In general, facial image-based remote photoplethysmography (rPPG) methods use color-based and patch-based region-of-interest (ROI) selection methods to estimate the blood volume pulse (BVP) and beats per minute (BPM). Anatomically, the thickness of the skin is not uniform in all areas of the face, so the same diffuse reflection information cannot be obtained in each area. In recent years, various studies have presented experimental results for their ROIs but did not provide a valid rationale for the proposed regions. In this paper, to see the effect of skin thickness on the accuracy of the rPPG algorithm, we conducted an experiment on 39 anatomically divided facial regions. Experiments were performed with seven algorithms (CHROM, GREEN, ICA, PBV, POS, SSR, and LGI) using the UBFC-rPPG and LGI-PPGI datasets considering 29 selected regions and two adjusted regions out of 39 anatomically classified regions. We proposed a BVP similarity evaluation metric to find a region with high accuracy. We conducted additional experiments on the TOP-5 regions and BOT-5 regions and presented the validity of the proposed ROIs. The TOP-5 regions showed relatively high accuracy compared to the previous algorithm’s ROI, suggesting that the anatomical characteristics of the ROI should be considered when developing a facial image-based rPPG algorithm.

This study presents a novel hybrid deep learning model for arrhythmia classification from electrocardiogram signals, utilizing the stockwell transform for feature extraction. As ECG signals are time-series data, they are transformed into the frequency domain to extract relevant features. Subsequently, a CNN is employed to capture local patterns, while a transformer architecture learns long-term dependencies. Unlike traditional CNN-based models that require R-peak detection, the proposed model operates without it and demonstrates superior accuracy and efficiency. The findings contribute to enhancing the accuracy of ECG-based arrhythmia diagnosis and are applicable to real-time monitoring systems. Specifically, the model achieves an accuracy of 97.8% on the Icentia11k dataset using four arrhythmia classes and 99.58% on the MIT-BIH dataset using five arrhythmia classes.

This paper presents a novel reference architecture for blockchain-enabled federated learning (BCFL), a state-of-the-art approach that amalgamates the strengths of federated learning and blockchain technology. We define smart contract functions, stakeholders and their roles, and the use of interplanetary file system (IPFS) as key components of BCFL and conduct a comprehensive analysis. In traditional centralized federated learning, the selection of local nodes and the collection of learning results for each round are merged under the control of a central server. In contrast, in BCFL, all these processes are monitored and managed via smart contracts. Additionally, we propose an extension architecture to support both cross-device and cross-silo federated learning scenarios. Furthermore, we implement and verify the architecture in a practical real-world Ethereum development environment. Our BCFL reference architecture provides significant flexibility and extensibility, accommodating the integration of various additional elements, as per specific requirements and use cases, thereby rendering it an adaptable solution for a wide range of BCFL applications. As a prominent example of extensibility, decentralized identifiers (DIDs) have been employed as an authentication method to introduce practical utilization within BCFL. This study not only bridges a crucial gap between research and practical deployment but also lays a solid foundation for future explorations in the realm of BCFL. The pivotal contribution of this study is the successful implementation and verification of a realistic BCFL reference architecture. We intend to make the source code publicly accessible shortly, fostering further advancements and adaptations within the community.

Cardiac auscultation is a cost-effective and noninvasive technique for cardiovascular disease detection. Recently, various studies have been underway for cardiac auscultation using deep learning, not doctors. When training a deep learning network, it is important to secure large amount of high-quality data. However, medical data are difficult to obtain, and in most cases the number of abnormal classes is insufficient. In this study, data augmentation is used to supplement the insufficient amount of data, and data generalization to generate data suitable for convolutional neural networks (CNN) is proposed. We demonstrate performance improvements by inputting them into the CNN. Our method achieves an overall performance of 96%, 81%, and 90% for sensitivity, specificity, and F1-score, respectively. Diagnostic accuracy was improved by 18% compared to when it was not used. Particularly, it showed excellent detection success rate for abnormal heart sounds. The proposed method is expected to be applied to an automatic diagnosis system to detect heart abnormalities and help prevent heart disease through early detection.

The photoplethysmography (PPG) signal contains various information that is related to CVD (cardiovascular disease). The remote PPG (rPPG) is a method that can measure a PPG signal using a face image taken with a camera, without a PPG device. Deep learning-based rPPG methods can be classified into three main categories. First, there is a 3D CNN approach that uses a facial image video as input, which focuses on the spatio-temporal changes in the facial video. The second approach is a method that uses a spatio-temporal map (STMap), and the video image is pre-processed using the point where it is easier to analyze changes in blood flow in time order. The last approach uses a preprocessing model with a dichromatic reflection model. This study proposed the concept of an axis projection network (APNET) that complements the drawbacks, in which the 3D CNN method requires significant memory; the STMap method requires a preprocessing method; and the dyschromatic reflection model (DRM) method does not learn long-term temporal characteristics. We also showed that the proposed APNET effectively reduced the network memory size, and that the low-frequency signal was observed in the inferred PPG signal, suggesting that it can provide meaningful results to the study when developing the rPPG algorithm.

Lignin is a complex biopolymer extracted from plant cell walls, playing a crucial role in structural integrity. As the second most abundant biopolymer after cellulose, lignin has significant industrial value in bioenergy, the chemical industry, and agriculture, gaining attention as a sustainable alternative to fossil fuels. Its composition changes during degradation, affecting its applications, making accurate analysis essential. Common lignin analysis methods include Thermogravimetric Analysis (TGA), Fourier-transform Infrared Spectroscopy (FT-IR), and Gas Chromatography–Mass Spectrometry (GC-MS). While GC-MS enables precise chemical identification, its high cost and time requirements limit frequent use in budget-constrained studies. To address this challenge, this study explores the feasibility of an artificial intelligence model that predicts the GC-MS analysis results of depolymerized lignin using data obtained from TGA and FT-IR analyses. The proposed model demonstrates potential but requires further validation across various lignin substrates for generalizability. Additionally, collaboration with organic chemists is essential to assess its practical applicability in real-world lignin and biomass research.