Yazar "Jiang, Mingzhe" seçeneğine göre listele

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    Detection of atrial fibrillation from variable-duration ECG signal based on time-adaptive densely network and feature enhancement strategy
    (IEEE-Institute Electrical Electronics Engineers Inc, 2023) Zhang, Xianbin; Jiang, Mingzhe; Polat, Kemal; Alhudhaif, Adi; Hemanth, Jude; Wu, Wanqing
    Atrial fibrillation (AF) is one of the clinic's most common arrhythmias with high morbidity and mortality. Developing an intelligent auxiliary diagnostic model of AF based on a body surface electrocardiogram (ECG) is necessary. Convolutional neural network (CNN) is one of the most commonly used models for AF recognition. However, typical CNN is not compatible with variable-duration ECG, so it is hard to demonstrate its universality and generalization in practical applications. Hence, this paper proposes a novel Time-adaptive densely network named MP-DLNet-F. The MP-DLNet module solves the problem of incompatibility between variable-duration ECG and 1D-CNN. In addition, the feature enhancement module and data imbalance processing module are respectively used to enhance the perception of temporal-quality information and decrease the sensitivity to data imbalance. The experimental results indicate that the proposed MP-DLNet-F achieved 87.98% classification accuracy, and F1-score of 0.847 on the CinC2017 database for 10-second cropped/padded single-lead ECG fragments. Furthermore, we deploy transfer learning techniques to test heterogeneous datasets, and in the CPSC2018 12-lead dataset, the method improved the average accuracy and F1-score by 21.81% and 16.14%, respectively. Experimental results indicate that our method can update the constructed model's parameters and precisely forecast AF with different duration distributions and lead distributions. Combining these advantages, MP-DLNet-F can exemplify all kinds of varied-duration or imbalance medical signal processing problems such as Electroencephalogram (EEG) and Photoplethysmography (PPG).
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    A novel facial emotion recognition method for stress inference of facial nerve paralysis patients
    (Pergamon-Elsevier Science Ltd, 2022) Xu, Cuiting; Yan, Chunchuan; Jiang, Mingzhe; Alenezi, Fayadh; Alhudhaif, Adi; Polat, Kemal
    Facial nerve paralysis results in muscle weakness or complete paralysis on one side of the face. Patients suffer from difficulties in speech, mastication and emotional expression, impacting their quality of life by causing anxiety and depression. The emotional well-being of a facial nerve paralysis patient is usually followed up during and after treatment as part of quality-of-life measures through questionnaires. The commonly used questionnaire may help recognize whether a patient has been through a depressive state but is unable to understand their basic emotions dynamically. Automatic emotion recognition from facial expression images could be a solution to help understand facial nerve paralysis patients, recognize their stress in advance, and assist their treatment. However, their facial expressions are different from healthy people due to facial muscle inability, which makes existing emotion recognition data and models from healthy people invalid. Recent studies on facial images mainly focus on the automatic diagnosis of facial nerve paralysis level and thus lack full basic emotions. Different nerve paralysis levels also increase inconsistency in expressing the same emotion among patients. To enable emotion recognition and stress inference from facial images for facial nerve paralysis patients, we established an emotional facial expressions dataset from 45 patients with six basic emotions. The problem of limited data size in building a deep learning model VGGNet was solved by leveraging facial images from healthy people in transfer learning. Our proposed model reached an accuracy of 66.58% recognizing basic emotions from patients, which was 19.63% higher than the model trained only from the facial nerve paralysis data and was 42.69% higher than testing directly on the model trained from healthy data. Logically, the results show that patients with less severe facial nerve paralysis reached a higher emotion recognition accuracy. Additionally, although disgust, anger, and fear were especially challenging to specify from each other, the accuracy was 85.97% recognizing any stress-related negative emotions, making stress inference feasible.