title: A novel similarity comparison approach for dynamic ECG series.
abstract: The heart sound signal is a reflection of heart and vascular system motion. Long-term continuous electrocardiogram (ECG) contains important information which can be helpful to prevent heart failure. A single piece of a long-term ECG recording usually consists of more than one hundred thousand data points in length, making it difficult to derive hidden features that may be reflected through dynamic ECG monitoring, which is also very time-consuming to analyze. In this paper, a Dynamic Time Warping based on MapReduce (MRDTW) is proposed to make prognoses of possible lesions in patients. Through comparison of a real-time ECG of a patient with the reference sets of normal and problematic cardiac waveforms, the experimental results reveal that our approach not only retains high accuracy, but also greatly improves the efficiency of the similarity measure in dynamic ECG series.
authors.affiliation: College of Computer, National University of Defense Technology, Changsha 410073, China.|Xiangyang School for NCOs, Xiangyang 441118, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Yin
authors.affiliation: College of Computer, National University of Defense Technology, Changsha 410073, China.
authors.forename: Xiaoqian
authors.initials: X
authors.lastname: Zhu
authors.affiliation: School of Engineering, The University of Hull, Cottingham Road, Hull, United Kingdom.
authors.forename: Shaodong
authors.initials: S
authors.lastname: Ma
authors.affiliation: College of Computer, National University of Defense Technology, Changsha 410073, China.
authors.forename: Shuqiang
authors.initials: S
authors.lastname: Yang
authors.affiliation: College of Computer, National University of Defense Technology, Changsha 410073, China.
authors.forename: Liqian
authors.initials: L
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: DTW; ECG series; MapReduce; Similarity; parallel