Algorithm for real-time detection of heart rate from noisy ECG signals supported by continuous blood pressure analysis

The algorithm proposed in this paper is designed for robust identification of the heart beat annotations in multimodal data, consisting of ECG signal and one or several continuous arterial blood pressure signals. In case the ECG signal is distorted or unavailable the heart beat annotations are detected in continuous blood pressure signal. The novelty of the proposed solution lays in the adaptation of the algorithm for implementation on a real time system, a weighted estimation of the average RR interval in ECG signal and application of abnormality index estimation algorithm in advance to RR interval estimation from arterial blood pressure signal. The algorithm proposed in this paper reduced the HR estimation error from 6% to 1-2% for various SAI thresholds. For both analysed signal datasets the amount of FP annotations were reduced by our proposed algorithm, especially for the training dataset, where the amount of FP indications was reduced nearly 4 times.

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