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Cardiovascular disease is the leading cause of death in the world. It is vital to prevent it by rapid diagnosis and appropriate management through periodic blood pressure (BP) measurement. Recently, many studies have been conducted on methods to measure BP without a cuff. One of the most common methods of predicting BP without a cuff is to use the correlation between pulse wave velocity (PWV) and BP. Studies that predict BP through PWV have two problems to overcome: 1) Additional efforts are required to extract PWV features manually from various biomedical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG); and 2) in predicting BP using biomedical signals from other people, individual periodic calibration is required because the correlation between PWV and BP differs from person to person. In this study, we proposed a cuffless BP prediction method based on a deep convolutional neural network (CNN) that can overcome the problems mentioned above. The proposed CNN method 1) can use raw signals for training without PWV feature extraction; and 2) automatically learns the characteristics of biomedical signals from other people to predict BP accurately without calibration. We propose two schemes: extraction through multiple dilated convolution, and concentration through strided convolution with a large kernel, to process sequential ECG and PPG signals through CNN. BP prediction performance was the best when both ECG and PPG signals were used together. To this end, we conducted extensive experiments on the different settings of the proposed method and constructed an effective learning model. The proposed method achieved excellent performance in predicting both systolic blood pressure and diastolic blood pressure over other known approaches. We also verified that the performance of our method fulfills international standard protocols, AAMI, and BHS.INDEX TERMS Biomedical signal processing, computational and artificial intelligence, health information management, machine learning, cuffless blood pressure measurement, deep learning, convolutional neural networks, end-to-end.

The effect of hot-pressing temperature on the densification behavior and mechanical properties of titanium diboride (TiB 2 ) was investigated. TiB 2 specimens were hot-pressed for 1 h at temperatures in the range of 1500°-1800°C, with an addition of 2.5 wt% of silicon nitride (Si 3 N 4 ) as a sintering aid. The density increased markedly at temperatures in the range of 1500°-1600°C and remained constant thereafter. The formation of a eutectic liquid at 1550°C was attributed to the steep increase in density. The hot-pressing temperature also improved the mechanical properties, such as the flexural strength, Vickers hardness, and fracture toughness of the specimens. Similar to the density, the mechanical properties improved remarkably at ϳ1550°C, so that optimum properties were obtainable at temperatures as low as 1600°C.