Key result
The proposed adaptive-filtering-based QRS complex detection method achieved a sensitivity of 99.49% and a positive predictivity of 99.72% when tested on the MIT-BIH ECG database.
The proposed adaptive-filtering-based QRS detection method offers high accuracy with low power consumption and area cost, making it suitable for wearable ECG devices.
Promising for low-power wearable ECG devices; leaves open need for prospective clinical validation.
Electrocardiogram (ECG) can be used as a valid way for diagnosing heart disease. To fulfill ECG processing in wearable devices by reducing computation complexity and hardware cost, two kinds of adaptive filters are designed to perform QRS complex detection and motion artifacts removal, respectively. The proposed design achieves a sensitivity of 99.49% and a positive predictivity of 99.72%, tested under the MIT-BIH ECG database. The proposed design is synthesized under the SMIC 65-nm CMOS technology and verified by post-synthesis simulation. Experimental results show that the power consumption and area cost of this design are of 160 μ W and 1.09 × 10 5 μ m 2 , respectively.
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Tian et al. (2017) studied Heart disease. Adaptive-filtering-based QRS complex detection method was evaluated on QRS complex detection sensitivity and positive predictivity. The proposed adaptive-filtering-based QRS complex detection method achieved a sensitivity of 99.49% and a positive predictivity of 99.72% when tested on the MIT-BIH ECG database.
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