Journal of Medical Engineering and Technology最新文献_第3页

News and Product Update. 新闻和产品更新。

Journal of Medical Engineering and Technology Pub Date : 2024-11-01 Epub Date: 2025-03-10 DOI: 10.1080/03091902.2025.2474849

J Fenner

引用次数: 0

An arrhythmia classification using a deep learning and optimisation-based methodology. 心律失常分类使用深度学习和优化为基础的方法。

Journal of Medical Engineering and Technology Pub Date : 2024-10-01 Epub Date: 2025-02-14 DOI: 10.1080/03091902.2025.2463574

Suvita Rani Sharma, Birmohan Singh, Manpreet Kaur

引用次数: 0

Hybrid attention-CNN model for classification of gait abnormalities using EMG scalogram images. 使用肌电图对步态异常进行分类的注意- cnn混合模型。

Journal of Medical Engineering and Technology Pub Date : 2024-10-01 Epub Date: 2025-02-12 DOI: 10.1080/03091902.2025.2462310

Pranshu C B S Negi, S S Pandey, Shiru Sharma, Neeraj Sharma

{"title":"Hybrid attention-CNN model for classification of gait abnormalities using EMG scalogram images.","authors":"Pranshu C B S Negi, S S Pandey, Shiru Sharma, Neeraj Sharma","doi":"10.1080/03091902.2025.2462310","DOIUrl":"10.1080/03091902.2025.2462310","url":null,"abstract":"This research aimed to develop an algorithm for classifying scalogram images generated from electromyography data of patients with Rheumatoid Arthritis and Prolapsed Intervertebral Disc. Electromyography is valuable for assessing muscle function and diagnosing neurological disorders, but limitations, such as background noise, cross-talk, and inter-subject variability complicate the interpretation and assessment. To mitigate this, the present study uses scalogram images and attention-network architecture. The algorithm utilises a combination of features extracted from an attention module and a convolution feature module, followed by classification using a Convolutional Neural Network classifier. A comparison of eight alternative architectures, including individual implementations of attention and convolution filters and a Convolutional Neural Network-only model, shows that the hybrid Convolutional Neural Network model proposed in this study outperforms the others. The model exhibits excellent discriminatory ability between gait abnormalities with an accuracy of 96.7%, a precision of 95.2%, a recall of 94.8%, and an Area Under Curve of 0.99. These findings suggest that the proposed model is highly accurate in classifying scalogram images of electromyography signals and may have significant clinical implications for early diagnosis and treatment planning.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"239-252"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400308","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}

引用次数: 0

A combination of deep learning models and type-2 fuzzy for EEG motor imagery classification through spatiotemporal-frequency features. 基于深度学习模型和2型模糊的脑电运动图像时空频率分类。

Journal of Medical Engineering and Technology Pub Date : 2024-10-01 Epub Date: 2025-02-14 DOI: 10.1080/03091902.2025.2463577

Ensong Jiang, Tangsen Huang, Xiangdong Yin

{"title":"A combination of deep learning models and type-2 fuzzy for EEG motor imagery classification through spatiotemporal-frequency features.","authors":"Ensong Jiang, Tangsen Huang, Xiangdong Yin","doi":"10.1080/03091902.2025.2463577","DOIUrl":"10.1080/03091902.2025.2463577","url":null,"abstract":"Developing a robust and effective technique is crucial for interpreting a user's brainwave signals accurately in the realm of biomedical signal processing. The variability and uncertainty present in EEG patterns over time, compounded by noise, pose notable challenges, particularly in mental tasks like motor imagery. Introducing fuzzy components can enhance the system's ability to withstand noisy environments. The emergence of deep learning has significantly impacted artificial intelligence and data analysis, prompting extensive exploration into assessing and understanding brain signals. This work introduces a hybrid series architecture called FCLNET, which combines Compact-CNN to extract frequency and spatial features alongside the LSTM network for temporal feature extraction. The activation functions in the CNN architecture were implemented using type-2 fuzzy functions to tackle uncertainties. Hyperparameters of the FCLNET model are tuned by the Bayesian optimisation algorithm. The efficacy of this approach is assessed through the BCI Competition IV-2a database and the BCI Competition IV-1 database. By incorporating type-2 fuzzy activation functions and employing Bayesian optimisation for tuning, the proposed architecture indicates good classification accuracy compared to the literature. Outcomes showcase the exceptional achievements of the FCLNET model, suggesting that integrating fuzzy units into other classifiers could lead to advancements in motor imagery-based BCI systems.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"262-275"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415610","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}

引用次数: 0

News and product update. 新闻和产品更新。

Journal of Medical Engineering and Technology Pub Date : 2024-10-01 Epub Date: 2025-02-19 DOI: 10.1080/03091902.2025.2461392

J Fenner

引用次数: 0

Synthetic photoplethysmogram (PPG) signal generation using a genetic programming-based generative model. 基于遗传规划生成模型的合成光体积脉搏图（PPG）信号生成。

Journal of Medical Engineering and Technology Pub Date : 2024-08-01 Epub Date: 2024-12-27 DOI: 10.1080/03091902.2024.2438150

Fatemeh Ghasemi, Majid Sepahvand, Maytham N Meqdad, Fardin Abdali Mohammadi

引用次数: 0

News and product update. 新闻和产品更新。

Journal of Medical Engineering and Technology Pub Date : 2024-08-01 Epub Date: 2024-12-04 DOI: 10.1080/03091902.2024.2426422

J Fenner

引用次数: 0

An idea for redo median sternotomy. 重做胸骨正中切开术的构想。

Journal of Medical Engineering and Technology Pub Date : 2024-08-01 Epub Date: 2024-12-09 DOI: 10.1080/03091902.2024.2435861

Kamal Fani

引用次数: 0

Comparative study of DCNN and image processing based classification of chest X-rays for identification of COVID-19 patients using fine-tuning. DCNN与基于图像处理的胸片分类在微调识别COVID-19患者中的对比研究

Journal of Medical Engineering and Technology Pub Date : 2024-08-01 Epub Date: 2024-12-09 DOI: 10.1080/03091902.2024.2438158

Amitesh Badkul, Inturi Vamsi, Radhika Sudha

{"title":"Comparative study of DCNN and image processing based classification of chest X-rays for identification of COVID-19 patients using fine-tuning.","authors":"Amitesh Badkul, Inturi Vamsi, Radhika Sudha","doi":"10.1080/03091902.2024.2438158","DOIUrl":"10.1080/03091902.2024.2438158","url":null,"abstract":"The conventional detection of COVID-19 by evaluating the CT scan images is tiresome, often experiences high inter-observer variability and uncertainty issues. This work proposes the automatic detection and classification of COVID-19 by analysing the chest X-ray images (CXR) with the deep convolutional neural network (DCNN) models through a fine-tuning and pre-training approach. CXR images pertaining to four health scenarios, namely, healthy, COVID-19, bacterial pneumonia and viral pneumonia, are considered and subjected to data augmentation. Two types of input datasets are prepared; in which dataset I contains the original image dataset categorised under four classes, whereas the original CXR images are subjected to image pre-processing via Contrast Limited Adaptive Histogram Equalisation (CLAHE) algorithm and Blackhat Morphological Operation (BMO) for devising the input dataset II. Both datasets are supplied as input to various DCNN models such as DenseNet, MobileNet, ResNet, VGG16, and Xception for achieving multi-class classification. It is observed that the classification accuracies are improved, and the classification errors are reduced with the image pre-processing. Overall, the VGG16 model resulted in better classification accuracies and reduced classification errors while accomplishing multi-class classification. Thus, the proposed work would assist the clinical diagnosis, and reduce the workload of the front-line healthcare workforce and medical professionals.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"213-222"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142796221","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}

引用次数: 0

News and product update. 新闻和产品更新。

Journal of Medical Engineering and Technology Pub Date : 2024-07-01 Epub Date: 2024-11-02 DOI: 10.1080/03091902.2024.2411080

John Fenner

引用次数: 0