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Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

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Analyzing Heart Rate Estimation from Vibrational Cardiography with Different Orientations.

Ezz Aboulezz, James Skoric, Yannick D'Mello

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|October 6, 2020

Summary

A new algorithm accurately detects heart rate using seismocardiography and gyrocardiography, proving effective regardless of body orientation for remote health monitoring.

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Area of Science:

Biomedical Engineering
Cardiovascular Physiology
Signal Processing

Background:

Remote health monitoring is crucial for managing cardiovascular diseases.
Seismocardiography (SCG) and Gyrocardiography (GCG) offer reliable heart rate data.
Developing efficient monitoring systems is essential for widespread adoption.

Purpose of the Study:

To develop and validate a simple, efficient algorithm for heart rate detection using SCG and GCG.
To assess the algorithm's accuracy across different body orientations and postures.
To establish the feasibility of using SCG and GCG for adaptable, everyday remote heart monitoring.

Main Methods:

SCG and GCG signals were recorded from 5 subjects using an Inertial Measurement Unit, Raspberry Pi, and BIOPAC system.
An autocorrelation-based algorithm was applied to detect heart rate from 2335 cardiac cycles.
Heart rate estimations were compared against an electrocardiography (ECG) reference using correlation coefficients.

Main Results:

The algorithm achieved high correlation with ECG: r-squared of 0.956 (supine) and 0.975 (standing).
Overall dataset correlation coefficient reached 0.965, demonstrating robust performance.
The algorithm successfully determined heart rate on a per-second basis, irrespective of subject orientation.

Conclusions:

The Autocorrelated Differential Algorithm reliably detects heart rate from SCG and GCG signals.
The algorithm's resistance to orientation changes enhances its adaptability for real-world applications.
This technology holds promise for improved remote cardiovascular health monitoring.