The deployment of wireless health wearables is increasing in the framework of mobile health monitoring. The power and processing efficiencies with data compression are key aspects. To this end, an efficient automated arrhythmia recognition method is devised. The aim of this work is to contribute to the realisation of modern wireless electrocardiogram (ECG) gadgets. The proposed system utilizes an intelligent combination of subsampling, denoising and wavelet transform based subbands decomposition. Onward, the subband’s statistical features are extracted and mutual information (MI) based dimension reduction is attained for an effective realization of the ECG wearable processing chain. The amount of information to be processed is reduced in a real-time manner by using subsampling. It brings a remarkable reduction in the proposed system's computational complexity compared to the fixed-rate counterparts. MI based features selection improves the classification performance in terms of precision and processing delay. Moreover, it enhances the compression gain and aptitudes an effective diminishing in the transmission activity. Experimental results show that the designed method attains a 4 times computational gain while assuring an appropriate quality of signal reconstruction. A 7.2-fold compression gain compared to conventional counterparts is also attained. The best classification accuracies of 97% and 99% are secured respectively for cases of 5-class and 4-class arrhythmia datasets. It shows that the suggested method realizes an effective recognition of arrhythmia.

在移动健康监控的框架中，无线健康可穿戴设备的部署正在增加。数据压缩的功率和处理效率是关键方面。为此，设计了一种有效的自动心律失常识别方法。这项工作的目的是为实现现代无线心电图（ECG）小工具做出贡献。所提出的系统利用了基于子带分解的子采样，去噪和小波变换的智能组合。然后，提取子带的统计特征，并实现基于互信息（MI）的降维，以有效实现ECG可穿戴处理链。通过使用子采样，可以实时减少要处理的信息量。它大大减少了拟议系统的数量” 与固定速率同类产品相比的计算复杂度。基于MI的特征选择在精度和处理延迟方面提高了分类性能。此外，它提高了压缩增益，并有效降低了传输活动。实验结果表明，所设计的方法在保证适当的信号重构质量的同时，获得了4倍的计算增益。与传统的同类产品相比，压缩增益也达到了7.2倍。对于5级和4级心律失常数据集，分别具有97％和99％的最佳分类准确率。结果表明，该方法可以有效识别心律失常。基于MI的特征选择在精度和处理延迟方面提高了分类性能。此外，它提高了压缩增益，并有效降低了传输活动。实验结果表明，所设计的方法在保证适当的信号重构质量的同时，获得了4倍的计算增益。与传统的同类产品相比，压缩增益也达到了7.2倍。对于5级和4级心律失常数据集，分别具有97％和99％的最佳分类准确率。结果表明，所提出的方法可以有效地识别心律失常。基于MI的特征选择在精度和处理延迟方面提高了分类性能。此外，它提高了压缩增益，并有效降低了传输活动。实验结果表明，所设计的方法在保证适当的信号重构质量的同时，获得了4倍的计算增益。与传统的同类产品相比，压缩增益也达到了7.2倍。对于5级和4级心律失常数据集，分别具有97％和99％的最佳分类准确率。结果表明，所提出的方法可以有效地识别心律失常。实验结果表明，所设计的方法在保证适当的信号重构质量的同时，获得了4倍的计算增益。与传统的同类产品相比，压缩增益也达到了7.2倍。对于5级和4级心律失常数据集，分别具有97％和99％的最佳分类准确率。结果表明，所提出的方法可以有效地识别心律失常。实验结果表明，所设计的方法在保证适当的信号重构质量的同时，获得了4倍的计算增益。与传统的同类产品相比，压缩增益也达到了7.2倍。对于5级和4级心律失常数据集，分别具有97％和99％的最佳分类准确率。结果表明，该方法可以有效识别心律失常。