Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> The authors describe the static charge sensitive bed (SCSB), which consists of a sensitive movement detector embedded in a mattress. When a subject rests on the bed, a single electrical signal, containing components reflective of cardiac, respiratory, and body movement related motion, is produced. The authors describe a digital signal processing technique. to separate the BCG (ballistocardiogram) from the SCSB signal. An evaluation of this algorithm was conducted using recordings from normal volunteers. Comparisons with simultaneously recorded reference signals indicated that the algorithm performed satisfactorily in a laboratory environment. The results presented demonstrate that valuable BCG information can be obtained from the SCSB signal after suitable processing. > <s> BIB001 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> Seismocardiography is a new non-invasive technique developed for recording and analyzing cardiac vibratory activity. At present, a single channel electrocardiogram (ECG) has to be recorded simultaneously to provide a timing reference. Beat detection and analysis of SCGs rely on peak detection of the ECG QRS complex. In the SCG of one beat, there is a dominant characteristic complex, called the W complex. This complex is analogous to the ECG's QRS complex as a beat timing reference, and may carry significant discriminant information for SCG classification. In this paper, an automated SCG characteristic W complex detection algorithm, using a wavelet-based multiscale edge detection technique, is described. Preliminary test results demonstrate that this technique is effective for localization of the W complex. <s> BIB002 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> A noninvasive and unconstrained real-time method to detect the respiration rhythm and pulse rate during sleep is presented. By employing the agrave trous algorithm of the wavelet transformation (WT), the respiration rhythm and pulse rate can be monitored in real-time from a pressure signal acquired with a pressure sensor placed under a pillow. The waveform for respiration rhythm detection is derived from the 26 scale approximation, while that for pulse rate detection is synthesized by combining the 24 and 25 scale details. To minimize the latency in data processing and realize the highest real-time performance, the respiration rhythm and pulse rate are estimated by using waveforms directly derived from the WT approximation and detail components without the reconstruction procedure. This method is evaluated with data collected from 13 healthy subjects. By comparing with detections from finger photoelectric plethysmograms used for pulse rate detection, the sensitivity and positive predictivity were 99.17% and 98.53%, respectively. Similarly, for respiration rhythm, compared with detections from nasal thermistor signals, results were 95.63% and 95.42%, respectively. This study suggests that the proposed method is promising to be used in a respiration rhythm and pulse rate monitor for real-time monitoring of sleep-related diseases during sleep <s> BIB003 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> A multichannel pressure sensing Emfit foil was integrated to a bed mattress for measuring ballistocardiograph signals during sleep. We calculated the heart beat interval with cepstrum method, by applying FFT for short time windows including pair of consequent heart beats. We decreased the variance of FFT by averaging the multichannel data in the frequency domain. Relative error of our method in reference to electrocardiograph RR interval was only 0.35% for 15 night recordings with six normal subjects, when 12% of data was automatically removed due to movement artifacts. Background motivation for this work is given from the studies applying heart rate variability for the sleep staging. <s> BIB004 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> This paper suggests a beat detection method for ballistocardiogram (BCG) from an unconstrained cardiac signal monitoring devices. A fiducial peak point of BCG is an I-J-K complex which corresponds with ventricle contraction and Electrocardiogram (ECG) QRS complex. The goal of the method is extraction of J peak without ECG synchronization. The detection method is based on a “template matching” rule evaluated using a correlation function in a local moving-window procedure. The total beat detection algorithm operates in two stages, template definition stage and beat detection stage with defined template in previous stage. In the first stage, the BCG template is constructed by the expert with an empirical analysis of BCG signal and measurement device. In the second stage, the correlation function calculates an accuracy of template with BCG signal using a local moving-window. The data analysis has been performed on the subjects tested at Seoul National University Hospital Sleep Medicine Center and presents 95.16% of sensitivity and 94.76% of positive predictivity value for the J peak detection. <s> BIB005 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> Polysomnography (PSG) involves simultaneous and continuous monitoring of relevant normal and abnormal physiological activity during sleep. At present, an electroencephalography-based rule is generally used for classifying sleep stages. However, scoring the PSG record is quite laborious and time consuming. In this paper, movement and cardiac activity were measured unobtrusively by a load-cell-installed bed, and sleep was classified into two stages: slow-wave sleep and non-slow-wave sleep. From the measured cardiac activity, we extracted heartbeat data and calculated heart rate variability parameters: standard deviation of R-R intervals SDNN, low frequency-to-high frequency ratio, alpha of detrended fluctuation analysis and correlation coefficient of R-R interval. The developed system showed a substantial concordance with PSG results when compared using a contingency test. The mean epoch-by-epoch agreement between the proposed method and PSG was 92.5% and Cohen's kappa was 0.62. <s> BIB006 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> We present an algorithm for obtaining the heart rate from the signal of a single, contact-less sensor recording the mechanical activity of the heart. This vital parameter is required on a beat-to-beat basis for applications in sleep analysis and heart failure disease management. Our approach bundles information from various sources for first robust estimates. These estimates are further refined in a second step. An unambiguous comparison with the ECG RR-intervals taken as reference is possible for 98.5% of the heart beats. In these cases, a mean absolute error of 17 ms for the inter-beat interval lengths has been achieved, over a test corpus of 20 whole nights. <s> BIB007 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> Purpose ::: With increasing concern for well-being and recent advances in biomedical technology, healthcare is extending beyond the realm of hospitals. Nonintrusive home healthcare monitoring is essential for ensuring long-term use and preservation of an uninterrupted daily life. Herein, we describe recent progress in nonintrusive measurements of ECG, PPG, respiration and blood pressure using sensors installed in everyday household equipment. <s> BIB008 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> 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. <s> BIB009 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> We introduced a novel non-constrained technique for estimating heart rate variability (HRV) using a ballistocardiogram (BCG). To assess whether the BCG signal can be used to analyse the cardiac autonomic modulation, HRV parameters derived from the BCG signal (ballistocardiographic HRV, B-HRV) were statistically compared with the HRV parameters from the ECG signal during rest and under two different experimental conditions that induce cardiac autonomic rhythm changes: the Valsalva manoeuvre and static exercise. Time domain, frequency domain and nonlinear analyses were individually performed on 15 healthy subjects to assess whether the BCG can be used to analyse the cardiac autonomic modulation under each condition. For all subjects, the proposed method had averages of relative errors of 5.01 ± 4.72, 5.64 ± 4.83 and 5.98 ± 5.80% for resting, Valsalva and post-exercise sessions, respectively, and the correlation coefficients between the reference (ECG) and proposed (BCG) methods are 0.97, 0.98 and 0.98, for resting, Valsalva and post-exercise sessions, respectively. During cardiac autonomic changes, the B-HRV parameters changed in a pattern that is very similar to the variations in the HRV parameters based on Student's t-test results. In addition, some of the B-HRV parameters changed according to cardiac autonomic rhythms controlled by sympathetic and parasympathetic activities during the experiments. These findings indicate that BCG can provide an accurate and reliable means to evaluate autonomic system activation by HRV in its unconstrained way. <s> BIB010 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> In this paper, a multimethod approach for heartbeat and respiration detection from an optical interferometric signal is proposed. Optical interferometer is a sensitive device that detects physical changes of optical-fiber length due to external perturbations. When in direct or indirect contact with human body (e.g., hidden in a bed mattress), mechanical and acoustic activity of cardiac muscle and respiration reflect in the interferometric signal, enabling entirely unobtrusive monitoring of heartbeat and respiration. A novel, two-phased multimethod approach was developed for this purpose. The first phase selects best performing combinations of detection methods on a training set of signals. The second phase applies the selected methods to test set of signals and fuses all the detections of vital signs. The test set consisted of 14 subjects cycling an ergometer until reaching their submaximal heart rate. The following resting periods were analyzed showing high efficiency (98.18 ± 1.40% sensitivity and 97.04 ± 4.95% precision) and accuracy (mean absolute error of beat-to-beat intervals 22±9 ms) for heartbeat detection, and acceptable efficiency (90.06 ± 7.49% sensitivity and 94.21 ± 3.70% precision) and accuracy (mean absolute error of intervals between respiration events 0.33 ± 0.14 s) for respiration detection. <s> BIB011 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> In this research, we proposed a method to estimate the time- and frequency-domain indices of pulse rate variability by using pulse waveforms separated from pressure signals measured during sleep. The pulse waveform is extracted from pressure signals based on wavelet transform, and heart beats are detected by an adaptive pulse template method. Through an evaluation based on 13 healthy subjects' pressure and photoplethysmography signals measured simultaneously during sleep, we found that the time- and frequency-domain indices of pulse rate variability estimated from pressure signals have a high correlation with those estimated from photoplethysmography signals. It suggests that pressure signals may serve as a convenient and low-cost tool to estimate pulse rate variability during sleep. <s> BIB012 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> Ballistocardiography (BCG) is a promising unobtrusive method for home e-healthcare systems, and has attracted increasing interest in recent years along with technological advances in related biomedical, electrical engineering and computer science fields. While existing systems have investigated the efficacy of BCG setups in bed, backrest, seat or scale positions, we propose to study BCG in headrest position that will allow new practical and portable applications. To this end, we designed and implemented a multi-modality sensing system including a high-sensitivity microbend fiber optic BCG sensor. In this preliminary study, we have collected multi-modality physiological data on 3 human subjects. We ran extensive analysis on BCG in correlation with ECG, and identified special characteristics of the signal in the new BCG setup. The result suggests that new appropriate computing techniques are necessary for accurately recovering the heart beat signal. Therefore, we developed a novel algorithm for heart beat detection. We evaluate the algorithm with the data and demonstrate that it can accurately compute heart rate intervals in the headrest BCG despite significant signal distortion. <s> BIB013 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> 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 cardiac vibration signals, specifically 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 inter-beat intervals. We have validated our method on over-night BCG recordings from 33 subjects (8 normal, 25 insomniacs). On this dataset, containing approximately one million heart beats, our method achieved a mean beat-to-beat interval error of 0.78% with a coverage of 72.69%. <s> BIB014 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> We propose and evaluate an unsupervised method for the estimation of heart rate variability (HRV) indices from ballistocardiograms (BCGs) recorded by a bed-mounted, electromechanical film (EMFi) sensor during sleep. After estimating the beat-to-beat intervals from the BCGs, short-term time- and frequency-domain HRV indices are computed and compared to an ECG reference. We evaluated signals recorded overnight from 8 subjects (approx. 212.000 heart beats). Our results show a good correlation (> 0.9) between BCG- and ECG- derived HRV indices and suggest that unsupervised long- term HRV monitoring using BCGs is indeed feasible. <s> BIB015 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> This paper deals with an optimized heartbeat detection multimethod by using maximum a posteriori probability (MAP). The approach was derived for unobtrusive fiber-optic measurements of cardiac activity. Multiple independent detection methods were selected and characterized by their delays and variability referring to cardiac electrical excitation (R waves). Validation of the approach was performed in two experiments involving 24 participants: 10 interferometric signals were recorded at rest, 14 with variable heart rate after physical exercise. The proposed MAP heartbeat detection was assessed by a cross validation in 250 iterations. Obtained results show the overall efficiency, which was estimated by a product of the sensitivity, precision, and variability of heartbeat detections, yields 97.04 ± 3.36% for the experiment with physical exercise and 97.07 ± 4.49% at rest. The method's accuracy guarantees that the heartbeat detections differ for 22 ± 5 ms and 22 ± 3 ms from the ECG reference in the two types of experiments, respectively. <s> BIB016 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> 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 ). <s> BIB017 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> A. Heartbeat Detection <s> We present a method for measuring beat-to-beat heart rate from ballistocardiograms acquired with force sensors. First, a model for the heartbeat shape is adaptively inferred from the signal using hierarchical clustering. Then, beat-to-beat intervals are detected by finding positions where the heartbeat shape best fits the signal. The method was validated with overnight recordings from 46 subjects in varying setups (sleep clinic, home, single bed, double bed, two sensor types). The mean beat-to-beat interval error was 13 ms and on an average 54% of the beat-to-beat intervals were detected. The method is part of a home-use e-health system for an unobtrusive sleep measurement. <s> BIB018
Since heart rate is regulated by the autonomic nervous system, the analysis of HRV is currently employed to obtain physiological and clinical information on the level of sympathetic and parasympathetic drive to the heart. Even though ECG is the most widely used biological signal to evaluate heart rate dynamics, BCG may also be used. Due to its easier application for monitoring in contrast to the inconvenience of attaching electrodes to the skin in ECG measurement, BCG may facilitate the assessment of heart rate dynamics in daily life BIB008 . Heartbeats may be identified by the J-wave peak in the BCG signal, i.e., the point of highest amplitude in the BCG waveform. Heart rate is evaluated by measuring the interval between consecutive J-peaks, the J-J interval. As there are many algorithms to detect the R-peak in ECG, there are also various methods to detect the J-peaks or heart beat from BCG. Since BCG can be measured in different settings with different type of sensors, the peak-detection algorithm should be selected to optimize the performance considering the characteristics of measured BCG. A heartbeat detection algorithm which showed high performance in R-peak detection from ECG can be applied with minor modification for J-peak detection. Generally the peak detection procedure is applied to select the highest value in amplitude as the J-peak within the sliding window after some preprocessing to increase signal-to-noise ratio (SNR) and to reject artifacts due to motion or other interferences. Choi et al. demonstrated increased detection performance with a dedicated algorithm, which finds local peaks in four divided subintervals within a period and selects the maximum peak as J-peak from these local peaks with some rejection rules BIB006 . Jansen et al. applied a detection method based on a "template matching" rule by evaluating a correlation function in a local moving window BIB001 , a method which was further refined and developed by Shin et al. BIB005 . Although this method requires template design in its first stage, Shin et al. successfully applied it to several types of BCG signals acquired from air mattress, load cells, and EMFi sensors. The results showed 95.2% of sensitivity and 94.8% of specificity in average for five subjects and three types of BCG signals. Additional methods for heartbeat detection from BCG signals include those which combine different estimators BIB011 , BIB007 , BIB016 , and methods which use wavelets to preprocess the signal prior to peak detection BIB003 , BIB002 . Heart rate was estimated from the spectral domain specially focusing on third harmonics of BCG signals acquired with fiber optic sensors BIB013 . The results showed an error less than 0.34 beat/min in 2-min averaged heart rate. Heartbeat intervals were calculated with the cepstrum method, by applying FFT for short time windows including pair of consequent heart beats BIB004 . Relative error of the method was 0.35% for 15 night recordings with six normal subjects after rejecting movement artifacts. Since the results of heart beat detection are not perfect, generally visual editing is required to correct the errors in peak detection for further application like HRV analysis. Multichannel fusion techniques have also been demonstrated recently for BCG-based heartbeat detection BIB004 , BIB017 . Recently, Paalasmaa et al. BIB018 and Brueser et al. BIB014 both verified heartbeat detection algorithms on large datasets containing hundreds of thousands of heartbeats recorded in uncontrolled environments. Paalasmaa et al. used hierarchical clustering to first infer a heartbeat shape from the recordings, then beat-tobeat intervals were found by determining positions at which this template best fit the signal. The mean beat-to-beat interval error was 13 ms from 46 subjects in the clinic, home, single bed, double bed, and with two sensor types. Brueser et al. demonstrated robust estimation of heartbeats for 33 subjects of which 25 were insomniacs, with a mean beat-to-beat interval error of 0.78%. Their method used three short-time estimators combined using a Bayesian approach to continuously estimate interbeat intervals. Automatic template learning approaches were also presented by Brueser et al. in 2011 with low error BIB009 . Performance of HRV analysis using BCG measured on weighing scale-type load cell is evaluated in reference to the ECG during the resting and under each condition of Valsalva and postexercise sessions that induce cardiac autonomic rhythm changes BIB010 . Time domain, frequency domain, and nonlinear domain HRV parameters were evaluated on 15 healthy subjects to assess the cardiac autonomic modulation under each of these conditions. For all subjects and for all experimental sessions, HRV parameters calculated from BCG peak intervals are statistically not different from those obtained from the reference ECG. The results showed high performance with relative errors of 5.0-6.0% and strong correlation of 0.97-0.98 in average for these three states compared with the results from ECG peaks. The errors were relatively high in HRV parameters reflecting the high-frequency characteristics of heart rates such as HF, LF/HF in the spectral analysis, pNN50 in time-domain analysis, and SD1 in nonlinear analysis. This is considered to be caused by the inaccuracy in detecting peak from the less sharp J-peak of BCG compared to the R-peak in ECG. HRV estimates with BCG have also been compared to the PPG, and the correlation between the two was found to be high BIB012 . Preliminary work was recently presented by Brueser et al. for unsupervised HRV estimation from BCG signals BIB015 .