Design of Interval Type-2 Fuzzy Systems for Classification of Blood Pressure Load

Author(s):  
Juan Carlos Guzmán ◽  
Patricia Melin ◽  
German Prado-Arechiga
2016 ◽  
Vol 4 (2) ◽  
pp. 167-177
Author(s):  
Abdulhamza Rajooj Hmood ◽  
Laith Abbas Aldabbagh

ABPM is the best method of detecting WCH in diabetic patients. We report ABPM findings in 100 diabetic individuals with WCH during 2 years. All of the referred subjects underwent casual and ambulatory blood pressure measurements. The mean age of participants was 41.43 year (±13.3 SD) and the age range from 19–74 years; majority were male (80%). The proportion of ambulatory-confirmed average 24-hours true hypertension was significantly reduced when the duration of the study was increased to 48 hours (P<0.001). Day-time systole (P=0.036) as well as night-time systole (P=0.001) and diastole (P<0.001) were statistically different during the two period of monitoring. Dipping pattern had increased from 53% to 60% (P=0.02) but non-dipper pattern was a common finding. Blood pressure load had been normalized in 19% of patients when the duration of study increases to 48 hours (P<0.001). WCH was common in T2DM and the best diagnostic tool was 48-hours ABPM. The dominant rhythm was dipper and absence of nocturnal drop was also common. Blood pressure load might be accurately determined with 48-hours ABPM.


Axioms ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 8 ◽  
Author(s):  
Juan Guzmán ◽  
Ivette Miramontes ◽  
Patricia Melin ◽  
German Prado-Arechiga

The use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. The use of fuzzy logic allows to design fuzzy classifiers, which have fuzzy rules and membership functions, which are designed based on the experience of an expert. In this particular case a fuzzy classifier of Mamdani type was built, with 21 rules, with two inputs and one output and the objective of this classifier is to perform blood pressure level classification based on knowledge of an expert which is represented in the fuzzy rules. Subsequently different architectures were made in type-1 and type-2 fuzzy systems for classification, where the parameters of the membership functions used in the design of each architecture were adjusted, which can be triangular, trapezoidal and Gaussian, as well as how the fuzzy rules are optimized based on the ranges established by an expert. The main contribution of this work is the design of the optimized interval type-2 fuzzy system with triangular membership functions. The final type-2 system has a better classification rate of 99.408% than the type-1 classifier developed previously in “Design of an optimized fuzzy classifier for the diagnosis of blood pressure with a new computational method for expert rule optimization” with 98%. In addition, we also obtained a better classification rate than the other architectures proposed in this work.


IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 1533-1545
Author(s):  
Chunsong Han ◽  
Dingding Song ◽  
Guangtao Ran ◽  
Jiafeng Yu

2001 ◽  
Vol 6 (3) ◽  
pp. 115-123 ◽  
Author(s):  
Giuseppe Mulè ◽  
Emilio Nardi ◽  
Giuseppe Andronico ◽  
Santina Cottone ◽  
Francesco Raspanti ◽  
...  

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