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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. [ABSTRACT FROM AUTHOR]

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.

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 r 2 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 r 2 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.

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.

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.

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.