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Aikon Health
HeartLinQ: Continuous Monitoring for Early Detection of Heart Failure

HeartLinQ is a European consortium project developing a non-invasive wearable for the continuous monitoring of heart failure patients. The consortium believes that early detection of clinical deterioration can save lives and prevent unnecessary hospital admissions. Data science is a key building block in turning raw sensor data into reliable, clinically actionable insights.

Expertise
Optimization & forecasting
Year
2026
result
Early detection of heart failure thanks to clinically validated digital biomarkers

Heart failure is one of the leading causes of hospital admission worldwide, placing a significant burden on patients, healthcare professionals, and healthcare systems alike. Many patients are readmitted within months of being discharged, often because early signs of clinical deterioration go unnoticed until it's too late. Existing home-monitoring solutions rely heavily on patients measuring themselves, which introduces inaccuracies exactly when reliable data matters most. What's missing is a way to continuously and objectively track a patient's condition outside the hospital, and translate that data into insights doctors can actually act on before a crisis develops.

HeartLinQ brings together a consortium of specialized partners, each contributing a distinct piece of the puzzle. AIKON Health leads the development of the wearable device itself; other partners contribute expertise in electronics, embedded systems, and clinical validation. Datacation's role in this collaboration is to turn raw sensor data into trustworthy, clinically meaningful information. This involves building and refining algorithms that clean and preprocess ECG and PPG signals, extracting reliable clinical features, and developing digital biomarkers together with a prediction model for heart failure. The project is co-financed by the European Union.

By the end of the project, HeartLinQ aims to deliver a validated, non-invasive wearable prototype capable of continuously monitoring heart failure-relevant parameters, including thoracic impedance, heart rate, and respiratory rate. This will be supported by validated algorithms for real-time monitoring and early detection of deterioration, tested through analytical studies with healthy volunteers and a clinical validation study involving 40-50 heart failure patients. The ultimate goal is to demonstrate that continuous remote monitoring is feasible in real-world care settings, enabling earlier intervention, more personalized treatment, and a meaningful step toward more proactive, patient-centered heart failure care.

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