Low-Power Unobtrusive ECG Sensor System for Wireless Power Transfer

Electrocardiogram (ECG) is one of the most widely used physiological signal that provides fundamental health information. Conventional ECG uses gel-type ECG electrodes, however, the gel-type ECG electrode is inconvenient for long-term or daily-life ECG monitoring. Capacitive ECG sensor was implemented to overcome the shortcoming of gel-type ECG electrodes. The wireless power transfer (WPT) technology can improve ubiquitous healthcare, but the power consumption is an important factor of the system based on WPT. Previous study did not consider the power consumption of the system. In this study, we implemented a low-power capacitive ECG monitoring system. The supply power of the system was 5V, and Bluetooth low energy (BLE) module was applied to decrease the power consumption in wireless data transmission. The total power consumption of the system was 16.4 mW.N

End-To-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism

Blood pressure (BP) is a vital sign that provides fundamental health information regarding patients. Continuous BP monitoring is important for patients with hypertension. Various studies have proposed cuff-less BP monitoring methods using pulse transit time. We propose an end-to-end deep learning architecture using only raw signals without the process of extracting features to improve the BP estimation performance using the attention mechanism. The proposed model consisted of a convolutional neural network, a bidirectional gated recurrent unit, and an attention mechanism. The model was trained by a calibration-based method, using the data of each subject. The performance of the model was compared to the model that used each combination of the three signals, and the model with the attention mechanism showed better performance than other state-of-the-art methods, including conventional linear regression method using pulse transit time (PTT). A total of 15 subjects were recruited, and electrocardiogram, ballistocardiogram, and photoplethysmogram levels were measured. The 95% confidence interval of the reference BP was [86.34, 143.74] and [51.28, 88.74] for systolic BP (SBP) and diastolic BP (DBP), respectively. The R 2 values were 0.52 and 0.49, and the mean-absolute-error values were 4.06 ± 4.04 and 3.33 ± 3.42 for SBP and DBP, respectively. In addition, the results complied with global standards. The results show the applicability of the proposed model as an analytical metric for BP estimation