New Automated Detection Method of OSA Based on Artificial Neural Networks Using P-Wave Shape and Time Changes

被引：7

作者：

Lweesy, Khaldon ^{[1
]}

Fraiwan, Luay ^{[1
]}

Khasawneh, Natheer ^{[2
]}

Dickhaus, Hartmut ^{[3
]}

机构：

[1] Jordan Univ Sci & Technol, Fac Engn, Dept Biomed Engn, Irbid 22110, Jordan

[2] Jordan Univ Sci & Technol, Dept Comp Engn, Irbid 22110, Jordan

[3] Heidelberg Univ, Dept Med Informat, Heidelberg, Germany

来源：

JOURNAL OF MEDICAL SYSTEMS | 2011年 / 35卷 / 04期

关键词：

Artificial neural network (ANN); Obstructive sleep apnea (OSA); P-wave; Automated detection of OSA; OBSTRUCTIVE SLEEP-APNEA; ELECTROCARDIOGRAM; DISPERSION; DURATION; LEVEL;

D O I：

10.1007/s10916-009-9409-z

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

This paper describes a new method for automatic detection of obstructive sleep apnea (OSA) based on artificial neural networks (ANN) using regular electrocardiogram (ECG) recordings. ECG signals were pre-processed and segmented to extract the P-waves; then three P-wave features were extracted: the P-wave duration (T (p) ), the P-wave dispersion (P (d) ), and the time interval from the peak of the P-wave to the R-wave (T (pr) ). Combinations of the three features were used as features for classification using ANN. For each feature combination studied, 70% of the input data was used for training the ANN, 15% for validating, and 15% for testing the results. Perfect agreement between expert's scores and the ANN scores was achieved when the ANN was applied on T (p) , P (d) , and T (pr) taken together, while substantial agreements were achieved when applying the ANN on the feature combinations T (p) and P (d) , and T (p) and T (pr) .

引用

页码：723 / 734

页数：12

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