Electrocardiogram Classification Using Reservoir Computing With Logistic Regression
Electrocardiogram Classification Using Reservoir Computing With Logistic Regression
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DOI：
10.1109/jbhi.2014.2332001
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发表时间：
2015-05-01
影响因子：
7.7
通讯作者：
Mirasso, Claudio R.
中科院分区：
文献类型：
--
作者：
Angel Escalona-Moran, Miguel;Soriano, Miguel C.;Mirasso, Claudio R.
一个适应的国家的最先进的方法被称为水库计算的信息处理是用来显示其效用的开放和耗时的问题心跳分类。按照美国医疗器械促进协会的指南使用MIT-BIH心律失常数据库。我们的方法需要一个计算成本低廉的心电图信号的预处理，导致一个快速的算法和接近实时分类解决方案。我们的多类分类结果表明，平均特异性为97.75%，平均准确性为98.43%。灵敏度和阳性预测值分别为84.83%和88.75%，这使得我们的方法在临床应用中具有重要意义。
An adapted state-of-the-art method of processing information known as Reservoir Computing is used to show its utility on the open and time-consuming problem of heartbeat classification. The MIT-BIH arrhythmia database is used following the guidelines of the Association for the Advancement of Medical Instrumentation. Our approach requires a computationally inexpensive preprocessing of the electrocardiographic signal leading to a fast algorithm and approaching a real-time classification solution. Our multiclass classification results indicate an average specificity of 97.75% with an average accuracy of 98.43%. Sensitivity and positive predicted value show an average of 84.83% and 88.75%, respectively, what makes our approach significant for its use in a clinical context.