Key result
Flexible AI biosensor patch tracks continuous BP with high accuracy, achieving r ~0.86 vs standard ABPM.
Why the study?
Monitoring vital signs and physiological responses during daily activities is useful for early diagnosis and prevention of cardiovascular diseases, motivating the development of flexible, wearable biosensors.
A novel flexible, wearable biosensor patch combined with a 1D-CNN deep learning model using single-channel ECG provides accurate, continuous, cuffless blood pressure estimation meeting AAMI and BHS standards.
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May support wearable BP monitoring; leaves open prospective validation before clinical adoption.
Monitoring the vital signs and physiological responses of the human body in daily activities is particularly useful for the early diagnosis and prevention of cardiovascular diseases. Here, we proposed a wireless and flexible biosensor patch for continuous and longitudinal monitoring of different physiological signals, including body temperature, blood pressure (BP), and electrocardiography. Moreover, these modalities for tracking body movement and GPS locations for emergency rescue have been included in biosensor devices. We optimized the flexible patch design with high mechanical stretchability and compatibility that can provide reliable and long-term attachment to the curved skin surface. Regarding smart healthcare applications, this research presents an Internet of Things-connected healthcare platform consisting of a smartphone application, website service, database server, and mobile gateway. The IoT platform has the potential to reduce the demand for medical resources and enhance the quality of healthcare services. To further address the advances in non-invasive continuous BP monitoring, an optimized deep learning architecture with one-channel electrocardiogram signals is introduced. The performance of the BP estimation model was verified using an independent dataset; this experimental result satisfied the Association for the Advancement of Medical Instrumentation, and the British Hypertension Society standards for BP monitoring devices. The experimental results demonstrated the practical application of the wireless and flexible biosensor patch for continuous physiological signal monitoring with Internet of Medical Things-connected healthcare applications.
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Phan et al. (2022) studied Healthy (n=5). Flexible biosensor patch with 1D-CNN BP estimation model vs. Commercial ambulatory BP monitoring (Oscar 2) was evaluated on Correlation coefficient for BP prediction (r = 0.86 for SBP, 0.84 for DBP). The proposed flexible biosensor patch with a 1D-CNN BP estimation model achieved high correlation coefficients for blood pressure prediction (r = 0.86 for SBP, 0.84 for DBP) compared to a commercial ambulatory monitor.
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