Biomedical Signal Processing and Control最新文献

{"title":"Experimental validation of a finite state controlled functional electrical stimulation walking system with real-time gait phase detection using a single wearable IMU sensor","authors":"Hikaru Yokoyama ,&nbsp;Koshi Shibagaki ,&nbsp;Suzufumi Arai ,&nbsp;Heather E. Williams ,&nbsp;Albert H. Vette ,&nbsp;Taishin Nomura ,&nbsp;Matija Milosevic","doi":"10.1016/j.bspc.2025.108154","DOIUrl":"10.1016/j.bspc.2025.108154","url":null,"abstract":"<div><div>Functional electrical stimulation (FES) is an effective tool for activating lower-limb muscles in gait rehabilitation. Precise timing of FES according to gait sub-phases is crucial for effective gait movements. However, most FES systems are controlled by open-loop muscle stimulation based on pre-determined timings or therapist input. Real-time detection of gait sub-phases and controlling FES systems accordingly could improve efficacy. Yet many prior approaches either require multiple sensors or detect only two main gait events: Heel Contact and Toe Off. Simplification is essential for clinical translation, and a single-sensor setup can potentially streamline FES control in practical rehabilitation contexts. To overcome this limitation, the present study present a novel real-time gait phase detection algorithm for Finite State Machine (FSM) control during walking, utilizing a single wearable Inertial Measurement Unit (IMU). The algorithm was validated by recording stimulation timings and comparing them to lower-limb kinematics from simultaneous optical motion capture. Our algorithm accurately identified four gait sub-phases in real-time, with only a small differences relative to off-line gait sub-phase timings (averaging −2.88 ms for T1, 67.2 ms for T2, −0.68 ms for T3, and 6.63 ms for T4). We observed that most FES onsets occurred just after the gait phase transition, typically within 50 ms. Overall, this approach is simple to implement and shows potential for real-time FSM control of FES in gait rehabilitation, though additional validation is required before clinical deployment.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108154"},"PeriodicalIF":4.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gait analysis and Machine learning algorithms to distinguish between Parkinson’s disease, amyotrophic lateral sclerosis, and Huntington’s disease","authors":"Yi Ru ,&nbsp;Xiangwei Zhao ,&nbsp;Yasamin Baghersad ,&nbsp;Hamid Taheri Andani ,&nbsp;Maboud Hekmatifar ,&nbsp;Belgacem Bouallegue","doi":"10.1016/j.bspc.2025.108216","DOIUrl":"10.1016/j.bspc.2025.108216","url":null,"abstract":"<div><div>Parkinson’s disease is a neurological disorder that is both common and complex. It affects the nervous system, resulting in tremors, sluggish movements, and imbalance. It is a chronic and progressive condition that is regarded as the most prevalent neurodegenerative disease following Alzheimer’s disease. Although timely diagnosis enables the provision of essential care, Parkinson’s disease identification can be difficult due to the presence of numerous neurological disorders. Movement disorders are early symptoms of neurological diseases, and motion dynamics is an effective method for diagnosing them. This method evaluates and diagnoses the disease by analyzing the gait patterns of patients. This study specifically demonstrated this method to distinguish between Parkinson’s disease and other neurological disorders. After being obtained from the Physiont database, the proposed method preprocessed signals. The database contained 15 signals from individuals with Parkinson’s disease and 49 signals from healthy individuals and those with other neurological conditions. The wavelet transform filter bank with default coefficients from MATLAB software was employed to denoise and enhance the acquired signals in order to achieve superior results. The fourth variant of Dubichs, which was appropriate for biomedical signals, implemented a discrete wavelet transform with eight decomposition levels. Subsequently, a set of statistical, temporal, frequency, and nonlinear characteristics of signal was extracted and prioritized according to energy. Once the optimal features were selected from these extractions, linear support vector machines (SVM) and nonlinear models (nearest neighbor (KNN) and multilayer perceptrons (MLPs) were employed to classify signal information. The proposed method’s validity was evaluated through confusion matrix analysis, as well as calculations of specificity, sensitivity, and accuracy. The results suggest that the SVM classifier had a substantial discriminative capacity for distinguishing between Parkinson’s and non-Parkinson’s patients. The support vector machine was the most accurate classifier, achieving an accuracy rate of 98.4% in its ability to distinguish between individuals with Parkinson’s disease and those without. Multilayer perceptrons (MLPs), linear support vector machines (SVM), and nonlinear models (KNN) were used to classify the signal information after the optimal features from these extractions were determined. The proposed method was evaluated by confusion matrix analysis, specificity, sensitivity, and accuracy. The results demonstrate that the SVM classifier was highly discriminative in differentiating patients with and without Parkinson's disease, with an overall accuracy of 98.4% when differentiating individuals with and without Parkinson's disease, making the support vector machine the most accurate classifier.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108216"},"PeriodicalIF":4.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge-aware Attentional Neural Network based healthcare big data analytics optimized with Weighted Velocity-Guided Grey Wolf Optimization Algorithm","authors":"N. Vasuki ,&nbsp;C. Anand ,&nbsp;P. Sukumar ,&nbsp;V.Suresh Babu","doi":"10.1016/j.bspc.2025.108160","DOIUrl":"10.1016/j.bspc.2025.108160","url":null,"abstract":"<div><div>A significant increase in data volumes, along with the attractive opportunities and potential arising from data analysis contributes to the idea of Big Data. The existing healthcare big data analytics methods face challenges in handling high-dimensional data, slow convergence and suboptimal feature selection. In this paper, a Knowledge-aware Attentional Neural Network based Healthcare Big Data Analytics optimized with Weighted Velocity-Guided Grey Wolf Optimization Algorithm (KANN-HBA-WVGGWOA) is proposed. Here, the input data are taken from PIMA Indians Diabetes dataset. Then the input data is pre-processed by utilizing Multiparticle Kalman filter (MKF) to calculate every data object value primarily. The feature selection utilizing Improved Bald Eagle Search Optimization Algorithm (IBESOA) to select the optimal features from the dataset. The selected features are given into Knowledge-aware Attentional Neural Network (KANN) to classify the data as diabetes and no diabetes. Finally, Weighted Velocity-Guided Grey Wolf Optimization Algorithm (WVGGWOA) is proposed to optimize the KANN classifier that precisely classifies the diabetes disease. The KANN-HBA-WVGGWOA method is implemented in Python. The proposed KANN-HBA-WVGGWOA method attains 1.28%, 2.22%, and 2.27% higher accuracy; 12.56%, 18.68%, and 19.49% less computational time compared to the existing models: Role of big data analytics for revolutionizing diabetes management including health care decision-making (BDA-LR-RDMH), Map reduce dependent big data framework utilizing associative kruskal poly kernel classifier for diabetic disorder prediction (BDF-MRPK-DDP) and the Implementation of ML approaches with big data along IoT to generate effectual prediction for health informatics (BD-KNN-PHI) respectively.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108160"},"PeriodicalIF":4.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate zone of inhibition measurement for rapid antimicrobial susceptibility testing","authors":"B. Sunanda,&nbsp;D.R. Ramesh Babu","doi":"10.1016/j.bspc.2025.107884","DOIUrl":"10.1016/j.bspc.2025.107884","url":null,"abstract":"<div><div>Antimicrobial Susceptibility Testing (AST) is a critical tool in combating bacterial infections and guiding effective antibiotic treatments. This paper introduces an automated algorithm leveraging YOLOv5 for accurately measuring the diameter of Zones of Inhibition (ZOIs) in AST plates is to address the limitations of manual measurement. The proposed system employs image processing techniques and object detection to identify antibiotics and ZOIs, enabling precise classification can be made as resistant, intermediate, or susceptible based on radius measured using Clinical and Laboratory Standards Institute (CLSI) guidelines. Special cases, such as overlapping and scattered zones, are managed through enhanced Harris-Stephens corner detection methods. The system was evaluated using 300 annotated images of Escherichia coli and Klebsiella pneumonia, achieving high accuracy in ZOI measurement and susceptibility classification. Results demonstrate the algorithm’s potential to enhance the reliability and efficiency of AST, offering a robust solution for clinical decision-making in the fight against antimicrobial resistance.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 107884"},"PeriodicalIF":4.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new algorithm for removing random motion noise of human body based on total variation","authors":"Yunqing Liu,&nbsp;Jiaqi Shang,&nbsp;Wei Chu,&nbsp;Fei Yan,&nbsp;Dongpo Xu,&nbsp;Siyuan Wu,&nbsp;He Huang,&nbsp;Xin Chen","doi":"10.1016/j.bspc.2025.108198","DOIUrl":"10.1016/j.bspc.2025.108198","url":null,"abstract":"<div><div>Millimeter-wave radar sensors have garnered significant attention in non-contact monitoring for detecting human vital signs, particularly respiration and heart rate. These sensors offer advantages such as compact size, lightweight design, and versatility in sensing across diverse scenarios. Among them, frequency-modulated continuous wave (FMCW) radar demonstrates considerable potential for vital sign monitoring. However, a critical challenge persists, human random motion noise, which spans the entire frequency domain, significantly interferes with accurate heartbeat signal detection. To address this issue, this paper proposes a total variation model-based approach. This method involves reconstructing the thoracic signal matrix, converting it into a grayscale image, and obtaining the sparse characteristics of noise and the underlying image structure based on the second-order gradient information in different directions of the image. By introducing the Lp pseudo-norm, the second-order regularization constraint terms in the horizontal and vertical directions are designed respectively. Meanwhile, a global sparse constraint term was designed based on the prior characteristics of the noise. Then, the noise and the sparse characteristics of the underlying image structure are utilized for denoising, and finally, the thoracic cavity signal is reverse-reconstructed. This framework effectively suppresses random body motion noise while preserving vital sign information. Experimental results demonstrate a notable improvement in signal-to-noise ratio (SNR), along with enhanced measurement accuracy for both respiratory rate and heart rate. The findings of this study not only advance the theoretical framework for non-contact vital sign monitoring but also underscore the practical utility of FMCW radar in real-world applications.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108198"},"PeriodicalIF":4.9,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “DIO-REGNET: Macular edema detection using Dingo optimized deep Reg network” [Biomed. Signal Proc. Contr. 109 (2025) 107941]","authors":"Gulfishan Firdose Ahmed ,&nbsp;Piyush Kumar Shukla ,&nbsp;Chukka Santhaiah ,&nbsp;Raju Barskar ,&nbsp;Noha Alduaiji ,&nbsp;Balamurali Pydi ,&nbsp;A. Chandrasekar","doi":"10.1016/j.bspc.2025.108195","DOIUrl":"10.1016/j.bspc.2025.108195","url":null,"abstract":"","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108195"},"PeriodicalIF":4.9,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAM-MyoNet: A fine-grained perception myocardial ultrasound segmentation network based on segment anything model with prior knowledge driven","authors":"Yuhan Ying ,&nbsp;Xingyu Fang ,&nbsp;Yiwen Zhao ,&nbsp;XinGang Zhao ,&nbsp;Yufeng Zhou ,&nbsp;Gang Du ,&nbsp;Ying Zhan ,&nbsp;Tian Gao ,&nbsp;Andi Li ,&nbsp;Dandan Sun ,&nbsp;Guoli Song","doi":"10.1016/j.bspc.2025.108117","DOIUrl":"10.1016/j.bspc.2025.108117","url":null,"abstract":"<div><div>The automatic segmentation of myocardial ultrasound images is critical for the early diagnosis of cardiac diseases and the assessment of cardiac function. However, this task remains highly challenging due to the poor image quality of cardiac ultrasound and the complex structural morphology. In recent years, Segment Anything Model (SAM) and its derivative algorithms have shown superior performance in various complex segmentation tasks due to their extensive prior knowledge and powerful inference ability. Despite its strength in perceiving and inferring the myocardial edges, the segmentation accuracy of SAM still needs to be improved. To better complete this task, we propose a novel network SAM-MyoNet based on SAM, which has three main contributions. First, we introduce the information augmentation and driving module (IADM), which utilizes prior knowledge to augment the original dataset and drive SAM for feature extraction, thereby generating high-quality preliminary segmentation predictions. Second, we introduce the fine-grained feature perception module (FFPM), which employs a dual-branch attentional mechanism to refine segmentation based on preliminary results. One branch enhances fine-grained features, while the other maintains overall morphological perception, further improving segmentation accuracy. Third, we incorporate shape supervision to improve the model’s learning of myocardial shape characteristics. To comprehensively evaluate the network’s performance, we conducted extensive experiments across four different myocardial ultrasound datasets. The results show that our SAM-MyoNet outperforms the current state-of-the-art (SOTA) methods. Furthermore, we validate its generalizability using multi-center data. Our code is released at <span><span>https://github.com/yingyuhan/SAM-MyoNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108117"},"PeriodicalIF":4.9,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inpainting of ultrasound cardiac tissues with consistent anatomical structures","authors":"Honghe Li,&nbsp;Jinzhu Yang,&nbsp;Mei Wei,&nbsp;Mingjun Qu,&nbsp;Yong Feng","doi":"10.1016/j.bspc.2025.108121","DOIUrl":"10.1016/j.bspc.2025.108121","url":null,"abstract":"<div><div>Image inpainting techniques play a crucial role in restoring occluded regions in medical images, ultimately enhancing the consistency of clinical diagnoses. Traditional inpainting methods often face challenges when attempting to restore complex anatomical structures and large occluded areas in medical ultrasound images. While recent deep learning-based techniques show promise, they still encounter issues such as loss of edge information and inconsistent reconstruction in occluded regions. In this paper, we introduce a Segmentation-guided Medical Ultrasound Inpainting framework designed to overcome these limitations. Our framework integrates segmentation-derived edge priors to guide the inpainting process, ensuring anatomical consistency and enhancing the recovery of fine details. We propose a Multi-Scale Mixed Residual Block to improve the model’s ability to restore large masked regions and a Deformable Edge Attention mechanism to preserve critical edge details during downsampling while minimizing the introduction of noise. Extensive experiments on two publicly available echocardiography datasets show that our method significantly outperforms state-of-the-art inpainting models in terms of PSNR, MAE, and SSIM metrics. The results highlight the potential of our approach to provide accurate, consistent ultrasound image reconstruction, making it a valuable tool for clinical diagnostics.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108121"},"PeriodicalIF":4.9,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrooculography signal generation with conditional diffusion models for eye movement classification","authors":"Hyun-Tae Choi,&nbsp;Eidmann Ammienn Bin Eh Mi,&nbsp;Bum-Kyu Kim,&nbsp;Won-Du Chang","doi":"10.1016/j.bspc.2025.108211","DOIUrl":"10.1016/j.bspc.2025.108211","url":null,"abstract":"<div><div>Electrooculography (EOG) is a biosignal that encodes the directional information of eye movements and is widely used in eye-tracking applications and human-computer interaction. These applications provide intuitive, accessible interfaces, making EOG valuable as a communication aid. Among these applications, eye writing—an approach in which users draw characters using eye movements—is distinguished for its ability to convey significantly more information than traditional methods. This technique has potential applications for individuals who rely on eye movements for communication, including those with amyotrophic lateral sclerosis. However, achieving high accuracy in eye writing typically requires deep learning models constrained by ethical and legal challenges in collecting large EOG datasets. In this study, we developed a diffusion-model-based generative framework for eye-written character recognition under limited-data conditions, addressing challenges in both accuracy and data availability. The model was implemented using conditional vectorized class information to further increase the diversity of the generated data by indicating the class. The effectiveness of the proposed method was assessed through visual representations of generated signals and comparison of classification accuracies. The model outperformed the generative adversarial network in terms of the visual quality of generated data. It also achieved classification accuracies of 94.63% for Arabic numerals and 80.36% for Japanese Katakana strokes when trained with ninefold data at intermediate steps. Despite having significantly more parameters, it achieved shorter inference time than TimeGAN, further demonstrating computational efficiency and feasibility. Additionally, adapting the proposed method to other bioelectric signals demonstrates significant potential, suggesting a flexible framework for future biosignal research and applications.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108211"},"PeriodicalIF":4.9,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cwin-Net: A channel window attention network for magnetic resonance image super-resolution","authors":"Jing Wang ,&nbsp;Yaoyao Ma ,&nbsp;Chao Xu,&nbsp;Minghang Chu,&nbsp;Zhiwei Fan,&nbsp;Di Wu","doi":"10.1016/j.bspc.2025.108119","DOIUrl":"10.1016/j.bspc.2025.108119","url":null,"abstract":"<div><div>In contemporary medical diagnostics and therapeutic interventions, high-fidelity magnetic resonance imaging (MRI) plays a pivotal role in ensuring accurate clinical assessments. However, prolonged MRI acquisition times present substantial challenges to both patient comfort and healthcare system efficiency. To address these limitations, we introduce Cwin-Net — a channel window attention network specifically designed for magnetic resonance image super-resolution. The proposed Cwin attention mechanism introduces two key innovations: First, by organically combining window-shift attention with channel weighting mechanisms, it achieves synergistic capture of both local details and global information. Second, the introduced dynamic channel weight adjustment mechanism enables the network to adaptively enhance the representation of key feature channels, thereby significantly improving detail preservation while maintaining global structure. Recognizing the inherent complexity of MR image characteristics, we implement a segmentation perceptual loss to incorporate anatomical prior knowledge, thereby prioritizing the reconstruction of clinically relevant textural patterns. The architecture further incorporates Feature Enhanced Blocks (FEB) to optimize deep feature integration, selectively amplifying diagnostically significant elements through learned weight parameters and frequency domain analysis. Extensive validation on the OASIS, ACDC, and Knee datasets demonstrates Cwin-Net’s superiority over state-of-the-art methods, achieving optimal performance in both quantitative metrics and visual quality assessments.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108119"},"PeriodicalIF":4.9,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}