Key result
A linear decreasing inertia variant of a particle swarm optimization-based adaptive filter achieved a sensitivity of 99.75%, positive predictivity of 99.83%, and detection error rate of 0.42% for QRS complex detection.
A novel PSO-based adaptive filter demonstrates high accuracy for QRS complex detection in ECG signals.
A new technique for QRS complex detection of electrocardiogram signals, using particle swarm optimisation (PSO)‐based adaptive filter (AF), is proposed. In the proposed method, the AF, based on PSO, is used to generate the feature. An effective detection algorithm, containing search‐backs for missed peaks, is also proposed. In the experiment, five PSO variants are tested on MIT‐BIH arrhythmia database. The linear decreasing inertia variant of PSO, achieves the best results with sensitivity, positive predictivity and detection error rate of 99.75, 99.83 and 0.42%, respectively. Effectiveness of the proposed method is validated by comparing fidelity parameter of proposed method with state‐of‐the‐art methods.
No takes yet. Share an insight, caveat, or question.
Jain et al. (2016) studied Arrhythmia (ECG signals). Particle swarm optimisation (PSO)‐based adaptive filter (AF) vs. State-of-the-art methods was evaluated on QRS complex detection (sensitivity, positive predictivity, and detection error rate). A linear decreasing inertia variant of a particle swarm optimization-based adaptive filter achieved a sensitivity of 99.75%, positive predictivity of 99.83%, and detection error rate of 0.42% for QRS complex detection.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: