Background
Cardiac conditions involving abnormal thickening of the heart muscle, such as hypertrophic cardiomyopathy (HCM) and left ventricular hypertrophy (LVH), present significant diagnostic challenges due to their overlapping symptoms and similar manifestations in standard ECG readings. Existing diagnostic methods—including echocardiograms, genetic testing, and traditional ECG criteria like Cornell and Sokolow-Lyon—are often expensive, require specialized equipment or expertise, and rely on time-domain analyses that may not capture the subtle differences in the heart's electrical activity. Although these methods provide valuable information, their limited ability to differentiate nuanced physiological changes has driven the need for more sophisticated analytical techniques, capable of translating the ECG signals into more revealing representations of cardiac dynamics for improved diagnosis.
Technology overview
A diagnostic system analyzes ECG signals by converting them from the time domain into the frequency (s-) domain using the Laplace transform, enabling the extraction of complex poles that represent the natural frequencies and shapes of heartbeats. An algorithm detects characteristic peak signatures in the s-domain that allow discrimination between hypertrophic cardiomyopathy (HCM) and left ventricular hypertrophy (LVH), based on the hypothesis that these conditions produce distinctive pole patterns in the complex plane. The approach leverages MATLAB for implementation, utilizes established ECG datasets, and provides a cost-effective method that can integrate with current ECG equipment without additional hardware, while preliminary validations using PhysioNet recordings support its potential for broader diagnostic applications.
Benefits
- Cost-effectiveness: The invention uses standard ECG equipment and analysis methods to deliver diagnostic results at a fraction of the cost of echocardiograms and genetic testing, which can cost thousands of dollars.
- Increased accessibility: By relying on existing ECG setups and avoiding the need for specialized hardware, the technique makes advanced cardiac diagnostics accessible in a broader range of clinical settings, unlike expensive imaging modalities.
- Enhanced diagnostic precision: The transformation of ECG signals from the time domain to the s-domain enables the identification of unique pole patterns associated with HCM and LVH, offering a more refined differentiation compared to traditional time-domain analyses such as the Cornell criteria and Sokolow-Lyon criteria.
- Technical innovation in signal analysis: Utilizing Laplace transform-based frequency domain analysis, the method captures complex dynamic behavior of the heart, providing insights that conventional ECG pattern recognition or AI-enabled classifiers may not detect.
- Scalability and integration: Implemented initially in MATLAB with plans for cross-platform adaptation, this solution can seamlessly integrate into existing clinical workflows and ECG machines, facilitating rapid adoption in hospitals and clinics.
- Potential for broader diagnostic applications: Beyond differentiating between HCM and LVH, the underlying analytical framework can be extended to diagnose other conditions using physiological signals, potentially transforming a wide array of diagnostic practices.
Applications
- Hospital and clinical cardiac diagnostics software: Enables hospitals and clinics to perform cost-effective, accurate differentiation between HCM and LVH using existing ECG equipment.
- Medical device manufacturer integration: Provides an upgrade opportunity for ECG device manufacturers to integrate the s‑domain analysis algorithm into their systems, enhancing diagnostic performance without extra hardware.
- Expanded diagnostic platform for physiological signals: Offers a foundation for adapting the algorithm to diagnose other diseases by analyzing various physiological signals, broadening its healthcare application.
