Search Results (7)

Recently, the ever-growing interest in the continuous monitoring of heart function in out-of-laboratory settings for an early diagnosis of cardiovascular diseases has led to the investigation of innovative methods for cardiac monitoring. Among others, wearables recording seismic waves induced on the chest surface by the mechanical activity of the heart are becoming popular. For what concerns wearable-based methods, cardiac vibrations can be recorded from the thorax in the form of acceleration, angular velocity, and/or displacement by means of accelerometers, gyroscopes, and fiber optic sensors, respectively. The present paper reviews the currently available wearables for measuring precordial vibrations. The focus is on sensor technology and signal processing techniques for the extraction of the parameters of interest. Lastly, the explored application scenarios and experimental protocols with the relative influencing factors are discussed for each technique. The goal is to delve into these three fundamental aspects (i.e., wearable system, signal processing, and application scenario), which are mutually interrelated, to give a holistic view of the whole process, beyond the sensor aspect alone. The reader can gain a more complete picture of this context without disregarding any of these 3 aspects. Full article

In recent years, cardiovascular diseases are on the rise, and they entail enormous health burdens on global economies. Cardiac vibrations yield a wide and rich spectrum of essential information regarding the functioning of the heart, and thus it is necessary to take advantage of this data to better monitor cardiac health by way of prevention in early stages. Specifically, seismocardiography (SCG) is a noninvasive technique that can record cardiac vibrations by using new cutting-edge devices as accelerometers. Therefore, providing new and reliable data regarding advancements in the field of SCG, i.e., new devices and tools, is necessary to outperform the current understanding of the State-of-the-Art (SoTA). This paper reviews the SoTA on SCG and concentrates on three critical aspects of the SCG approach, i.e., on the acquisition, annotation, and its current applications. Moreover, this comprehensive overview also presents a detailed summary of recent advancements in SCG, such as the adoption of new techniques based on the artificial intelligence field, e.g., machine learning, deep learning, artificial neural networks, and fuzzy logic. Finally, a discussion on the open issues and future investigations regarding the topic is included. Full article

A 2D floor plan (FP) often contains structural, decorative, and functional elements and annotations. Vectorization of floor plans (VFP) is an object detection task that involves the localization and recognition of different structural primitives in 2D FPs. The detection results can be used to generate 3D models directly. The conventional pipeline of VFP often consists of a series of carefully designed complex algorithms with insufficient generalization ability and suffer from low computing speed. Considering the VFP is not suitable for deep learning-based object detection frameworks, this paper proposed a new VFP framework to solve this problem based on a generative adversarial network (GAN). First, a private dataset called ZSCVFP is established. Unlike current public datasets that only own not more than 5000 black and white samples, ZSCVFP contains 10,800 colorful samples disturbed by decorative textures in different styles. Second, a new edge-extracting GAN (EdgeGAN) is designed for the new task by formulating the VFP task as an image translation task innovatively that involves the projection of the original 2D FPs into a primitive space. The output of EdgeGAN is a primitive feature map, each channel of which only contains one category of the detected primitives in the form of lines. A self-supervising term is introduced to the generative loss of EdgeGAN to ensure the quality of generated images. EdgeGAN is faster than the conventional and object-detection-framework-based pipeline with minimal performance loss. Lastly, two inspection modules that are also suitable for conventional pipelines are proposed to check the connectivity and consistency of PFM based on the subspace connective graph (SCG). The first module contains four criteria that correspond to the sufficient conditions of a fully connected graph. The second module that classifies the category of all subspaces via one single graph neural network (GNN) should be consistent with the text annotations in the original FP (if available). The reason is that GNN treats the adjacent matrix of SCG as weights directly. Thus, GNN can utilize the global layout information and achieve higher accuracy than other common classifying methods. Experimental results are given to illustrate the efficiency of the proposed EdgeGAN and inspection approaches. Full article

Gyrocardiography (GCG) is a non-invasive technique of analyzing cardiac vibrations by a MEMS (microelectromechanical system) gyroscope placed on a chest wall. Although its history is short in comparison with seismocardiography (SCG) and electrocardiography (ECG), GCG becomes a technique which may provide additional insight into the mechanical aspects of the cardiac cycle. In this review, we describe the summary of the history, definition, measurements, waveform description and applications of gyrocardiography. The review was conducted on about 55 works analyzed between November 2016 and September 2020. The aim of this literature review was to summarize the current state of knowledge in gyrocardiography, especially the definition, waveform description, the physiological and physical sources of the signal and its applications. Based on the analyzed works, we present the definition of GCG as a technique for registration and analysis of rotational component of local cardiac vibrations, waveform annotation, several applications of the gyrocardiography, including, heart rate estimation, heart rate variability analysis, hemodynamics analysis, and classification of various cardiac diseases. Full article

This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets ( $p>0.05$ from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found ( $p<0.01$ ) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors. Full article

This paper presents an unsupervised methodology to analyze SeismoCardioGram (SCG) signals. Starting from raw accelerometric data, heartbeat complexes are extracted and annotated, using a two-step procedure. An unsupervised calibration procedure is added to better adapt to different user patterns. Results show that the performance scores achieved by the proposed methodology improve over related literature: on average, 98.5% sensitivity and 98.6% precision are achieved in beat detection, whereas RMS (Root Mean Square) error in heartbeat interval estimation is as low as 4.6 ms. This allows SCG heartbeat complexes to be reliably extracted. Then, the morphological information of such waveforms is further processed by means of a modular Convolutional Variational AutoEncoder network, aiming at extracting compressed, meaningful representation. After unsupervised training, the VAE network is able to recognize different signal morphologies, associating each user to its specific patterns with high accuracy, as indicated by specific performance metrics (including adjusted random and mutual information score, completeness, and homogeneity). Finally, a Linear Model is used to interpret the results of clustering in the learned latent space, highlighting the impact of different VAE architectural parameters (i.e., number of stacked convolutional units and dimension of latent space). Full article

Alzheimer’s disease (AD) is a devastating neurodegenerative disorder characterized by the presence of extracellular senile plaques primarily composed of Aβ peptides and intracellular neurofibrillary tangles (NFTs) composed of hyperphosphorylated tau proteins. Olfactory dysfunction is an early clinical phenotype in AD and was reported to be attributable to the presence of NFTs, senile Aβ plaques in the olfactory bulb (OB). Our previous research found that selenomethionine (Se-Met), a major form of selenium (Se) in organisms, effectively increased oxidation resistance as well as reduced the generation and deposition of Aβ and tau hyperphosphorylation in the olfactory bulb of a triple transgenic mouse model of AD (3×Tg-AD), thereby suggesting a potential therapeutic option for AD. In this study, we further investigated changes in the transcriptome data of olfactory bulb tissues of 7-month-old triple transgenic AD (3×Tg-AD) mice treated with Se-Met (6 µg/mL) for three months. Comparison of the gene expression profile between Se-Met-treated and control mice revealed 143 differentially expressed genes (DEGs). Among these genes, 21 DEGs were upregulated and 122 downregulated. The DEGs were then annotated against the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The results show that upregulated genes can be roughly classified into three types. Some of them mainly regulate the regeneration of nerves, such as Fabp7, Evt5 and Gal; some are involved in improving cognition and memory, such as Areg; and some are involved in anti-oxidative stress and anti-apoptosis, such as Adcyap1 and Scg2. The downregulated genes are mainly associated with inflammation and apoptosis, such as Lrg1, Scgb3a1 and Pglyrp1. The reliability of the transcriptomic data was validated by quantitative real time polymerase chain reaction (qRT-PCR) for the selected genes. These results were in line with our previous study, which indicated therapeutic effects of Se-Met on AD mice, providing a theoretical basis for further study of the treatment of AD by Se-Met. Full article