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Noninvasive acoustical detection of coronary artery disease using the adaptive line enhancer method
M Akay1, W Welkowitz, J L Semmlow
1Biomedical Engineering Department, Rutgers State University, Piscataway, NJ 08855.
Insights
This study introduces a novel signal processing method to detect coronary artery disease using heart sounds. The technique accurately identified diseased patients by analyzing diastolic heart sounds, confirming high-frequency energy is linked to coronary stenosis.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Heart sounds may indicate occluded coronary arteries.
- Previous analysis of diastolic heart sounds revealed additional frequency components in coronary artery disease (CAD) patients.
Purpose of the Study:
- To explore advanced signal processing for noninvasive detection of CAD.
- To present a new signal-processing approach using adaptive line enhancing (ALE) and spectral estimation of diastolic heart sounds.
Main Methods:
- Utilized a two-stage process: ALE for noise reduction and spectral estimation (AR or ARMA) for parameter extraction.
- Analyzed diastolic heart sounds recorded at the bedside.
- Employed model parameters, including power spectral density (PSD) and AR/ARMA poles, for diagnosis.
Main Results:
- The new method correctly identified normal and abnormal recordings in 39 out of 43 cases.
- Confirmed an association between high-frequency energy (above 400 Hz) and coronary stenosis.
Conclusions:
- The presented signal-processing approach shows promise for noninvasive CAD detection.
- Diastolic heart sound analysis, particularly high-frequency components, is valuable for diagnosing coronary artery stenosis.
Abstract:
Previous studies have indicated that heart sounds may contain information which is useful in the detection of occluded coronary arteries. Specifically, previous work based on analysing heart sounds recorded during the diastolic portion of the cardiac cycle, when blood flow through the coronary arteries is maximum, has shown that additional frequency components are present in patients with coronary artery disease. To further explore the application of advanced signal processing techniques to the noninvasive detection of coronary artery disease, a new signal-processing approach is presented using adaptive line enhancing (ALE) and spectral estimation of diastolic heart sounds taken from recordings made at the patient's bedside. This approach comprises two cascaded processes. In the first the ALE method is used to enhance the diastolic heart sounds and eliminate background noise. In the second process, either autoregressive (AR) or autoregressive moving average (ARMA) spectral methods are used to estimate the model parameters. Model parameters (the power spectral density (PSD) functions and the poles of the AR or ARMA method) were used to diagnose patients as diseased or normal. Results showed that normal and abnormal recordings were correctly identified in 39 of 43 cases using the new method. These results also confirm that high-frequency energy above 400 Hz is associated with coronary stenosis.