Improvement of Force-Sensor-Based Heart Rate Estimation Using Multichannel Data Fusion

Abstract

The aim of this paper is to present and evaluate algorithms for heartbeat interval estimation from multiple spatially distributed force sensors integrated into a bed. Moreover, the benefit of using multichannel systems as opposed to a single sensor is investigated. While it might seem intuitive that multiple channels are superior to a single channel, the main challenge lies in finding suitable methods to actually leverage this potential. To this end, two algorithms for heart rate estimation from multichannel vibration signals are presented and compared against a single-channel sensing solution. The first method operates by analyzing the cepstrum computed from the average spectra of the individual channels, while the second method applies Bayesian fusion to three interval estimators, such as the autocorrelation, which are applied to each channel. This evaluation is based on 28 night-long sleep lab recordings during which an eight-channel polyvinylidene fluoride-based sensor array was used to acquire cardiac vibration signals. The recruited patients suffered from different sleep disorders of varying severity. From the sensor array data, a virtual single-channel signal was also derived for comparison by averaging the channels. The single-channel results achieved a beat-to-beat interval error of 2.2% with a coverage (i.e., percentage of the recording which could be analyzed) of 68.7%. In comparison, the best multichannel results attained a mean error and coverage of 1.0% and 81.0%, respectively. These results present statistically significant improvements of both metrics over the single-channel results (p < 0.05).

Step Estimator Based on a Wearable ECG Monitor

“…Before peak detection implementation, step and gait signals have been proceeded using preprocessing signal which is identical in [30]. e detection accuracy was calculated by comparing the number of peak from step signal with gait signal using (11), which is that the error is obtained by subtracting two times the number of gait peaks with the number of signal peaks, as given in (12). We multiply the number of gait peaks by factor 2 because one gait cycle represents two-step cycles:…”

“…During exercise, the ECG signal will be interfered by body movement. Most studies of heart rate detection are based on motion artifact cancelation [9][10][11] and heart rate estimation [12,13]. But none of researchers used the motion artifact as advantageous motion signal.…”

A Novel 12-Lead ECG T-Shirt with Active Electrodes

“…The first step was to filter the obtained ECG data with a 3rd order Butterworth bandpass filter between 2 and 20 Hz. In order to obtain the RR intervals (beat-to-beat intervals) , a method for beat-to-beat interval identification of multi-channel data based on self-similarity features was applied for the reference and T-shirt data [27]. Using this method, beat-to-beat intervals in any kind of cardiac-related data can be determined as long as a repetitive pattern is detectable.…”

“…The Bland-Altman diagram for all subjects is presented in Figure 9; the RR intervals extracted from both devices are compared in this diagram. The RR intervals used in this section were extracted from the Einthoven II lead with the algorithm by Brueser [27]. Furthermore, only artifact-free data were selected for the diagram, so the mean coverage was 87.2%.…”

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

“…All curves are in arbitrary units, the quality metric Q is defined as the ratio of the peak height to the area under the curve. towards a multimodal, N-channel setting [13], where each estimator is calculated for each of the N channels, is straightforward,…”