Robust inter-beat interval estimation in cardiac vibration signals

Abstract

Abstract. Reliable and accurate estimation of instantaneous frequencies of physiological rhythms, such as heart rate, is critical for many healthcare applications. Robust estimation is especially challenging when novel unobtrusive sensors are used for continuous health monitoring in uncontrolled environments, because these sensors can create significant amounts of potentially unreliable data. We propose a new flexible algorithm for the robust estimation of local (beat-to-beat) intervals from ballistocardiograms (BCGs) recorded by an unobtrusive bed-mounted sensor. This sensor allows the measurement of motions of the body which are caused by cardiac activity.Our method requires neither a training phase nor any prior knowledge about the morphology of the heart beats in the analyzed waveforms. Instead, three short-time estimators are combined using a Bayesian approach to continuously estimate the interbeat intervals. We have validated our method on over-night BCG recordings from 33 subjects (8 normal, 25 insomniacs). On this dataset, containing approximately 1 million heart beats, our method achieved a mean beat-to-beat interval error of 0.94 % with a coverage of 70.96 %.

Remote monitoring of breathing dynamics using infrared thermography

“…To extract the instantaneous breathing frequencies, the algorithm introduced by Brüser et al [31,32] was implemented. These authors proposed a new algorithm that allows a robust estimation of local beat-to-beat intervals in physiological time series [33].…”

“…It aims to find the absolute difference between samples. Brüser et al [31,32] adapted this method by using an adaptive window. This new approach is given by The current estimator can be intended as an indirect peak detector, since it considers only the signal amplitude.…”

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

“…For the second set, a robust interval estimation approach [4] was used. This algorithm estimates beat-to-beat intervals without peak-detection on the raw signal, but exploits its self-similarity.…”

Beat-to-beat heart rate estimation fusing multimodal video and sensor data

“…Unobtrusive acquisition of biosignals for health-and wellness applications has experienced increasing popularity in recent years [1][2][3][4]. In particular, monitoring of the heart rate and its variability outside the classical scenarios such as hospitals and sleep laboratories is an active area of research.…”

“…Moreover, some sort of illumination and a direct line of sight is required, which complicates applications like sleep monitoring. Here, other modalities that can easily be integrated into the mattress, such as capacitive electrocardiography (cECG) [4] or BCG [3], have proven to allow the accurate and robust determination of beat-to-beat intervals (BBI) for HRV analysis. Vast portions of daily life in post-industrial countries are spent sitting, often in front of computers.…”