Current QRS detection algorithms were evaluated based on robustness to noise, parameter choice, and numerical efficiency to identify suitable methods for implementation on battery-operated mobile devices.
Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.
Elgendi et al. (Tue,) conducted a review in Cardiovascular diseases (ECG analysis). QRS detection algorithms was evaluated on Algorithm performance (robustness to noise, parameter choice, numerical efficiency). Current QRS detection algorithms were evaluated based on robustness to noise, parameter choice, and numerical efficiency to identify suitable methods for implementation on battery-operated mobile devices.
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