Biomedical Engineering: Applications, Basis and Communications最新文献

{"title":"New corrected QT formula optimal for different heart rates: A comparative study","authors":"S. Karimulla, Arya Bhardwaj, V. Sangareddi, J. Sivaraman","doi":"10.4015/s1016237222500430","DOIUrl":"https://doi.org/10.4015/s1016237222500430","url":null,"abstract":"","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"58 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89459989","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":"Taylor-IIWO: Taylor Improved Invasive Weed Optimization enabled Deep Quantum Neural Network for brain tumor segmentation and classification","authors":"A. Ratna Raju, S. Pabboju, R. Rajeswara Rao","doi":"10.4015/s1016237222500442","DOIUrl":"https://doi.org/10.4015/s1016237222500442","url":null,"abstract":"","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"13 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83183581","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":"PERFORMANCE ENHANCEMENT OF DEEP NEURAL NETWORK BASED AUTOMATIC VOICE DISORDER DETECTION SYSTEM WITH DATA AUGMENTATION — DETECTION OF LEUKOPLAKIA: A CASE STUDY","authors":"D. K. Thennal, Vrinda V. Nair, R. Indudharan, D. Gopinath","doi":"10.4015/s1016237222500417","DOIUrl":"https://doi.org/10.4015/s1016237222500417","url":null,"abstract":"Laryngeal pathologies resulting in voice disorders are normally diagnosed using invasive methods such as rigid laryngoscopy, flexible nasopharyngo-laryngoscopy and stroboscopy, which are expensive, time-consuming and often inconvenient to patients. Automatic Voice Disorder Detection (AVDD) systems are used for non-invasive screening to give an indicative direction to the physician as a preliminary diagnosis. Deep neural networks, known for their superior discrimination capabilities, can be used for AVDD Systems, provided there are sufficient samples for training. The most popular datasets used for developing AVDD systems lack sufficient samples in several pathological categories. Leukoplakia — a premalignant lesion, which may progress to carcinoma unless detected early — is one such pathology. Data augmentation is a technique used in deep learning environments to increase the size of the training datasets which lack sufficient samples for effective data analysis and classification. This study aims at investigating the performance enhancement of a deep learning-based AVDD system through a novel time domain data augmentation technique named ‘TempAug’. This method segments each data sample into short voice segments, so as to get multiple data from each sample, thereby generating a larger database (augmented database) for training a deep learning model. A deep neural network model, Long Short-Term Memory (LSTM) with Short Term Fourier Transform (STFT) coefficients as input features for classification, was used in this study for the detection of the voice disorder Leukoplakia. A series of experiments were done to investigate the effect of data augmentation and to find the optimum duration for segmentation. Based on experimental results, a detection strategy was developed and evaluated using an AVDD system, which gave an accuracy of 81.25%. The percentage increase in accuracy was found to be 46.9% with respect to the accuracy obtained for unaugmented data.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"10 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78589073","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":"D-DAGNet: AN IMPROVED HYBRID DEEP NETWORK FOR AUTOMATED CLASSIFICATION OF GLAUCOMA FROM OCT IMAGES","authors":"A. P. Sunija, V. Gopi, Adithya K. Krishna","doi":"10.4015/s1016237222500429","DOIUrl":"https://doi.org/10.4015/s1016237222500429","url":null,"abstract":"The introduction of Optical Coherence Tomography (OCT) in ophthalmology has resulted in significant progress in the early detection of glaucoma. Traditional approaches to identifying retinal diseases comprise an analysis of medical history and manual assessment of retinal images. Manual diagnosis is time-consuming and requires considerable human expertise, without which, errors could be costly to human sight. The use of artificial intelligence such as machine learning techniques in image analysis has been gaining ground in recent years for accurate, fast and cost-effective diagnosis from retinal images. This work proposes a Directed Acyclic Graph (DAG) network that combines Depthwise Convolution (DC) to decisively recognize early-stage retinal glaucoma from OCT images. The proposed method leverages the benefits of both depthwise convolution and DAG. The Convolutional Neural Network (CNN) information obtained in the proposed architecture is processed as per the partial order over the nodes. The Grad-CAM method is adopted to quantify and visualize normal and glaucomatous OCT heatmaps to improve diagnostic interpretability. The experiments were performed on LFH_Glaucoma dataset composed of 1105 glaucoma and 1049 healthy OCT scans. The proposed faster hybrid Depthwise-Directed Acyclic Graph Network (D-DAGNet) achieved an accuracy of 0.9995, precision of 0.9989, recall of 1.0, F1-score of 0.9994 and AUC of 0.9995 with only 0.0047 M learnable parameters. Hybrid D-DAGNet enhances network training efficacy and significantly reduces learnable parameters required for identification of the features of interest. The proposed network overcomes the problems of overfitting and performance degradation due to accretion of layers in the deep network, and is thus useful for real-time identification of glaucoma features from retinal OCT images.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"17 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79223082","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":"DIAGNOSIS OF BRAIN TUMOR USING MULTISCALE CONVOLUTION NEURAL NETWORK","authors":"Homayoon Yektaei, Hanieh Yektaei, Yasaman Hoseyni","doi":"10.4015/s1016237222500405","DOIUrl":"https://doi.org/10.4015/s1016237222500405","url":null,"abstract":"Nowadays, the number of patients with brain tumors is steadily increasing, diagnosis and isolation of the tumor play an important role in the process of treatment and surgery. Due to the high error of manual segmentation of the tumor, algorithms that perform this operation with less error are of great importance. Convolutional neural networks have made great progress in the field of medical imaging. The use of imaging techniques and pattern recognition in the diagnosis and automatic determination of brain tumors by MRI imaging reduces errors, human error and speeds up detection. The artificial convolutional neural network (CNN) has been widely used in the diagnosis of intelligent cancers and has significantly reduced the error rate. Therefore, in this paper, we present a new method using a combination of convolutional and multi-scale artificial neural network that has significantly increased the accuracy of tumor diagnosis. This study presents a multidisciplinary convolution neural network (MCNN) approach to classifying tumors that can be used as an important part of automated diagnosis systems for accurate cancer diagnosis. Based on the MCNN structure, which presents the MRI image to several deep convolutional neural networks of varying sizes and resolutions, the stage of extracting classical hand-made features is avoided. This approach proposes better classification rates than the classical methods. This study uses a multi-scale convolution technique to achieve a detection accuracy of 95/4%, which shows the efficiency of the proposed method.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"71 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74354074","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":"EFFICIENT RETINAL IMAGE ENHANCEMENT USING MORPHOLOGICAL OPERATIONS","authors":"Ashanand, M. Kaur","doi":"10.4015/s1016237222500338","DOIUrl":"https://doi.org/10.4015/s1016237222500338","url":null,"abstract":"Manual analysis of retinal images is a complicated and time-consuming task for ophthalmologists. Retinal images are susceptible to non-uniform illumination, poor contrast, transmission error, and noise problems. For the detection of retinal abnormalities, an efficient technique is required that can identify the presence of retinal complications. This paper proposes a methodology to enhance retinal images that use morphological operations to improve the contrast and bring out the fine details in the suspicious region. The enhancement plays a vital role in detecting abnormalities in the retinal images. Luminance gain metric ([Formula: see text] is obtained from Gamma correction on luminous channel of [Formula: see text]*[Formula: see text]*[Formula: see text] (hue, saturation, and value) color model of retinal image to improve luminosity. The efficiency and strength of the proposed methodology are evaluated using the performance evaluation parameters peak signal to noise ratio (PSNR), mean square error (MSE), mean absolute error (MAE), feature structural similarity index metric (FSIM), structural similarity index metric (SSIM), spectral residual index metric (SRSIM), Reyligh feature similarity index metric (RFSIM), absolute mean brightness error (AMBE), root mean square error (RMSE), image quality index (IQI), and visual similarity index (VSI). It has been revealed from the results and statistical analysis using the Friedman test that the proposed method outperforms existing state-of-the-art enhancement techniques.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"19 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82713732","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":"NOVEL FINDINGS ON CARDIORESPIRATORY ROLE SWAPPING BY ASSESSING NONLINEAR DYNAMICAL INTERACTIONS","authors":"Pei-Chen Lo","doi":"10.4015/s1016237222500399","DOIUrl":"https://doi.org/10.4015/s1016237222500399","url":null,"abstract":"This paper reports the innovative results of applying nonlinear dynamical theory to heart-rate (HR) and respiratory (RP) sequences to explore Yin–Yang (passive–active) interacting phenomenon of cardiorespiratory function. Organ systems inside human body interact mutually in the manner of swapping the active–passive role, according to Yin–Yang theory in traditional Chinese medicine. Yang as an active source rules over Yin as a passive load. Nonlinear inter-driven (NLID) factor, [Formula: see text], based on recurrence analysis is proposed in this study. NLID factor characterizes the nonlinear interactions between two nonlinear dynamical systems, cardiac and respiratory systems. NLID factor is computed by quantifying the HR and RP recurrent plots constructed from phase trajectories based on embedding theorem and phase-space reconstruction. Heterogeneous patterns of HR and RP recurrence plots in continuous-attention task reflect highly unpredictable system dynamics. Heterogeneous HR recurrence patterns are surprisingly associated with the nearly homogeneous RP recurrence plot at resting state, indicating rather weak cardiorespiratory interaction. HR recurrence in Zen exhibits notable regularity in spite of the irregular RP recurrence behaviors. NLID factor reveals prominent Yang energy delivered by RP system that dominates over the cardiac system in Zen-meditation group. Such findings draw our attention to the possibly extraordinary self-regularity power of heart (and autonomic nervous system) in the Zen-meditation state.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"32 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87047200","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":"AN EXTRACTION AND CLASSIFICATION BASED ON EMD AND LSSVM OF EPILEPTIC EEG","authors":"Xia Zhang, C. Yan","doi":"10.4015/s101623722250034x","DOIUrl":"https://doi.org/10.4015/s101623722250034x","url":null,"abstract":"The epilepsy EEG signal with nonstationary and nonlinear characteristics is a typical electroencephalographic (EEG), which have been solved by the proposed method of using least squares support vector machine (LSSVM) as the classifiers and empirical mode decomposition (EMD) as the feature extraction methods to achieve epileptic seizure detection with signal analysis and processing. In this proposed method, EMD is used to select three intrinsic mode functions (IMF) with high correlation to replace the original signals, which has been employed to solve the nonlinear and nonstationary problems in feature extraction and infusion, and then, the feature can be employed to feed to the recognition engine named LSSVM, and its parameters is optimized by particle swarm optimization (PSO). The study uses publicly available EEG database from the University of Bonn (UoB), and there are 7960 EEG segments in the complete dataset, among which are nine recognition problems marked as Z-N, Z-F, Z-S, O-N, O-F,O-S N-S F-S and Z-O-N-F-S, the average classification accuracy of Z-N, Z-F, Z-S, O-N, O-F, O-S, N-S and F-S can be generally obtained as highly as 90%, the Z-O-N-F-S training set and test set classification accuracy are 98.8% and 88%, which had been used to verify the effectiveness and robustness of this proposed method on feature extraction, To the best of our knowledge, the excellent performance of the proposed method has shown that this method can be employed to track the patient’s healthy state and monitor the moment of epilepsy seizure.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"11 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77739186","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":"EFFECTIVE SEMANTIC FEATURES TO IMPROVE RETRIEVAL OF LUNG NODULES IN CT SCAN IMAGES","authors":"M. Alizade, A. H. Foruzan","doi":"10.4015/s1016237222500326","DOIUrl":"https://doi.org/10.4015/s1016237222500326","url":null,"abstract":"Successful treatment of a patient depends on the accurate determination of the disease type. The advent of big data facilitates the retrieving of medical images and helps physicians in reliable diagnoses using content-based medical image retrieval systems (CBMIR). They consist of a feature extraction module and a distance metric. The extracted textural or deep-based features identify different types of diseases. In the proposed retrieval algorithm, we use the gray level cooccurrence matrix as the common textural characteristics and integrate them with semantic attributes. The semantic features are the geometric characteristics of the tumor that a radiologist employ to distinguish between benign and malignant tumors. These high-level attributes include the Euler number, margin smoothness, and the aspect ratio of the lesion’s size. We used the Minkowski distance measure for computing the similarity of images and applied the proposed algorithm to 200 CT-scan data containing lung lesions obtained from the LIDC database. The types of lesions were benign and malignant. Employing an ablation study, we proved the effectiveness of the semantic feature. The precision of the retrieval results is 93% which is promising compared to recent studies. In the future, we plan to define other kinds of semantic attributes to distinguish stages 1–5 of lung tumors as well.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"9 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77059104","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":"DESIGN AND IMPLEMENTATION OF A PORTABLE AUTOMATED AUDIOMETER FOR HEARING CLASSIFICATION USING MACHINE LEARNING APPROACHES","authors":"V. Raja Sankari, U. Snekhalatha, T. Rajalakshmi","doi":"10.4015/s1016237222500351","DOIUrl":"https://doi.org/10.4015/s1016237222500351","url":null,"abstract":"Audiometric tests can identify the hearing loss at specific frequencies using the audiogram. The aim and objectives of the study were (i) to develop an automated audiometer for self-diagnosing the hearing ability of the patient; (ii) to extract the features from the acoustic signals and to classify the normal and profound hearing loss patients using different machine learning algorithms; (iii) to validate the hearing loss classification using six-frequency average (6-FA) method based on simple linear regression analysis and machine learning algorithms. The study is conducted among 150 patients, including 75 patients with normal hearing ability and 75 patients with profound hearing loss. The total population of 150 underwent audiometric test both in the soundproof audiometric room and in the normal field environment. Based on the patient response, the intensity and frequency are changed automatically, and the audiogram is plotted by the principle of Artificial Neural Network learning procedures. The overall accuracy produced by classification of normal and profound hearing loss patients using Support Vector Machine (SVM), k-Nearest Neighbor classifier, and Naïve Bayes classifier is 97%, 96%, and 95%, respectively. The results indicated that the SVM classifier outperforms the other two classifiers well. The preliminary audiometric test can be performed remotely and then consulted with an audiologist. Thus, the patient could operate the developed prototype independently and get a consultation from trained medical personnel.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"68 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85981314","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}