| Popis: |
Wearable devices have gained significant popularity for continuous Electrocardiogram (ECG) monitoring due to their compactness and convenience. Day-by-day and 24/7 monitoring without gaps is demanded to promptly detect unusual symptoms that are short-term warning signs of dangerous diseases. Cost-saving criteria are the highest priority to extend the use time of devices and maintain the system operation. However, not all measured signals are useful because the signal quality will be affected by many things such as motion artifacts and muscle noise. Demands on classifying and sending only the usable signal is a requirement, it can save the battery on wearable devices and the expense on the server side (i.e., cloud-based processing), such as storage and processing resources. Therefore, Signal Quality Indices (SQIs) have been developed and researched to determine signal quality to meet the above requirements. This study introduces a novel SQI approach to classify signals. The proposed method has three contributions: 1) exponentially Weighted Mean-Variance (EWMV), a lightweight equation, to identify peaks, followed by applying an adaptive threshold to define peaks that have the same shape as R-peaks; 2) outlier elimination process is proposed to enhance the accuracy; and 3) maximal Overlap Discrete Wavelet Transform (MODWT) is employed to categorize ECG signals into a new class, potentially containing signals relevant to pathological analysis. Experimental results demonstrate that our algorithm achieves the highest sensitivity for both noisy and noiseless data sets. Specifically, it achieved a sensitivity of 99.31% for clean signals and 97.69% for noisy signals. In the case of the PhysioNet challenge data set, while our sensitivity of 96.37% may not be the highest, our accuracy stands out at 95.10%, surpassing other methods recently reviewed. Additionally, our approach demonstrates the lowest trade-off between sensitivity and accuracy among the surveyed SQI techniques. |
