A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography.

Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at -15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of -19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.

Identification of the Vibrations Corresponding with Heart Sounds using Vibrational Cardiography.

Cardiography enables diagnostic and preventive care in hospitals and outpatient scenarios. However, most heart monitors do not distinguish the phases of the cardiac cycle. The transition between phases is indicated by the primary heart sounds. OBJECTIVE: Automatically identify the vibrations corresponding to both heart sounds. METHODS: Cardiac activity was monitored for 15 subjects while at rest, during exertion, and while performing static breath holds. The subjects consisted of 6 males and 9 females between the ages of 18-39 years with no known cardiorespiratory ailments. Motion corresponding to the heart sounds was identified using vibrational cardiography (VCG). The waveforms were processed to obtain quantities associated with their linear jerk and rotational kinetic energy. RESULTS: The ability to identity the first vibration was evaluated using the heart rate as a figure of merit. Its correlation with electrocardiography (ECG) measurements produced a r2 coefficient of 0.9887. The second vibration was compared with impedance cardiography (ICG) based on its delay from the ECG R-peak, and the fraction of the beat duration occupied by left ventricular ejection time. The comparisons produced r2 values of 0.251 and 0.2797, respectively. CONCLUSION: The vibrations corresponding to both primary heart sounds have the potential to be analyzed using VCG. SIGNIFICANCE: This study provides evidence of the feasibility of using VCG in identifying mechanical cardiovascular function. It facilitates non-invasive cardiac health monitoring in daily life.

Neural Network-based Classification of Static Lung Volume States using Vibrational Cardiography.

Non-invasive health monitoring has the potential to improve the delivery and efficiency of medical treatment. OBJECTIVE: This study was aimed at developing a neural network to classify the lung volume state of a subject (i.e. high lung volume (HLV) or low lung volume (LLV), where the subject had fully inhaled or exhaled, respectively) by analyzing cardiac cycles extracted from vibrational cardiography (VCG) signals. METHODS: A total of 15619 cardiac cycles were recorded from 50 subjects, of which 9989 cycles were recorded in the HLV state and the remaining 5630 cycles were recorded in the LLV state. A 1D convolutional neural network (CNN) was employed to classify the lung volume state of these cardiac cycles. RESULTS: The CNN model was evaluated using a train/test split of 80/20 on the data. The developed model was able to correctly classify the lung volume state of 99.4% of the testing data. CONCLUSION: VCG cardiac cycles can be classified based on lung volume state using a CNN. SIGNIFICANCE: These results provide evidence of a correlation between VCG and respiration volume, which could inform further analysis into VCG-based cardio-respiratory monitoring.

Analyzing Heart Rate Estimation from Vibrational Cardiography with Different Orientations.

Remote health monitoring is a widely discussed topic due to its potential to improve quality and delivery of medical treatment and the global increase in cardiovascular diseases. OBJECTIVE: Seismocardiography and Gyrocardiography have been shown to provide reliable heart rate information. A simple and efficient setup was developed for the monitoring of mechanical signals at the sternum. An algorithm based in autocorrelation was run on subjects with different orientations in order to detect heart rate. METHODS: Subjects performed several tests where both SCG and GCG were recorded using an inertial measurement unit, a Raspberry Pi and a BIOPAC acquisition system. A total of 2335 cardiac cycles were obtained from 5 subjects. Heart rate was determined on a per second basis and compared with an electrocardiography (ECG) reference by correlation coefficients. Ensemble averages were used to visualize differences in VCG morphology. RESULTS: Heart rate estimation obtained from VCG signals across all 5 subjects was referenced with ECG and achieved an r-squared correlation coefficient of 0.956 when supine and 0.975 when standing, compared to 0.965 across the entire dataset. CONCLUSION: Autocorrelated Differential Algorithm was able to successfully detect heart rate, regardless of orientation and posture. SIGNIFICANCE: Changes in orientation of the body during measurement introduce inaccuracies. This work shows that the algorithm is resistant to orientation and more adaptable to everyday life.

Relationship of the Respiration Waveform to a Chest Worn Inertial Sensor.

Demand of portable health monitoring has been growing due to increasing cardiovascular and respiratory diseases. While both cardiovascular monitoring and respiratory monitoring have been developed independently, there lacks a simple integrated solution to monitor both simultaneously. Seismocardiography (SCG), a method of recording cardiac vibrations with an accelerometer can also be used to extract respiratory information via low frequency chest oscillations. This study used an inertial measurement unit which pairs a 3-axis accelerometer and a 3-axis gyroscope to monitor respiration while maintaining optimum placement protocol for recording SCG. Additionally, the connection between inertial measurement and both respiratory rate and volume were explored based on their correlation with a Spirometer. Respiratory volume was shown to have moderate correlation with chest motion with an average best-case correlation coefficient of 0.679 across acceleration and gyration. The techniques described will assist the design of future SCG algorithms by understanding the sources behind their modulation from respiration. This paper shows that a simplified processing technique can be added to SCG algorithms for respiration monitoring.

Real-Time Cardiac Beat Detection and Heart Rate Monitoring from Combined Seismocardiography and Gyrocardiography.

Cardiography is an indispensable element of health care. However, the accessibility of at-home cardiac monitoring is limited by device complexity, accuracy, and cost. We have developed a real-time algorithm for heart rate monitoring and beat detection implemented in a custom-built, affordable system. These measurements were processed from seismocardiography (SCG) and gyrocardiography (GCG) signals recorded at the sternum, with concurrent electrocardiography (ECG) used as a reference. Our system demonstrated the feasibility of non-invasive electro-mechanical cardiac monitoring on supine, stationary subjects at a cost of $100, and with the SCG-GCG and ECG algorithms decoupled as standalone measurements. Testing was performed on 25 subjects in the supine position when relaxed, and when recovering from physical exercise, to record 23,984 cardiac cycles at heart rates in the range of 36-140 bpm. The correlation between the two measurements had r2 coefficients of 0.9783 and 0.9982 for normal (averaged) and instantaneous (beat identification) heart rates, respectively. At a sampling frequency of 250 Hz, the average computational time required was 0.088 s per measurement cycle, indicating the maximum refresh rate. A combined SCG and GCG measurement was found to improve accuracy due to fundamentally different noise rejection criteria in the mutually orthogonal signals. The speed, accuracy, and simplicity of our system validated its potential as a real-time, non-invasive, and affordable solution for outpatient cardiac monitoring in situations with negligible motion artifact.

Effect of Static Respiratory Volume on the Waveform of Cardiac-induced Sternal Vibrations.

Cardio-respiratory activity originating in the chest creates vibrations that diffuse through the organs to the thoracic wall. The vibrational waves were detected in all six degrees of freedom by an inertial motion sensor at the xiphoid process of the sternum. Vibrational cardiography (VCG) combines the detection of vibrations via acceleration, termed as seismocardiography, and gyration, termed as gyrocardiography. The objective of this study was to determine the effect of static respiration volume on the morphology of cardiac-induced waveforms in the VCG signal. In this study, 24 subjects were tested while holding breath at peak inhalation, and at peak exhalation. Ensemble averages of the waveforms showed larger variations in the signal when the lungs were inhaled for both the primary and secondary heart sounds. Inter-subject variability was accounted for by averaging all waveforms and calculating the root mean squared value over a sliding window of 60 milliseconds. The peak amplitudes of both heart sounds were consistently larger for high lung volumes. However, the ratio of primary to the secondary heart sound was found to be inversely proportional to lung volume. These opposing effects offer a strong analysis tool for the determination of relative inhalation volume using VCG morphology alone.

Wearable Fiber Optic Sensors for Biomechanical Sensing via Joint Angle Detection.

Monitoring human biomechanical movement is necessary for the analysis and development of kinesthetic exercise techniques in physical rehabilitation, professional sports, and performance arts. Optical fiber technology offers an attractive solution to motion capture sensing in terms of size, robustness, signal fidelity, and efficiency. We report on the development of PDMS-based fiber optic strain sensors for biomechanical sensing in real-time via the evaluation of skeletal joint angles. The fibers were fabricated using an elastomer and gel combination in a 3:2 ratio. The elasticity and optical loss of this novel fiber material was experimentally characterized for two fiber diameters of 3 mm and 5 mm. The experimental stress-strain behavior was fitted to a 3D hyperelastic Mooney-Rivlin model to obtain C01 and C10 material constants of 0.022 MPa and 0.0308 MPa respectively. Transmission monotonically decreased in response to a stress applied in both the longitudinal (elongation) and lateral (bending) directions. The sensors were demonstrated in a motion sensing implementation by monitoring the joint angle at the elbow in real-time. Measurements indicated a consistent performance of both fiber diameters over the range of motion of the elbow corresponding to flexion and extension. The optical loss increased by 0.1784 dB and 0.1147 dB for each degree of flexion with standard deviation error in measurement of 3.525° and 4.672° for the 3 mm and 5 mm fiber diameters, respectively. The results demonstrate the potential of this system for real-time, wearable biomechanical sensing, motion capture systems, and as a feedback mechanism in prosthetics and robotics.