On patients from the MIT-BIH Arrhythmia Database, we achieve a median sensitivity of 94.59% and positive predictive value of 96.24%, for the binary classification task of separating premature ventricular contractions (PVCs), a type of ectopic beat, from non-PVCs.
我们提出了一种基于直推式迁移学习的自适应二元分类算法。我们在心电图（ECG）分析的背景下阐述了该方法。从大量患者中获得的知识会自动适用于患者的记录，以准确检测异位搏动。对于麻省理工学院-贝斯以色列女执事医疗中心心律失常数据库中的患者，在将室性早搏（PVC）（一种异位搏动类型）与非PVC进行二元分类的任务中，我们实现了94.59%的中位灵敏度和96.24%的阳性预测值。
