Cardiomtec provides a handheld single‑lead ECG device that captures raw heart signals and uploads them to a cloud analytics platform. Its machine‑learning algorithms generate risk scores and early‑warning alerts, which clinicians can review via a secure web portal and integrate with telehealth or EHR systems. The solution enables at‑home cardiac monitoring for high‑risk adults, supporting preventive care.
Funding
Funding not disclosed
Founders
Product
Problem
Many individuals at risk for coronary artery disease, atrial fibrillation, and related cardiac conditions remain undiagnosed because conventional ECG testing requires clinic visits, trained personnel, and expensive equipment. Asymptomatic patients, particularly those with type 2 diabetes, often lack a convenient way to monitor early electrophysiological changes that could indicate disease onset.
Solution
Cardiomtec offers a compact, home‑use ECG device that records the heart’s electrical activity and transmits the raw signal to a cloud‑based analytics platform. Proprietary signal‑processing algorithms extract clinically relevant features and apply machine‑learning models to identify deviations suggestive of early‑stage cardiac pathology. Results are presented through a secure web portal that physicians can review remotely, enabling timely referral for confirmatory diagnostics. The system supports continuous or periodic self‑testing, allowing users to track risk trends without visiting a medical facility. By integrating automated risk assessment with telehealth connectivity, the solution facilitates proactive, preventive cardiac care.
Target Audience
The primary users are adults with elevated cardiovascular risk—especially type 2 diabetics and individuals with a family history of heart disease—who seek at‑home monitoring, as well as clinicians who require remote cardiac risk assessments for preventive care programs.
Features
- Handheld, single‑lead ECG sensor with Bluetooth Low Energy connectivity for real‑time data capture
- On‑device preprocessing to reduce motion artefacts and ensure high‑quality signal acquisition
- Cloud‑hosted analytics pipeline employing supervised machine‑learning models trained on annotated cardiac datasets
- Automated risk scoring and early‑warning alerts delivered via a patient mobile app and clinician dashboard
- End‑to‑end encryption and HIPAA‑compliant data storage, with role‑based access controls for physicians
- API integration with telemedicine platforms and electronic health record systems using FHIR standards
- User‑friendly interface that guides placement, records measurements, and stores longitudinal trends for each user