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Ready to Redefine
RadiHeart
RadiHeart 率先通過 PPG 信號進行連續血壓監測，為常規生理管理提供實時數據。
我們的人工智能解決方案還可以應用於心律失常、左心室射出率和心率變異性的功能。我們目前正在開發這些功能。
RadiHeart 改變了心臟護理，包括心律失常檢測、HRV 分析和 LVEF 評估。
動機
由於當前飲食習慣和生活方式的改變，慢性病的患病率不斷上升。
許多人患有高血壓、心髒病等慢性疾病。
RadiRad 旨在通過為這些患者提供實時生理信號監測來幫助改變他們的生活。
鑑於高血壓和心臟病患者需要長時間的血壓和心率數據監測，傳統的生理信號監測受到位置和檢測設備的限制。
因此，我們開發了RadiHeart來解決這些因素。
Current physiological condition monitor method
Physiological monitoring currently requires various devices, such as a pulse oximeter for measuring blood oxygen levels, a sphygmomanometer for blood pressure readings, and physiological monitors with EKG leads for electrocardiogram signals.
Blood pressure measurements typically fall into two categories: invasive and non-invasive.
In urgent situations such as hospitals or surgery rooms, invasive blood pressure monitoring is typically used, while non-invasive blood pressure monitoring is commonly used for everyday monitoring.
Non-invasive blood pressure monitoring has the following key characteristics:
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It requires the cuff to measure blood pressure, with the results representing a single measurement.
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Instruments used for blood pressure monitoring typically require calibration.
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Blood pressure monitors are generally inconveniently for carrying outside.
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Physiological condition monitor method with RadiHeart
RadiHeart uses deep learning algorithms to process PPG signals and convert them into ABP signals collected by pulse oximeters.
With the ABP signals, RadiHeart can calculate continuous blood pressure readings and also estimate the HRV values.
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Through RadiHeart, users can easily obtain continuous blood pressure & HRV evaluation.
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