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1.
Bol. latinoam. Caribe plantas med. aromát ; 23(2): 180-198, mar. 2024. ilus, tab, graf
Article in English | LILACS | ID: biblio-1538281

ABSTRACT

India's commercial advancement and development depend heavily on agriculture. A common fruit grown in tropical settings is citrus. A professional judgment is required while analyzing an illness because different diseases have slight variati ons in their symptoms. In order to recognize and classify diseases in citrus fruits and leaves, a customized CNN - based approach that links CNN with LSTM was developed in this research. By using a CNN - based method, it is possible to automatically differenti ate from healthier fruits and leaves and those that have diseases such fruit blight, fruit greening, fruit scab, and melanoses. In terms of performance, the proposed approach achieves 96% accuracy, 98% sensitivity, 96% Recall, and an F1 - score of 92% for ci trus fruit and leave identification and classification and the proposed method was compared with KNN, SVM, and CNN and concluded that the proposed CNN - based model is more accurate and effective at identifying illnesses in citrus fruits and leaves.


El avance y desarrollo comercial de India dependen en gran medida de la agricultura. Un tipo de fruta comunmente cultivada en en tornos tropicales es el cítrico. Se requiere un juicio profesional al analizar una enfermedad porque diferentes enfermedades tienen ligeras variaciones en sus síntomas. Para reconocer y clasificar enfermedades en frutas y hojas de cítricos, se desarrolló e n esta investigación un enfoque personalizado basado en CNN que vincula CNN con LSTM. Al utilizar un método basado en CNN, es posible diferenciar automáticamente entre frutas y hojas más saludables y aquellas que tienen enfermedades como la plaga de frutas , el verdor de frutas, la sarna de frutas y las melanosis. En términos de desempeño, el enfoque propuesto alcanza una precisión del 96%, una sensibilidad del 98%, una recuperación del 96% y una puntuación F1 del 92% para la identificación y clasificación d e frutas y hojas de cítricos, y el método propuesto se comparó con KNN, SVM y CNN y se concluyó que el modelo basado en CNN propuesto es más preciso y efectivo para identificar enfermedades en frutas y hojas de cítricos.


Subject(s)
Citrus/classification , Citrus/parasitology , Neural Networks, Computer , Plant Leaves/classification , Plant Leaves/parasitology , Artificial Intelligence/trends , Fruit/classification , Fruit/growth & development
2.
Rev. bras. oftalmol ; 83: e0006, 2024. tab, graf
Article in Portuguese | LILACS | ID: biblio-1535603

ABSTRACT

RESUMO Objetivo: Obter imagens de fundoscopia por meio de equipamento portátil e de baixo custo e, usando inteligência artificial, avaliar a presença de retinopatia diabética. Métodos: Por meio de um smartphone acoplado a um dispositivo com lente de 20D, foram obtidas imagens de fundo de olhos de pacientes diabéticos; usando a inteligência artificial, a presença de retinopatia diabética foi classificada por algoritmo binário. Resultados: Foram avaliadas 97 imagens da fundoscopia ocular (45 normais e 52 com retinopatia diabética). Com auxílio da inteligência artificial, houve acurácia diagnóstica em torno de 70 a 100% na classificação da presença de retinopatia diabética. Conclusão: A abordagem usando dispositivo portátil de baixo custo apresentou eficácia satisfatória na triagem de pacientes diabéticos com ou sem retinopatia diabética, sendo útil para locais sem condições de infraestrutura.


ABSTRACT Introduction: To obtain fundoscopy images through portable and low-cost equipment using artificial intelligence to assess the presence of DR. Methods: Fundus images of diabetic patients' eyes were obtained by using a smartphone coupled to a device with a 20D lens. By using artificial intelligence (AI), the presence of DR was classified by a binary algorithm. Results: 97 ocular fundoscopy images were evaluated (45 normal and 52 with DR). Through AI diagnostic accuracy around was 70% to 100% in the classification of the presence of DR. Conclusion: The approach using a low-cost portable device showed satisfactory efficacy in the screening of diabetic patients with or without diabetic retinopathy, being useful for places without infrastructure conditions.


Subject(s)
Humans , Male , Female , Adolescent , Adult , Middle Aged , Aged , Algorithms , Artificial Intelligence , Diabetic Retinopathy/diagnostic imaging , Photograph/instrumentation , Fundus Oculi , Ophthalmoscopy/methods , Retina/diagnostic imaging , Mass Screening , Neural Networks, Computer , Diagnostic Techniques, Ophthalmological/instrumentation , Machine Learning , Smartphone , Deep Learning
4.
Rev. colomb. cir ; 38(3): 439-446, Mayo 8, 2023. fig, tab
Article in Spanish | LILACS | ID: biblio-1438420

ABSTRACT

Introducción. Debido a la ausencia de modelos predictivos estadísticamente significativos enfocados a las complicaciones postoperatorias en el manejo quirúrgico del neumotórax, desarrollamos un modelo, utilizando redes neurales, que identifica las variables independientes y su importancia para reducir la incidencia de complicaciones. Métodos. Se realizó un estudio retrospectivo en un centro asistencial, donde se incluyeron 106 pacientes que requirieron manejo quirúrgico de neumotórax. Todos fueron operados por el mismo cirujano. Se desarrolló una red neural artificial para manejo de datos con muestras limitadas; se optimizaron los datos y cada algoritmo fue evaluado de forma independiente y mediante validación cruzada, para obtener el menor error posible y la mayor precisión con el menor tiempo de respuesta. Resultados. Las variables de mayor importancia según su peso en el sistema de decisión de la red neural (área bajo la curva 0,991) fueron el abordaje por toracoscopia video asistida (OR 1,131), el uso de pleurodesis con talco (OR 0,994) y el uso de autosuturas (OR 0,792; p<0,05). Discusión. En nuestro estudio, los principales predictores independientes asociados a mayor riesgo de complicaciones fueron el neumotórax de etiología secundaria y el neumotórax recurrente. Adicionalmente, confirmamos que las variables asociadas a reducción de riesgo de complicaciones postoperatorias tuvieron significancia estadística. Conclusión. Identificamos la toracoscopia video asistida, el uso de autosuturas y la pleurodesis con talco como posibles variables asociadas a menor riesgo de complicaciones. Se plantea la posibilidad de desarrollar una herramienta que facilite y apoye la toma de decisiones, por lo cual es necesaria la validación externa en estudios prospectivos


Introduction. Due to the absence of statistically significant predictive models focused on postoperative complications in the surgical management of pneumothorax, we developed a model using neural networks that identify the independent variables and their importance in reducing the incidence of postoperative complications. Methods. A retrospective single-center study was carried out, where 106 patients who required surgical management of pneumothorax were included. All patients were operated by the same surgeon. An artificial neural network was developed to manage data with limited samples. The data is optimized and each algorithm is evaluated independently and through cross-validation to obtain the lowest possible error and the highest precision with the shortest response time. Results. The most important variables according to their weight in the decision system of the neural network (AUC 0.991) were the approach via video-assisted thoracoscopy (OR 1.131), use of pleurodesis with powder talcum (OR 0.994) and use of autosutures (OR 0.792, p<0.05). Discussion. In our study, the main independent predictors associated with a higher risk of complications are pneumothorax of secondary etiology and recurrent pneumothorax. Additionally, we confirm that the variables associated with a reduction in the risk of postoperative complications have statistical significance. Conclusion. We identify video-assisted thoracoscopy, use of autosuture and powder talcum pleurodesis as possible variables associated with a lower risk of complications and raise the possibility of developing a tool that facilitates and supports decision-making, for which external validation in prospective studies is necessary


Subject(s)
Humans , Pneumothorax , Artificial Intelligence , Neural Networks, Computer , Postoperative Complications , Talc , Thoracoscopy
5.
Chinese Journal of Industrial Hygiene and Occupational Diseases ; (12): 177-182, 2023.
Article in Chinese | WPRIM | ID: wpr-970734

ABSTRACT

Objective: To construct and verify a light-weighted convolutional neural network (CNN), and explore its application value for screening the early stage (subcategory 0/1 and stage Ⅰ of pneumoconiosis) of coal workers' pneumoconiosis (CWP) from digital chest radiography (DR) . Methods: A total of 1225 DR images of coal workers who were examined at an Occupational Disease Prevention and Control Institute in Anhui Province from October 2018 to March 2021 were retrospectively collected. All DR images were collectively diagnosed by 3 radiologists with diagnostic qualifications and gave diagnostic results. There were 692 DR images with small opacity profusion 0/- or 0/0 and 533 DR images with small opacity profusion 0/1 to stage Ⅲ of pneumoconiosis. The original chest radiographs were preprocessed differently to generate four datasets, namely 16-bit grayscale original image set (Origin16), 8-bit grayscale original image set (Origin 8), 16-bit grayscale histogram equalized image set (HE16) and 8-bit grayscale histogram equalized image set (HE8). The light-weighted CNN, ShuffleNet, was applied to train the generated prediction model on the four datasets separately. The performance of the four models for pneumoconiosis prediction was evaluated on a test set containing 130 DR images using measures such as the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, and Youden index. The Kappa consistency test was used to compare the agreement between the model predictions and the physician diagnosed pneumoconiosis results. Results: Origin16 model achieved the highest ROC area under the curve (AUC=0.958), accuracy (92.3%), specificity (92.9%), and Youden index (0.8452) for predicting pneumoconiosis, with a sensitivity of 91.7%. And the highest consistency between identification and physician diagnosis was observed for Origin16 model (Kappa value was 0.845, 95%CI: 0.753-0.937, P<0.001). HE16 model had the highest sensitivity (98.3%) . Conclusion: The light-weighted CNN ShuffleNet model can efficiently identify the early stages of CWP, and its application in the early screening of CWP can effectively improve physicians' work efficiency.


Subject(s)
Humans , Retrospective Studies , Anthracosis/diagnostic imaging , Pneumoconiosis/diagnostic imaging , Coal Mining , Neural Networks, Computer , Coal
6.
Journal of Biomedical Engineering ; (6): 51-59, 2023.
Article in Chinese | WPRIM | ID: wpr-970673

ABSTRACT

Fetal electrocardiogram (ECG) signals provide important clinical information for early diagnosis and intervention of fetal abnormalities. In this paper, we propose a new method for fetal ECG signal extraction and analysis. Firstly, an improved fast independent component analysis method and singular value decomposition algorithm are combined to extract high-quality fetal ECG signals and solve the waveform missing problem. Secondly, a novel convolutional neural network model is applied to identify the QRS complex waves of fetal ECG signals and effectively solve the waveform overlap problem. Finally, high quality extraction of fetal ECG signals and intelligent recognition of fetal QRS complex waves are achieved. The method proposed in this paper was validated with the data from the PhysioNet computing in cardiology challenge 2013 database of the Complex Physiological Signals Research Resource Network. The results show that the average sensitivity and positive prediction values of the extraction algorithm are 98.21% and 99.52%, respectively, and the average sensitivity and positive prediction values of the QRS complex waves recognition algorithm are 94.14% and 95.80%, respectively, which are better than those of other research results. In conclusion, the algorithm and model proposed in this paper have some practical significance and may provide a theoretical basis for clinical medical decision making in the future.


Subject(s)
Algorithms , Neural Networks, Computer , Electrocardiography , Databases, Factual , Fetus
7.
Journal of Biomedical Engineering ; (6): 44-50, 2023.
Article in Chinese | WPRIM | ID: wpr-970672

ABSTRACT

In this paper, we propose a multi-scale mel domain feature map extraction algorithm to solve the problem that the speech recognition rate of dysarthria is difficult to improve. We used the empirical mode decomposition method to decompose speech signals and extracted Fbank features and their first-order differences for each of the three effective components to construct a new feature map, which could capture details in the frequency domain. Secondly, due to the problems of effective feature loss and high computational complexity in the training process of single channel neural network, we proposed a speech recognition network model in this paper. Finally, training and decoding were performed on the public UA-Speech dataset. The experimental results showed that the accuracy of the speech recognition model of this method reached 92.77%. Therefore, the algorithm proposed in this paper can effectively improve the speech recognition rate of dysarthria.


Subject(s)
Humans , Dysarthria/diagnosis , Speech , Speech Perception , Algorithms , Neural Networks, Computer
8.
Chinese Journal of Medical Instrumentation ; (6): 43-46, 2023.
Article in Chinese | WPRIM | ID: wpr-971301

ABSTRACT

OBJECTIVE@#To use the low-cost anesthesia monitor for realizing anesthesia depth monitoring, effectively assist anesthesiologists in diagnosis and reduce the cost of anesthesia operation.@*METHODS@#Propose a monitoring method of anesthesia depth based on artificial intelligence. The monitoring method is designed based on convolutional neural network (CNN) and long and short-term memory (LSTM) network. The input data of the model include electrocardiogram (ECG) and pulse wave photoplethysmography (PPG) recorded in the anesthesia monitor, as well as heart rate variability (HRV) calculated from ECG, The output of the model is in three states of anesthesia induction, anesthesia maintenance and anesthesia awakening.@*RESULTS@#The accuracy of anesthesia depth monitoring model under transfer learning is 94.1%, which is better than all comparison methods.@*CONCLUSIONS@#The accuracy of this study meets the needs of perioperative anesthesia depth monitoring and the study reduces the operation cost.


Subject(s)
Artificial Intelligence , Neural Networks, Computer , Heart Rate , Electrocardiography , Photoplethysmography/methods , Anesthesia
9.
Biomedical and Environmental Sciences ; (12): 1123-1135, 2023.
Article in English | WPRIM | ID: wpr-1007892

ABSTRACT

OBJECTIVE@#This study aimed to develop an artificial neural network (ANN) model combined with dietary retinol intake from different sources to predict the risk of non-alcoholic fatty liver disease (NAFLD) in American adults.@*METHODS@#Data from the 2007 to 2014 National Health and Nutrition Examination Survey (NHANES) 2007-2014 were analyzed. Eligible subjects ( n = 6,613) were randomly divided into a training set ( n 1 = 4,609) and a validation set ( n 2 = 2,004) at a ratio of 7:3. The training set was used to identify predictors of NAFLD risk using logistic regression analysis. An ANN was established to predict the NAFLD risk using a training set. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the accuracy of the model using the training and validation sets.@*RESULTS@#Our study found that the odds ratios ( ORs) and 95% confidence intervals ( CIs) of NAFLD for the highest quartile of plant-derived dietary retinol intake (i.e., provitamin A carotenoids, such as β-carotene) ( OR = 0.75, 95% CI: 0.57 to 0.99) were inversely associated with NAFLD risk, compared to the lowest quartile of intake, after adjusting for potential confounders. The areas under the ROC curves were 0.874 and 0.883 for the training and validation sets, respectively. NAFLD occurs when its incidence probability is greater than 0.388.@*CONCLUSION@#The ANN model combined with plant-derived dietary retinol intake showed a significant effect on NAFLD. This could be applied to predict NAFLD risk in the American adult population when government departments formulate future health plans.


Subject(s)
Adult , Humans , Vitamin A , Non-alcoholic Fatty Liver Disease/epidemiology , Nutrition Surveys , Diet , Neural Networks, Computer
10.
Acta Physiologica Sinica ; (6): 937-945, 2023.
Article in Chinese | WPRIM | ID: wpr-1007802

ABSTRACT

The present study aims to establish comprehensive evaluation models of physical fitness of the elderly based on machine learning, and provide an important basis to monitor the elderly's physique. Through stratified sampling, the elderly aged 60 years and above were selected from 10 communities in Nanchang City. The physical fitness of the elderly was measured by the comprehensive physical assessment scale based on our previous study. Fuzzy neural network (FNN), support vector machine (SVM) and random forest (RF) models for comprehensive physical evaluation of the elderly people in communities were constructed respectively. The accuracy, sensitivity and specificity of the comprehensive physical fitness evaluation models constructed by FNN, SVM and RF were above 0.85, 0.75 and 0.89, respectively, with the FNN model possessing the best prediction performance. FNN, RF and SVM models are valuable in the comprehensive evaluation and prediction of physical fitness, which can be used as tools to carry out physical evaluation of the elderly.


Subject(s)
Aged , Humans , Physical Fitness , Neural Networks, Computer , Exercise , Machine Learning
11.
Chinese Medical Journal ; (24): 2706-2711, 2023.
Article in English | WPRIM | ID: wpr-1007693

ABSTRACT

BACKGROUND@#Distinguishing between primary clear cell carcinoma of the liver (PCCCL) and common hepatocellular carcinoma (CHCC) through traditional inspection methods before the operation is difficult. This study aimed to establish a Faster region-based convolutional neural network (RCNN) model for the accurate differential diagnosis of PCCCL and CHCC.@*METHODS@#In this study, we collected the data of 62 patients with PCCCL and 1079 patients with CHCC in Beijing YouAn Hospital from June 2012 to May 2020. A total of 109 patients with CHCC and 42 patients with PCCCL were randomly divided into the training validation set and the test set in a ratio of 4:1.The Faster RCNN was used for deep learning of patients' data in the training validation set, and established a convolutional neural network model to distinguish PCCCL and CHCC. The accuracy, average precision, and the recall of the model for diagnosing PCCCL and CHCC were used to evaluate the detection performance of the Faster RCNN algorithm.@*RESULTS@#A total of 4392 images of 121 patients (1032 images of 33 patients with PCCCL and 3360 images of 88 patients with CHCC) were uesd in test set for deep learning and establishing the model, and 1072 images of 30 patients (320 images of nine patients with PCCCL and 752 images of 21 patients with CHCC) were used to test the model. The accuracy of the model for accurately diagnosing PCCCL and CHCC was 0.962 (95% confidence interval [CI]: 0.931-0.992). The average precision of the model for diagnosing PCCCL was 0.908 (95% CI: 0.823-0.993) and that for diagnosing CHCC was 0.907 (95% CI: 0.823-0.993). The recall of the model for diagnosing PCCCL was 0.951 (95% CI: 0.916-0.985) and that for diagnosing CHCC was 0.960 (95% CI: 0.854-0.962). The time to make a diagnosis using the model took an average of 4 s for each patient.@*CONCLUSION@#The Faster RCNN model can accurately distinguish PCCCL and CHCC. This model could be important for clinicians to make appropriate treatment plans for patients with PCCCL or CHCC.


Subject(s)
Humans , Liver Neoplasms/pathology , Retrospective Studies , Carcinoma, Hepatocellular/pathology , Neural Networks, Computer
12.
Singapore medical journal ; : 91-97, 2023.
Article in English | WPRIM | ID: wpr-969646

ABSTRACT

With the advent of artificial intelligence (AI), machines are increasingly being used to complete complicated tasks, yielding remarkable results. Machine learning (ML) is the most relevant subset of AI in medicine, which will soon become an integral part of our everyday practice. Therefore, physicians should acquaint themselves with ML and AI, and their role as an enabler rather than a competitor. Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree and application domain in computer vision and natural language processing through specific examples. We discuss how machines are already being used to augment the physician's decision-making process, and postulate the potential impact of ML on medical practice and medical research based on its current capabilities and known limitations. Moreover, we discuss the feasibility of full machine autonomy in medicine.


Subject(s)
Humans , Artificial Intelligence , Machine Learning , Algorithms , Neural Networks, Computer , Medicine
13.
Chinese Journal of Medical Instrumentation ; (6): 402-405, 2023.
Article in Chinese | WPRIM | ID: wpr-982253

ABSTRACT

OBJECTIVE@#In order to improve the accuracy of the current pulmonary nodule location detection method based on CT images, reduce the problem of missed detection or false detection, and effectively assist imaging doctors in the diagnosis of pulmonary nodules.@*METHODS@#Propose a novel method for detecting the location of pulmonary nodules based on multiscale convolution. First, image preprocessing methods are used to eliminate the noise and artifacts in lung CT images. Second, multiple adjacent single-frame CT images are selected to be concatenate into multi-frame images, and the feature extraction is carried out through the artificial neural network model U-Net improved by multi-scale convolution to enhanced feature extraction capability for pulmonary nodules of different sizes and shapes, so as to improve the accuracy of feature extraction of pulmonary nodules. Finally, using point detection to improve the loss function of U-Net training process, the accuracy of pulmonary nodule location detection is improved.@*RESULTS@#The accuracy of detecting pulmonary nodules equal or larger than 3 mm and smaller than 3 mm are 98.02% and 96.94% respectively.@*CONCLUSIONS@#This method can effectively improve the detection accuracy of pulmonary nodules on CT image sequence, and can better meet the diagnostic needs of pulmonary nodules.


Subject(s)
Humans , Lung Neoplasms/diagnostic imaging , Solitary Pulmonary Nodule/diagnostic imaging , Tomography, X-Ray Computed , Neural Networks, Computer
14.
Chinese Journal of Medical Instrumentation ; (6): 258-263, 2023.
Article in Chinese | WPRIM | ID: wpr-982224

ABSTRACT

Atrial fibrillation is a common arrhythmia, and its diagnosis is interfered by many factors. In order to achieve applicability in diagnosis and improve the level of automatic analysis of atrial fibrillation to the level of experts, the automatic detection of atrial fibrillation is very important. This study proposes an automatic detection algorithm for atrial fibrillation based on BP neural network (back propagation network) and support vector machine (SVM). The electrocardiogram (ECG) segments in the MIT-BIH atrial fibrillation database are divided into 10, 32, 64, and 128 heartbeats, respectively, and the Lorentz value, Shannon entropy, K-S test value and exponential moving average value are calculated. These four characteristic parameters are used as the input of SVM and BP neural network for classification and testing, and the label given by experts in the MIT-BIH atrial fibrillation database is used as the reference output. Among them, the use of atrial fibrillation in the MIT-BIH database, the first 18 cases of data are used as the training set, and the last 7 cases of data are used as the test set. The results show that the accuracy rate of 92% is obtained in the classification of 10 heartbeats, and the accuracy rate of 98% is obtained in the latter three categories. The sensitivity and specificity are both above 97.7%, which has certain applicability. Further validation and improvement in clinical ECG data will be done in next study.


Subject(s)
Humans , Atrial Fibrillation/diagnosis , Support Vector Machine , Heart Rate , Algorithms , Neural Networks, Computer , Electrocardiography
15.
Journal of Zhejiang University. Medical sciences ; (6): 243-248, 2023.
Article in English | WPRIM | ID: wpr-982041

ABSTRACT

The application of artificial neural network algorithm in pathological diagnosis of gastrointestinal malignant tumors has become a research hotspot. In the previous studies, the algorithm research mainly focused on the model development based on convolutional neural networks, while only a few studies used the combination of convolutional neural networks and recurrent neural networks. The research contents included classical histopathological diagnosis and molecular typing of malignant tumors, and the prediction of patient prognosis by utilizing artificial neural networks. This article reviews the research progress on artificial neural network algorithm in the pathological diagnosis and prognosis prediction of digestive tract malignant tumors.


Subject(s)
Humans , Neural Networks, Computer , Algorithms , Prognosis , Gastrointestinal Neoplasms/diagnosis
16.
Journal of Biomedical Engineering ; (6): 544-551, 2023.
Article in Chinese | WPRIM | ID: wpr-981574

ABSTRACT

The synergistic effect of drug combinations can solve the problem of acquired resistance to single drug therapy and has great potential for the treatment of complex diseases such as cancer. In this study, to explore the impact of interactions between different drug molecules on the effect of anticancer drugs, we proposed a Transformer-based deep learning prediction model-SMILESynergy. First, the drug text data-simplified molecular input line entry system (SMILES) were used to represent the drug molecules, and drug molecule isomers were generated through SMILES Enumeration for data augmentation. Then, the attention mechanism in the Transformer was used to encode and decode the drug molecules after data augmentation, and finally, a multi-layer perceptron (MLP) was connected to obtain the synergy value of the drugs. Experimental results showed that our model had a mean squared error of 51.34 in regression analysis, an accuracy of 0.97 in classification analysis, and better predictive performance than the DeepSynergy and MulinputSynergy models. SMILESynergy offers improved predictive performance to assist researchers in rapidly screening optimal drug combinations to improve cancer treatment outcomes.


Subject(s)
Electric Power Supplies , Neural Networks, Computer , Antineoplastic Agents/pharmacology
17.
Journal of Biomedical Engineering ; (6): 536-543, 2023.
Article in Chinese | WPRIM | ID: wpr-981573

ABSTRACT

Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed a new PPG signal quality assessment by fusing multi-class features with multi-scale series information to address the problems of traditional machine learning methods with low accuracy and deep learning methods requiring a large number of samples for training. The multi-class features were extracted to reduce the dependence on the number of samples, and the multi-scale series information was extracted by a multi-scale convolutional neural network and bidirectional long short-term memory to improve the accuracy. The proposed method obtained the highest accuracy of 94.21%. It showed the best performance in all sensitivity, specificity, precision, and F1-score metrics, compared with 6 quality assessment methods on 14 700 samples from 7 experiments. This paper provides a new method for quality assessment in small samples of PPG signals and quality information mining, which is expected to be used for accurate extraction and monitoring of clinical and daily PPG physiological information.


Subject(s)
Photoplethysmography , Machine Learning , Neural Networks, Computer
18.
Journal of Biomedical Engineering ; (6): 474-481, 2023.
Article in Chinese | WPRIM | ID: wpr-981565

ABSTRACT

In the diagnosis of cardiovascular diseases, the analysis of electrocardiogram (ECG) signals has always played a crucial role. At present, how to effectively identify abnormal heart beats by algorithms is still a difficult task in the field of ECG signal analysis. Based on this, a classification model that automatically identifies abnormal heartbeats based on deep residual network (ResNet) and self-attention mechanism was proposed. Firstly, this paper designed an 18-layer convolutional neural network (CNN) based on the residual structure, which helped model fully extract the local features. Then, the bi-directional gated recurrent unit (BiGRU) was used to explore the temporal correlation for further obtaining the temporal features. Finally, the self-attention mechanism was built to weight important information and enhance model's ability to extract important features, which helped model achieve higher classification accuracy. In addition, in order to mitigate the interference on classification performance due to data imbalance, the study utilized multiple approaches for data augmentation. The experimental data in this study came from the arrhythmia database constructed by MIT and Beth Israel Hospital (MIT-BIH), and the final results showed that the proposed model achieved an overall accuracy of 98.33% on the original dataset and 99.12% on the optimized dataset, which demonstrated that the proposed model can achieve good performance in ECG signal classification, and possessed potential value for application to portable ECG detection devices.


Subject(s)
Humans , Electrocardiography , Algorithms , Cardiovascular Diseases , Databases, Factual , Neural Networks, Computer
19.
Journal of Biomedical Engineering ; (6): 450-457, 2023.
Article in Chinese | WPRIM | ID: wpr-981562

ABSTRACT

The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.


Subject(s)
Humans , Bayes Theorem , Neural Networks, Computer , Algorithms , Brain , Cognitive Dysfunction/diagnosis
20.
Journal of Biomedical Engineering ; (6): 418-425, 2023.
Article in Chinese | WPRIM | ID: wpr-981558

ABSTRACT

The brain-computer interface (BCI) based on motor imagery electroencephalography (MI-EEG) enables direct information interaction between the human brain and external devices. In this paper, a multi-scale EEG feature extraction convolutional neural network model based on time series data enhancement is proposed for decoding MI-EEG signals. First, an EEG signals augmentation method was proposed that could increase the information content of training samples without changing the length of the time series, while retaining its original features completely. Then, multiple holistic and detailed features of the EEG data were adaptively extracted by multi-scale convolution module, and the features were fused and filtered by parallel residual module and channel attention. Finally, classification results were output by a fully connected network. The application experimental results on the BCI Competition IV 2a and 2b datasets showed that the proposed model achieved an average classification accuracy of 91.87% and 87.85% for the motor imagery task, respectively, which had high accuracy and strong robustness compared with existing baseline models. The proposed model does not require complex signals pre-processing operations and has the advantage of multi-scale feature extraction, which has high practical application value.


Subject(s)
Humans , Time Factors , Brain , Electroencephalography , Imagery, Psychotherapy , Neural Networks, Computer
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