Key result
A new algorithm model combining ECG, PPG, and Korotkoff Sound for intelligent wearable devices increased blood pressure measurement accuracy to ±5 mmHg.
Why the study?
High blood pressure is an important cause of morbidity and mortality among chronic patients, driven by low measurement accuracy and lack of real-time monitoring.
Does a new algorithm model based on ECG, PPG, and Korotkoff Sound improve blood pressure measurement accuracy in intelligent wearable devices?
Does a new algorithm model based on ECG, PPG, and Korotkoff Sound improve blood pressure measurement accuracy in intelligent wearable devices?
A new algorithm combining ECG, PPG, and Korotkoff Sound improves blood pressure measurement accuracy in wearable devices to within ±5 mmHg.
Answers are grounded in this paper and cite their sources.
May support multi-signal wearable BP development; leaves open prospective validation before clinical adoption.
High blood pressure is an important cause of morbidity and mortality among chronic patient population, due to low accuracy of the measurement machine and lack of real-time monitoring. This paper tries to find a more accurate calculation method of blood pressure measurement data in the dynamic measurement process by studying data processing acquisition method, wavelength measurement method and filtering processing method of intelligent wearable device. It tried various measures, such as correction of the existing algorithm analysis model, extracting the typical measurement data, designing a new variable scalar value, analyzing the data correlation after the changes of dynamic comparative analysis of data correlation, and verifying the effectiveness and accuracy of proposed method via dynamic sensors data collection experiment. This paper combined hardware design and reflected wavelength selection, designed a new transform personal health elements relation algorithm model to reflect the change of blood pressure, which is a feasible approach to increase blood pressure measurement accuracy to +-5mmhg. The photoelectric blood pressure measurement model is improved through developing new measurement mode based on ECG (Electrocardiogram), PPG (Photoplethysmography) and Korotkoff Sound, the blood pressure monitoring accuracy for smart wearable device is also enhanced, which could provide referential data for determining human health status and disease development status.
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Qin et al. (2019) studied High blood pressure. New transform personal health elements relation algorithm model based on ECG, PPG, and Korotkoff Sound was evaluated on Blood pressure measurement accuracy. A new algorithm model combining ECG, PPG, and Korotkoff Sound for intelligent wearable devices increased blood pressure measurement accuracy to ±5 mmHg.
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