Similarity Measurement Study For Bio-Signal Waveform Based on Tunnel Morph
Since Bio-signal contain rich diagnosis information for medical treatment, the modeling, featuring, classification and diagnostic analysis of the similarity measurement on such waveforms bear great significance in medical research. Getting inspiration from anti-aliasing analysis in signal processing field, a tunnel morph outcome model is presented for different types of disease related to bio-signal waveform. One similarity measurement strategy based on tunnel morph model is proposed in the paper. Instead of linear value measuring on waveforms, the curve characteristics of waveforms are also taken in consideration. Along with the tunnel morph model, a set of integrity definitions are given in the paper, including segmentation, measurement function selection, tunnel morph width, and best partition point assigning. The similarity measurement strategy is validated through a case study on AECG (Ambulatory Electrocardiogram). The data are from MIT/BIH, each of them contains 30 minutes ECG waveform. In the experiment, the sensitivity and positive predictivity are of the evaluation standard on different strategies. From the result, the effectiveness of tunnel morph strategy is higher than city block distance, Euclidean distance and correlation coefficient. And the results also support the advantage of similarity measurement based on tunnel morph model.
similarity measurement bio-signal waveform tunnel morph ECG waveform weight
Gang Zheng JiaQi Guo-jie Shi Min Dai
School of computer and communication,Tianjin University of Technology Tianjin, China Laboratory of biologic signal and intelligent processing,Tianjin University of Technology Tianjin, C
国际会议
上海
英文
1674-1680
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）