Scaling to Multimodal and Multichannel Heart Sound Classification with Synthetic and Augmented Biosignals
arXiv:2509.11606v4 Announce Type: replace-cross Abstract: Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.9 million deaths each year. Early detection is critical, creating a demand for accurate and inexpensive pre-screening methods. Deep learning has recently been applied to classify abnormal heart sounds indicative of CVDs using synchronised phonocardiogram (PCG) and electrocardiogram (ECG) signals, as well as multichannel PCG (mPCG). However, state-of-the-art architectures remain underutilised due to the limited availability of synchro
Advances in deep learning and biosignal processing are enabling more sophisticated AI applications in healthcare, particularly for diagnostics previously limited by data availability.
This development represents a significant step towards scalable, affordable, and accurate early detection of cardiovascular diseases, potentially transforming global healthcare access and outcomes.
The ability to scale multimodal and multichannel heart sound classification using synthetic and augmented data addresses previous limitations in dataset size and diversity for training advanced AI models.
- · Healthcare AI companies
- · Medical device manufacturers
- · Patients with cardiovascular disease
- · Developing nations
- · Traditional diagnostic methods
- · Healthcare systems reliant on manual interpretations
Widespread adoption of AI-powered heart sound diagnostics for pre-screening and early intervention.
Reduced burden on cardiology specialists and improved patient outcomes through earlier disease management.
Potential for integration into smart wearables and home monitoring systems, democratizing cardiovascular health screening.
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Read at arXiv cs.LG