Biomedical Engineering: Applications, Basis and Communications最新文献

{"title":"ANALYSIS OF HRV FOR POSTURAL CHANGE OF YOUNG ADULTS USING SIGNAL PROCESSING METHODS","authors":"Ankit Soni, K. Rawal","doi":"10.4015/s1016237222500284","DOIUrl":"https://doi.org/10.4015/s1016237222500284","url":null,"abstract":"Heart rate variability (HRV) is a fundamental physiological marker for assessing the autonomic nervous system’s (ANS) reaction. The response of the ANS is influenced by a variety of physical activities, i.e. body postural change. This paper aims to analyze the effect of physical activities such as postural change on HRV. To study this, a dataset of 56 subjects electrocardiogram (ECG) was self-recorded in two body postures (i) Supine and (ii) Standing. For the acquisition of ECG, the BIOPAC[Formula: see text]MP36 system has been used at a 500[Formula: see text]Hz sampling frequency. Further, HRV signals of each subject from recorded ECG have been extracted and selected linear, and nonlinear techniques have been used to determine the effect of postural shift on it. Further, the spearman rank correlation coefficient has been evaluated between the calculated parameters to determine the correlation between linear and nonlinear parameters. The obtained results indicate that the HRV is at a higher scale in the supine posture, while it is at a lower scale when the posture has been changed from supine to standing. The change that occurred in the response of HRV with the postural change indicates that the sympathetic activation of ANS is increased in the standing body posture.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"22 8-9 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80266755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COLORECTAL POLYP DETECTION USING IMAGE ENHANCEMENT AND SCALED YOLOv4 ALGORITHM","authors":"J. Nisha, V. Gopi, P. Palanisamy","doi":"10.4015/s1016237222500260","DOIUrl":"https://doi.org/10.4015/s1016237222500260","url":null,"abstract":"Colorectal cancer (CRC) is the common cancer-related cause of death globally. It is now the third leading cause of cancer-related mortality worldwide. As the number of instances of colorectal polyps rises, it is more important than ever to identify and diagnose them early. Object detection models have recently become popular for extracting highly representative features. Colonoscopy is shown to be a useful diagnostic procedure for examining anomalies in the digestive system’s bottom half. This research presents a novel image-enhancing approach followed by a Scaled YOLOv4 Network for the early diagnosis of polyps, lowering the high risk of CRC therapy. The proposed network is trained using the CVC ClinicDB and the CVC ColonDB and the Etis Larib database are used for testing. On the CVC ColonDB database, the performance metrics are precision (95.13%), recall (74.92%), F1-score (83.19%), and F2-score (89.89%). On the ETIS Larib database, the performance metrics are precision (94.30%), recall (77.30%), F1-score (84.90%), and F2-score (80.20%). On both the databases, the suggested methodology outperforms the present one in terms of F1-score, F2-score, and precision compared to the futuristic method. The proposed Yolo object identification model provides an accurate polyp detection strategy in a real-time application.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"35 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81458793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MULTI-SCALE DIRECTED ACYCLIC GRAPH-CNN FOR AUTOMATED CLASSIFICATION OF DIABETIC RETINOPATHY FROM OCT IMAGES","authors":"A. P. Sunija, Adithya K. Krishna, V. Gopi, P. Palanisamy","doi":"10.4015/s1016237222500259","DOIUrl":"https://doi.org/10.4015/s1016237222500259","url":null,"abstract":"Diabetic Retinopathy (DR) is the principal cause of vision loss that interrupts the regular interaction of vascular, neural, and retinal constituents leading to impaired neuronal function and retinal abnormalities. Diagnosis of DR from Optical Coherence Tomography (OCT) image is difficult and time-consuming because several small features must be identified and graded, which results in a strenuous diagnosis when integrated with the complexity of the grading system. This study focuses on classifying DR from normal Spectral Domain-OCT (SD-OCT) images using the Directed Acyclic Graph (DAG) network without any pre-processing techniques. The proposed DAG-CNN model comprises 16 convolutional blocks, which learns multi-scale features automatically from multiple layers in the convolutional network and combines them effectively for the DR and normal prediction. The proposed model is tested on the public OCTID_DR and private LFH_DR SD-OCT databases containing DR and healthy OCT images. The model achieved an accuracy, precision, recall, F1-score, and AUC on OCTID_DR database of 0.9841, 0.9727, 0.9818, 0.9772, and 0.9836, respectively; and on LFH_DR database the respective values are 0.9988, 1, 0.9976, 0.9988, and 0.9988 with only 0.1569 Million of learnable parameters. This method significantly reduces the number of learnable parameters and the model’s computational complexity in terms of memory required and FLoating point OPerations (FLOPs). Guided Gradient-weighted Class Activation Mapping (Grad-CAM) is performed to highlight the regions of SD-OCT images that contribute to the decision of the classifier. Our model significantly surpasses the accuracy of the existing models with lower resource consumption and higher real-time performance.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"21 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86002640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AUTOMATED CLASSIFICATION OF AUTISM SPECTRUM DISORDER USING EEG SIGNALS AND CONVOLUTIONAL NEURAL NETWORKS","authors":"Qaysar Mohi ud Din, A. Jayanthy","doi":"10.4015/s101623722250020x","DOIUrl":"https://doi.org/10.4015/s101623722250020x","url":null,"abstract":"Children suffering from Autism Spectrum Disorder (ASD) have impaired social communication, interaction and restricted and repetitive behaviors. ASD is caused by abnormal brain developments which give rise to the behavioral characteristics associated with ASD. The clinical diagnosis of ASD is performed on the basis of behavioral assessment and it causes a time delay in early intervention, as there is a time gap between abnormal brain developments and associated behavioral characteristics. Electroencephalography (EEG) is a technique which measures the electrical activity produced by the brain and it has been used to detect several neurological disorders. Studies have shown that there is a variation in the EEG signals of a normal subject and EEG signals of ASD subjects. In this study, we obtained scalograms of EEG signals by using Continuous Wavelet Transform (CWT). Pre-trained deep Convolutional Neural Networks (CNNs) such as GoogLeNet, AlexNet, MobileNet and SqueezeNet were used for extracting the features from scalograms and classification of obtained scalograms from EEG signals of normal and ASD subjects. We also used Support Vector Machine (SVM) algorithm and Relevance Vector Machine (RVM) for classification of the features extracted by the deep CNNs. The GoogLeNet, AlexNet, MobileNet and SqueezeNet deep CNNs achieved a validation accuracy of 75%, 75.84%, 79.45% and 82.98% in classifying the scalograms generated from EEG signals. The SVM achieved an accuracy of 71.6%, 74.76%, 70.70% and 81.47% using GoogleNet, Mobilenet, AlexNet and SqueezeNet for scalogram feature extraction. The RVM achieved an accuracy of 65.5%, 69.9%, 65.3% and 72.59% when used for classification using the features generated from GoogLeNet, AlexNet, MobileNet and SqueezeNet.The SqueezeNet deep CNN performed better than GoogLeNet, AlexNet and MobileNet for classification of the EEG scalograms. The feature extraction using SqueezeNet also resulted in better classification accuracy obtained by SVM and RVM. The results indicate that pre-trained models can be used for classifying the ASD using scalograms of the EEG signals.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"9 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75293897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COMPUTER-AIDED THERAPY USING AUTOMATIC SPEECH RECOGNITION TECHNIQUE FOR DELAYED LANGUAGE DEVELOPMENT CHILDREN","authors":"Hala S. Abuelmakarem, S. Fawzi, A. Quriba, Ahmed Elbialy, A. Kandil","doi":"10.4015/s1016237222500235","DOIUrl":"https://doi.org/10.4015/s1016237222500235","url":null,"abstract":"Objectives: This study aims to develop a computer-aided therapy (CAT) application to help children who suffer from delayed language development (DLD) improve their language, especially during the COVID-19 pandemic. Methods: The implemented system teaches the children their body parts using the Egyptian dialect. Two datasets were collected from healthy children (2800 words) and unhealthy children (236 words) who have DLD at the clinic. The model is implemented using a speaker-independent isolated word recognizer based on a discrete-observation hidden Markov model (DHMM) classifier. After the speech signal preprocessing step, K-means algorithm generated a codebook to cluster the speech segments. This task was completed under the MATLAB environment. The graphical user interface was implemented successfully under the C# umbrella to complete the CAT application task. The system was tested on healthy and DLD children. Also, in a small clinical trial, five children who have DLD tested the program in an actual trial to monitor their pronunciation progress during therapeutic sessions. Results: The max recognition rate was 95.25% for the healthy children dataset, while 93.82% for the DLD dataset. Conclusion: DHMM was implemented successfully using nine and five states based on different codebook sizes (160, 200). The implemented system achieved a high recognition rate using both datasets. The children enjoyed using the application because it was interactive. Children who have DLD can use speech recognition applications.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"397 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80299744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ACCURATE CLASSIFICATION OF MOTOR UNIT DISCHARGES FROM DYNAMIC SURFACE EMG SIGNALS","authors":"Jinbao He, Zaifei Luo, Qinbo Hu","doi":"10.4015/s1016237222500181","DOIUrl":"https://doi.org/10.4015/s1016237222500181","url":null,"abstract":"In order to correctly identify the motor unit action potential trains (MUAPTs) in estimated discharges from dynamic surface electromyogram (EMG), an approach for accurate classification of motor unit (MU) discharges is presented. First, the estimated discharges are obtained manually, then the estimated discharges are classified as MUAPTs based on the MU location, which combines the MU depth with the MU plane position. During verification in dynamic muscle contractions, the advanced tripole model is introduced. At SNRs of 10, 20 and 30[Formula: see text]dB, the MUAPTs were identified with true positive rate (TPR) of 91.1[Formula: see text]5.5%, 95.2[Formula: see text]3.7% and 96.1[Formula: see text]2.9%. The results also show that the MU location can be used as a simple method for identifying MUAPT from estimated discharges and selecting reliably decomposed discharges. The newly introduced method is a robust and reliable indicator of MUAPT identification accuracy.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"306 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73161879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A RISK CLASSIFICATION SYSTEM FOR ELDERLY FALLS USING SUPPORT VECTOR MACHINE","authors":"Chi-Chih Wu, C. Chiu, Su-Yi Fu","doi":"10.4015/s101623722250017x","DOIUrl":"https://doi.org/10.4015/s101623722250017x","url":null,"abstract":"Falls are a multi-factor problem that poses a serious risk to the elderly. Approximately, 60% of falls are caused by a number of known factors, including the environment, which accounts for approximately 25–45% of falling risk. Most of the remainder results from a lack of personal balance control. Falling can cause long-term disabilities in the elderly, sometimes resulting in lower quality of life, and is also associated with increased medical expenses and personal care costs. In this study, we developed a falling assessment system to evaluate and classify individuals into four graded falling risk groups. During the test, all subjects were required to wear a self-developed dynamic measurement system and to perform two balance tests: a “Timed Up and Go Test” and a “30-Second Chair Stand Test.” We obtained 29 characteristic parameters from the data recorded during these tests. Next, we performed group classification. Eigenvalues were normalized, and a principal component analysis (PCA) was performed. After identifying informative characteristic parameters, support vector machine (SVM) was used to classify individuals as members of one of the four falling risk groups. These included low-, moderate-, high-, and extreme-risk groups. Using unreduced data of the 29 characteristic parameters extracted from the two balance tests, the accuracy of the SVM classification in allocating individuals to the correct group was 97.5%. After PCA, the 29 characteristic parameters were reduced to eight principal components, and the SVM classification method using these eight principal components was 93.25%.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"39 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83626341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CALCIFICATION CLUSTERS AND LESIONS ANALYSIS IN MAMMOGRAM USING MULTI-ARCHITECTURE DEEP LEARNING ALGORITHMS","authors":"H. Tsai, Chia-Shin Wei, Ya-Chu Hsieh, I-Miao Chen, Pin-Yu Yeh, Darren Shih, Chiun-Li Chin","doi":"10.4015/s1016237222500223","DOIUrl":"https://doi.org/10.4015/s1016237222500223","url":null,"abstract":"Today, radiologists observe a mammogram to determine whether breast tissue is normal. However, calcifications on the mammogram are so small that sometimes radiologists cannot locate them without a magnified observation to make a judgment. If clusters formed by malignant calcifications are found, the patient should undergo a needle localization surgical biopsy to determine whether the calcification cluster is benign or malignant. However, a needle localization surgical biopsy is an invasive examination. This invasive examination leaves scars, causes pain, and makes the patient feel uncomfortable and unwilling to receive an immediate biopsy, resulting in a delay in treatment time. The researcher cooperated with a medical radiologist to analyze calcification clusters and lesions, employing a mammogram using a multi-architecture deep learning algorithm to solve these problems. The features of the location of the cluster and its benign or malignant status are collected from the needle localization surgical biopsy images and medical order and are used as the target training data in this study. This study adopts the steps of a radiologist examination. First, VGG16 is used to locate calcification clusters on the mammogram, and then the Mask R-CNN model is used to find micro-calcifications in the cluster to remove background interference. Finally, an Inception V3 model is used to analyze whether the calcification cluster is benign or malignant. The prediction precision rates of VGG16, Mask R-CNN, and Inception V3 in this study are 93.63%, 99.76%, and 88.89%, respectively, proving that they can effectively assist radiologists and help patients avoid undergoing a needle localization surgical biopsy.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"127 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85716361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CLASSIFICATION OF INFORMATIVE FRAMES IN COLONOSCOPY VIDEO BASED ON IMAGE ENHANCEMENT AND PHOG FEATURE EXTRACTION","authors":"J. Nisha, V. Gopi, P. Palanisamy","doi":"10.4015/s1016237222500156","DOIUrl":"https://doi.org/10.4015/s1016237222500156","url":null,"abstract":"Colonoscopy allows doctors to check the abnormalities in the intestinal tract without any surgical operations. The major problem in the Computer-Aided Diagnosis (CAD) of colonoscopy images is the low illumination condition of the images. This study aims to provide an image enhancement method and feature extraction and classification techniques for detecting polyps in colonoscopy images. We propose a novel image enhancement method with a Pyramid Histogram of Oriented Gradients (PHOG) feature extractor to detect polyps in the colonoscopy images. The approach is evaluated across different classifiers, such as Multi-Layer Perceptron (MLP), Adaboost, Support Vector Machine (SVM), and Random Forest. The proposed method has been trained using the publicly available databases CVC ClinicDB and tested in ETIS Larib and CVC ColonDB. The proposed approach outperformed the existing state-of-the-art methods on both databases. The reliability of the classifiers performance was examined by comparing their F1 score, precision, F2 score, recall, and accuracy. PHOG with Random Forest classifier outperformed the existing methods in terms of recall of 97.95%, precision 98.46%, F1 score 98.20%, F2 score of 98.00%, and accuracy of 98.21% in the CVC-ColonDB. In the ETIS-LARIB dataset it attained a recall value of 96.83%, precision 98.65%, F1 score 97.73%, F2 score 98.59%, and accuracy of 97.75%. We observed that the proposed image enhancement method with PHOG feature extraction and the Random Forest classifier will help doctors to evaluate and analyze anomalies from colonoscopy data and make decisions quickly.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"22 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78845815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A CLUSTERING-BASED FUSION SYSTEM FOR BLASTOMERE LOCALIZATION","authors":"Shimaa M. Khder, Eman A. H. Mohamed, I. Yassine","doi":"10.4015/s1016237222500211","DOIUrl":"https://doi.org/10.4015/s1016237222500211","url":null,"abstract":"Microscopic digital image processing paves the way for study and evaluation of blastomere identification and localization as a preprocessing step for the embryos selection for the In VitroFertilization (IVF) transfer. Computer vision aims at developing automated image system to localize and grade blastomeres before injection. In this paper, we propose a clustering-based system that supports the localization and counting of blastomeres. The dataset, employed in this study, is formed of 50 Images collected at Assisted Reproduction Technology (ART) Unit, International Islamic Center for Population Studies and Research, Al-Azhar University, Egypt. The proposed system is formed of 2 modules named preprocessing and segmentation modules, where different algorithms were investigated for each module. The preprocessing module includes Image denoising and enhancement tasks. Whereas the edge enhancement investigates the performance of Ostu’s thresholding, Canny and Sobel edge detection techniques, while employing Circular Hough Transform (CHT) for the segmentation task. A fusion-based algorithm was then employed to merge the segmented Blastomeres of the previously defined systems to boost the performance through integrated blastomeres, as well the confidence in localization. The fusion-based algorithm showed very promising results reaching an average Precision, sensitivity, and Overall Quality of 87.9%, 92.9%, and 82.3%, respectively.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"36 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89044928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}