| 张春云赵捷贾慧琳李斐.基于支持向量机的室颤信号检测算法[J].现代生物医学进展英文版,2012,12(9):1751-1754. |
| 基于支持向量机的室颤信号检测算法 |
| Ventricular Fibrillation Detection Algorithm Based on SupportVector Machine |
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| DOI： |
| 中文关键词: 室性纤颤（VF） TCI 支持向量机（SVM） |
| 英文关键词: Ventricular Fibrillation TCI Support Vector Machine |
| 基金项目:山东省自然科学基金（ZR2010HM020）；济南市科技发展计划项目（201102005） |
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| 中文摘要: |
| 目的：实现室颤信号与非室颤信号的分类，进而实现室颤信号的检测。方法：本文引入了一种基于支持向量机（Support Vector
Machine, SVM）和改进的越限区间算法（TCI）的新算法，其中支持向量机在处理分类和模式识别等问题中具有很大的优势。该
算法采用4s 的滑动窗技术，并利用改进后的越限区间算法（Threshold Crossing Interval，TCI）方法提取心电信号的特征。新算法的
实现如下：在每一滑动窗内采用改进的后的绝对值阈值，计算中间2s 内的平均越限间隔值。并以此TCI 值作为特征参数，输入一
个预先设计好的二分类支持向量机中，从而实现分类。结果：成功实现了室颤信号的检测，通过计算该方法的灵敏度、精确度、预
测性和准确度且与其他方法相比较，可知此新算法总体可靠性优于其他方法。结论：该算法能够实现室颤信号的实时监测，且简
单易行，易于实现，较适合实时的心电监测以及除颤仪器。 |
| 英文摘要: |
| Objective: To realize the discrimination of ventricular fibrillation (VF) and non-ventricular fibrillation (non-VF), accordingly
detection of VF. Methods: The new algorithm was based on support vector machine (SVM) and the improved (TCI) algorithm.
The SWM has great advantages in processing classification and pattern recognition. The new algorithm utilized 4-s-sliding-window technology
and the improved TCI to extract features of ECG. It was implemented as follows: by using absolute thresholds, calculated average
threshold crossing intervals of the middle 2s segment in every sliding window, and then input the TCI values into a binary classification
support vector machine, finally, the discrimination was realized. Results: VF and non-VF were classified successfully. It shows that the
new algorithm was superior to other classical algorithms by calculating quality parameters. Conclusions: This new algorithm can be used
for real time VF detection. It is easier to implement and has greater advantages in real-time execution. It is suitable for ECG monitoring
and defibrillator. |
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