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Ready to Redefine
Main Features
RadiHeart is designed for patients with chronic illnesses, providing real-time physiological data through a compact and user-friendly finger sensor that simultaneously measures continuous blood pressure and SpO2 levels.
RadiHeart utilizes a deep learning algorithm to analyze PPG signals from the finger sensor for SpO2 and continuous blood pressure readings.
The algorithmic results from ABP readings can calculate the HRV index, providing insights into rest and stress management by assessing the user's sympathetic and parasympathetic nerve arousal levels.
In order to activate the arrhythmia and LVEF functionalities, an EKG input is required.
Our deep learning AI algorithm will analyze the results obtained from the EKG and calculate them. This can aid doctors in expediting the diagnosis process and quickly identifying abnormal intervals and waveforms.
Useful tools
Daily/Monthly report
Group users
BP trend
Our RadiHeart user interface comes with a range of useful tools to help users, including an option to create reports based on daily, weekly, or monthly blood pressure measurements.
These reports are convenient for both users and doctors to keep track of blood pressure changes over time.
RadiHeart also allows multiple users to monitor family members' health.
Continuous Blood Pressure
RadiHeart's cutting-edge technology enables accurate and real-time continuous blood pressure monitoring & blood oxygen level. By analyzing PPG signals and obtaining ABP signals, RadiHeart's system provides unparalleled accuracy and reliability in blood pressure monitoring.
RadiHeart provides comprehensive data with just one finger sensor:
1. Real-time blood oxygen saturation detection
2. Heart rate (HR)
3. Pulse rate (PR)
4. Perfusion index (PI)
5. Real-time Systolic blood pressure (SBP)
6. Real-time Diastolic blood pressure (DBP)
7. Real-time Mean arterial pressure (MAP)
User Interface
Instruction
Daily trend
User Interface
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HRV Index (Heart Rate Variability)
Heart rate variability can be affected by multiple factors like age, gender, ethnicity, stress, smoking, medications, exercise, and the ageing process. The overall heart rate variability varies in individuals due to these factors. Additionally, pathological factors such as myocardial infarction, heart failure, and diabetes also contribute to changes in heart rate variability.
Our HRV Index predictive applications have extended to various fields.
The lack of dynamic flexibility due to autonomic nervous system imbalance leads to declining health.
Factors affecting HRV
Factors
Clinical research
Clinical research
Factors affecting HRV
Factors
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Arrhythmia Detection
We integrate our technology with all ECG devices.
Our AI automatically detects abnormalities and assists physicians in efficiently identifying problems from an electrogram report.
LVEF
(left ventricular ejection fraction)
Current heart failure guidelines stress the significance of asymptomatic cardiac dysfunction as a preceding stage in the progression towards clinically apparent heart failure.
Most cardiac dysfunctions can be prevented by early detection.
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