Ballistocardial Signal-Based Personal Identification Using Deep Learning for the Non-Invasive and Non-Restrictive Monitoring of Vital Signs.

Owing to accelerated societal aging, the prevalence of elderly individuals experiencing solitary or sudden death at home has increased. Therefore, herein, we aimed to develop a monitoring system that utilizes piezoelectric sensors for the non-invasive and non-restrictive monitoring of vital signs, including the heart rate and respiration, to detect changes in the health status of several elderly individuals. A ballistocardiogram with a piezoelectric sensor was tested using seven individuals. The frequency spectra of the biosignals acquired from the piezoelectric sensors exhibited multiple peaks corresponding to the harmonics originating from the heartbeat. We aimed for individual identification based on the shapes of these peaks as the recognition criteria. The results of individual identification using deep learning techniques revealed good identification proficiency. Altogether, the monitoring system integrated with piezoelectric sensors showed good potential as a personal identification system for identifying individuals with abnormal biological signals.

Non-contact assessment of cardiac physiology using FO-MVSS-based ballistocardiography: a promising approach for heart failure evaluation.

Continuous monitoring of cardiac motions has been expected to provide essential cardiac physiology information on cardiovascular functioning. A fiber-optic micro-vibration sensing system (FO-MVSS) makes it promising. This study aimed to explore the correlation between Ballistocardiography (BCG) waveforms, measured using an FO-MVSS, and myocardial valve activity during the systolic and diastolic phases of the cardiac cycle in participants with normal cardiac function and patients with congestive heart failure (CHF). A high-sensitivity FO-MVSS acquired continuous BCG recordings. The simultaneous recordings of BCG and electrocardiogram (ECG) signals were obtained from 101 participants to examine their correlation. BCG, ECG, and intracavitary pressure signals were collected from 6 patients undergoing cardiac catheter intervention to investigate BCG waveforms and cardiac cycle phases. Tissue Doppler imaging (TDI) measured cardiac time intervals in 51 participants correlated with BCG intervals. The BCG recordings were further validated in 61 CHF patients to assess cardiac parameters by BCG. For heart failure evaluation machine learning was used to analyze BCG-derived cardiac parameters. Significant correlations were observed between cardiac physiology parameters and BCG's parameters. Furthermore, a linear relationship was found betwen IJ amplitude and cardiac output (r = 0.923, R2 = 0.926, p < 0.001). Machine learning techniques, including K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and XGBoost, respectively, demonstrated remarkable performance. They all achieved average accuracy and AUC values exceeding 95% in a five-fold cross-validation approach. We establish an electromagnetic-interference-free and non-contact method for continuous monitoring of the cardiac cycle and myocardial contractility and measure the different phases of the cardiac cycle. It presents a sensitive method for evaluating changes in both cardiac contraction and relaxation in the context of heart failure assessment.

BCG Signal Quality Assessment Based on Time-Series Imaging Methods.

This paper describes a signal quality classification method for arm ballistocardiogram (BCG), which has the potential for non-invasive and continuous blood pressure measurement. An advantage of the BCG signal for wearable devices is that it can easily be measured using accelerometers. However, the BCG signal is also susceptible to noise caused by motion artifacts. This distortion leads to errors in blood pressure estimation, thereby lowering the performance of blood pressure measurement based on BCG. In this study, to prevent such performance degradation, a binary classification model was created to distinguish between high-quality versus low-quality BCG signals. To estimate the most accurate model, four time-series imaging methods (recurrence plot, the Gramain angular summation field, the Gramain angular difference field, and the Markov transition field) were studied to convert the temporal BCG signal associated with each heartbeat into a 448 × 448 pixel image, and the image was classified using CNN models such as ResNet, SqueezeNet, DenseNet, and LeNet. A total of 9626 BCG beats were used for training, validation, and testing. The experimental results showed that the ResNet and SqueezeNet models with the Gramain angular difference field method achieved a binary classification accuracy of up to 87.5%.

Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review.

Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activity at the body's center of mass and on the chest, respectively. Since their inception, their potential for evaluating cardiovascular health has been studied. However, both BCG and SCG are impacted by respiration, leading to a periodic modulation of these signals. As a result, data processing algorithms have been developed to exclude the respiratory signals, or recording protocols have been designed to limit the respiratory bias. Reviewing the present status of the literature reveals an increasing interest in applying these techniques to extract respiratory information, as well as cardiac information. The possibility of simultaneous monitoring of respiratory and cardiovascular signals via BCG or SCG enables the monitoring of vital signs during activities that require considerable mental concentration, in extreme environments, or during sleep, where data acquisition must occur without introducing recording bias due to irritating monitoring equipment. This work aims to provide a theoretical and practical overview of cardiopulmonary interaction based on BCG and SCG signals. It covers the recent improvements in extracting respiratory signals, computing markers of the cardiorespiratory interaction with practical applications, and investigating sleep breathing disorders, as well as a comparison of different sensors used for these applications. According to the results of this review, recent studies have mainly concentrated on a few domains, especially sleep studies and heart rate variability computation. Even in those instances, the study population is not always large or diversified. Furthermore, BCG and SCG are prone to movement artifacts and are relatively subject dependent. However, the growing tendency toward artificial intelligence may help achieve a more accurate and efficient diagnosis. These encouraging results bring hope that, in the near future, such compact, lightweight BCG and SCG devices will offer a good proxy for the gold standard methods for assessing cardiorespiratory function, with the added benefit of being able to perform measurements in real-world situations, outside of the clinic, and thus decrease costs and time.

Validation of Dozee, a Ballistocardiography-based Device, for Contactless and Continuous Heart Rate and Respiratory Rate Measurement.

Long-term acquisition of respiratory and heart signals is useful in a variety of applications, including sleep analysis, monitoring of respiratory and heart disorders, and so on. Ballistocardiography (BCG), a non-invasive technique that measures micro-body vibrations caused by cardiac contractions as well as motion caused by breathing, snoring, and body movements, would be ideal for long-term vital parameter acquisition. Turtle Shell Technologies Pvt. Ltd.'s Dozee device, which is based on BCG, is a contactless continuous vital parameters monitoring system. It is designed to measure Heart Rate (HR) and Respiratory Rate (RR) continuously and without contact in a hospital setting or at home. A validation study for HR and RR was conducted using Dozee by comparing it to the vitals obtained from the FDA-approved Patient Monitor. This was done in a sleep laboratory setting over 110 nights in 51 subjects to evaluate HR and over 20 nights in 17 subjects to evaluate RR at the National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India. Approximately 789 hours data for HR and approximately 112 hours data for RR was collected. Dozee was able to achieve a mean absolute error of 1.72 bpm for HR compared to the gold standard ECG. A mean absolute error of ∼1.24 breaths/min was obtained in determining RR compared to currently used methods. Dozee is ideal for long-term contactless monitoring of vital parameters due to its low mean absolute errors in measuring both HR and RR. Clinical Relevance- Continuous and long-term vitals monitoring is known to enable early screening of clinical deterioration, improve patient outcomes and reduce mortality. Current methods of continuous monitoring are overly complex, costly, and rely heavily on patient compliance. The proposed remote vitals monitoring solution based on BCG was found to be at par with gold standard methods of recording HR and RR. As a result, clinicians can use it to effectively monitor patients in both the hospital and at home.

Estimation of the respiratory rate from ballistocardiograms using the Hilbert transform.

BACKGROUND: Measuring the respiratory rate is usually associated with discomfort for the patient due to contact sensors or a high time demand for healthcare personnel manually counting it. METHODS: In this paper, two methods for the continuous extraction of the respiratory rate from unobtrusive ballistocardiography signals are introduced. The Hilbert transform is used to generate an amplitude-invariant phase signal in-line with the respiratory rate. The respiratory rate can then be estimated, first, by using a simple peak detection, and second, by differentiation. RESULTS: By analysis of a sleep laboratory data set consisting of nine records of healthy individuals lasting more than 63 h and including more than 59,000 breaths, a mean absolute error of as low as 0.7 BPM for both methods was achieved. CONCLUSION: The results encourage further assessment for hospitalised patients and for home-care applications especially with patients suffering from diseases of the respiratory system like COPD or sleep apnoea.

Beat to BEAT - Non-Invasive Investigation of Cardiac Function on the International Space Station.

This paper describes the protocol of the microgravity experiment BEAT (Ballistocardiography for Extraterrestrial Applications and Long-Term Missions). The current study makes use of signal acquisition of cardiac parameters with a high-precision Ballistocardiography (BCG)/Seismocardiography (SCG) measurement system, which is integrated in a smart shirt (SmartTex). The goal is to evaluate the feasibility of this concept for continuous wearable monitoring and wireless data transfer. BEAT is part of the "Wireless Compose-2" (WICO2) project deployed on the International Space Station (ISS) that will provide wireless network infrastructure for scientific, localization and medical experiments.

Digital Optical Ballistocardiographic System for Activity, Heart Rate, and Breath Rate Determination during Sleep.

In this work, we present a ballistocardiographic (BCG) system for the determination of heart and breath rates and activity of a user lying in bed. Our primary goal was to simplify the analog and digital processing usually required in these kinds of systems while retaining high performance. A novel sensing approach is proposed consisting of a white LED facing a digital light detector. This detector provides precise measurements of the variations of the light intensity of the incident light due to the vibrations of the bed produced by the subject's breathing, heartbeat, or activity. Four small springs, acting as a bandpass filter, connect the boards where the LED and the detector are mounted. Owing to the mechanical bandpass filtering caused by the compressed springs, the proposed system generates a BCG signal that reflects the main frequencies of the heartbeat, breathing, and movement of the lying subject. Without requiring any analog signal processing, this device continuously transmits the measurements to a microcontroller through a two-wire communication protocol, where they are processed to provide an estimation of the parameters of interest in configurable time intervals. The final information of interest is wirelessly sent to the user's smartphone by means of a Bluetooth connection. For evaluation purposes, the proposed system has been compared with typical BCG systems showing excellent performance for different subject positions. Moreover, applied postprocessing methods have shown good behavior for information separation from a single-channel signal. Therefore, the determination of the heart rate, breathing rate, and activity of the patient is achieved through a highly simplified signal processing without any need for analog signal conditioning.

Non-Contact Heartbeat Detection Based on Ballistocardiogram Using UNet and Bidirectional Long Short-Term Memory.

Benefiting from non-invasive sensing tech- nologies, heartbeat detection from ballistocardiogram (BCG) signals is of great significance for home-care applications, such as risk prediction of cardiovascular disease (CVD) and sleep staging, etc. In this paper, we propose an effective deep learning model for automatic heartbeat detection from BCG signals based on UNet and bidirectional long short-term memory (Bi-LSTM). The developed deep learning model provides an effective solution to the existing challenges in BCG-aided heartbeat detection, especially for BCG in low signal-to-noise ratio, in which the waveforms in BCG signals are irregular due to measured postures, rhythm and artifact motion. For validations, performance of the proposed detection is evaluated by BCG recordings from 43 subjects with different measured postures and heart rate ranges. The accuracy of the detected heartbeat intervals measured in different postures and signal qualities, in comparison with the R-R interval of ECG, is promising in terms of mean absolute error and mean relative error, respectively, which is superior to the state-of-the-art methods. Numerical results demonstrate that the proposed UNet-BiLSTM model performs robust to noise and perturbations (e.g. respiratory effort and artifact motion) in BCG signals, and provides a reliable solution to long term heart rate monitoring.

ResNet-BiLSTM: A Multiscale Deep Learning Model for Heartbeat Detection Using Ballistocardiogram Signals.

As the heartbeat detection from ballistocardiogram (BCG) signals using force sensors is interfered by respiratory effort and artifact motion, advanced signal processing algorithms are required to detect the J-peak of each BCG signal so that beat-to-beat interval can be identified. However, existing methods generally rely on rule-based detection of a fixed size, without considering the rhythm features in a large time scale covering multiple BCG signals. Methods. This paper develops a deep learning framework based on ResNet and bidirectional long short-term memory (BiLSTM) to conduct beat-to-beat detection of BCG signals. Unlike the existing methods, the proposed network takes multiscale features of BCG signals as the input and, thus, can enjoy the complementary advantages of both morphological features of one BCG signal and rhythm features of multiple BCG signals. Different time scales of multiscale features for the proposed model are validated and analyzed through experiments. Results. The BCG signals recorded from 21 healthy subjects are conducted to verify the performance of the proposed heartbeat detection scheme using leave-one-out cross-validation. The impact of different time scales on the detection performance and the performance of the proposed model for different sleep postures are examined. Numerical results demonstrate that the proposed multiscale model performs robust to sleep postures and achieves an averaged absolute error (E abs) and an averaged relative error (E rel) of the heartbeat interval relative to the R-R interval of 9.92 ms and 2.67 ms, respectively, which are superior to those of the state-of-the-art detection protocol. Conclusion. In this work, a multiscale deep-learning model for heartbeat detection using BCG signals is designed. We demonstrate through the experiment that the detection with multiscale features of BCG signals can provide a superior performance to the existing works. Further study will examine the ultimate performance of the multiscale model in practical scenarios, i.e., detection for patients suffering from cardiovascular disorders with night-sleep monitoring.

Ballistocardiogram artifact removal in simultaneous EEG-fMRI using generative adversarial network.

Due to its advantages of high temporal and spatial resolution, the technology of simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) acquisition and analysis has attracted much attention, and has been widely used in various research fields of brain science. However, during the fMRI of the brain, ballistocardiogram (BCG) artifacts can seriously contaminate the EEG. As an unpaired problem, BCG artifact removal now remains a considerable challenge. Aiming to provide a solution, this paper proposed a novel modular generative adversarial network (GAN) and corresponding training strategy to improve the network performance by optimizing the parameters of each module. In this manner, we hope to improve the local representation ability of the network model, thereby improving its overall performance and obtaining a reliable generator for BCG artifact removal. Moreover, the proposed method does not rely on additional reference signal or complex hardware equipment. Experimental results show that, compared with multiple methods, the technique presented in this paper can remove the BCG artifact more effectively while retaining essential EEG information.

A Comparison of Alternative Approaches to MR Cardiac Triggering: A Pilot Study at 3 Tesla.

This pilot comparative study evaluates the usability of the alternative approaches to magnetic resonance (MR) cardiac triggering based on ballistocardiography (BCG): fiber-optic sensor (O-BCG) and pneumatic sensor (P-BCG). The comparison includes both the objective and subjective assessment of the proposed sensors in comparison with a gold standard of ECG-based triggering. The objective evaluation included several image quality assessment (IQA) parameters, whereas the subjective analysis was performed by 10 experts rating the diagnostic quality (scale 1 - 3, 1 corresponding to the best image quality and 3 the worst one). Moreover, for each examination, we provided the examination time and comfort rating (scale 1 - 3). The study was performed on 10 healthy subjects. All data were acquired on a 3 T SIEMENS MAGNETOM Prisma. In image quality analysis, all approaches reached comparable results, with ECG slightly outperforming the BCG-based methods, especially according to the objective metrics. The subjective evaluation proved the best quality of ECG (average score of 1.68) and higher performance of P-BCG (1.97) than O-BCG (2.03). In terms of the comfort rating and total examination time, the ECG method achieved the worst results, i.e. the highest score and the longest examination time: 2.6 and 10:49 s, respectively. The BCG-based alternatives achieved comparable results (P-BCG 1.5 and 8:06 s; OBCG 1.9, 9:08 s). This study confirmed that the proposed BCG-based alternative approaches to MR cardiac triggering offer comparable quality of resulting images with the benefits of reduced examination time and increased patient comfort.

The kinocardiograph for assessment of changes in haemodynamic load in patients with chronic heart failure with reduced ejection fraction.

AIMS: The kinocardiograph (KCG) is an unobtrusive device, consisting of a chest sensor, which records local thoracic vibrations produced in result of cardiac contraction and ejection of blood into the great vessels [seismocardiography (SCG)], and a lower back sensor, which records micromovements of the body in reaction to blood flowing through the vasculature [ballistocardiography (BCG)]. SCG and BCG signals are translated to the integral of cardiac kinetic energy (iK) and cardiac maximum power (Pmax), which might be promising metrics for future telemonitoring purposes in heart failure (HF). As a first step of validation, this study aimed to determine whether iK and Pmax are responsive to exercise-induced changes in the haemodynamic load of the heart in HF patients. METHODS AND RESULTS: Fifteen patients with stable HF with reduced ejection fraction performed a submaximal exercise protocol. KCG and cardiac ultrasound measurements were obtained both at rest and at submaximal exercise. BCG iK over the cardiac cycle (CC) increased significantly (0.0026 ± 0.0017 to 0.0052 ± 0.0061 mJ.s.; P = 0.01) during exercise, in contrast to a non-significant increase in SCG iK CC. BCG Pmax CC increased significantly (0.92 ± 0.89 to 2.03 ± 1.95 mJ/s; P = 0.02), in contrast to a non-significant increase in SCG Pmax CC. When analysing the systolic phase of the CC, similar patterns were found. Cardiac output (CO) ratio (i.e. CO exercise/CO rest) showed a moderate, significant correlation with BCG Pmax CC ratio (r = +0.65; P = 0.008) and with SCG Pmax CC ratio (r = +0.54; P = 0.04). CONCLUSIONS: iK and Pmax measured with the KCG, preferentially using BCG, are responsive to changes in the haemodynamic load of the heart in HF patients. The combination of the BCG and SCG sensor might be of added value to fully understand changes in haemodynamics and to discriminate between an HF patient and a healthy individual.

Data-driven beamforming technique to attenuate ballistocardiogram artefacts in electroencephalography-functional magnetic resonance imaging without detecting cardiac pulses in electrocardiography recordings.

Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a very promising non-invasive neuroimaging technique. However, EEG data obtained from the simultaneous EEG-fMRI are strongly influenced by MRI-related artefacts, namely gradient artefacts (GA) and ballistocardiogram (BCG) artefacts. When compared to the GA correction, the BCG correction is more challenging to remove due to its inherent variabilities and dynamic changes over time. The standard BCG correction (i.e., average artefact subtraction [AAS]), require detecting cardiac pulses from simultaneous electrocardiography (ECG) recording. However, ECG signals are also distorted and will become problematic for detecting reliable cardiac peaks. In this study, we focused on a beamforming spatial filtering technique to attenuate all unwanted source activities outside of the brain. Specifically, we applied the beamforming technique to attenuate the BCG artefact in EEG-fMRI, and also to recover meaningful task-based neural signals during an attentional network task (ANT) which required participants to identify visual cues and respond accurately. We analysed EEG-fMRI data in 20 healthy participants during the ANT, and compared four different BCG corrections (non-BCG corrected, AAS BCG corrected, beamforming + AAS BCG corrected, beamforming BCG corrected). We demonstrated that the beamforming approach did not only significantly reduce the BCG artefacts, but also significantly recovered the expected task-based brain activity when compared to the standard AAS correction. This data-driven beamforming technique appears promising especially for longer data acquisition of sleep and resting EEG-fMRI. Our findings extend previous work regarding the recovery of meaningful EEG signals by an optimized suppression of MRI-related artefacts.

Assessment of left ventricular twist by 3D ballistocardiography and seismocardiography compared with 2D STI echocardiography in a context of enhanced inotropism in healthy subjects.

Ballistocardiography (BCG) and Seismocardiography (SCG) assess the vibrations produced by cardiac contraction and blood flow, respectively, by means of micro-accelerometers and micro-gyroscopes. From the BCG and SCG signals, maximal velocities (VMax), integral of kinetic energy (iK), and maximal power (PMax) can be computed as scalar parameters, both in linear and rotational dimensions. Standard echocardiography and 2-dimensional speckle tracking imaging echocardiography were performed on 34 healthy volunteers who were infused with increasing doses of dobutamine (5-10-20 µg/kg/min). Linear VMax of BCG predicts the rates of left ventricular (LV) twisting and untwisting (both p < 0.0001). The linear PMax of both SCG and BCG and the linear iK of BCG are the best predictors of the LV ejection fraction (LVEF) (p < 0.0001). This result is further confirmed by mathematical models combining the metrics from SCG and BCG signals with heart rate, in which both linear PMax and iK strongly correlate with LVEF (R = 0.7, p < 0.0001). In this setting of enhanced inotropism, the linear VMax of BCG, rather than the VMax of SCG, is the metric which best explains the LV twist mechanics, in particular the rates of twisting and untwisting. PMax and iK metrics are strongly associated with the LVEF and account for 50% of the variance of the LVEF.

Cardiovascular adaptation to simulated microgravity and countermeasure efficacy assessed by ballistocardiography and seismocardiography.

Head-down bed rest (HDBR) reproduces the cardiovascular effects of microgravity. We tested the hypothesis that regular high-intensity physical exercise (JUMP) could prevent this cardiovascular deconditioning, which could be detected using seismocardiography (SCG) and ballistocardiography (BCG). 23 healthy males were exposed to 60-day HDBR: 12 in a physical exercise group (JUMP), the others in a control group (CTRL). SCG and BCG were measured during supine controlled breathing protocols. From the linear and rotational SCG/BCG signals, the integral of kinetic energy ([Formula: see text]) was computed on each dimension over the cardiac cycle. At the end of HDBR, BCG rotational [Formula: see text] and SCG transversal [Formula: see text] decreased similarly for all participants (- 40% and - 44%, respectively, p < 0.05), and so did orthostatic tolerance (- 58%, p < 0.01). Resting heart rate decreased in JUMP (- 10%, p < 0.01), but not in CTRL. BCG linear [Formula: see text] decreased in CTRL (- 50%, p < 0.05), but not in JUMP. The changes in the systolic component of BCG linear iK were correlated to those in stroke volume and VO2 max (R = 0.44 and 0.47, respectively, p < 0.05). JUMP was less affected by cardiovascular deconditioning, which could be detected by BCG in agreement with standard markers of the cardiovascular condition. This shows the potential of BCG to easily monitor cardiac deconditioning.

Single-layered ultra-soft washable smart textiles for all-around ballistocardiograph, respiration, and posture monitoring during sleep.

Good sleep is considered to be the cornerstone for maintaining both physical and mental health. However, nearly one billion people worldwide suffer from various sleep disorders. To date, polysomnography (PSG) is the most commonly used sleep-monitoring technology,however, it is complex, intrusive, expensive and uncomfortable. Unfortunately, present noninvasive monitoring technologies cannot simultaneously achieve high sensitivity, multi-parameter monitoring and comfort. Here, we present a single-layered, ultra-soft, smart textile for all-around physiological parameters monitoring and healthcare during sleep. With a high-pressure sensitivity of 10.79 mV/Pa, a wide working frequency bandwidth from 0 Hz to 40 Hz, good stability, and decent washability, the single-layered ultra-soft smart textile is simultaneously capable of real-time detection and tracking of dynamic changes in sleep posture, and subtle respiration and ballistocardiograph (BCG) monitoring. Using the set of patient generated health data, an obstructive sleep apnea-hypopnea syndrome (OSAHS) monitoring and intervention system was also developed to improve the sleep quality and prevent sudden death during sleep. This work is expected to pave a new and practical pathway for physiological monitoring during sleep.

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.

A Feasible Feature Extraction Method for Atrial Fibrillation Detection From BCG.

Atrial fibrillation (AF) is the most frequently occurring form of arrhythmia, which induces multiple fatal diseases and impairs the quality of life in patients; thus, the study of the diagnostic methods for detecting AF is clinically important. Here, we present a feature extraction method for the detection of AF using a ballistocardiogram (BCG), which is based on a physiological signal database collected by a non-contact sensor. The BCG signals, including both with AF and sinus rhythm (SR), were collected from 37 subjects during overnight sleep (approximately 8 h). The signals were split into 2915 1-min segments (AF: 1494, SR: 1421) without overlap and labeled as AF and SR. BCG signals were transformed into BCG energy signals in order to highlight the features of AF and SR BCG signals; and four new data sequences representing different characteristics of the BCG energy signals were generated. The mean value, variance, skewness, and kurtosis of the four data sequences were calculated and 16 features were extracted for each segment. Five machine learning algorithms were used for classification. The results of this study show that the support vector machine performed the best among the five tested classifiers and achieved sensitivity, precision, and accuracy of 0.968, 0.928, and 0.945, respectively. These results indicate that the proposed feature extraction method can be well applied to AF and SR classification and may lay foundations for the development of systems for long-term home cardiac monitoring and AF screening.

Mitigation of Instrument-Dependent Variability in Ballistocardiogram Morphology: Case Study on Force Plate and Customized Weighing Scale.

The objective of this study was to investigate the measurement instrument-dependent variability in the morphology of the ballistocardiogram (BCG) waveform in human subjects and computational methods to mitigate the variability. The BCG was measured in 22 young healthy subjects using a high-performance force plate and a customized commercial weighing scale under upright standing posture. The timing and amplitude features associated with the major I, J, K waves in the BCG waveforms were extracted and quantitatively analyzed. The results indicated that 1) the I, J, K waves associated with the weighing scale BCG exhibited delay in the timings within the cardiac cycle relative to the ECG R wave as well as attenuation in the absolute amplitudes than the respective force plate counterparts, whereas 2) the time intervals between the I, J, K waves were comparable. Then, two alternative computational methods were conceived in an attempt to mitigate the discrepancy between force plate versus weighing-scale BCG: a transfer function and an amplitude-phase correction. The results suggested that both methods effectively mitigated the discrepancy in the timings and amplitudes associated with the I, J, K waves between the force plate and weighing-scale BCG. Hence, signal processing may serve as a viable solution to the mitigation of the instrument-induced morphological variability in the BCG, thereby facilitating the standardized analysis and interpretation of the timing and amplitude features in the BCG across wide-ranging measurement platforms.