Photoacoustic (PA) microscopy allows imaging of the soft biological tissue based on optical absorption contrast and spatial ultrasound resolution. One of the major applications of PA imaging is its characterization of microvasculature. However, the strong PA signal from skin layer overshadowed the subcutaneous blood vessels leading to indirectly reconstruct the PA images in human study. Addressing the present situation, we examined a deep learning (DL) automatic algorithm to achieve high-resolution and high-contrast segmentation for widening PA imaging applications. In this research, we propose a DL model based on modified U-Net for extracting the relationship features between amplitudes of the generated PA signal from skin and underlying vessels. This study illustrates the broader potential of hybrid complex network as an automatic segmentation tool for the in vivo PA imaging. With DL-infused solution, our result outperforms the previous studies with achieved real-time semantic segmentation on large-size high-resolution PA images.

One of the best ways to monitor the health of the heart is to regularly record its electrical activity by using an electrocardiogram (ECG). Abnormal ECG signals may indicate conditions such as heart attack, arrhythmia, or heart defects. There are many ECG devices available which can detect and amplify this differential biological signal from the heart, allowing a lot of information to be collected quickly. The ECG is often small and easy to use, but its power is supplied from regular batteries, which need to be replaced after a certain period of use. This causes discomfort for elderly users. To overcome this limitation, in this paper, we aim to develop a solar-powered, portable Bluetooth device for ECG measurements. The device can be interfaced with smartphones or other wireless devices via Bluetooth by a distance up to 100 m. The ECG device was designed to use solar energy, which is also the main power source. Following the solar energy harvesting circuit is a solar panel with an output voltage of 2.4 V and a power out of 0.25 W. We optimized the design to have a very low power consumption and in sleep mode the current consumption is only around 40 µA. The device was designed with 24-bit resolution and a sampling frequency of up to 2133 Hz, which can allow high accuracy ECG measurements. The device is not only used for heart rate monitoring, but it can also assist doctors in analyzing ECG signals with a high accuracy via embedded operating software.

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