Detection of heart beats in multimodal data: a robust beat-to-beat interval estimation approach

Abstract

The heart rate and its variability play a vital role in the continuous monitoring of patients, especially in the critical care unit. They are commonly derived automatically from the electrocardiogram as the interval between consecutive heart beat. While their identification by QRS-complexes is straightforward under ideal conditions, the exact localization can be a challenging task if the signal is severely contaminated with noise and artifacts. At the same time, other signals directly related to cardiac activity are often available. In this multi-sensor scenario, methods of multimodal sensor-fusion allow the exploitation of redundancies to increase the accuracy and robustness of beat detection.In this paper, an algorithm for the robust detection of heart beats in multimodal data is presented. Classic peak-detection is augmented by robust multi-channel, multimodal interval estimation to eliminate false detections and insert missing beats. This approach yielded a score of 90.70 and was thus ranked third place in the PhysioNet/Computing in Cardiology Challenge 2014: Robust Detection of Heart Beats in Muthmodal Data follow-up analysis.In the future, the robust beat-to-beat interval estimator may directly be used for the automated processing of multimodal patient data for applications such as diagnosis support and intelligent alarming.

Nonnegative Matrix Factorization and Random Forest for Classification of Heart Sound Recordings in the Spectral Domain

“…Although originally developed for cardiac vibration signals, the algorithm has since been proven useful on a variety of cardiac signals [10,11] Table 3. Leave-one-out cross-validation of the training data.…”

Fusing QRS Detection, Waveform Features, and Robust Interval Estimation with a Random Forest to Classify Atrial Fibrillation

“…The original algorithm was developed for the analysis of ballistocardiography and is based on the assumption that consecutive beats exhibit similar morphologies. Even so, the algorithm has proven useful for beat detection in clinical data [5] and for the reduction of false alarms in the intensive care unit [6]. For every window with index j of the signal of interest, the most likely interval η j as well as a quality metric q j is reported.…”

Noise Resistance of Several Top-Scored Heart Beat Detectors

“…In this regard, I selected the top-ranked algorithms of Pangerc et al [2], Johnson et al [3], Hoog Antink et al [4], de Cooman et al [5] and Vollmer [7] to analyze the robustness against noise in detail. A Linux system was appropriately configured (Lenovo IdeaCentre K330, Intel Core i5-2300 CPU @ 2.80GHz, 6GB RAM: openSUSE 13.2, Matlab 2015b, Compiler gcc-4.7.4, WFDB Toolbox von Physionet wfdb-10.5.24) to make detectors executable which were programmed in C or C++.…”