Ibaida Ayman, Khalil Ibrahim
School of Computer Science and IT - RMIT University-Melbourne, VIC 3000, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2013-6. doi: 10.1109/IEMBS.2010.5627888.
Since ECG is huge in size sending large volume data over resource constrained wireless networks is power consuming and will reduce the energy of nodes in Body Sensor Networks (BSN). Therefore, compression of ECGs and diagnosis of diseases from compressed ECGs will play key roles in enhancing the life-time of body sensor networks. Moreover, discrimination between ventricular Tachycardia and Ventricular Fibrillation is of crucial importance to save human life. Existing algorithms work only on plain text ECGs to distinguish between the two, and therefore, not suitable in BSN. VT and VF are often similar in patterns and in filtration of noise and improper attribute selection in compressed ECGs will make it even harder to classify them properly. In this paper, a supervised attribute selection algorithm called Correlation Based Feature Selection (CFS) [4] is used to filter the unwanted attributes and select the most relevant attributes. We then use the selected attributes to train and classify VT and VF using Radial Basis Function (RBF) Neural Network and k-nearest neighbour techniques. We experimented with 103 ECG samples taken from MIT-BIH Malignant Ventricular Ectopy Database. Results showed that accuracy can be as high as 93.3% when attribute selection is used and large number of training samples are provided.
由于心电图数据量巨大，在资源受限的无线网络上传输大量数据会消耗能量，并会降低人体传感器网络（BSN）中节点的能量。因此，心电图的压缩以及从压缩后的心电图中进行疾病诊断将在延长人体传感器网络的寿命方面发挥关键作用。此外，区分室性心动过速和心室颤动对于挽救人类生命至关重要。现有算法仅对普通文本心电图进行处理以区分两者，因此不适用于人体传感器网络。室性心动过速和心室颤动在模式以及噪声过滤方面通常相似，并且在压缩心电图中选择不当的属性会使正确分类它们变得更加困难。在本文中，一种名为基于相关性的特征选择（CFS）[4]的监督属性选择算法被用于过滤不需要的属性并选择最相关的属性。然后，我们使用所选属性，通过径向基函数（RBF）神经网络和k近邻技术对室性心动过速和心室颤动进行训练和分类。我们对从麻省理工学院 - 贝斯以色列女执事医疗中心恶性室性心律失常数据库中获取的103个心电图样本进行了实验。结果表明，当使用属性选择并提供大量训练样本时，准确率可高达93.3%。
