Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <s> The principle of the static charge-sensitive-bed (SCSB) method is described. The method is simple and inexpensive. From the SCSB recording the ballistocardiogram (BCG) and respiratory movement can be simultaneously recorded by selective filtering of the original signal. The SCSB recording thus enables continuous long-term monitoring of the BCG, heart rate, respiratory rate, respiratory amplitude, and body movements. There are no electrodes or cables connected to the subject. The SCSB-BCG signal was studied by comparing the SCSB method with the conventional ultralow-frequency (ULF) acceleration BCG. The wave forms at rest and the amplitude responses to physical exercise were studied by both methods. Multiple recordings of the BCG, heart rate, respiratory movement, respiratory rate, and body movements after exercise and during sleep using the SCSB method are presented. The SCSB method opens new approaches to long-term studies of the regulation of myocardial performance, heart rate, and respiration. Applications of the SCSB method for clinical sleep studies, patient monitoring and cardiovascular screening examinations are discussed. <s> BIB001 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <s> This paper reports the development of an unconstrained sensing technique for monitoring the respiration and heartbeats during sleep using a polyvinylidene fluoride (PVDF) piezopolymer film sensor with the aim that the sensor can be used on the ordinary bed together with the condition that the use of the sensor does not interfere the daily sleep of the patient under measurement. A PVDF film is used as the sensory material in the sensor system. The film is placed under the sheet at the location of the thorax to pick up the fluctuation of the pressure on the bed caused by the respiratory movement and heartbeats. Wavelet multiresolution decomposition analysis is used for the detection of respiration and heartbeat from the sensor output. It is shown that the respiration and heartbeats can simultaneously be detected by the sensor with the use of the wavelet multiresolution decomposition analysis. <s> BIB002 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <s> This paper describes a novel method to estimate sleep stage through noninvasive and unrestrained means. The Rechtschaffen and Kales (R-K) method is a standard to estimate sleep stage. However, it involves restraining the examinee and, thus, induces psychological stress. Furthermore, it requires specialists with a high degree of technical expertise and the use of an expensive polygraph. The sleep estimation method presented here is based on the noninvasive and unrestrained pneumatic biomeasurement method presented by the authors. Sleep stage transition in overnight sleep and the relationship between sleep stage and biosignals measured using the pneumatic method was analyzed and from the results, a mathematical model of sleep was created. Based on this model, a sleep stage estimator, including a sleep stage classifier and observer, was designed. The sleep state transition equation was the basis for the design of this observer, while the observed relationships were the basis for designing a classifier. Agreement of the estimated sleep stages with those obtained using the R-K method for the non-REM stage was 82.6%, for the REM stage was 38.3% and for Wake was 70.5%, including disagreement. However, the new method might ultimately result in better estimation of sleep stage due to the fact that it does not physically restrain the patient and does not induce psychological stress. <s> BIB003 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <s> The cardio-respiratory signal is a fundamental vital sign used for assessment of a patient's status. Additionally, the cardio-respiratory signal provides a great deal of information to healthcare providers wishing to monitor healthy individuals. The air mattress sensor system allows the measurement of the respiration and heart beat movements without the use of a harness or sensor on the subject's body, which eliminates the difficulties these pose for long term measurements. In order to increase the sensitivity, a differential measurement technique between two air cells was used. The concept of a balancing tube between two air cells is suggested in order to increase the robustness against postural changes during the measurements. With this balancing tube, the meaningful frequency range could be selected using a pneumatic method. A mathematical model was constructed and validation experiments were performed for step and sinusoidal input signals. This technique was applied to measurements of respiration and heart beat movements in the supine posture on the bed, which showed potential for applications in sleep analysis, unconstrained healthcare monitoring and neonate monitoring. <s> BIB004 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <s> We have developed a noninvasive pneumatics-based system by which to measure heartbeat, respiration, snoring, and body movements of a subject in bed. A thin, air-sealed cushion is placed under the bed mattress of the subject and the small movements attributable to human automatic vital functions are measured as changes in pressure using a pressure sensor having an almost flat frequency response from 0.1 to 5 kHz and a sensitivity of 56 mV/Pa. Using the newly developed system, heartbeat, respiration, apnea, snoring and body movements are clearly measured. In addition, the optimal signal-to-noise (S/N) ratio by which to evaluate the reliability of the heart rate measurement is presented. Heart rates were measured for four different body postures, 13 different subjects, four different bed mattresses, and three different sensor positions. For these measurements, the S/N ratios ranged from 15.9 to 23.5 dB, and so were determined to be reliable. <s> BIB005 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <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> BIB006 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <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> BIB007 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <s> Techniques such as ballistocardiography (BCG) that can provide noninvasive long-term physiological monitoring have gained interest due to a growing recognition of adverse effects from poor sleep and sleep disorders. The noninvasive analysis of physiological signals (NAPS) system is a BCG-based monitoring system developed to measure heart rate, breathing rate, and musculoskeletal movement that shows promise as a general sleep analysis tool. Overnight sleep studies were conducted on 40 healthy subjects during a clinical trial at the University of Virginia. The NAPS system's measures of heart rate and breathing rate were compared to ECG, pulse oximetry, and respiratory inductance plethysmography (RIP). The subjects were split into a training dataset and a validation dataset, maintaining similar demographics in each set. The NAPS system accurately detected heart rate, averaged over the prescribed 30-s epochs, to within less than 2.72 beats per minute of ECG, and accurately detected breathing rate, averaged over the same epochs, to within 2.10 breaths per minute of RIP bands used in polysomnography. <s> BIB008 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <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> BIB009 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <s> We describe a system for the evaluation of the sleep macrostructure on the basis of Emfit sensor foils placed into bed mattress and of advanced signal processing. The signals on which the analysis is based are heart-beat interval (HBI) and movement activity obtained from the bed sensor, the relevant features and parameters obtained through a time-variant autoregressive model (TVAM) used as feature extractor, and the classification obtained through a hidden Markov model (HMM). Parameters coming from the joint probability of the HBI features were used as input to a HMM, while movement features are used for wake period detection. A total of 18 recordings from healthy subjects, including also reference polysomnography, were used for the validation of the system. When compared to wake-nonrapid-eye-movement (NREM)-REM classification provided by experts, the described system achieved a total accuracy of 79±9% and a kappa index of 0.43±0.17 with only two HBI features and one movement parameter, and a total accuracy of 79±10% and a kappa index of 0.44±0.19 with three HBI features and one movement parameter. These results suggest that the combination of HBI and movement features could be a suitable alternative for sleep staging with the advantage of low cost and simplicity. <s> BIB010 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <s> This study presents different methods for automatic sleep classification based on heart rate variability (HRV), respiration and movement signals recorded through bed sensors. Two methods for feature extraction have been implemented: time variant-autoregressive model (TVAM) and wavelet discrete transform (WDT); the obtained features are fed into two classifiers: Quadratic (QD) and Linear (LD) discriminant for staging sleep in REM, nonREM and WAKE periods. The performances of all the possible combinations of feature extractors and classifiers are compared in terms of accuracy and kappa index, using clinical polysomographyc evaluation as golden standard. 17 recordings from healthy subjects, including also polisomnography, were used to train and test the algorithms. When automatic classification is compared. QD-TVAM algorithm achieved a total accuracy of 76.81 ± 7.51 % and kappa index of 0.55 ± 0.10, while LD-WDT achieved a total accuracy of 79 ± 10% and kappa index of 0.51 ± 0.17. The results suggest that a good sleep evaluation can be achieved through non-conventional recording systems that could be used outside sleep centers. <s> BIB011 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <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> BIB012 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <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> BIB013 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <s> This paper shows the design of a fiber-based sensor for living activities in human body and the results of a laboratory evaluation carried out on it. The authors have developed a device that allows for monitoring the vibrations of human body evoked by living activities-breathing and cardiac rhythm. The device consists of a Bragg grating inscribed into a single mode optical fiber and operating on a wavelength of around 1550 nm. The fiber Bragg grating (FBG) is mounted inside a pneumatic cushion to be placed between the backrest of the seat and the back of the monitored person. Deformations of the cushion, involving deformations of the FBG, are proportional to the vibrations of the body leaning on the cushion. Laboratory studies have shown that the sensor allows for obtaining dynamic strains on the sensing FBG in the range of 50-124 μ strain caused by breathing and approximately 8.3 μstrain induced by heartbeat, which are fully measurable by today's FBG interrogation systems. The maximum relative measurement error of the presented sensor is 12%. The sensor's simple design enables it to be easily implemented in pilot's and driver's seats for monitoring the physiological condition of pilots and drivers. <s> BIB014 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <s> We describe an online sleep monitoring service, based on unobtrusive ballistocardiography (BCG) measurement in an ordinary bed. The novelty of the system is that the sleep tracking web application is based on measurements from a fully unobtrusive sensor. The BCG signal is measured with a piezoelectric film sensor under the mattress topper, and sent to the web server for analysis. Heart rate and respiratory variation, activity, sleep stages, and stress reactions are inferred based on the signal. The sleep information is presented to the user along with measurements of the sleeping environment (temperature, noise, luminosity) and user-logged tags (e.g. stress, alcohol, exercise). The approach is designed for long-term use at home, allowing users to follow the development of their sleep over months and years. The service has also a medical use, as sleep disorder patients can be measured for long periods before and after interventions. <s> BIB015 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <s> A bed sensor with multiple pressure sensitive non-contacting electrodes has been applied for unobtrusive monitoring during sleep. The novelty is in using multichannel algorithms to improve extraction of the heart rate and respiration signal from the recorded ballistocardiographic (BCG) data. Heart rate is extracted by using a sliding Fourier Transform, and after averaging the sensor channels in the frequency domain, the attained resolution enables to detect individual heart beat intervals (HBI) and estimate the heart rate variability (HRV). The respiration signal is calculated from the low pass filtered BCG signals by updating the linear coefficients with an adaptive principal component analysis (PCA) model. In comparison to the reference ECG R-R interval, the relative error of the HBI has been 0.40 % with 88 % measurement coverage for the healthy subjects during normal sleep. The error of the respiratory rate estimated from the bed sensor has been 1.5 % in comparison with the respiratory inductive plethysmogram (RIP). For the group of patients having different kinds of suspected sleep disorders, the measurement coverage varied a lot between subjects due to increased movement artifacts. In this case, some examples of detecting respiratory disorders with bed sensor signals are shown. <s> BIB016 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <s> This study proposes a method for detecting the heartbeat intervals of a person lying on a bed from ballistocardiographic signals recorded unobtrusively with four dynamic force sensors located under the bed posts. The method does not recognize individual heartbeats, but the intervals where the correlation between two consecutive signal segments maximizes. This study evaluated the performance of the method with nine subjects in 1-h long recordings and achieved 91% beat-to-beat interval (BBI) recognition coverage; 98.6% of the detected BBIs differed less than 50 ms from the values calculated from a reference electrocardiogram signal. This study also evaluated the reliability of two parameters of heart rate variability that have been used in sleep quality assessment in several studies and are usually calculated for 30 s epochs. The results suggest that the method is able to provide sufficient reliability for using the data in evaluation of sleep quality. <s> BIB017 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <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> BIB018 </s> Ballistocardiography and Seismocardiography: A Review of Recent Advances <s> C. Bed-Based BCG Systems <s> Fragmented sleep due to frequent awakenings represents a major cause of impaired daytime performance and adverse health outcomes. Currently, the gold standard for studying and assessing sleep fragmentation is polysomnography (PSG). Here, we propose an alternative method for real-time detection of nocturnal awakening via ballistocardiography using an unobtrusive polyvinylidene fluoride (PVDF) film sensor on a bed mattress. From ballistocardiogram, heart rate and body movement information were extracted to develop an algorithm for classifying sleeping and awakening epochs. In total, ten normal subjects (mean age 38.7 ± 14.6 years) and ten patients with obstructive sleep apnea (OSA) (mean age 44.2 ± 16.5 years) of varying symptom severity participated in this study. Our study detected awakening epochs with an average sensitivity of 85.3% and 85.2%, specificity of 98.4% and 97.7%, accuracy of 97.4% and 96.5%, and Cohen's kappa coefficient of 0.83 and 0.81 for normal subjects and OSA patients, respectively. Also, sleep efficiency was estimated using detected awakening epochs and then compared with PSG results. Mean absolute errors in sleep efficiency were 1.08% and 1.44% for normal subjects and OSA patients, respectively. The results presented here indicate that our suggested method could be reliably applied to real-time nocturnal awakening detection and sleep efficiency estimation. Furthermore, our method may ultimately be an effective tool for long-term, home monitoring of sleep-wake behavior. <s> BIB019
BCG can be applied in evaluating the sleep stages and sleep related disorders in more comfortable environment replacing some functions done by polysomnography (PSG). Since BCGbased technology does not require attaching electrodes on patient body surface, it has advantage over ECG of not disturbing subject's ordinary sleep behaviors in collecting data. BCG measurement can be integrated with the subject's sleeping environment using several types of sensors, the first of which was a static charge sensitive bed by Alihanka et al. BIB001 , and more recently the following implementations: Pressure sensor in the air mattress BIB004 or in pad BIB008 , film-type force sensors BIB017 or load cells in the legs of bed BIB009 , microbend fiber optic BCG sensor BIB018 BIB013 BIB014 , EMFi sensors BIB007 , piezoelectric film sensors BIB015 or polyvinylidene fluoride (PVDF) sensors BIB002 in the mattress pad, strain gauges BIB012 , pneumatic BIB005 , and hydraulic BIB006 sensors. Some researchers have also proposed the use of sensor arrays rather than single sensors to improve robustness BIB016 ,  . As these sensors can usually provide the additional information on respiration and body movement as well as heart beats, this information can be incorporated with the BCG to generate sleep evaluating parameters more accurately, as well as other applications such as early warning in the general ward, or home monitoring, where rhythm and dynamics can be monitored over extended periods of time for predictive analytics. Sleep stages have mainly been classified into two levels slow wave sleep or non-slow wave sleep (SWS/non-SWS), or three levels (wake/REM/NREM) based on BCG. The earliest implementation of BCG based sleep staging was by Watanabe and Watanabe BIB003 . Two stage classification between SWS and non-SWS was performed based on BCG with movement measured unobtrusively by a load cell installed bed BIB009 . Based on calculated heart rate variability (HRV) parameters, they achieved the mean agreement of 92.5% (kappa index of 0.62). Sleep efficiency was evaluated by detecting nocturnal awakening epochs in BCG measured using PVDF sensors on bed mattress BIB019 , based on the principle that awakening during sleep is related with subtle changes in heart rate; thus, awakening epochs can be detected based on HRV parameters. They achieved the classification accuracy of 97.4% (kappa index of 0.83) and 96.5% (kappa index of 0.81) and evaluated the sleep efficiency with absolute error of 1.08% and 1.44% for normal subjects and obstructive sleep apnea patients, respectively. Three stage classification (Wake/REM/NREM) of sleep has been derived using the analyses of spectral components of the heartbeats extracted from multichannel BCG based on EMFi sensors BIB010 . By applying a hidden Markov model only on BCG, they achieved a total accuracy of 79% (kappa index of 0.43) compared to clinical sleep staging from PSG. The performance was enhanced by combining the time variant-autoregressive model (TVAM) and wavelet discrete transform (WDT) with the quadratic (QD) or linear discriminant (LD) analysis BIB011 . The QD-TVAM algorithm achieved a total accuracy of 76.8% (kappa index of 0.55), while LD-WDT achieved a total accuracy of 79% (kappa index of 0.51). Although there was also a study done for sleep stage classification into four levels (wake/REM/deep sleep/light sleep) with ECG  , four-level sleep stage classification with BCG is not reported yet. With the ECG signal, Tanida et al. classified the sleep stage with HRV analyzed for each 60-s epoch of ECG and calculated at three frequency band powers. Their results for minute-by minute agreement rate ranged from 32% to 72% with an average of 56% for ten healthy women. Sleep monitoring based on BCG technology has a potential to provide both continuous and longitudinal information on a subjects' sleep quality and may take a role as a predictive screening method prior to the sleep studies based on PSG. It could also fill the gap among PSG of whole night examination and portable ambulatory PSG, which can be applied at home and simplified with, for example, a wrist worn movement sensor.