Analyzing Heart Rate Estimation from Vibrational Cardiography with Different Orientations.

UNLABELLED

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.