Pub Date : 2024-06-08DOI: 10.4015/s1016237224500212
Deepjyoti Kalita, Shiyona Dash, Khalid B. Mirza
The utilization of Electroencephalogram (EEG) as a non-invasive tool to investigate neurological disorders, particularly epilepsy, by capturing pathological biosignal markers indicative of seizures, sets the backdrop for this research endeavor. While previous studies have harnessed deep learning techniques for seizure detection, a pressing need persists for a resource-efficient model that demands minimal training data and time yet upholds commendable specificity and sensitivity. In response to this gap, we introduce an innovative deep Gated Recurrent Unit (GRU)– Long Short-Term Memory (LSTM) network, coined as EpiNET, purposefully crafted for the prediction of epileptic seizures using EEG data. A distinctive feature of EpiNET is its integration of statistical, spectral, and temporal features, chosen for their computational simplicity, thereby enhancing the model’s efficiency. The model is meticulously trained and validated on diverse patient datasets sourced from the CHB-MIT Scalp EEG database, outshining existing deep learning networks regarding seizure prediction accuracy. EpiNET boasts remarkable metrics, with reported sensitivity, accuracy, and specificity values standing at 92.54 ±?0.41%, 96.15 ±?0.45%, and 97.73 ±?0.58%, respectively. This underscores the efficacy of EpiNET while upholding a lean model structure, addressing concerns regarding computational efficiency. A ground-breaking aspect of this study is the introduction of a GRU-LSTM-based deep learning model capable of predicting epileptic seizures at least 2 h (120 min) in advance, marking a significant stride towards timely intervention and heightened patient care. In summary, this research not only advances the field of neurological disorder prediction but also underscores the paramount importance of resource efficiency in model development.
{"title":"EPINET: AN OPTIMIZED, RESOURCE EFFICIENT DEEP GRU-LSTM NETWORK FOR EPILEPTIC SEIZURE PREDICTION","authors":"Deepjyoti Kalita, Shiyona Dash, Khalid B. Mirza","doi":"10.4015/s1016237224500212","DOIUrl":"https://doi.org/10.4015/s1016237224500212","url":null,"abstract":"The utilization of Electroencephalogram (EEG) as a non-invasive tool to investigate neurological disorders, particularly epilepsy, by capturing pathological biosignal markers indicative of seizures, sets the backdrop for this research endeavor. While previous studies have harnessed deep learning techniques for seizure detection, a pressing need persists for a resource-efficient model that demands minimal training data and time yet upholds commendable specificity and sensitivity. In response to this gap, we introduce an innovative deep Gated Recurrent Unit (GRU)– Long Short-Term Memory (LSTM) network, coined as EpiNET, purposefully crafted for the prediction of epileptic seizures using EEG data. A distinctive feature of EpiNET is its integration of statistical, spectral, and temporal features, chosen for their computational simplicity, thereby enhancing the model’s efficiency. The model is meticulously trained and validated on diverse patient datasets sourced from the CHB-MIT Scalp EEG database, outshining existing deep learning networks regarding seizure prediction accuracy. EpiNET boasts remarkable metrics, with reported sensitivity, accuracy, and specificity values standing at 92.54 ±?0.41%, 96.15 ±?0.45%, and 97.73 ±?0.58%, respectively. This underscores the efficacy of EpiNET while upholding a lean model structure, addressing concerns regarding computational efficiency. A ground-breaking aspect of this study is the introduction of a GRU-LSTM-based deep learning model capable of predicting epileptic seizures at least 2 h (120 min) in advance, marking a significant stride towards timely intervention and heightened patient care. In summary, this research not only advances the field of neurological disorder prediction but also underscores the paramount importance of resource efficiency in model development.","PeriodicalId":503224,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368861","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}
Pub Date : 2024-06-01DOI: 10.4015/s1016237224500157
Mohamed A. Naser, Wael A. Moeaz, M. T. El-Wakad, Mohamed S. Abdo
In the area of tissue engineering, single screw extrusion (SSE) has gained attention due to its versatility and efficiency in fabricating polymer-based scaffolds. Furthermore, advancements such as the implementation of extrusion techniques and the integration of bioactive agents have significantly expanded the capabilities of SSE. This study aims to investigate the configuration of a custom-designed plastic extrusion for tissue engineering, highlighting its potential in fabricating suture technology for various regenerative biomedical applications. Furthermore, the challenges and future perspectives in SSE technology are discussed, with a focus on the need for additional research to optimize processing parameters, enhance structure bioactivity, and facilitate clinical usage. SSE provides precise regulation of structure morphology, mechanical properties, and porosity, which are critical factors that influence cell behavior and tissue regeneration. Overall, SSE holds great promise as a scalable and cost-effective manufacturing technique for producing polymer-based structures with tailored properties, advancing the field of tissue engineering towards effective clinical solutions. The paper provides a comprehensive overview of a filament extruder production machine that is capable of manufacturing high-quality filament sutures (FS) using thermoplastic materials, specifically bio-protein derived from human serum albumin. The main focus of the paper is to explain the design and operation principles of the filament extruder. The extruder is equipped with a die that can measure a range starting from 2.5 mm and going down to smaller scales. This allows for the extrusion of filaments with a diameter as small as 1.75 mm. Although the design of the extrusion apparatus closely resembles that of commercially available machines, the focus here is on its adaptability and cost-effectiveness for laboratory-scale production. Overall, the research contributes to advancing the understanding of extrusion processing technologies in the context of biomedical applications, with a specific focus on utilizing human serum albumin-derived thermoplastics for manufacturing FS.
{"title":"DESIGN A SINGLE SCREW EXTRUDER FOR POLYMER-BASED TISSUE ENGINEERING","authors":"Mohamed A. Naser, Wael A. Moeaz, M. T. El-Wakad, Mohamed S. Abdo","doi":"10.4015/s1016237224500157","DOIUrl":"https://doi.org/10.4015/s1016237224500157","url":null,"abstract":"In the area of tissue engineering, single screw extrusion (SSE) has gained attention due to its versatility and efficiency in fabricating polymer-based scaffolds. Furthermore, advancements such as the implementation of extrusion techniques and the integration of bioactive agents have significantly expanded the capabilities of SSE. This study aims to investigate the configuration of a custom-designed plastic extrusion for tissue engineering, highlighting its potential in fabricating suture technology for various regenerative biomedical applications. Furthermore, the challenges and future perspectives in SSE technology are discussed, with a focus on the need for additional research to optimize processing parameters, enhance structure bioactivity, and facilitate clinical usage. SSE provides precise regulation of structure morphology, mechanical properties, and porosity, which are critical factors that influence cell behavior and tissue regeneration. Overall, SSE holds great promise as a scalable and cost-effective manufacturing technique for producing polymer-based structures with tailored properties, advancing the field of tissue engineering towards effective clinical solutions. The paper provides a comprehensive overview of a filament extruder production machine that is capable of manufacturing high-quality filament sutures (FS) using thermoplastic materials, specifically bio-protein derived from human serum albumin. The main focus of the paper is to explain the design and operation principles of the filament extruder. The extruder is equipped with a die that can measure a range starting from 2.5 mm and going down to smaller scales. This allows for the extrusion of filaments with a diameter as small as 1.75 mm. Although the design of the extrusion apparatus closely resembles that of commercially available machines, the focus here is on its adaptability and cost-effectiveness for laboratory-scale production. Overall, the research contributes to advancing the understanding of extrusion processing technologies in the context of biomedical applications, with a specific focus on utilizing human serum albumin-derived thermoplastics for manufacturing FS.","PeriodicalId":503224,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"137 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141281531","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}
Pub Date : 2024-02-13DOI: 10.4015/s1016237224500078
Seyedeh Maryam Zareh Moayedi, A. Rezai, Seyedeh Shahrbanoo Falahieh Hamidpour
In this paper, an intelligent method is developed for improving the performance of the Computer-Aided Detection (CAD) system. The research objective is to improve the performance of the CAD system in Breast Cancer (BC) detection with high accuracy using thermal images. The research strategy is efficient using feature extraction, feature selection, classification and artificial intelligence methods. In the developed method, the features in the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) are extracted from images. The features are selected using the firefly feature selection algorithm. These selected features are observed to be relevant for the abnormality detection in healthy and unhealthy breasts. The [Formula: see text]-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision-Tree (D-Tree) classifiers are then applied to these features for the detection of malignancy in the breast. The breast thermograms of 200 subjects available at the Database for Mastology Research for breast research using InfraRed images, DMR-IR database, are considered for evaluation of our intelligent method. The results demonstrate that the accuracy is 98.8%, 81.5%, and 95%, the sensitivity is 99%, 83.15%, and 95.91%, and the specificity is 98.2%, 80%, and 94.11% when using SVM, kNN, and D-Tree classifier algorithm, respectively. This reveals the effectiveness of our intelligent method to improve the accuracy of the CAD system in the BC detection.
{"title":"TOWARD EFFECTIVE BREAST CANCER DETECTION IN THERMAL IMAGES USING EFFICIENT FEATURE SELECTION ALGORITHM AND FEATURE EXTRACTION METHODS","authors":"Seyedeh Maryam Zareh Moayedi, A. Rezai, Seyedeh Shahrbanoo Falahieh Hamidpour","doi":"10.4015/s1016237224500078","DOIUrl":"https://doi.org/10.4015/s1016237224500078","url":null,"abstract":"In this paper, an intelligent method is developed for improving the performance of the Computer-Aided Detection (CAD) system. The research objective is to improve the performance of the CAD system in Breast Cancer (BC) detection with high accuracy using thermal images. The research strategy is efficient using feature extraction, feature selection, classification and artificial intelligence methods. In the developed method, the features in the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) are extracted from images. The features are selected using the firefly feature selection algorithm. These selected features are observed to be relevant for the abnormality detection in healthy and unhealthy breasts. The [Formula: see text]-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision-Tree (D-Tree) classifiers are then applied to these features for the detection of malignancy in the breast. The breast thermograms of 200 subjects available at the Database for Mastology Research for breast research using InfraRed images, DMR-IR database, are considered for evaluation of our intelligent method. The results demonstrate that the accuracy is 98.8%, 81.5%, and 95%, the sensitivity is 99%, 83.15%, and 95.91%, and the specificity is 98.2%, 80%, and 94.11% when using SVM, kNN, and D-Tree classifier algorithm, respectively. This reveals the effectiveness of our intelligent method to improve the accuracy of the CAD system in the BC detection.","PeriodicalId":503224,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"61 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139841662","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}
Pub Date : 2024-02-13DOI: 10.4015/s1016237224500078
Seyedeh Maryam Zareh Moayedi, A. Rezai, Seyedeh Shahrbanoo Falahieh Hamidpour
In this paper, an intelligent method is developed for improving the performance of the Computer-Aided Detection (CAD) system. The research objective is to improve the performance of the CAD system in Breast Cancer (BC) detection with high accuracy using thermal images. The research strategy is efficient using feature extraction, feature selection, classification and artificial intelligence methods. In the developed method, the features in the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) are extracted from images. The features are selected using the firefly feature selection algorithm. These selected features are observed to be relevant for the abnormality detection in healthy and unhealthy breasts. The [Formula: see text]-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision-Tree (D-Tree) classifiers are then applied to these features for the detection of malignancy in the breast. The breast thermograms of 200 subjects available at the Database for Mastology Research for breast research using InfraRed images, DMR-IR database, are considered for evaluation of our intelligent method. The results demonstrate that the accuracy is 98.8%, 81.5%, and 95%, the sensitivity is 99%, 83.15%, and 95.91%, and the specificity is 98.2%, 80%, and 94.11% when using SVM, kNN, and D-Tree classifier algorithm, respectively. This reveals the effectiveness of our intelligent method to improve the accuracy of the CAD system in the BC detection.
{"title":"TOWARD EFFECTIVE BREAST CANCER DETECTION IN THERMAL IMAGES USING EFFICIENT FEATURE SELECTION ALGORITHM AND FEATURE EXTRACTION METHODS","authors":"Seyedeh Maryam Zareh Moayedi, A. Rezai, Seyedeh Shahrbanoo Falahieh Hamidpour","doi":"10.4015/s1016237224500078","DOIUrl":"https://doi.org/10.4015/s1016237224500078","url":null,"abstract":"In this paper, an intelligent method is developed for improving the performance of the Computer-Aided Detection (CAD) system. The research objective is to improve the performance of the CAD system in Breast Cancer (BC) detection with high accuracy using thermal images. The research strategy is efficient using feature extraction, feature selection, classification and artificial intelligence methods. In the developed method, the features in the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) are extracted from images. The features are selected using the firefly feature selection algorithm. These selected features are observed to be relevant for the abnormality detection in healthy and unhealthy breasts. The [Formula: see text]-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision-Tree (D-Tree) classifiers are then applied to these features for the detection of malignancy in the breast. The breast thermograms of 200 subjects available at the Database for Mastology Research for breast research using InfraRed images, DMR-IR database, are considered for evaluation of our intelligent method. The results demonstrate that the accuracy is 98.8%, 81.5%, and 95%, the sensitivity is 99%, 83.15%, and 95.91%, and the specificity is 98.2%, 80%, and 94.11% when using SVM, kNN, and D-Tree classifier algorithm, respectively. This reveals the effectiveness of our intelligent method to improve the accuracy of the CAD system in the BC detection.","PeriodicalId":503224,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"2 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139781921","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：