Key result
A novel joint data compression and QRS detection algorithm achieved a lossless bit compression ratio of 2.286x, a sensitivity of 99.64%, and a positive prediction of 99.81% on the MIT/BIH database.
A novel joint QRS detection and data compression algorithm offers high accuracy and reduced computational complexity, making it suitable for wearable ambulatory ECG devices.
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May enable efficient wearable ECG monitoring; leaves open prospective clinical validation.
This paper presents a novel electrocardiogram (ECG) processing technique for joint data compression and QRS detection in a wireless wearable sensor. The proposed algorithm is aimed at lowering the average complexity per task by sharing the computational load among multiple essential signal-processing tasks needed for wearable devices. The compression algorithm, which is based on an adaptive linear data prediction scheme, achieves a lossless bit compression ratio of 2.286x. The QRS detection algorithm achieves a sensitivity (Se) of 99.64% and positive prediction (+P) of 99.81% when tested with the MIT/BIH Arrhythmia database. Lower overall complexity and good performance renders the proposed technique suitable for wearable/ambulatory ECG devices.
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Yong Lian (2014) studied Electrocardiogram monitoring. Joint QRS detection and data compression algorithm was evaluated on QRS detection sensitivity and positive prediction, and data compression ratio. A novel joint data compression and QRS detection algorithm achieved a lossless bit compression ratio of 2.286x, a sensitivity of 99.64%, and a positive prediction of 99.81% on the MIT/BIH database.
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