Fractal Dimension-based Methodology for Sudden Cardiac Death Prediction

被引：0

作者：

Lopez-Caracheo, Francisco ^{[1
]}

Bazaldua Camacho, Antonio ^{[1
]}

Perez-Ramirez, Carlos A. ^{[1
]}

Valtierra-Rodriguez, Martin ^{[2
]}

Dominguez-Gonzalez, Aurelio ^{[2
]}

Amezquita-Sanchez, Juan P. ^{[2
]}

机构：

[1] Univ Autonoma Queretaro, Fac Ingn, Campus Aeropuerto,Carretera Chichimequillas S-N, Santiago De Queretaro 76140, Queretaro, Mexico

[2] Univ Autonoma Queretaro, Fac Ingn, Campus San Juan del Rio,Rio Moctezuma 249, San Juan Del Rio 76809, Queretaro, Mexico

来源：

2018 IEEE INTERNATIONAL AUTUMN MEETING ON POWER, ELECTRONICS AND COMPUTING (ROPEC) | 2018年

关键词：

Sudden Cardiac Death; Prediction; ECG signals; Fractal Dimension; Time analysis;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Sudden cardiac death (SCD) is a disease that can be regarded as one of the principal death causes in the society. Hence, if the SCD event can be predicted in the earliest stage possible, it will allow saving people lives because they will receive timely medical procedures. In this paper, a methodology to predict SCD of an automatic manner using ECG signals, fractal dimension (FD), and artificial neural networks is presented. Three FD methods are investigated, Higuchi fractal dimension, Box dimension, and Katz fractal dimension. The effectiveness of the proposed methodology for predicting a SCD event is demonstrated using a database of 38 patients, 20 with SCD and 18 normal, provided by MIT-BIH (Boston's Beth Israel Hospital). The results show an accuracy of 91.4% 14 minutes prior to SCD event.

引用

页数：6

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