IEEE J Biomed Health Inform. 2014 Mar;18(2):703-13. doi: 10.1109/JBHI.2013.2279595.
This paper proposes a rule-based decision-making diagnosis system to evaluate arteriovenous shunt (AVS) stenosis for long-term hemodialysis treatment of patients using fuzzy petri nets (FPNs). AVS stenoses are often associated with blood sounds, resulting from turbulent flow over the narrowed blood vessel. Phonoangiography provides a noninvasive technique to monitor the sounds of the AVS. Since the power spectra changes in frequency and amplitude with the degree of AVS stenosis, it is difficult to make a human-made decision to judge the degree using a combination of those variances. The Burg autoregressive (AR) method is used to estimate the frequency spectra of a phonoangiographic signal and identify the characteristic frequencies. A rule-based decision-making method, FPNs, is designed as a decision-making system to evaluate the degree of stenosis (DOS) in routine examinations. For 42 long-term follow-up patients, the examination results show the proposed diagnosis system has greater efficiency in evaluating AVS stenosis.
本文提出了一种基于规则的决策诊断系统,使用模糊 Petri 网(FPN)评估长期血液透析治疗患者的动静脉分流(AVS)狭窄。AVS 狭窄通常与血流声音有关,是由于狭窄血管中的湍流引起的。血管声描记术提供了一种监测 AVS 声音的非侵入性技术。由于功率谱随 AVS 狭窄程度在频率和幅度上发生变化,因此很难通过组合这些变化来做出人工决策来判断狭窄程度。Burg 自回归(AR)方法用于估计血管声描记信号的频谱并识别特征频率。基于规则的决策方法,即模糊 Petri 网(FPN),被设计为决策系统,以评估常规检查中的狭窄程度(DOS)。对于 42 名长期随访患者,检查结果表明,所提出的诊断系统在评估 AVS 狭窄方面具有更高的效率。