Ballistocardiography Can Estimate Beat-to-Beat Heart Rate Accurately at Night in Patients After Vascular Intervention.

While bed-integrated ballistocardiography (BCG) has potential clinical applications such as unobtrusive monitoring of patients staying in the general hospital ward, it has so far mainly gained interest in the wellness domain. In this article, the potential of BCG to monitor hospitalized patients after surgical intervention was assessed. Long-term BCG recordings (mean duration 17.7 h) of 14 patients were performed with an EMFit QS bed sensor. In addition, ten healthy subjects were recorded during sleep (mean duration 7.8 h). Using an iterative algorithm, beat-to-beat intervals (BBIs) and the ultra-short-term heart-rate-variability (HRV) parameters standard deviation of NN intervals (SDNN) and root mean square of successive differences (RMSSD) were estimated and compared to an ECG reference in terms of average estimation error and temporal coverage. While the absolute BBI estimation error was found to be higher when full-day patient data was used (16.5 ms), no significant difference between healthy subjects (12.7 ms) and patient nighttime data (11.0 ms) was observed. Nevertheless, temporal coverage of BBI estimation was significantly lower in patients (39.3% overall, 51.7% at night) compared to the healthy sleepers (73.2%). This resulted in reduced HRV estimation coverage (9.7% vs. 37.2%) at comparable estimation error levels.

Unobtrusive Nocturnal Heartbeat Monitoring by a Ballistocardiographic Sensor in Patients with Sleep Disordered Breathing.

Sleep disordered breathing (SDB) is known for fluctuating heart rates and an increased risk of developing arrhythmias. The current reference for heartbeat analysis is an electrocardiogram (ECG). As an unobtrusive alternative, we tested a sensor foil for mechanical vibrations to perform a ballistocardiography (BCG) and applied a novel algorithm for beat-to-beat cycle length detection. The aim of this study was to assess the correlation between beat-to-beat cycle length detection by the BCG algorithm and simultaneously recorded ECG. In 21 patients suspected for SDB undergoing polysomnography, we compared ECG to simultaneously recorded BCG data analysed by our algorithm. We analysed 362.040 heartbeats during a total of 93 hours of recording. The baseline beat-to-beat cycle length correlation between BCG and ECG was r s = 0.77 (n = 362040) with a mean absolute difference of 15 ± 162 ms (mean cycle length: ECG 923 ± 220 ms; BCG 908 ± 203 ms). After filtering artefacts and improving signal quality by our algorithm, the correlation increased to r s = 0.95 (n = 235367) with a mean absolute difference in cycle length of 4 ± 72 ms (ECG 920 ± 196 ms; BCG 916 ± 194 ms). We conclude that our algorithm, coupled with a BCG sensor foil provides good correlation of beat-to-beat cycle length detection with simultaneously recorded ECG.

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

Coverage and accuracy of unobtrusively measured biosignals are generally relatively low compared to clinical modalities. This can be improved by exploiting redundancies in multiple channels with methods of sensor fusion. In this paper, we demonstrate that two modalities, skin color variation and head motion, can be extracted from the video stream recorded with a webcam. Using a Bayesian approach, these signals are fused with a ballistocardiographic signal obtained from the seat of a chair with a mean absolute beat-to-beat estimation error below 25 milliseconds and an average coverage above 90% compared to an ECG reference.

Heartbeat Cycle Length Detection by a Ballistocardiographic Sensor in Atrial Fibrillation and Sinus Rhythm.

BACKGROUND: Heart rate monitoring is especially interesting in patients with atrial fibrillation (AF) and is routinely performed by ECG. A ballistocardiography (BCG) foil is an unobtrusive sensor for mechanical vibrations. We tested the correlation of heartbeat cycle length detection by a novel algorithm for a BCG foil to an ECG in AF and sinus rhythm (SR). METHODS: In 22 patients we obtained BCG and synchronized ECG recordings before and after cardioversion and examined the correlation between heartbeat characteristics. RESULTS: We analyzed a total of 4317 heartbeats during AF and 2445 during SR with a correlation between ECG and BCG during AF of r = 0.70 (95% CI 0.68-0.71, P < 0.0001) and r = 0.75 (95% CI 0.73-0.77, P < 0.0001) during SR. By adding a quality index, artifacts could be reduced and the correlation increased for AF to 0.76 (95% CI 0.74-0.77, P < 0.0001, n = 3468) and for SR to 0.85 (95% CI 0.83-0.86, P < 0.0001, n = 2176). CONCLUSION: Heartbeat cycle length measurement by our novel algorithm for BCG foil is feasible during SR and AF, offering new possibilities of unobtrusive heart rate monitoring. This trial is registered with IRB registration number EK205/11. This trial is registered with clinical trials registration number NCT01779674.

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

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.

Ambient and Unobtrusive Cardiorespiratory Monitoring Techniques.

Monitoring vital signs through unobtrusive means is a goal which has attracted a lot of attention in the past decade. This review provides a systematic and comprehensive review over the current state of the field of ambient and unobtrusive cardiorespiratory monitoring. To this end, nine different sensing modalities which have been in the focus of current research activities are covered: capacitive electrocardiography, seismo- and ballistocardiography, reflective photoplethysmography (PPG) and PPG imaging, thermography, methods relying on laser or radar for distance-based measurements, video motion analysis, as well as methods using high-frequency electromagnetic fields. Current trends in these subfields are reviewed. Moreover, we systematically analyze similarities and differences between these methods with respect to the physiological and physical effects they sense as well as the resulting implications. Finally, future research trends for the field as a whole are identified.

Improvement of force-sensor-based heart rate estimation using multichannel data fusion.

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).

Monte-Carlo Simulation and Automated Test Bench for Developing a Multichannel NIR-Based Vital-Signs Monitor.

Unobtrusive, long-term monitoring of cardiac (and respiratory) rhythms using only non-invasive vibration sensors mounted in beds promises to unlock new applications in home and low acuity monitoring. This paper presents a novel concept for such a system based on an array of near infrared (NIR) sensors placed underneath a regular bed mattress. We focus on modeling and analyzing the underlying technical measurement principle with the help of a 2D model of a polyurethane foam mattress and Monte-Carlo simulations of the opto-mechanical interaction responsible for signal genesis. Furthermore, a test rig to automatically and repeatably impress mechanical vibrations onto a mattress is introduced and used to identify the properties of a prototype implementation of the proposed measurement principle. Results show that NIR-based sensing is capable of registering miniscule deformations of the mattress with a high spatial specificity. As a final outlook, proof-of-concept measurements with the sensor array are presented which demonstrate that cardiorespiratory movements of the body can be detected and that automatic heart rate estimation at competitive error levels is feasible with the proposed approach.

Robust sensor fusion of unobtrusively measured heart rate.

Contactless vital sign measurement technologies often have the drawback of severe motion artifacts and periods in which no signal is available. However, using several identical or physically different sensors, redundancy can be used to decrease the error in noncontact heart rate estimation, while increasing the time period during which reliable data are available. In this paper, we show for the first time two major results in case of contactless heart rate measurements deduced from a capacitive ECG and optical pulse signals. First, an artifact detection is an essential preprocessing step to allow a reliable fusion. Second, the robust but computationally efficient median already provides good results; however, using a Bayesian approach, and a short time estimation of the variance, best results in terms of difference to reference heart rate and temporal coverage can be achieved. In this paper, six sensor signals were used and coverage increased from 0-90% to 80-94%, while the difference between the estimated heart rate and the gold standard was less than ±2 BPM.

Model-based verification of a non-linear separation scheme for ballistocardiography.

The current rise in popularity of ballisto-cardiography-related research has led to the development of new sensor concepts and recording methods. Measuring the ballistocardiogram using bed mounted pressure sensors opens up new possibilities for home monitoring applications. The signals measured with these sensors contain a mixture of cardiac and respiratory components, which can be used for detection of comorbidities of heart failure like apnea or arrhythmia. However, the separation of the cardiac and respiratory components has proven to be difficult, since there is significant overlap in the spectra of both components. In this paper, an algorithm for the separation task is presented, which can overcome the problem of overlapping spectra. Additionally, a model has been developed for the generation of artificial ballistocardiograms, which are used to analyze the separation performance. Furthermore, the algorithm is tested on preliminary data from a clinical study.

Non-contact monitoring techniques - Principles and applications.

This work gives an overview about some non-contact methods for monitoring of physiological activity. In particular, the focus is on ballistocardiography, capacitive ECG, Infrared Thermography, Magnetic Impedance Monitroing and Photoplethymographic Imaging. The principles behind the methods are described and an inside into possible medical applications is offered.

Multi-channel optical sensor-array for measuring ballistocardiograms and respiratory activity in bed.

Our work covers improvements in sensors and signal processing for unobtrusive, long-term monitoring of cardiac (and respiratory) rhythms using only non-invasive vibration sensors. We describe a system for the unobtrusive monitoring of vital signs by means of an array of novel optical ballistocardiography (BCG) sensors placed underneath a regular bed mattress. Furthermore, we analyze the systems spatial sensitivity and present proof-of-concept results comparing our system to a more conventional BCG system based on a single electromechanical-film (EMFi) sensor. Our preliminary results suggest that the proposed optical multi-channel system could have the potential to reduce beat-to-beat heart rate estimation errors, as well as enable the analysis of more complex breathing patterns.

Adaptive beat-to-beat heart rate estimation in ballistocardiograms.

A ballistocardiograph records the mechanical activity of the heart. We present a novel algorithm for the detection of individual heart beats and beat-to-beat interval lengths in ballistocardiograms (BCGs) from healthy subjects. An automatic training step based on unsupervised learning techniques is used to extract the shape of a single heart beat from the BCG. Using the learned parameters, the occurrence of individual heart beats in the signal is detected. A final refinement step improves the accuracy of the estimated beat-to-beat interval lengths. Compared to many existing algorithms, the new approach offers heart rate estimates on a beat-to-beat basis. The agreement of the proposed algorithm with an ECG reference has been evaluated. A relative beat-to-beat interval error of 1.79% with a coverage of 95.94% was achieved on recordings from 16 subjects.

Applying machine learning to detect individual heart beats in ballistocardiograms.

Ballistocardiography is a technique in which the mechanical activity of the heart is recorded. We present a novel algorithm for the detection of individual heart beats in ballistocardiograms (BCGs). In a training step, unsupervised learning techniques are used to identify the shape of a single heart beat in the BCG. The learned parameters are combined with so-called "heart valve components" to detect the occurrence of individual heart beats in the signal. A refinement step improves the accuracy of the estimated beat-to-beat interval lengths. Compared to other algorithms this new approach offers heart rate estimates on a beat-to-beat basis and is designed to cope with arrhythmias. The proposed algorithm has been evaluated in laboratory and home settings for its agreement with an ECG reference. A beat-to-beat interval error of 14.16 ms with a coverage of 96.87% was achieved. Averaged over 10 s long epochs, the mean heart rate error was 0.39 bpm.