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Photoplethysmography (PPG) is a non-invasive and cost-efficient optical technique used to assess blood volume variations in the microcirculation. PPG technology is widely used in a variety of wearable sensors to investigate the cardiovascular system. Recent studies have demonstrated the utility of PPG analysis for carrying out large-scale screening to prevent and detect diabetes. However, most of these studies require feature extraction and/or several pre-processing steps. Over the past few years, the advent of deep learning has significantly impacted the analysis of biomedical signals. Despite their success in other fields, however, very few studies have focused on the application of deep learning to raw PPG signals for detecting diabetes. Existing studies have proposed large models trained on large amounts of data. In this paper, we present a Light CNN-based model for screening the presence of type 2 diabetes using a single raw pulse extracted from photoplethysmographic signals. In addition to the baseline architecture, we evaluate different model architectures that take as input age and biological sex or PPG handcrafted features. Furthermore, we apply transfer learning to all the tested architectures to evaluate the effectiveness of harnessing pre-trained models in detecting diabetes. We tested a model pre-trained on a general PPG shape dataset and another model pre-trained on a dataset containing hypertension PPG signals. Our model scored an AUC of 75.5 when trained with raw PPG waves, age, and biological sex without applying transfer learning, which is competitive with current state of the art.

Hand-vein biometrics as a high-security pattern has received more and more attention. One of the open issues in hand-vein verification is the lack of robustness against image quality degradation, which may comprise the verification accuracy. To achieve robust verification, vein feature extraction approaches, especially vein texture segmentation, have been extensively investigated. In recent years, deep neural networks have achieved promising results in medical image segmentation and have been brought into vein verification, but current solutions suffer from two challenges for vein segmentation: 1) lacking the labeling data, which is expensive to obtain and 2) the incorrect label data obtained by manual labeling scheme or automatic labeling scheme may strongly influence parameters when the network is trained, which may degrade the verification performance. This paper proposes an iterative deep belief network (DBN) to extract vein features based on the initial label data, which are automatically generated using a very limited a priori knowledge and iteratively corrected by our DBN. First, a known handcrafted vein image segmentation technique is employed to automatically label vein pixel and background pixel. A training dataset is constructed based on the patches centered on the labeled pixels. Second, a DBN is trained on the resulting database to predict the probability of each pixel to belong to be a vein pixel given a patch centered on it. The vein patterns are segmented using a probability threshold of 0.5. The resulting vein features are employed to reconstruct the training dataset, based on which the network is retrained. During the iterative procedure, the incorrect labels of training data are statistically corrected, which enables DBN to effectively learn what a finger-vein pattern is by learning the difference between vein patterns and background ones. The experimental results on two public hand-vein databases show a significant improvement in terms of hand-vein verification accuracy.