Biomedical Signal Processing and Control最新文献

{"title":"Deep learning-based real-time diagnosis of cardiac diseases through behavioral changes in ECG signals","authors":"Muktesh Gupta,&nbsp;Rajesh Wadhvani,&nbsp;Akhtar Rasool","doi":"10.1016/j.bspc.2025.107532","DOIUrl":"10.1016/j.bspc.2025.107532","url":null,"abstract":"<div><div>The heart’s crucial task is pumping blood to vital organs. Heart diseases are the leading global cause of death. Early identification of abnormal cardiac signals may lead to diagnosis and prevention of disease. The electrocardiogram (ECG) is a low-cost, non-invasive approach with great potential to identify cardiac diseases in real-time monitoring. However, obstructions such as dealing with noisy ECG signals, rapid signal processing, and difficulty identifying changes continue to hinder the seamless and accurate detection of cardiac disease. A novel three-phase framework is proposed, which first identifies abrupt changes in ECG signal behavior and subsequently determines the cause of these changes through disease classification. This facilitates the early identification of abnormalities, improving diagnostic accuracy and timely intervention. The preprocessing phase is integrated with a Change Point Detection (CPD) module, enabling the framework to be entirely end-to-end adaptive. The CPD model utilizes an autoencoder to capture essential characteristics of the ECG signal in the latent space. These characteristics are then fused with the other time domain features to enhance the accuracy of the stack ensemble-based classifier. The proposed method processes incoming data sequentially for real-time analysis. Experiments were performed using two real-world datasets provided by the Physikalisch-Technische Bundesanstalt Institute. The outcomes indicate the suggested approach efficiently and reliably manages real-time ECG signal monitoring, surpassing other benchmark strategies.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"104 ","pages":"Article 107532"},"PeriodicalIF":4.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155299","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":"Prediction of colorectal cancer microsatellite instability and tumor mutational burden from histopathological images using multiple instance learning","authors":"Wenyan Wang ,&nbsp;Wei Shi ,&nbsp;Chuanqi Nie ,&nbsp;Weipeng Xing ,&nbsp;Hailong Yang ,&nbsp;Feng Li ,&nbsp;Jinyang Liu ,&nbsp;Geng Tian ,&nbsp;Bing Wang ,&nbsp;Jialiang Yang","doi":"10.1016/j.bspc.2025.107608","DOIUrl":"10.1016/j.bspc.2025.107608","url":null,"abstract":"<div><div>Recent advancements in deep learning have enabled the prediction of microsatellite instability (MSI) and tumor mutational burden (TMB) status of colorectal cancer (CRC) patients using whole slide histopathological images (WSIs). However, current methods suffer from poor prediction accuracy and lack interpretability, which hinders their clinical application. To address this, we propose a new cascaded two-stage multiple instance learning (MIL) method called CasNet for predicting MSI and TMB. CasNet employs a supervised ResNet model to extract informative image features from patches within the WSI. It then evaluates the importance of each patch using a gradient-based class activation graph (Grad-CAM) and an attention mechanism. On the CRC dataset from the cancer genome atlas (TCGA), CasNet achieved an area-under-the-curve (AUC) of 0.909 for predicting MSI status and a mean AUC of 0.8818 in 5-fold cross-validation for TMB prediction, outperforming seven other state-of-the-art methods. Furthermore, we demonstrate the robustness of CasNet by achieving AUC scores of 0.88 and 0.84 for MSI and TMB predictions, respectively, using only 40% of the samples for training. To enhance the interpretability of CasNet, a segmentation method based on Hover-Net is utilized to analyze the differences in cell content between MSI and MSS groups. Overall, CasNet is an accurate and interpretable method for predicting MSI and TMB, making it a promising in predicting biomarkers even with limited training data.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"104 ","pages":"Article 107608"},"PeriodicalIF":4.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155329","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":"Scalogram sets based motor imagery EEG classification using modified vision transformer: A comparative study on scalogram sets","authors":"Balendra,&nbsp;Pranshu CBS Negi,&nbsp;Neeraj Sharma,&nbsp;Shiru Sharma","doi":"10.1016/j.bspc.2025.107640","DOIUrl":"10.1016/j.bspc.2025.107640","url":null,"abstract":"<div><div>Nowadays, motor imagery (MI) electroencephalogram (EEG) is mainly utilized for brain computer interface (BCI) based prosthetic device developments and involves the accurate classification of EEG signals. However, the major challenges are inter-subject and intra-subject variability, presence of noise and artifacts in the acquired EEG signal, this results in low average classification accuracy. To address these issues, the present work proposes an innovative algorithm based on scalogram set formation and modified vision transformer (MViT) model for classification of EEG data. The proposed scalogram sets formed by organizing scalograms of fundamental wavelets as well as their combinations and the proposed Modified Vision Transformer model employs both serial and parallel feeding of initial patches through consecutive transformer blocks, enhancing information flow and extracting diverse features. To verify and validate the proposed methodology, the BCI Competition IV 2b dataset was utilized. The MViT with Morlet and Shannon scalogram set performed accuracies of 86.34 % for intra-subject and 76.19 % for inter-subject classification. The proposed approach performed best among state-of-the-art methods with an average improvement of 3.46 % for intra-subject and 1.75 % for inter-subject in accuracy highlighting the robustness and reliability of the proposed methodology.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"104 ","pages":"Article 107640"},"PeriodicalIF":4.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155333","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":"Lower limb joint angle estimation based on surface electromyography signals","authors":"Hongzhan Lv,&nbsp;Yunrui Wang,&nbsp;Boda Hao","doi":"10.1016/j.bspc.2025.107563","DOIUrl":"10.1016/j.bspc.2025.107563","url":null,"abstract":"<div><div>Accurate human motion estimation is crucial for effective and safe human-robot interaction when using robotic devices for rehabilitation or performance enhancement. Accurately estimating joint angles is challenging due to the strong dynamics and time-varying properties. To address these issues, a feature extraction and joint angle estimation method was proposed to achieve accurate estimation of lower limb movements. To enhance the stability of raw data, the variational mode decomposition (VMD) algorithm was employed to decompose the sEMG signals. The Archimedes optimization algorithm (AOA) optimized two key parameters of VMD: the number of intrinsic mode functions (IMF) components and the penalty factor. Additionally, principal component analysis (PCA) was applied to extract key factors from the feature sequence, reducing the model’s input dimensions. Finally, the Informer model was used for dynamic temporal modeling of the multivariable feature sequences. Experimental results demonstrated that the method accurately estimated knee and hip joint angles using sEMG signals from only three lower limb muscles. For various gait estimations, the root mean square error (RMSE) was approximately 0.386, and the mean absolute error (MAE) averaged 0.296. Compared to LSTM and ELM, the accuracy of angle estimation was improved by at least 3 times. The accuracy was significantly higher than that of existing methods. Therefore, the method can be effectively applied to the angle estimation of complex lower limb movement patterns. This has significant implications for the motion control of exoskeletons, dynamic prostheses, and rehabilitation robots.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"104 ","pages":"Article 107563"},"PeriodicalIF":4.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155343","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":"Temporal electroencephalography features unveiled via olfactory stimulus as biomarkers for mild Alzheimer’s disease","authors":"Bilal Orkan Olcay ,&nbsp;Murat Pehlivan ,&nbsp;Bilge Karaçalı","doi":"10.1016/j.bspc.2025.107566","DOIUrl":"10.1016/j.bspc.2025.107566","url":null,"abstract":"<div><h3>Aim</h3><div>Our primary aim is to capture and use the timings of the characteristic brain responses to olfactory stimulation for mild Alzheimer’s disease diagnosis purposes.</div></div><div><h3>Proposed method</h3><div>Our method identifies the timings of short-lived signal segments where characteristic distances between pre- and post-stimulus relative spectral energies are attained for each EEG channel and frequency band. These timings and timing-derived features were subsequently used in a leave-one-subject-out cross-validation scenario to assess the diagnostic performance of our framework. We evaluated seven distinct statistical distance measures to determine the most effective one for characterizing the neurological conditions of the subjects.</div></div><div><h3>Results</h3><div>The average cross-validation performance shows that our framework achieved 87.50% diagnosis performance. The frequently used features were mainly derived from the delta and alpha activity of the prefrontal region (Fp1) and the beta activity of the parietal region (Pz), which agree with the current findings of olfaction biophysics.</div></div><div><h3>Comparison with existing methods</h3><div>We compared the performance of our method with that of four existing methods in the literature. Our method outperformed these four methods. Moreover, our method elicited the highest accuracy when the clinical olfactory score (UPSIT) was included as a feature.</div></div><div><h3>Conclusions</h3><div>Our analysis framework reveals a significant alteration of the timing organization of the brain that emerged upon olfactory stimulation in Alzheimer’s patients. The timings of characteristic response and the features calculated via these timings contribute to Alzheimer’s disease diagnosis performance remarkably. The perspective proposed here may facilitate early diagnosis, thereby facilitating the exploration of novel therapeutic and treatment strategies.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"104 ","pages":"Article 107566"},"PeriodicalIF":4.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154660","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":"Dynamic chest Electrical Impedance Tomography with mixed statistical shape reconstruction","authors":"Shangjie Ren ,&nbsp;Baorui Bai ,&nbsp;Hai Rong ,&nbsp;Chenke Zhang ,&nbsp;Feng Dong","doi":"10.1016/j.bspc.2025.107533","DOIUrl":"10.1016/j.bspc.2025.107533","url":null,"abstract":"<div><div>Electrical Impedance Tomography (EIT) is a promising noninvasive imaging technique, particularly valuable in biomedical applications due to its inherent safety and capability for real-time monitoring. However, enhancing image quality in chest EIT presents significant challenges due to the dynamic nature of chest conductivity and the complexity of accurately capturing heart and lung functionality. To overcome these problems, a Mixed Statistical Shape Representation (MSSR) method is proposed. This innovative approach integrates a mixed statistical shape representation with a spatiotemporal regularization strategy, specifically tailored to tackle the dynamic and complex conductivity reconstruction problem inherent in chest EIT. The proposed MSSR method has been validated using numerical simulations and experimental data. Compared to conventional image reconstruction methods, the MSSR approach has shown superior performance in reconstructing chest conductivity changes, especially in capturing the dynamic functionality of the heart. This validation offers improved diagnostic and monitoring capabilities of EIT in biomedical applications.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"104 ","pages":"Article 107533"},"PeriodicalIF":4.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155298","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":"FCS-TPNet: Fusion of fNIRS chromophore signals to construct temporal-spatial graph representation for topological networks","authors":"Lin Yang ,&nbsp;Jiacheng Gu ,&nbsp;Jun Chen ,&nbsp;Dongrui Gao ,&nbsp;Manqing Wang","doi":"10.1016/j.bspc.2025.107528","DOIUrl":"10.1016/j.bspc.2025.107528","url":null,"abstract":"<div><div>Functional near-infrared spectroscopy (fNIRS) is a non-invasive, portable brain imaging technology capable of objectively reflect cognitive states. Recently, Graph Convolutional Networks (GCNs) have gained prominence for exploring functional connectivity patterns between brain regions to identify cognitive states. However, current GCNs used for fNIRS ignore the delayed hemodynamic responses and the correlated information between HbO and HbR when constructing fNIRS graph representations. Additionally, previous study without considering the dynamic nature of inter-channel relations may not adequately capture the changes of brain network connectivity patterns. To address these issues, we introduce an innovative neural network, named FCS-TPNet. First, we propose a dual-signal hemodynamic information interaction module to learn hemodynamic features with latency-based adaptive convolutional kernels, and capture the correlation between HbO and HbR by point-wise convolution. Then, we construct a dynamic graph convolution module to obtain complex topological patterns between channels by continuously updating learnable parameters. Several experiments are performed to assess the performance of the proposed model in two-class tasks (mental arithmetic, MA and word generation, WG) and ternary scenarios task (UFFT). In 5 × 5-fold cross-validation experiments, FCS-TPNet achieves the best average accuracy of 79.16 % and 74.53 % in MA and WG, respectively. For UFFT, FCS-TPNet obtains highest classification results of 75.82 %. In subject-independent experiments, our model achieves best accuracy of 81.84 %, 78.21 % and 78.40 % on MA, WG and UFFT, respectively. Ultimately, the results present the efficiency and generalization ability of the proposed model in different cognitive tasks.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"104 ","pages":"Article 107528"},"PeriodicalIF":4.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154718","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":"Discrimination of invasive ductal and lobular carcinoma of the breast based on the combination of enhanced Legendre polynomial, kinetic features and deep learning features","authors":"Ali M. Hasan ,&nbsp;Noor K.N. Al-Waely ,&nbsp;Hadeel K. Aljobouri ,&nbsp;Hamid A. Jalab ,&nbsp;Rabha W. Ibrahim ,&nbsp;Farid Meziane","doi":"10.1016/j.bspc.2025.107546","DOIUrl":"10.1016/j.bspc.2025.107546","url":null,"abstract":"<div><div>The fifth most common cause of cancer-related deaths among women worldwide is breast cancer, which is the most common cancer among women globally. Early detection of breast cancer through regular screenings and awareness of symptoms can lead to better prognosis and more effective treatment options. Breast cancer is a diverse disease that comes in more than 20 varieties. It is generally divided into two categories based on histology: in-situ carcinoma and invasive (infiltrating) carcinoma. These categories are further divided into four subcategories based on the location of the tumor’s origin: invasive ductal carcinoma (IDC), invasive lobular carcinoma (ILC), lobular carcinoma in situ (LCIS), and ductal carcinoma in situ (DCIS). The aim of this study is to use medical image processing and machine learning to accurately diagnose invasive lobular carcinoma (ILC) and invasive and lobular carcinoma (IDC) of the breast. This study develops a novel hybrid feature extraction model to improve the diagnosis accuracy of breast cancer. Deep learning, kinetic, and enhanced Legendre polynomial features have been combined to create a hybrid feature model that enhances IDC and ILC discrimination. The proposed model consists of four stages: data collection, preprocessing, feature extraction, and classification. The publicly available DCE-MRI dataset was used in this study, and the results showed classification accuracy of 97.99% in combined post-contrast-1 model. Overall, the results demonstrated the benefits of the hybrid feature extraction model and the fact that this study is non-invasive, uses only medical image processing, and does not require biopsies to enhance treatments.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"104 ","pages":"Article 107546"},"PeriodicalIF":4.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154737","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":"CRAT: Advanced transformer-based deep learning algorithms in OCT image classification","authors":"Mingming Yang ,&nbsp;Junhui Du ,&nbsp;Ruichan Lv","doi":"10.1016/j.bspc.2025.107544","DOIUrl":"10.1016/j.bspc.2025.107544","url":null,"abstract":"<div><h3>Objectives</h3><div>The primary retinal optical coherence tomography (OCT) images usually have speckle noise, which may lower the diagnostic accuracy. In this research, we developed a transformer-based deep learning algorithm named Class-Re-Attention Transformers (CRAT), which presented advanced performance to quickly and accurately predict possible retinal diseases and further pathological changes from easily accessible OCT images.</div></div><div><h3>Materials and methods</h3><div>In this context, a comprehensive collection of 109,371 retinal OCT images was curated. This collection encompasses 24,562 images indicative of AMD, 37,494 images representative of CNV, 11,598 images associated with DME, 8,896 images depicting drusen, and 26,821 images classified as normal. Among them, 190 images are used as the external test set, and they are from Xi ’an Ninth Hospital. CRA can enhance the learning of deep features and the integration of classification information through the synergy of Re-attention mechanism and attention-like layer. The Re-attention block helps mitigate the risk of Attention collapse, while the class-attention Layer enhances the classification performance by specifically handling the relationship between Class labels and features. This enhancement facilitates efficient diagnosis, leveraging the extracted features.</div></div><div><h3>Result</h3><div>In order to assess the performance of CRAT, the accuracy, precision and recall rate, specificity, and F1 score were used as the main index, which provide a comprehensive performance evaluation of the proposed algorithm. The results demonstrated that the average accuracy, average precision, average recall, average specificity and average F1 score of the five eye categories (AMD, CNV, DME, Drusen and Normal) perform well on the internal test dataset, which reached 94.40%, 94.42%, 94.39%, 98.60%, and 97.76%, respectively. And the results on the external test dataset are 97.33%, 96.33%, 97.08%, 99.17%, and 98.74%, respectively.</div></div><div><h3>Conclusion</h3><div>CRA block can reduce the influence of image noise on diagnostic results. The proposed method can help ophthalmologists to quickly and accurately predict the likely occurrence of retinal diseases.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"104 ","pages":"Article 107544"},"PeriodicalIF":4.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154719","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":"Sleep apnea syndrome classification based om temporal ECG and SPO2 by using multimodal multichannel transfer module with squeeze and excitation","authors":"Mingfeng Jiang ,&nbsp;Lijun Lou ,&nbsp;Wei Zhang ,&nbsp;Xiaocheng Yang ,&nbsp;Zhefeng Wang ,&nbsp;Yongquan Wu ,&nbsp;Wei Ke ,&nbsp;Ling Xia","doi":"10.1016/j.bspc.2025.107589","DOIUrl":"10.1016/j.bspc.2025.107589","url":null,"abstract":"<div><div>Sleep Apnea Syndrome (SAS) is a prevalent sleep disorder characterized by intermittent pauses in breathing during sleep. If undiagnosed and untreated, SAS can have significant adverse effects on the human physiological system. Polysomnography (PSG) has been regarded as a gold-standard examination method for diagnosing sleep snoring (sleep apnea-hypopnea syndrome, OSAHS), but is often seen as inconvenient due to its complex operational requirements. This study introduces a novel method for SAS detection using temporal ECG and SPO2 signals via a CNN-RNN based Multimodal Multichannel Transfer Module with Squeeze and Excitation (MMTM-SE). Three hybrid CNN-RNN models were developed to extract features from ECG and SPO2 data. These extracted features were then fused through MMTM-SE structure, so as to enhance the correlation between different modalities and adaptively recalibrate channel features. The proposed method was validated by using the Apnea-ECG database across three deep learning networks. The experimental results show that the proposed approach outperformed existing methods, achieving a highest detection accuracy of 98.9%.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"104 ","pages":"Article 107589"},"PeriodicalIF":4.9,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154096","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}