Key result
A fast approach for QRS detection and ECG compression achieved sensitivities and positive predictions surpassing 99.9% in some databases, with a global data saving of 90.35%.
A novel parameterless and computationally efficient algorithm for QRS detection and ECG compression achieves high sensitivity (>99.9%) and significant data saving (90.35%).
May enable efficient ECG monitoring in constrained settings; leaves open prospective clinical validation before adoption.
OBJECTIVE: This paper presents a fast approach to detect QRS complexes based on a simple analysis of the temporal ECG structure. METHODS: The ECG is processed through several steps involving noise removal, feature detection, and feature analysis. The obtained feature set, which holds most of the ECG information while requiring low data storage, constitutes a lossy compressed version of the ECG. RESULTS: The experiments, performed using 12 different ECG databases, emphasize the advantages of our proposal. For example, 130-min ECG recordings are processed in average in 0.77 s. Also, sensitivities and positive predictions surpass 99.9% in some databases, and a global data saving of 90.35% is achieved. CONCLUSION AND SIGNIFICANCE: When compared to other approaches, this study offers a parameterless and computationally efficient alternative for QRS complex detection and lossy ECG compression. Moreover, some of the presented techniques are general enough to be used by other ECG analysis tools. Finally, the documented source code corresponding to this study is publicly available.
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Antoni Burguera (2018) studied ECG analysis. Fast approach to detect QRS complexes based on temporal ECG structure analysis vs. Other approaches was evaluated on QRS detection sensitivity, positive prediction, and data saving. A fast approach for QRS detection and ECG compression achieved sensitivities and positive predictions surpassing 99.9% in some databases, with a global data saving of 90.35%.
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