A threshold-independent QRS detection algorithm using the dynamic plosion index achieved performance comparable to state-of-the-art algorithms on the MIT-BIH database.
A novel threshold-independent QRS detection algorithm using the dynamic plosion index achieves performance comparable to state-of-the-art methods on the MIT-BIH database.
Detection of QRS serves as a first step in many automated ECG analysis techniques. Motivated by the strong similarities between the signal structures of an ECG signal and the integrated linear prediction residual (ILPR) of voiced speech, an algorithm proposed earlier for epoch detection from ILPR is extended to the problem of QRS detection. The ECG signal is pre-processed by high-pass filtering to remove the baseline wandering and by half-wave rectification to reduce the ambiguities. The initial estimates of the QRS are iteratively obtained using a non-linear temporal feature, named the dynamic plosion index suitable for detection of transients in a signal. These estimates are further refined to obtain a higher temporal accuracy. Unlike most of the high performance algorithms, this technique does not make use of any threshold or differencing operation. The proposed algorithm is validated on the MIT-BIH database using the standard metrics and its performance is found to be comparable to the state-of-the-art algorithms, despite its threshold independence and simple decision logic.
Ramakrishnan et al. (Wed,) conducted a other in QRS detection in ECG signals. Threshold-independent QRS detection algorithm using the dynamic plosion index vs. State-of-the-art algorithms was evaluated on QRS detection performance on the MIT-BIH database. A threshold-independent QRS detection algorithm using the dynamic plosion index achieved performance comparable to state-of-the-art algorithms on the MIT-BIH database.