1
|
Ansari MY, Qaraqe M, Righetti R, Serpedin E, Qaraqe K. Enhancing ECG-based heart age: impact of acquisition parameters and generalization strategies for varying signal morphologies and corruptions. Front Cardiovasc Med 2024; 11:1424585. [PMID: 39027006 PMCID: PMC11254851 DOI: 10.3389/fcvm.2024.1424585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 06/04/2024] [Indexed: 07/20/2024] Open
Abstract
Electrocardiogram (ECG) is a non-invasive approach to capture the overall electrical activity produced by the contraction and relaxation of the cardiac muscles. It has been established in the literature that the difference between ECG-derived age and chronological age represents a general measure of cardiovascular health. Elevated ECG-derived age strongly correlates with cardiovascular conditions (e.g., atherosclerotic cardiovascular disease). However, the neural networks for ECG age estimation are yet to be thoroughly evaluated from the perspective of ECG acquisition parameters. Additionally, deep learning systems for ECG analysis encounter challenges in generalizing across diverse ECG morphologies in various ethnic groups and are susceptible to errors with signals that exhibit random or systematic distortions To address these challenges, we perform a comprehensive empirical study to determine the threshold for the sampling rate and duration of ECG signals while considering their impact on the computational cost of the neural networks. To tackle the concern of ECG waveform variability in different populations, we evaluate the feasibility of utilizing pre-trained and fine-tuned networks to estimate ECG age in different ethnic groups. Additionally, we empirically demonstrate that finetuning is an environmentally sustainable way to train neural networks, and it significantly decreases the ECG instances required (by more than 100 × ) for attaining performance similar to the networks trained from random weight initialization on a complete dataset. Finally, we systematically evaluate augmentation schemes for ECG signals in the context of age estimation and introduce a random cropping scheme that provides best-in-class performance while using shorter-duration ECG signals. The results also show that random cropping enables the networks to perform well with systematic and random ECG signal corruptions.
Collapse
Affiliation(s)
- Mohammed Yusuf Ansari
- Electrical and Computer Engineering, Texas A&M University, College Station, TX, United States
- Electrical and Computer Engineering, Texas A&M University at Qatar, Doha, Qatar
| | - Marwa Qaraqe
- Electrical and Computer Engineering, Texas A&M University at Qatar, Doha, Qatar
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Raffaella Righetti
- Electrical and Computer Engineering, Texas A&M University, College Station, TX, United States
| | - Erchin Serpedin
- Electrical and Computer Engineering, Texas A&M University, College Station, TX, United States
| | - Khalid Qaraqe
- Electrical and Computer Engineering, Texas A&M University at Qatar, Doha, Qatar
| |
Collapse
|
2
|
Jiang M, Bian F, Zhang J, Huang T, Xia L, Chu Y, Wang Z, Jiang J. Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features. Physiol Meas 2024; 45:055017. [PMID: 38697203 DOI: 10.1088/1361-6579/ad46e1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 05/02/2024] [Indexed: 05/04/2024]
Abstract
Objective.Myocardial infarction (MI) is one of the most threatening cardiovascular diseases. This paper aims to explore a method for using an algorithm to autonomously classify MI based on the electrocardiogram (ECG).Approach.A detection method of MI that fuses continuous T-wave area (C_TWA) feature and ECG deep features is proposed. This method consists of three main parts: (1) The onset of MI is often accompanied by changes in the shape of the T-wave in the ECG, thus the area of the T-wave displayed on different heartbeats will be quite different. The adaptive sliding window method is used to detect the start and end of the T-wave, and calculate the C_TWA on the same ECG record. Additionally, the coefficient of variation of C_TWA is defined as the C_TWA feature of the ECG. (2) The multi lead fusion convolutional neural network was implemented to extract the deep features of the ECG. (3) The C_TWA feature and deep features of the ECG were fused by soft attention, and then inputted into the multi-layer perceptron to obtain the detection result.Main results.According to the inter-patient paradigm, the proposed method reached a 97.67% accuracy, 96.59% precision, and 98.96% recall on the PTB dataset, as well as reached 93.15% accuracy, 93.20% precision, and 95.14% recall on the clinical dataset.Significance.This method accurately extracts the feature of the C_TWA, and combines the deep features of the signal, thereby improving the detection accuracy and achieving favorable results on clinical datasets.
Collapse
Affiliation(s)
- Mingfeng Jiang
- School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou, People's Republic of China
| | - Feibiao Bian
- School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou, People's Republic of China
| | - Jucheng Zhang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, People's Republic of China
| | - Tianhai Huang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| | - Ling Xia
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou, People's Republic of China
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, People's Republic of China
| | - Yonghua Chu
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| | - Zhikang Wang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| | - Jun Jiang
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| |
Collapse
|
3
|
Palma-Ramírez D, Ross-Veitía BD, Font-Ariosa P, Espinel-Hernández A, Sanchez-Roca A, Carvajal-Fals H, Nuñez-Alvarez JR, Hernández-Herrera H. Deep convolutional neural network for weld defect classification in radiographic images. Heliyon 2024; 10:e30590. [PMID: 38726185 PMCID: PMC11079250 DOI: 10.1016/j.heliyon.2024.e30590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/12/2024] Open
Abstract
The quality of welds is critical to the safety of structures in construction, so early detection of irregularities is crucial. Advances in machine vision inspection technologies, such as deep learning models, have improved the detection of weld defects. This paper presents a new CNN model based on ResNet50 to classify four types of weld defects in radiographic images: crack, pore, non-penetration, and no defect. Stratified cross-validation, data augmentation, and regularization were used to improve generalization and avoid over-fitting. The model was tested on three datasets, RIAWELC, GDXray, and a private dataset of low image quality, obtaining an accuracy of 98.75 %, 90.255 %, and 75.83 %, respectively. The model proposed in this paper achieves high accuracies on different datasets and constitutes a valuable tool to improve the efficiency and effectiveness of quality control processes in the welding industry. Moreover, experimental tests show that the proposed approach performs well on even low-resolution images.
Collapse
Affiliation(s)
- Dayana Palma-Ramírez
- Postgraduate Program Doctorate in Applied Computer Engineering School of Computer Engineering. University of Valparaiso. Valparaiso, Chile
| | - Bárbara D. Ross-Veitía
- Production Engineering Doctorate Postgraduate Program Federal Technological University of Paraná (UTFPR) - Ponta Grossa Campus. PR, Brazil
| | - Pablo Font-Ariosa
- Defectoscopy and Welding Technical Services Company, Road O'Burke km. 2½ Pastorita, Cienfuegos, Cuba
| | - Alejandro Espinel-Hernández
- National Center for Applied Electromagnetism (CNEA), Universidad de Oriente, Ave. de Las Américas s/n, 90100, Santiago de Cuba, Cuba
| | - Angel Sanchez-Roca
- Intranox SL Pol. La Portalada C/ Circunde, 23 26006, Logroño, La Rioja, Spain
| | - Hipólito Carvajal-Fals
- Pesquisador Visitante. Departamento de Engenharia de Manufatura e Materiais. Universidade Estadual de Campinas. SP, Brazil
| | - José R. Nuñez-Alvarez
- Energy Department, Universidad de la Costa, (CUC), Calle 58 # 55-66, Barranquilla, 080002, Colombia
| | - Hernan Hernández-Herrera
- Faculty of Engineering, Universidad Simón Bolívar, Carrera 59 #59-132, Barranquilla, 080002, Colombia
| |
Collapse
|
4
|
Xie J, Wang Z, Yu Z, Ding Y, Guo B. Prototype Learning for Medical Time Series Classification via Human-Machine Collaboration. SENSORS (BASEL, SWITZERLAND) 2024; 24:2655. [PMID: 38676273 PMCID: PMC11054195 DOI: 10.3390/s24082655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/15/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024]
Abstract
Deep neural networks must address the dual challenge of delivering high-accuracy predictions and providing user-friendly explanations. While deep models are widely used in the field of time series modeling, deciphering the core principles that govern the models' outputs remains a significant challenge. This is crucial for fostering the development of trusted models and facilitating domain expert validation, thereby empowering users and domain experts to utilize them confidently in high-risk decision-making contexts (e.g., decision-support systems in healthcare). In this work, we put forward a deep prototype learning model that supports interpretable and manipulable modeling and classification of medical time series (i.e., ECG signal). Specifically, we first optimize the representation of single heartbeat data by employing a bidirectional long short-term memory and attention mechanism, and then construct prototypes during the training phase. The final classification outcomes (i.e., normal sinus rhythm, atrial fibrillation, and other rhythm) are determined by comparing the input with the obtained prototypes. Moreover, the proposed model presents a human-machine collaboration mechanism, allowing domain experts to refine the prototypes by integrating their expertise to further enhance the model's performance (contrary to the human-in-the-loop paradigm, where humans primarily act as supervisors or correctors, intervening when required, our approach focuses on a human-machine collaboration, wherein both parties engage as partners, enabling more fluid and integrated interactions). The experimental outcomes presented herein delineate that, within the realm of binary classification tasks-specifically distinguishing between normal sinus rhythm and atrial fibrillation-our proposed model, albeit registering marginally lower performance in comparison to certain established baseline models such as Convolutional Neural Networks (CNNs) and bidirectional long short-term memory with attention mechanisms (Bi-LSTMAttns), evidently surpasses other contemporary state-of-the-art prototype baseline models. Moreover, it demonstrates significantly enhanced performance relative to these prototype baseline models in the context of triple classification tasks, which encompass normal sinus rhythm, atrial fibrillation, and other rhythm classifications. The proposed model manifests a commendable prediction accuracy of 0.8414, coupled with macro precision, recall, and F1-score metrics of 0.8449, 0.8224, and 0.8235, respectively, achieving both high classification accuracy as well as good interpretability.
Collapse
Affiliation(s)
| | - Zhu Wang
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China; (J.X.); (Z.Y.); (Y.D.); (B.G.)
| | | | | | | |
Collapse
|
5
|
Sattar S, Mumtaz R, Qadir M, Mumtaz S, Khan MA, De Waele T, De Poorter E, Moerman I, Shahid A. Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets. SENSORS (BASEL, SWITZERLAND) 2024; 24:2484. [PMID: 38676101 PMCID: PMC11054468 DOI: 10.3390/s24082484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 04/05/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024]
Abstract
ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs.
Collapse
Affiliation(s)
- Shoaib Sattar
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (S.S.); (M.A.K.)
| | - Rafia Mumtaz
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (S.S.); (M.A.K.)
| | - Mamoon Qadir
- Federal Government Poly Clinic Hospital, Islamabad 44000, Pakistan;
| | - Sadaf Mumtaz
- NUST School of Health Sciences (NSHS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan;
| | - Muhammad Ajmal Khan
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (S.S.); (M.A.K.)
| | - Timo De Waele
- IDLab, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (T.D.W.); (E.D.P.); (I.M.); (A.S.)
| | - Eli De Poorter
- IDLab, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (T.D.W.); (E.D.P.); (I.M.); (A.S.)
| | - Ingrid Moerman
- IDLab, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (T.D.W.); (E.D.P.); (I.M.); (A.S.)
| | - Adnan Shahid
- IDLab, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (T.D.W.); (E.D.P.); (I.M.); (A.S.)
| |
Collapse
|
6
|
Terzi MB, Arikan O. Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram. BIOMED ENG-BIOMED TE 2024; 69:79-109. [PMID: 37823386 DOI: 10.1515/bmt-2022-0406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 08/25/2023] [Indexed: 10/13/2023]
Abstract
OBJECTIVES Coronary artery diseases (CADs) are the leading cause of death worldwide and early diagnosis is crucial for timely treatment. To address this, our study presents a novel automated Artificial Intelligence (AI)-based Hybrid Anomaly Detection (AIHAD) technique that combines various signal processing, feature extraction, supervised, and unsupervised machine learning methods. By jointly and simultaneously analyzing 12-lead cardiac sympathetic nerve activity (CSNA) and electrocardiogram (ECG) data, the automated AIHAD technique performs fast, early, and accurate diagnosis of CADs. METHODS In order to develop and evaluate the proposed automated AIHAD technique, we utilized the fully labeled STAFF III and PTBD databases, which contain the 12-lead wideband raw recordings non-invasively acquired from 260 subjects. Using these wideband raw recordings, we developed a signal processing technique that simultaneously detects the 12-lead CSNA and ECG signals of all subjects. Using the pre-processed 12-lead CSNA and ECG signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of CADs. Using the extracted discriminative features, we developed a supervised classification technique based on Artificial Neural Networks (ANNs) that simultaneously detects anomalies in the 12-lead CSNA and ECG data. Furthermore, we developed an unsupervised clustering technique based on Gaussian mixture models (GMMs) and Neyman-Pearson criterion, which robustly detects outliers corresponding to CADs. RESULTS Using the automated AIHAD technique, we have, for the first time, demonstrated a significant association between the increase in CSNA signals and anomalies in ECG signals during CADs. The AIHAD technique achieved highly reliable detection of CADs with a sensitivity of 98.48 %, specificity of 97.73 %, accuracy of 98.11 %, positive predictive value of 97.74 %, negative predictive value of 98.47 %, and F1-score of 98.11 %. Hence, the automated AIHAD technique demonstrates superior performance compared to the gold standard diagnostic test ECG in the diagnosis of CADs. Additionally, it outperforms other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it significantly increases the detection performance of CADs by taking advantage of the diversity in different data types and leveraging their strengths. Furthermore, its performance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or classify CADs. Additionally, it has a very low implementation time, which is highly desirable for real-time detection of CADs. CONCLUSIONS The proposed automated AIHAD technique may serve as an efficient decision-support system to increase physicians' success in fast, early, and accurate diagnosis of CADs. It may be highly beneficial and valuable, particularly for asymptomatic patients, for whom the diagnostic information provided by ECG alone is not sufficient to reliably diagnose the disease. Hence, it may significantly improve patient outcomes by enabling timely treatments and considerably reducing the mortality of cardiovascular diseases (CVDs).
Collapse
Affiliation(s)
- Merve Begum Terzi
- Faculty of Engineering, Electrical and Electronics Engineering Department, Bilkent University, Ankara, Türkiye
| | - Orhan Arikan
- Faculty of Engineering, Electrical and Electronics Engineering Department, Bilkent University, Ankara, Türkiye
| |
Collapse
|
7
|
Tasci B, Tasci G, Dogan S, Tuncer T. A novel ternary pattern-based automatic psychiatric disorders classification using ECG signals. Cogn Neurodyn 2024; 18:95-108. [PMID: 38406197 PMCID: PMC10881455 DOI: 10.1007/s11571-022-09918-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
Neuropsychiatric disorders are one of the leading causes of disability. Mental health problems can occur due to various biological and environmental factors. The absence of definitive confirmatory diagnostic tests for psychiatric disorders complicates the diagnosis. It's critical to distinguish between bipolar disorder, depression, and schizophrenia since their symptoms and treatments differ. Because of brain-heart autonomic connections, electrocardiography (ECG) signals can be changed in behavioral disorders. In this research, we have automatically classified bipolar, depression, and schizophrenia from ECG signals. In this work, a new hand-crafted feature engineering model has been proposed to detect psychiatric disorders automatically. The main objective of this model is to accurately detect psychiatric disorders using ECG beats with linear time complexity. Therefore, we collected a new ECG signal dataset containing 3,570 ECG beats with four categories. The used categories are bipolar, depression, schizophrenia, and control. Furthermore, a new ternary pattern-based signal classification model has been proposed to classify these four categories. Our proposal contains four essential phases, and these phases are (i) multileveled feature extraction using multilevel discrete wavelet transform and ternary pattern, (ii) the best features selection applying iterative Chi2 selector, (iii) classification with artificial neural network (ANN) to calculate lead wise results and (iv) calculation the voted/general classification accuracy using iterative majority voting (IMV) algorithm. tenfold cross-validation is one of the most used validation techniques in the literature, and this validation model gives robust classification results. Using ANN with tenfold cross-validation, lead-by-lead and voted results have been calculated. The lead-by-lead accuracy range of the proposed model using the ANN classifier is from 73.67 to 89.19%. By deploying the IMV method, the general classification performance of our ternary pattern-based ECG classification model is increased from 89.19 to 96.25%. The findings and the calculated classification accuracies (single lead and voted) clearly demonstrated the success of the proposed ternary pattern-based advanced signal processing model. By using this model, a new wearable device can be proposed.
Collapse
Affiliation(s)
- Burak Tasci
- Vocational School of Technical Sciences, Firat University, 23119 Elazig, Turkey
| | - Gulay Tasci
- Department of Psychiatry, Elazığ Fethi Sekin City Hospital, Elazığ, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Turker Tuncer
- Department of Psychiatry, Elazığ Fethi Sekin City Hospital, Elazığ, Turkey
| |
Collapse
|
8
|
Zhang X, Liu S, Wang X, Li Y. A fragmented neural network ensemble method and its application to image classification. Sci Rep 2024; 14:2291. [PMID: 38280964 PMCID: PMC10821914 DOI: 10.1038/s41598-024-52945-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/25/2024] [Indexed: 01/29/2024] Open
Abstract
In recent years, deep neural networks have evolved rapidly in engineering technology, with models becoming larger and deeper. However, for most companies, developing large models is extremely costly and highly risky. Researchers usually focus on the performance of the model, neglecting its cost and accessibility. In fact, most regular business scenarios do not require high-level AI. A simple and inexpensive modeling method for fulfilling certain demands for practical applications of AI is needed. In this paper, a Fragmented neural network method is proposed. Inspired by the random forest algorithm, both the samples and features are randomly sampled on image data. Images are randomly split into smaller pieces. Weak neural networks are trained using these fragmented images, and many weak neural networks are then ensembled to build a strong neural network by voting. In this way, sufficient accuracy is achieved while reducing the complexity and data volume of each base learner, enabling mass production through parallel and distributed computing. By conducting experiments on the MNIST and CIFAR10 datasets, we build a model pool using FNN, CNN, DenseNet, and ResNet as the basic network structure. We find that the accuracy of the ensemble weak network is significantly higher than that of each base learner. Meanwhile, the accuracy of the ensemble network is highly dependent on the performance of each base learner. The accuracy of the ensemble network is comparable to or even exceeds that of the full model and has better robustness. Unlike other similar studies, we do not pursue SOTA models. Instead, we achieved results close to the full model with a smaller number of parameters and amount of data.
Collapse
Affiliation(s)
- Xu Zhang
- School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, China
| | - Shuai Liu
- School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, China.
| | - Xueli Wang
- School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, China
| | - Yumei Li
- School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, China
| |
Collapse
|
9
|
Chopannejad S, Roshanpoor A, Sadoughi F. Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE-Tomek method to detect cardiac arrhythmia based on 12 -lead electrocardiogram signals. Digit Health 2024; 10:20552076241234624. [PMID: 38449680 PMCID: PMC10916475 DOI: 10.1177/20552076241234624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 01/26/2024] [Indexed: 03/08/2024] Open
Abstract
Objectives Cardiac arrhythmia is one of the most severe cardiovascular diseases that can be fatal. Therefore, its early detection is critical. However, detecting types of arrhythmia by physicians based on visual identification is time-consuming and subjective. Deep learning can develop effective approaches to classify arrhythmias accurately and quickly. This study proposed a deep learning approach developed based on a Chapman-Shaoxing electrocardiogram (ECG) dataset signal to detect seven types of arrhythmias. Method Our DNN model is a hybrid CNN-BILSTM-BiGRU algorithm assisted by a multi-head self-attention mechanism regarding the challenging problem of classifying various arrhythmias of ECG signals. Additionally, the synthetic minority oversampling technique (SMOTE)-Tomek technique was utilized to address the data imbalance problem to detect and classify cardiac arrhythmias. Result The proposed model, trained with a single lead, was tested using a dataset containing 10,466 participants. The performance of the algorithm was evaluated using a random split validation approach. The proposed algorithm achieved an accuracy of 98.57% by lead II and 98.34% by lead aVF for the classification of arrhythmias. Conclusion We conducted an analysis of single-lead ECG signals to evaluate the effectiveness of our proposed hybrid model in diagnosing and classifying different types of arrhythmias. We trained separate classification models using each individual signal lead. Additionally, we implemented the SMOTE-Tomek technique along with cross-entropy loss as a cost function to address the class imbalance problem. Furthermore, we utilized a multi-headed self-attention mechanism to adjust the network structure and classify the seven arrhythmia classes. Our model achieved high accuracy and demonstrated good generalization ability in detecting ECG arrhythmias. However, further testing of the model with diverse datasets is crucial to validate its performance.
Collapse
Affiliation(s)
- Sara Chopannejad
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Arash Roshanpoor
- Department of Computer, Yadegar-e-Imam Khomeini (RAH), Janat-abad Branch, Islamic Azad University, Tehran, Iran
| | - Farahnaz Sadoughi
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
10
|
Minic A, Jovanovic L, Bacanin N, Stoean C, Zivkovic M, Spalevic P, Petrovic A, Dobrojevic M, Stoean R. Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:9878. [PMID: 38139724 PMCID: PMC10747899 DOI: 10.3390/s23249878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 11/28/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023]
Abstract
Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive test using a network of up to 12 sensors (electrodes) on the chest and limbs to create an electrocardiogram (ECG). By visually observing these readings, experienced professionals can make accurate diagnoses and, if needed, request further testing. However, the training and experience needed to make accurate diagnoses are significant. This work explores the potential of recurrent neural networks for anomaly detection in ECG readings. Furthermore, to attain the best possible performance for these networks, training parameters, and network architectures are optimized using a modified version of the well-established particle swarm optimization algorithm. The performance of the optimized models is compared to models created by other contemporary optimizers, and the results show significant potential for real-world applications. Further analyses are carried out on the best-performing models to determine feature importance.
Collapse
Affiliation(s)
- Ana Minic
- Teacher Education Faculty, University of Pristina in Kosovska Mitrovica, 38220 Kosovska Mitrovica, Serbia;
| | - Luka Jovanovic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Nebojsa Bacanin
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Catalin Stoean
- Department of Computer Science, Faculty of Sciences, University of Craiova, 200585 Craiova, Romania;
| | - Miodrag Zivkovic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Petar Spalevic
- Faculty of Technical Science, University of Pristina in Kosovska Mitrovica, Filipa Visnjica bb, 38220 Kosovska Mitrovica, Serbia;
| | - Aleksandar Petrovic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Milos Dobrojevic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Ruxandra Stoean
- Department of Computer Science, Faculty of Sciences, University of Craiova, 200585 Craiova, Romania;
| |
Collapse
|
11
|
Wu J, Zou X, Tao R, Zheng G. Nonlinear regression of remaining surgery duration from videos via Bayesian LSTM-based deep negative correlation learning. Comput Med Imaging Graph 2023; 110:102314. [PMID: 37988845 DOI: 10.1016/j.compmedimag.2023.102314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 10/06/2023] [Accepted: 11/14/2023] [Indexed: 11/23/2023]
Abstract
In this paper, we address the problem of estimating remaining surgery duration (RSD) from surgical video frames. We propose a Bayesian long short-term memory (LSTM) network-based Deep Negative Correlation Learning approach called BD-Net for accurate regression of RSD prediction as well as estimation of prediction uncertainty. Our method aims to extract discriminative visual features from surgical video frames and model the temporal dependencies among frames to improve the RSD prediction accuracy. To this end, we propose to train an ensemble of Bayesian LSTMs on top of a backbone network by the way of deep negative correlation learning (DNCL). More specifically, we deeply learn a pool of decorrelated Bayesian regressors with sound generalization capabilities through managing their intrinsic diversities. BD-Net is simple and efficient. After training, it can produce both RSD prediction and uncertainty estimation in a single inference run. We demonstrate the efficacy of BD-Net on publicly available datasets of two different types of surgeries: one containing 101 cataract microscopic surgeries with short durations and the other containing 80 cholecystectomy laparoscopic surgeries with relatively longer durations. Experimental results on both datasets demonstrate that the proposed BD-Net achieves better results than the state-of-the-art (SOTA) methods. A reference implementation of our method can be found at: https://github.com/jywu511/BD-Net.
Collapse
Affiliation(s)
- Junyang Wu
- Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800 Dongchuan Road, 200240 Shanghai, China
| | - Xiaoyang Zou
- Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800 Dongchuan Road, 200240 Shanghai, China
| | - Rong Tao
- Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800 Dongchuan Road, 200240 Shanghai, China
| | - Guoyan Zheng
- Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800 Dongchuan Road, 200240 Shanghai, China.
| |
Collapse
|
12
|
Ansari MY, Qaraqe M, Charafeddine F, Serpedin E, Righetti R, Qaraqe K. Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade. Artif Intell Med 2023; 146:102690. [PMID: 38042607 DOI: 10.1016/j.artmed.2023.102690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 12/04/2023]
Abstract
Twelve lead electrocardiogram signals capture unique fingerprints about the body's biological processes and electrical activity of heart muscles. Machine learning and deep learning-based models can learn the embedded patterns in the electrocardiogram to estimate complex metrics such as age and gender that depend on multiple aspects of human physiology. ECG estimated age with respect to the chronological age reflects the overall well-being of the cardiovascular system, with significant positive deviations indicating an aged cardiovascular system and a higher likelihood of cardiovascular mortality. Several conventional, machine learning, and deep learning-based methods have been proposed to estimate age from electronic health records, health surveys, and ECG data. This manuscript comprehensively reviews the methodologies proposed for ECG-based age and gender estimation over the last decade. Specifically, the review highlights that elevated ECG age is associated with atherosclerotic cardiovascular disease, abnormal peripheral endothelial dysfunction, and high mortality, among many other cardiovascular disorders. Furthermore, the survey presents overarching observations and insights across methods for age and gender estimation. This paper also presents several essential methodological improvements and clinical applications of ECG-estimated age and gender to encourage further improvements of the state-of-the-art methodologies.
Collapse
Affiliation(s)
- Mohammed Yusuf Ansari
- Texas A&M University, College Station, TX, USA; Texas A&M University at Qatar, Doha, Qatar.
| | - Marwa Qaraqe
- Division of Information and Computing Technology, Hamad Bin Khalifa University, Doha, Qatar; Texas A&M University at Qatar, Doha, Qatar
| | | | | | | | | |
Collapse
|
13
|
Delfan N, Forouzanfar M. Hybrid Deep Morpho-Temporal Framework for Oscillometric Blood Pressure Measurement. IEEE J Biomed Health Inform 2023; 27:5293-5301. [PMID: 37651480 DOI: 10.1109/jbhi.2023.3310868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Oscillometric blood pressure (BP) measurement devices are widely utilized as the primary automated BP measurement tools in non-specialist environments. However, their accuracy and reliability vary under different settings and for different age groups and health conditions. An essential constraint of current oscillometric BP measurement devices is their analysis algorithms' incapacity to capture the BP information encoded in the pattern of recorded oscillometric pulses to its fullest extent. In this article, we propose a new 2D oscillometric data representation that enables a full characterization of arterial system and empowers the application of deep learning to extract the most informative features correlated with BP. A hybrid convolutional-recurrent neural network was developed to capture the oscillometric pulses morphological information as well as their temporal evolution over the cuff deflation period from the 2D structure, and estimate BP. The performance of the proposed method was verified on three oscillometric databases collected from the wrist and upper arms of 245 individuals. It was found that it achieves a mean error and a standard deviation of error of as low as 0.08 mmHg and 2.4 mmHg in the estimation of systolic BP, and 0.04 mmHg and 2.2 mmHg in the estimation of diastolic BP, respectively. Our proposed method outperformed the state-of-the-art techniques and satisfied the current international standards for BP monitors by a wide margin. The proposed method shows promise toward robust and objective BP estimation in a variety of patients and monitoring situations.
Collapse
|
14
|
Nguyen T, Nguyen P, Tran D, Pham H, Nguyen Q, Le T, Van H, Do B, Tran P, Le V, Nguyen T, Tran L, Pham H. Ensemble learning of myocardial displacements for myocardial infarction detection in echocardiography. Front Cardiovasc Med 2023; 10:1185172. [PMID: 37900571 PMCID: PMC10613081 DOI: 10.3389/fcvm.2023.1185172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 09/18/2023] [Indexed: 10/31/2023] Open
Abstract
Background Early detection and localization of myocardial infarction (MI) can reduce the severity of cardiac damage through timely treatment interventions. In recent years, deep learning techniques have shown promise for detecting MI in echocardiographic images. Existing attempts typically formulate this task as classification and rely on a single segmentation model to estimate myocardial segment displacements. However, there has been no examination of how segmentation accuracy affects MI classification performance or the potential benefits of using ensemble learning approaches. Our study investigates this relationship and introduces a robust method that combines features from multiple segmentation models to improve MI classification performance by leveraging ensemble learning. Materials and Methods Our method combines myocardial segment displacement features from multiple segmentation models, which are then input into a typical classifier to estimate the risk of MI. We validated the proposed approach on two datasets: the public HMC-QU dataset (109 echocardiograms) for training and validation, and an E-Hospital dataset (60 echocardiograms) from a local clinical site in Vietnam for independent testing. Model performance was evaluated based on accuracy, sensitivity, and specificity. Results The proposed approach demonstrated excellent performance in detecting MI. It achieved an F1 score of 0.942, corresponding to an accuracy of 91.4%, a sensitivity of 94.1%, and a specificity of 88.3%. The results showed that the proposed approach outperformed the state-of-the-art feature-based method, which had a precision of 85.2%, a specificity of 70.1%, a sensitivity of 85.9%, an accuracy of 85.5%, and an accuracy of 80.2% on the HMC-QU dataset. On the external validation set, the proposed model still performed well, with an F1 score of 0.8, an accuracy of 76.7%, a sensitivity of 77.8%, and a specificity of 75.0%. Conclusions Our study demonstrated the ability to accurately predict MI in echocardiograms by combining information from several segmentation models. Further research is necessary to determine its potential use in clinical settings as a tool to assist cardiologists and technicians with objective assessments and reduce dependence on operator subjectivity. Our research codes are available on GitHub at https://github.com/vinuni-vishc/mi-detection-echo.
Collapse
Affiliation(s)
- Tuan Nguyen
- VinUni-Illinois Smart Health Center, VinUniversity, Hanoi, Vietnam
- College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam
| | - Phi Nguyen
- Institute for Artificial Intelligence, VNU University of Engineering and Technology, Hanoi, Vietnam
| | - Dai Tran
- Cardiovascular Center, E Hospital, Hanoi, Vietnam
| | - Hung Pham
- Vietnam National Heart Institute, Bach Mai Hospital, Hanoi, Vietnam
| | - Quang Nguyen
- Vietnam National Heart Institute, Bach Mai Hospital, Hanoi, Vietnam
| | - Thanh Le
- Vietnam National Heart Institute, Bach Mai Hospital, Hanoi, Vietnam
| | - Hanh Van
- Vietnam National Heart Institute, Bach Mai Hospital, Hanoi, Vietnam
| | - Bach Do
- Vietnam National Heart Institute, Bach Mai Hospital, Hanoi, Vietnam
| | - Phuong Tran
- Vietnam National Heart Institute, Bach Mai Hospital, Hanoi, Vietnam
| | - Vinh Le
- Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam
| | - Thuy Nguyen
- Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam
| | - Long Tran
- Institute for Artificial Intelligence, VNU University of Engineering and Technology, Hanoi, Vietnam
| | - Hieu Pham
- VinUni-Illinois Smart Health Center, VinUniversity, Hanoi, Vietnam
- College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam
| |
Collapse
|
15
|
Legé D, Gergelé L, Prud’homme M, Lapayre JC, Launey Y, Henriet J. A Deep Learning-Based Automated Framework for Subpeak Designation on Intracranial Pressure Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:7834. [PMID: 37765896 PMCID: PMC10537288 DOI: 10.3390/s23187834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023]
Abstract
The intracranial pressure (ICP) signal, as monitored on patients in intensive care units, contains pulses of cardiac origin, where P1 and P2 subpeaks can often be observed. When calculable, the ratio of their relative amplitudes is an indicator of the patient's cerebral compliance. This characterization is particularly informative for the overall state of the cerebrospinal system. The aim of this study is to develop and assess the performances of a deep learning-based pipeline for P2/P1 ratio computation that only takes a raw ICP signal as an input. The output P2/P1 ratio signal can be discontinuous since P1 and P2 subpeaks are not always visible. The proposed pipeline performs four tasks, namely (i) heartbeat-induced pulse detection, (ii) pulse selection, (iii) P1 and P2 designation, and (iv) signal smoothing and outlier removal. For tasks (i) and (ii), the performance of a recurrent neural network is compared to that of a convolutional neural network. The final algorithm is evaluated on a 4344-pulse testing dataset sampled from 10 patient recordings. Pulse selection is achieved with an area under the curve of 0.90, whereas the subpeak designation algorithm identifies pulses with a P2/P1 ratio > 1 with 97.3% accuracy. Although it still needs to be evaluated on a larger number of labeled recordings, our automated P2/P1 ratio calculation framework appears to be a promising tool that can be easily embedded into bedside monitoring devices.
Collapse
Affiliation(s)
- Donatien Legé
- Sophysa, 91400 Orsay, France;
- DISC Department, FEMTO-ST, Université de Franche-Comté, 25000 Besançon, France; (J.-C.L.); (J.H.)
| | - Laurent Gergelé
- Intensive Care Unit, University Hospital of Saint-Etienne, 42100 Saint-Etienne, France;
| | | | - Jean-Christophe Lapayre
- DISC Department, FEMTO-ST, Université de Franche-Comté, 25000 Besançon, France; (J.-C.L.); (J.H.)
| | - Yoann Launey
- Intensive Care Unit, University Hospital of Rennes, 35000 Rennes, France;
| | - Julien Henriet
- DISC Department, FEMTO-ST, Université de Franche-Comté, 25000 Besançon, France; (J.-C.L.); (J.H.)
| |
Collapse
|
16
|
Fan L, Shi X, Wang Z, Zhang R, Zhang J. Disease identification method based on graph features between pulse cycles. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
|
17
|
Pham BT, Le PT, Tai TC, Hsu YC, Li YH, Wang JC. Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction. SENSORS (BASEL, SWITZERLAND) 2023; 23:2993. [PMID: 36991703 PMCID: PMC10051525 DOI: 10.3390/s23062993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/23/2023] [Accepted: 02/24/2023] [Indexed: 06/19/2023]
Abstract
An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset.
Collapse
Affiliation(s)
- Bach-Tung Pham
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan
| | - Phuong Thi Le
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City 320317, Taiwan
| | - Tzu-Chiang Tai
- Department of Computer Science and Information Engineering, Providence University, Taichung City 43301, Taiwan
| | - Yi-Chiung Hsu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City 320317, Taiwan
| | - Yung-Hui Li
- AI Research Center, Hon Hai Research Institute, New Taipei City 236, Taiwan
| | - Jia-Ching Wang
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan
| |
Collapse
|
18
|
Long J, Chen Y, Cao D, Chen P, Yang M. Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process. ACS OMEGA 2023; 8:5437-5450. [PMID: 36816643 PMCID: PMC9933188 DOI: 10.1021/acsomega.2c06624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
Solvent deasphalting (SDA) is a complex multiscale continuous process. The operation mode of the SDA process is not considered in the related data-driven model. Therefore, this paper proposes a time lag process prediction model with multiple operation modes to solve the above problem. First, based on random forests, the relative importance of initial input variables in the SDA process on DAO yield and Conradson carbon residual are studied and features are selected according to the results. Then, the stack denoising autoencoder (SDAE) is used to reconstruct the data and obtain the nonlinear mapping information of hidden layers of SDAE and achieve feature dimension reduction. SDAE can improve clustering accuracy of fuzzy c-means, and the operation mode of SDA process is accurately divided. Long short-term memory (LSTM) is used to establish a multicondition LSTM model. Compared with the traditional LSTM model, the multicondition LSTM model has a higher prediction accuracy with R 2 > 0.95. The sensitivity analyses of the properties of feed and operating conditions on DAO yield are consistent with the principle of two-phase countercurrent extraction in the SDA process. In addition, the benchmark test of the Tennessee Eastman process shows that the proposed method is also effective in the fault detection of other processes. Because the multicondition LSTM can predict the future process measurement data according to operating mode, it can better avoid the false alarm problem and predict the fault earlier.
Collapse
|
19
|
Al-mousa A, Baniissa J, Hashem T, Ibraheem T. Enhanced electrocardiogram machine learning-based classification with emphasis on fusion and unknown heartbeat classes. Digit Health 2023; 9:20552076231187608. [PMID: 37469959 PMCID: PMC10353025 DOI: 10.1177/20552076231187608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 06/23/2023] [Indexed: 07/21/2023] Open
Abstract
Building an electrocardiogram (ECG) heartbeat classification model is essential for early arrhythmia detection. This research aims to build a reliable model that can classify heartbeats into five heartbeat types: normal beat (N), supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), fusion beat (F), and unknown beat (Q), with a focus on enhancing the predictions of the uncommon Q and F heartbeats. The base dataset used is the MIT-BIH SupraVentricular Database, which was used to train and compare the performance of five machine learning models: logistic regression, Random Forest (RF), K-nearest neighbor, linear support vector machine, and linear discriminant analysis. In addition to using the synthetic minority oversampling technique, data extracted from multiple databases for the F and Q classes were combined with the original base dataset. These methods resulted in significant improvement in the recall for the rare F and Q classes when compared to the literature. The RF algorithm produced the best performance with an accuracy of 97% and recall values equal to 97%, 93%, 95%, 95%, and 30% for N, SVEB, VEB, F, and Q, respectively.
Collapse
Affiliation(s)
- Amjed Al-mousa
- Computer Engineering Department, Princess Sumaya University for Technology, Amman, Jordan
| | - Joud Baniissa
- Computer Engineering Department, Princess Sumaya University for Technology, Amman, Jordan
| | - Tala Hashem
- Computer Engineering Department, Princess Sumaya University for Technology, Amman, Jordan
| | - Tala Ibraheem
- Computer Engineering Department, Princess Sumaya University for Technology, Amman, Jordan
| |
Collapse
|
20
|
Abbas Q, Hussain A, Baig AR. Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers. Diagnostics (Basel) 2022; 12:diagnostics12123109. [PMID: 36553116 PMCID: PMC9777096 DOI: 10.3390/diagnostics12123109] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
The major cause of death worldwide is due to cardiovascular disorders (CVDs). For a proper diagnosis of CVD disease, an inexpensive solution based on phonocardiogram (PCG) signals is proposed. (1) Background: Currently, a few deep learning (DL)-based CVD systems have been developed to recognize different stages of CVD. However, the accuracy of these systems is not up-to-the-mark, and the methods require high computational power and huge training datasets. (2) Methods: To address these issues, we developed a novel attention-based technique (CVT-Trans) on a convolutional vision transformer to recognize and categorize PCG signals into five classes. The continuous wavelet transform-based spectrogram (CWTS) strategy was used to extract representative features from PCG data. Following that, a new CVT-Trans architecture was created to categorize the CWTS signals into five groups. (3) Results: The dataset derived from our investigation indicated that the CVT-Trans system had an overall average accuracy ACC of 100%, SE of 99.00%, SP of 99.5%, and F1-score of 98%, based on 10-fold cross validation. (4) Conclusions: The CVD-Trans technique outperformed many state-of-the-art methods. The robustness of the constructed model was confirmed by 10-fold cross-validation. Cardiologists can use this CVT-Trans system to help patients with the diagnosis of heart valve problems.
Collapse
Affiliation(s)
- Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Ayyaz Hussain
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan
| | - Abdul Rauf Baig
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
- Correspondence: ; Tel.: +966-563336816
| |
Collapse
|
21
|
Hassannataj Joloudari J, Mojrian S, Nodehi I, Mashmool A, Kiani Zadegan Z, Khanjani Shirkharkolaie S, Alizadehsani R, Tamadon T, Khosravi S, Akbari Kohnehshari M, Hassannatajjeloudari E, Sharifrazi D, Mosavi A, Loh HW, Tan RS, Acharya UR. Application of artificial intelligence techniques for automated detection of myocardial infarction: a review. Physiol Meas 2022; 43. [PMID: 35803247 DOI: 10.1088/1361-6579/ac7fd9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 07/08/2022] [Indexed: 11/11/2022]
Abstract
Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals worldwide. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG and some other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and some other biophysical signals.
Collapse
Affiliation(s)
- Javad Hassannataj Joloudari
- Computer Engineering, University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, South Khorasan, 9717434765, Iran (the Islamic Republic of)
| | - Sanaz Mojrian
- Mazandaran University of Science and Technology, Mazandaran Province, Babol, Danesh 5, No. Sheykh Tabarasi, Iran, Babol, 47166-85635, Iran (the Islamic Republic of)
| | - Issa Nodehi
- University of Qom, Qom, shahid khodakaram blvd، Iran, Qom, Qom, 1519-37195, Iran (the Islamic Republic of)
| | - Amir Mashmool
- University of Geneva, Via del Molo, 65, 16128 Genova GE, Italy, Geneva, Geneva, 16121, ITALY
| | - Zeynab Kiani Zadegan
- University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, 9717434765, Iran (the Islamic Republic of)
| | - Sahar Khanjani Shirkharkolaie
- Mazandaran University of Science and Technology, Mazandaran Province, Babol, Danesh 5, No. Sheykh Tabarasi, Iran, Babol, 47166-85635, Iran (the Islamic Republic of)
| | - Roohallah Alizadehsani
- Deakin University - Geelong Waterfront Campus, IISRI, Geelong, Victoria, 3220, AUSTRALIA
| | - Tahereh Tamadon
- University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, 9717434765, Iran (the Islamic Republic of)
| | - Samiyeh Khosravi
- University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, 9717434765, Iran (the Islamic Republic of)
| | - Mitra Akbari Kohnehshari
- Bu Ali Sina University, QFRQ+V8H District 2, Hamedan, Iran, Hamedan, Hamedan, 6516738695, Iran (the Islamic Republic of)
| | - Edris Hassannatajjeloudari
- Maragheh University of Medical Sciences, 87VG+9J6, Maragheh, East Azerbaijan Province, Iran, Maragheh, East Azerbaijan, 55158-78151, Iran (the Islamic Republic of)
| | - Danial Sharifrazi
- Islamic Azad University Shiraz, Shiraz University, Iran, Shiraz, Fars, 74731-71987, Iran (the Islamic Republic of)
| | - Amir Mosavi
- Faculty of Informatics, Obuda University, Faculty of Informatics, Obuda University, Budapest, Hungary, Budapest, 1034, HUNGARY
| | - Hui Wen Loh
- Singapore University of Social Sciences, SG, Clementi Rd, 463, Singapore 599494, Singapore, 599491, SINGAPORE
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, 5 Hospital Dr, Singapore 169609, Singapore, 168752, SINGAPORE
| | - U Rajendra Acharya
- Electronic Computer Engineering Division, Ngee Ann Polytechnic, 535 Clementi Road, Singapore 599489, Singapore, 599489, SINGAPORE
| |
Collapse
|
22
|
Collision Localization and Classification on the End-Effector of a Cable-Driven Manipulator Applied to EV Auto-Charging Based on DCNN-SVM. SENSORS 2022; 22:s22093439. [PMID: 35591128 PMCID: PMC9102926 DOI: 10.3390/s22093439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/26/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022]
Abstract
With the increasing popularity of electric vehicles, cable-driven serial manipulators have been applied in auto-charging processes for electric vehicles. To ensure the safety of the physical vehicle-robot interaction in this scenario, this paper presents a model-independent collision localization and classification method for cable-driven serial manipulators. First, based on the dynamic characteristics of the manipulator, data sets of terminal collision are constructed. In contrast to utilizing signals based on torque sensors, our data sets comprise the vibration signals of a specific compensator. Then, the collected data sets are applied to construct and train our collision localization and classification model, which consists of a double-layer CNN and an SVM. Compared to previous works, the proposed method can extract features without manual intervention and can deal with collision when the contact surface is irregular. Furthermore, the proposed method is able to generate the location and classification of the collision at the same time. The simulated experiment results show the validity of the proposed collision localization and classification method, with promising prediction accuracy.
Collapse
|
23
|
Saidi F, Trabelsi Z. A hybrid deep learning-based framework for future terrorist activities modeling and prediction. EGYPTIAN INFORMATICS JOURNAL 2022. [DOI: 10.1016/j.eij.2022.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
24
|
Xiong P, Lee SMY, Chan G. Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review. Front Cardiovasc Med 2022; 9:860032. [PMID: 35402563 PMCID: PMC8990170 DOI: 10.3389/fcvm.2022.860032] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/18/2022] [Indexed: 12/24/2022] Open
Abstract
Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and non-invasive approach in MI detection, localization, diagnosis, and prognosis. Population-based screening with ECG can detect MI early and help prevent it but this method is too labor-intensive and time-consuming to carry out in practice unless artificial intelligence (AI) would be able to reduce the workload. Recent advances in using deep learning (DL) for ECG screening might rekindle this hope. This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years, covering convolutional neural network (CNN), long short-term memory (LSTM), convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), residual neural network (ResNet), and autoencoder (AE). In this period, CNN obtained the best popularity in both MI detection and localization, and the highest performance has been obtained from CNN and ResNet model. The reported maximum accuracies of the six different methods are all beyond 97%. Considering the usage of different datasets and ECG leads, the network that trained on 12 leads ECG data of PTB database has obtained higher accuracy than that on smaller number leads data of other datasets. In addition, some limitations and challenges of the DL techniques are also discussed in this review.
Collapse
Affiliation(s)
- Ping Xiong
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Simon Ming-Yuen Lee
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Ging Chan
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China
| |
Collapse
|
25
|
Joolee JB, Uddin MA, Jeon S. Deep multi-model fusion network based real object tactile understanding from haptic data. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03181-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
26
|
A Shallow Domain Knowledge Injection (SDK-Injection) Method for Improving CNN-Based ECG Pattern Classification. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
ECG pattern classification for identifying the progress status of various heart diseases is a typical nonlinear problem. Therefore, deep learning-based automatic ECG diagnosis is being widely studied, and for this purpose, the CNN is mainly used to classify ECG patterns. In this case, it is hard to expect any further improvement in accuracy after optimizing the parameters. We propose a shallow domain knowledge injection method that can improve the accuracy of the existing parameter-optimized CNN. The proposed method can improve the accuracy by effectively injecting shallow domain knowledge, that can be acquired by non-medical experts, into the existing parameter-optimized CNN. The experiments show that the proposed method can be applied to both heart disease diagnoses and general ECG classification tasks, while improving the existing accuracy for both types of tasks.
Collapse
|
27
|
Dual-Hybrid Modeling for Option Pricing of CSI 300ETF. INFORMATION 2022. [DOI: 10.3390/info13010036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The reasonable pricing of options can effectively help investors avoid risks and obtain benefits, which plays a very important role in the stability of the financial market. The traditional single option pricing model often fails to meet the ideal expectations due to its limited conditions. Combining an economic model with a deep learning model to establish a hybrid model provides a new method to improve the prediction accuracy of the pricing model. This includes the usage of real historical data of about 10,000 sets of CSI 300 ETF options from January to December 2020 for experimental analysis. Aiming at the prediction problem of CSI 300ETF option pricing, based on the importance of random forest features, the Convolutional Neural Network and Long Short-Term Memory model (CNN-LSTM) in deep learning is combined with a typical stochastic volatility Heston model and stochastic interests CIR model in parameter models. The dual hybrid pricing model of the call option and the put option of CSI 300ETF is established. The dual-hybrid model and the reference model are integrated with ridge regression to further improve the forecasting effect. The results show that the dual-hybrid pricing model proposed in this paper has high accuracy, and the prediction accuracy is tens to hundreds of times higher than the reference model; moreover, MSE can be as low as 0.0003. The article provides an alternative method for the pricing of financial derivatives.
Collapse
|