A Novel Algorithm for HRV Estimation from Short-Term Acoustic Recordings at Neck.

Area of Science:

• Biomedical Engineering
• Physiology
• Signal Processing

Background:

• Heart rate variability (HRV) is crucial for monitoring autonomic nervous system (ANS) function.
• Current noninvasive HRV monitoring often relies on electrocardiogram (ECG) signals.
• Acoustic signals from the neck offer a potential alternative for HRV analysis.

Purpose of the Study:

• To develop and validate a novel algorithm for HRV analysis using acoustic data from the suprasternal notch.
• To assess the accuracy of the proposed method compared to traditional ECG recordings.
• To demonstrate the feasibility of noninvasive HRV monitoring via neck-based acoustic sensors.

Main Methods:

• An algorithm was developed to analyze acoustic signals recorded from the suprasternal notch.
• The Hilbert-Huang Transform (HHT) was employed for empirical data analysis.
• The K-means algorithm was used to detect S1 and S2 heart sounds for cardiac cycle segmentation.
• Time-domain HRV analysis was performed on short-term recordings from 10 subjects.

Main Results:

• The proposed algorithm showed close agreement with reference ECG signals for HRV analysis.
• Instantaneous heart rate (IHR) derived from acoustic data achieved 95.78% accuracy for S1 sounds and 92.35% for S2 sounds.
• The method successfully segmented the cardiac cycle using detected S1 and S2 sounds.

Conclusions:

• The proposed algorithm provides accurate HRV analysis from acoustic signals recorded at the neck.
• This noninvasive approach offers a promising alternative for monitoring ANS activity.
• Wearable acoustic sensors at the suprasternal notch can effectively capture cardiac signals for HRV assessment.