Atrial fibrillation (AF) is the most frequently occurring form of arrhythmia, which induces multiple fatal diseases and impairs the quality of life in patients; thus, the study of the diagnostic methods for detecting AF is clinically important. Here we present a feature extraction method for the detection of AF using a ballistocardiogram (BCG), which is based on a physiological signal database collected by a non-contact sensor. The BCG signals, including both with AF and sinus rhythm (SR), were collected from 37 subjects during overnight sleep (approximately 8 hours). The signals were split into 2915 one-minute segments (AF: 1494, SR: 1421) without overlap and labeled as AF and SR. BCG signals were transformed into BCG energy signals in order to highlight the features of AF and SR BCG signals; and four new data sequences representing different characteristics of the BCG energy signals were generated. The mean value, variance, skewness, and kurtosis of the four data sequences were calculated and 16 features were extracted for each segment. Five machine learning algorithms were used for classification. The results of this study show that the support vector machine (SVM) performed the best among the five tested classifiers and achieved sensitivity, precision, and accuracy of 0.968, 0.928, and 0.945, respectively. These results indicate that the proposed feature extraction method can be well applied to AF and SR classification and may lay foundations for the development of systems for long-term home cardiac monitoring and AF screening.

Purpose: In cardiac radiofrequency (RF) ablation, RF energy is often used to create a series of transmural lesions for blocking accessory conduction pathways. Electrode–tissue contact force (CF) is one of the key determinants of lesion formation during RF ablation. Low electrode–tissue CF is associated with ineffective RF lesion formation, whereas excessive CF may increase the risk of steam pop and perforation. By using finite element analysis, we studied lesion size and features at different values of electrode–tissue CF in cardiac RF ablation. Materials and methods: A computer-model-coupled electrode–tissue CF field, RF electric field, and thermal field were developed to study temperature distribution and lesion dimensions in cardiac tissue subjected to CF of 2, 5, 10, 20, 30, and 40 g with identical RF voltage and duration. Results: Increasing CF was associated with an increase in lesion depth, width, and cross-section area. The lesion cross-section area exhibited a linear increase, and the lesion width was significantly greater than lesion depth under the identical ablation condition. The relationship between CF value and lesion size is a power function: Lesion Size = a × CF^b (Lesion Depth = 3.17 × CF^0.14 and Lesion Width = 5.17 × CF^0.14). Conclusions: This study confirmed that CF is a major determinant of RF lesion size and that electrode–tissue CF affects the amount of power dissipated in tissue. At a constant RF voltage and application time, RF lesion size increases as CF increases.

There are two major challenges in automated heart sound analysis: segmentation and classification. An efficient segmentation is capable of providing valuable diagnostic information of patients. In addition, it is crucial for some feature-extraction based classification methods. Therefore, the segmentation of heart sound is of significant value. This paper presents an automatic heart sound segmentation method that combines the time-domain analysis, frequency-domain analysis and time–frequency-domain analysis. Employing this method, the boundaries of heart sound components are first located, and the components are then recognized. Finally, the heart sounds are divided into several segments on the basis of the results of boundary localization and component identification. In order to evaluate the performance of the proposed method, quantitative experiments are performed on an authoritative heart sound database. The experimental results show that the boundary localization has a sensitivity (Se) of 100%, a positive predictive value (PPV) of 99.3% and an accuracy (Acc) of 99.93%. Moreover, the Se, PPV and Acc of component identification reach 98.63, 99.86 and 98.49%, respectively. The proposed method shows reliable performance on the segmentation of heart sounds. Compared with previous works, this method can be applied to not only normal heart sounds, but also the sounds with S3, S4 and murmurs, thus greatly increasing the applied range.

Motor function assessment is crucial for post-stroke rehabilitation. Conventional evaluation methods are subjective, heavily depending on the experience of therapists. In light of the strong correlation between the stroke severity level and the performance of activities of daily living (ADLs), we explored the possibility of automatically evaluating the upper-limb Brunnstrom Recovery Stage (BRS) via three typical ADLs (tooth brushing, face washing and drinking). Multimodal data (acceleration, angular velocity, surface electromyography) were synchronously collected from 5 upper-limb-worn sensor modules. The performance of BRS evaluation system is known to be variable with different system parameters (e.g., number of sensor modules, feature types and classifiers). We systematically searched for the optimal parameters from different data segmentation strategies (five window lengths and four overlaps), 42 types of features, 12 feature optimization techniques and 9 classifiers with the leave-one-subject-out cross-validation. To achieve reliable and low-cost monitoring, we further explored whether it was possible to obtain a satisfactory result using a relatively small number of sensor modules. As a result, the proposed approach can correctly recognize the stages of all 27 participants using only three sensor modules with the optimized data segmentation parameters (window length: 7s, overlap: 50%), extracted features (simple square integral, slope sign change, modified mean absolute value 1 and modified mean absolute value 2), the feature optimization method (principal component analysis) and the logistic regression classifier. According to the literature, this is the first study to comprehensively optimize sensor configuration and parameters in each stage of the BRS classification framework. The proposed approach can serve as a factor-screening tool towards the automatic BRS classification and is promising to be further used at home.

An effective approach for lowering CO₂ emissions from industrial processes is the absorption method of CO₂ capture. Because of the high energy required to break the bond of the carbamate ions during the stripping process, the intramolecular interaction (repulsive interaction) of N_carbamate-C_carbamate was discovered to be the main interaction. Therefore, the purpose of this study is to examine the amine-CO₂ bond strength for various carbamate molecules during the regeneration process using molecular dynamic modelling, as well as to explore the diffusion coefficient of various amines. The results of interaction intensity show that in comparison to other studied amines for inter – and intra-molecular interactions N, N, N′, N′-tetramethylpropane-1,3-diamine (TMPAD), N-dimethylcyclohexanamine (DMCA) and 4-diethylamino-2-butanol (DEAB) were easy to break. It shows that the TMPAD has the highest repulsive interaction; consequently, it needs the lowest energy to break the carbamate ion in the stripping process as compared to MEA. The order of regeneration energy required to break the bond for N_carbamate-C_carbamate in various selected amines from higher to lower is MEA > 2MAE > PZ > 2DMAE > 2EAE > AMP > 1DMA2P > MCA > DMCA > DEAB > TMPAD. The diffusion coefficient data for various blends reveal that the 2MAE-DEAB blend has the highest rate of diffusion when compared to other blends (2MAE-1DMA2P and 2MAE-TMPAD).

Atrial fibrillation (AF) is the most common arrhythmia and can seriously threaten patient health. Research on AF detection carries important clinical significance. This manuscript proposes an AF detection method based on ballistocardiogram (BCG) signals collected by a noncontact sensor. We first constructed a BCG signal dataset consisting of 28,214 ten-second nonoverlapping segments collected from 45 inpatients during overnight sleep, including 9438 for AF, 9570 for sinus rhythm (SR), and 9206 for motion artifacts (MA). Then, we designed a residual convolutional neural network (CNN) for AF detection. The network has four modules, namely a downsampling convolutional module, a local feature learning module, a global feature learning module, and a classification module, and it extracts local and global features from BCG signals for AF detection. The model achieved precision, sensitivity, specificity, F1 score, and accuracy of 96.8%, 93.7%, 98.4%, 95.2%, and 96.8%, respectively. The results indicate that the AF detection method proposed in this manuscript could serve as a basis for long-term screening of AF at home based on BCG signal acquisition.