Revolutionizing Cardiac Monitoring: How DigiBeat’s Cartometric Cardiography Bridges the Diagnostic Gap
- Elena Fraser
- May 16
- 3 min read
Current home-based cardiac monitoring technologies have failed to close a crucial gap in cardiac care: spatial precision. From thumb ECGs to adhesive patches and wrist-worn devices, today’s tools collect data from limited, fixed anatomical sites. As a result, they miss much of the electrical and acoustic complexity of the heart’s function—a limitation that leads to diagnostic blind spots and missed opportunities for early intervention (Hannun et al., 2019; Attia et al., 2019). DigiBeat is changing this with its pioneering use of cartometric cardiography—a novel approach that blends spatial modeling with multimodal sensing to bring clinical-grade cardiac insight into the home.
The Spatial Blind Spot in Cardiac Monitoring
The heart is a dynamic, three-dimensional organ. Its electrical and mechanical signals propagate across the thoracic region in complex ways. Yet, most commercial monitoring devices observe from a single vantage point. Even advanced wearables with multiple sensors fall short by collecting data from isolated positions, failing to capture spatial variations essential to diagnosing conditions like regional ischemia or conduction abnormalities (van der Velde et al., 2021; McConnell et al., 2022).
While multi-lead ECG systems used in hospitals do solve this problem, they rely on exact sensor placement, expert interpretation, and expensive hardware—rendering them impractical for home use. A study by Bond (2012) revealed that incorrect electrode placement leads to misdiagnoses in up to 24% of cases. Clearly, the need for spatially accurate, yet user-friendly solutions is pressing.
What Is Cartometric Cardiography?
Cartometric cardiography is DigiBeat’s breakthrough approach designed to eliminate this spatial mismatch. Rather than relying on raw signal recordings from static sensors, DigiBeat’s system captures acoustic and electrical data from multiple thoracic sites using guided repositioning. Each measurement is mapped relative to the heart’s anatomy using built-in spatial referencing. Think of it as internal mapping for external measurement—a system that adapts to each user's body and still delivers clinically meaningful data.
Unlike traditional devices that require expert placement, the DigiBeat system uses inertial sensors and intelligent guidance to reposition the same sensors across multiple sites. The spatial model integrates these signals, compensating for individual variation and enabling robust interpretation of waveform morphology, timing, and amplitude. This results in accurate physiological metrics without needing a cardiologist at the bedside.
Why This Matters
Cartometric cardiography offers three key advantages over existing solutions:
Comprehensive Diagnostic Coverage By gathering multi-site data, DigiBeat replicates the broad coverage of clinical ECG setups enabling detection of spatially distinct cardiac abnormalities.
Affordability and Scalability A single set of reusable sensors, guided across multiple positions, reduces hardware costs and improves usability. This makes DigiBeat a viable tool for population-scale remote monitoring.
Empowering Lay Users Patients can conduct self-guided exams at home. Sophisticated algorithms handle data interpretation, making cardiac insights accessible without expert input.
Transforming Remote Cardiac Care
DigiBeat’s cartometric system isn’t just about monitoring—it’s about enabling on-demand, longitudinal surveillance of cardiac health. It supports early detection of disease progression or regression, facilitates therapy adjustments, and helps reduce costly emergency visits by providing actionable insights before symptoms escalate.
This innovation also addresses a critical care access challenge. In many parts of the U.S., patients wait over 50 days to see a cardiologist after a primary care referral. Meanwhile, emergency visits for cardiac events can cost upwards of $11,000 per visit. DigiBeat provides a bridge—bringing clinical-grade insights to the home, empowering patients and clinicians alike to act early and appropriately.
References:
Attia, Z. I., et al. (2019). An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet, 394(10201), 861-867.
Bond, R. R. (2012). The importance of electrode placement in 12-lead ECGs. Clinical Cardiology Journal, 25(2), 78–84.
Goldberger, A. L., et al. (2018). Clinical Electrocardiography: A Simplified Approach. Elsevier Health Sciences.
Hannun, A. Y., et al. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65-69.
Martínez, J. P., et al. (2020). Advances in ECG acquisition and interpretation: From traditional to smart and portable technologies. Sensors, 20(19), 5524.
McConnell, M. V., et al. (2022). Wearables in cardiology: A roadmap from research to clinical practice. JACC: Digital Health, 1(3), 350–366.
van der Velde, E. T., et al. (2021). Evaluation of wearable ECG devices for home use: A clinical perspective. European Heart Journal – Digital Health, 2(4), 557–565.
Zheng, Y., et al. (2020). High-precision mapping of ventricular arrhythmias using multi-lead ECG: Current challenges and future directions. Journal of Electrocardiology, 58, 15–22.
