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Published on: May 8, 2021
An empirical study of modified beat SQI based majority voting fusion method for heart-rate estimation in noisy
1Department of Instrumentation and Control, Government Engineering College, Gandhinagar, India.
Insights
This study introduces a novel fusion method (MMVF) to improve heart rate estimation from noisy cardiovascular signals by incorporating non-cardiovascular signals. The MMVF method enhances accuracy, even with severe signal corruption, ensuring reliable heart rate monitoring.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Cardiovascular Monitoring
Background:
- Traditional heart rate estimation relies on electrocardiogram (ECG) and arterial blood pressure (ABP) signals, which are susceptible to noise, compromising accuracy.
- Non-cardiovascular (NC) signals offer a complementary data source for robust heart rate estimation when cardiovascular signals are degraded.
Discussion:
- The proposed modified beat signal quality index-based majority voting fusion (MMVF) method effectively handles prolonged noise in cardiovascular signals.
- MMVF assigns uniform weights, allowing non-cardiovascular signals to contribute significantly when cardiovascular signals are corrupted, improving overall reliability.
Key Insights:
- Fusion of NC signals with noisy ECG and ABP signals using MMVF significantly enhances heart rate estimation accuracy, achieving up to 98.81% improvement over single ECG detectors.
- The MMVF method demonstrates robust heart rate estimation within clinical error limits, even with extremely noisy cardiovascular signals (ECG SNR down to -70 dB, ABP 100% noise duration).
- This approach proves effective for heart rate estimation in the presence of severe noise and R-peak artifacts in ECG signals.
Outlook:
- Further validation of the MMVF method across diverse clinical populations and noise conditions is warranted.
- Integration of MMVF into real-time monitoring systems could enhance patient safety and diagnostic capabilities.
- Exploration of advanced signal processing techniques for NC signal feature extraction may further boost performance.
Abstract:
Objectives. Most existing heartbeat-detection algorithms rely heavily on cardiovascular signals, namely electrocardiogram (ECG) and arterial blood pressure (ABP), which are often corrupted by noise, leading to unreliable heart-rate estimates. Simultaneously recorded non-cardiovascular (NC) signals help with reliable heart-rate estimates when both cardiovascular signals are corrupted by noise. This study aims to: (i) propose a modified beat signal quality index-based majority voting fusion (MMVF) method to deal with extremely noisy cardiovascular signals; (ii) generate synthetic noise datasets from standard PhysioNet datasets by adding different types of ECG noises, i.e. baseline wander (bw), electrode motion (em), muscle artifact (ma), and realistic artificial ABP noises in clean or low-noise ECG and ABP signals, respectively; and (iii) analyze the effectiveness of the MMVF method for heart-rate estimation with different combinations of beat detectors.Approach.The modified beat signal quality index in the proposed method can identify the quality of the signal even when it contains long durations of noise. The MMVF method assigns uniform weights to the beats detected from all multimodal physiological signals, thus enabling effective participation of beats from NC signals when both cardiovascular signals are corrupted.Results.Fusion of the NC signals with noisy cardiovascular signals using the MMVF method improves heart-rate estimation accuracy over that of single ECG beat detectors like gqrs, epltd, and slope sum function and Teager-Kaiser energy (SSF-TKE) up to 98.81%, 97.95%, and 87.98%, respectively. This method has yielded robust heart-rate estimation within clinically acceptable error limits in concurrently highly noisy cardiovascular signals (ECG: up to a signal-to-noise ratio (SNR) of -70 dB and ABP: up to 100% noise duration in noisy segments) by their fusion with NC signals.Significance.This study serves as empirical evidence for the robustness of the MMVF method in scenarios where there are extremely noisy cardiovascular signals and NC signals with ECG R-peak artifacts.
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