Revolutionary ECG diagnostics powered by AI/ML and backed by global network infrastructure
Our proprietary artificial intelligence and machine learning model revolutionises ECG interpretation by incorporating patient-specific context. Unlike traditional ECG interpretation systems, our AI/ML model takes into account each patient's current medical conditions, medications, and clinical history to provide more accurate and personalised diagnostic insights.
Our AI/ML system continuously improves, learning from new data while maintaining patient privacy and data protection standards.
Validated against traditional interpretation methods, our AI provides reliable clinical insights.
Our model is trained on a diverse, multi-source dataset designed to reflect the breadth of real-world cardiac presentations. Data is drawn from four complementary sources to maximise coverage across patient demographics, conditions, and clinical contexts:
Public ECG Datasets
Established, openly available ECG corpora used to provide a broad foundation of labelled waveform data across a wide range of cardiac conditions.
Proprietary Collected Data
ECG recordings collected directly through HyperLXC Medical's own devices and clinical partnerships, capturing real-world signal quality and patient variety.
Synthetic & Augmented Data
Algorithmically generated and augmented samples used to increase representation of rare conditions and edge cases that are underrepresented in real-world corpora.
EHR & Patient Context Data
Structured electronic health record data — including active medications, diagnoses, and clinical history — paired with ECG recordings to train the model's contextual reasoning.
The defining characteristic of our model is its ability to reason about an ECG in the context of the individual patient — not in isolation. During training, ECG waveform data is paired with structured patient context, teaching the model to weight its interpretation differently depending on clinical factors such as:
This fusion approach means the same waveform pattern can be interpreted appropriately as normal or clinically significant depending on who the patient is — reducing both false positives and missed findings.
All training data — whether sourced from public datasets, proprietary collections, or paired EHR records — is handled under a strict data governance framework:
HyperLXC Medical operates its own Autonomous System (AS208915) providing enterprise-grade network infrastructure with global reach and exceptional reliability. We maintain presence at major internet exchanges including AMS-IX Amsterdam, ERA-IX Amsterdam, and LONAP London, ensuring optimal routing and connectivity for healthcare services worldwide.
Compact and lightweight for use in any clinical setting, from GP surgeries to hospital wards.
Real-time AI analysis providing diagnostic insights within seconds of ECG capture.
Seamless cloud integration for remote monitoring, data storage, and multi-device management.
Intuitive interface requiring minimal training, designed with clinical workflows in mind.
Our commitment to advancing cardiac diagnostics drives continuous research and development in:
We welcome collaboration with research institutions, NHS trusts, and private healthcare providers to advance the field of cardiac diagnostics.