College of Engineering, Effat University, Jeddah 21478, Saudi Arabia.
Communication & Signal processing Lab, Energy and Technology Centre, Effat University, Jeddah 21478, Saudi Arabia.
Sensors (Basel). 2021 Feb 22;21(4):1511. doi: 10.3390/s21041511.
The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals. The method combines multirate processing, wavelet decomposition and frequency content-based subband coefficient selection and machine learning techniques. Multirate processing and features selection is used to reduce the amount of information processed thus reducing the computational complexity of the proposed system relative to the equivalent fixed-rate solutions. Frequency content-dependent subband coefficient selection enhances the compression gain and reduces the transmission activity and computational cost of the post cloud-based classification. We have used MIT-BIH dataset for our experiments. To avoid overfitting and biasness, the performance of considered classifiers is studied by using five-fold cross validation (5CV) and a novel proposed partial blind protocol. The designed method achieves more than 12-fold computational gain while assuring an appropriate signal reconstruction. The compression gain is 13 times compared to fixed-rate counterparts and the highest classification accuracies are 97.06% and 92.08% for the 5CV and partial blind cases, respectively. Results suggest the feasibility of detecting cardiac arrhythmias using the proposed approach.
可穿戴设备在基于云的健康监测系统中的使用正在增加。在这种情况下，信号压缩、计算和功率效率起着至关重要的作用。在这方面，我们提出了一种基于心电图 (ECG) 信号的心血管疾病诊断的有效方法。该方法结合了多速率处理、小波分解和基于频率内容的子带系数选择以及机器学习技术。多速率处理和特征选择用于减少处理的信息量，从而降低与等效固定速率解决方案相比，所提出系统的计算复杂度。基于频率内容的子带系数选择提高了压缩增益，并降低了基于云的分类后的传输活动和计算成本。我们已经使用 MIT-BIH 数据集进行了实验。为了避免过拟合和偏差，通过使用五折交叉验证 (5CV) 和一种新提出的部分盲协议来研究所考虑的分类器的性能。所设计的方法在确保适当的信号重建的同时，实现了超过 12 倍的计算增益。与固定速率相比，压缩增益提高了 13 倍，5CV 和部分盲情况下的最高分类准确率分别为 97.06%和 92.08%。结果表明，使用所提出的方法检测心律失常是可行的。
