One of the most significant indicator of heart disease is arrhythmia showing heartbeat patterns. Thus, early and accurate detection of arrythmia types by categorization of heartbeats is important. In this paper, we introduce an ECG beat classifier system integrating two main parts: feature extraction and classification. For the first part, we consider the features observed in the time–frequency ( t , f ) plane where the ECG is projected using a variant of Stockwell transform. For the second part, the framework of semi-supervised SVM with asymmetric costs (AS3VM) has been applied for assessment of the obtained feature sets performance. Notice that four heartbeat types have been considered: normal beats ( N ), left and right bundle branch blocks ( L and R ) and premature ventricular contractions ( V ). The proposed method has been evaluated on PhysionNet’s MIT-BIT arrythmia database. The obtained results show that the suggested approach achieves significant separability of the classes and thus, able to make prediction accuracies of $$99.35\%$$ 99.35 % , $$98.73\%$$ 98.73 % , $$98.57\%$$ 98.57 % and $$99.44\%$$ 99.44 % for, respectively, N , L , R and V beats.

中文翻译：

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优化的时频特征和半监督 SVM 对心跳分类

心脏病最重要的指标之一是显示心跳模式的心律失常。因此，通过对心跳进行分类来及早准确地检测心律失常类型是重要的。在本文中，我们介绍了一个集成了特征提取和分类两个主要部分的心电搏动分类器系统。对于第一部分，我们考虑在时频 (t, f) 平面中观察到的特征，其中使用斯托克韦尔变换的变体投影 ECG。对于第二部分，具有非对称成本的半监督 SVM（AS3VM）框架已被应用于评估所获得的特征集性能。请注意，已经考虑了四种心跳类型：正常心跳 (N)、左右束支传导阻滞（L 和 R）和室性早搏 (V)。所提出的方法已在 PhysionNet 的 MIT-BIT 心律失常数据库上进行了评估。获得的结果表明，所建议的方法实现了类的显着可分离性，因此能够使预测准确度达到 $$99.35\%$$ 99.35 % 、$$98.73\%$$ 98.73 % 、$$98.57\%$$ 98.57 % 和$$99.44\%$$ 99.44 % 分别用于 N 、 L 、 R 和 V 节拍。