Wave Intensity Analysis Combined With Machine Learning can Detect Impaired Stroke Volume in Simulations of Heart
Ryan M Reavette1, Spencer J Sherwin2, Meng-Xing Tang1
1Department of Bioengineering, Imperial College London, London, United Kingdom.
Insights
A new noninvasive wave intensity analysis shows promise for diagnosing heart failure. Machine learning applied to diameter-velocity measurements achieved high accuracy in detecting impaired heart performance in individuals, potentially improving early screening.
Area of Science:
- Cardiovascular physiology
- Biomedical engineering
- Medical diagnostics
Background:
- Heart failure (HF) has high mortality rates in the UK, partly due to delayed diagnosis.
- Current diagnostic methods lack specificity, simplicity, and affordability.
- Invasive pressure-velocity wave intensity analysis shows group-level alterations in HF but lacks individual diagnostic potential.
Purpose of the Study:
- To investigate the diagnostic potential of noninvasive wave intensity analysis for detecting impaired heart performance in individuals.
- To explore the use of arterial diameter and velocity measurements for wave intensity analysis.
- To assess the efficacy of machine learning in identifying HF from noninvasive wave intensity metrics.
Main Methods:
- Generation of a virtual population of 2000 elderly subjects (1000 healthy, 1000 with impaired stroke volume).
- Calculation of wave intensity metrics from noninvasive diameter and velocity waveforms in carotid, brachial, and radial arteries.
- Application of a support vector classifier (machine learning) to differentiate between healthy and impaired stroke volume groups.
Main Results:
- Individual wave intensity metrics showed significant overlap between healthy and impaired groups.
- No single metric reliably distinguished between normal and impaired stroke volume.
- Machine learning (support vector classifier) achieved high performance: 99% recall and 95% precision.
Conclusions:
- Noninvasive diameter-velocity wave intensity analysis alone is insufficient for individual HF diagnosis.
- Machine learning significantly enhances the diagnostic capability of noninvasive wave intensity metrics.
- This approach holds substantial potential for improving heart failure screening and diagnosis.
Abstract:
Heart failure is treatable, but in the United Kingdom, the 1-, 5- and 10-year mortality rates are 24.1, 54.5 and 75.5%, respectively. The poor prognosis reflects, in part, the lack of specific, simple and affordable diagnostic techniques; the disease is often advanced by the time a diagnosis is made. Previous studies have demonstrated that certain metrics derived from pressure-velocity-based wave intensity analysis are significantly altered in the presence of impaired heart performance when averaged over groups, but to date, no study has examined the diagnostic potential of wave intensity on an individual basis, and, additionally, the pressure waveform can only be obtained accurately using invasive methods, which has inhibited clinical adoption. Here, we investigate whether a new form of wave intensity based on noninvasive measurements of arterial diameter and velocity can detect impaired heart performance in an individual. To do so, we have generated a virtual population of two-thousand elderly subjects, modelling half as healthy controls and half with an impaired stroke volume. All metrics derived from the diameter-velocity-based wave intensity waveforms in the carotid, brachial and radial arteries showed significant crossover between groups-no one metric in any artery could reliably indicate whether a subject's stroke volume was normal or impaired. However, after applying machine learning to the metrics, we found that a support vector classifier could simultaneously achieve up to 99% recall and 95% precision. We conclude that noninvasive wave intensity analysis has significant potential to improve heart failure screening and diagnosis.
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