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Stamate E, Piraianu AI, Ciobotaru OR, Crassas R, Duca O, Fulga A, Grigore I, Vintila V, Fulga I, Ciobotaru OC. Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years. Diagnostics (Basel) 2024; 14:1103. [PMID: 38893630 PMCID: PMC11172021 DOI: 10.3390/diagnostics14111103] [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: 04/22/2024] [Revised: 05/12/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. RESULTS We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. CONCLUSIONS The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.
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Affiliation(s)
- Elena Stamate
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Alin-Ionut Piraianu
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Oana Roxana Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
| | - Rodica Crassas
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Oana Duca
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Ionica Grigore
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Vlad Vintila
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Clinical Department of Cardio-Thoracic Pathology, University of Medicine and Pharmacy “Carol Davila” Bucharest, 37 Dionisie Lupu Street, 4192910 Bucharest, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Octavian Catalin Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
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Aleksandra S, Robert K, Klaudia K, Dawid L, Mariusz S. Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions. ARCHIVES OF ACADEMIC EMERGENCY MEDICINE 2024; 12:e22. [PMID: 38572221 PMCID: PMC10988184 DOI: 10.22037/aaem.v12i1.2110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Introduction The burgeoning burden on emergency departments is a global challenge that we have been confronting for many years. Emerging artificial intelligence (AI)-based solutions may constitute a critical component in the optimization of these units. This systematic review was conducted to thoroughly examine and summarize the currently available AI solutions, assess potential benefits from their implementation, and identify anticipated directions of further development in this fascinating and rapidly evolving field. Methods This systematic review utilized data compiled from three key scientific databases: PubMed (2045 publications), Scopus (877 publications), and Web of Science (2495 publications). After meticulous removal of duplicates, we conducted a detailed analysis of 2052 articles, including 147 full-text papers. From these, we selected 51 of the most pertinent and representative publications for the review. Results Overall the present research indicates that due to high accuracy and sensitivity of machine learning (ML) models it's reasonable to use AI in support of doctors as it can show them the potential diagnosis, which could save time and resources. However, AI-generated diagnoses should be verified by a doctor as AI is not infallible. Conclusions Currently available AI algorithms are capable of analysing complex medical data with unprecedented precision and speed. Despite AI's vast potential, it is still a nascent technology that is often perceived as complicated and challenging to implement. We propose that a pivotal point in effectively harnessing this technology is the close collaboration between medical professionals and AI experts. Future research should focus on further refining AI algorithms, performing comprehensive validation, and introducing suitable legal regulations and standard procedures, thereby fully leveraging the potential of AI to enhance the quality and efficiency of healthcare delivery.
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Affiliation(s)
- Szymczyk Aleksandra
- Department of Emergency Medicine, Medical University of Gdansk, Smoluchowskiego 17, 80-214 Gdansk, Poland
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Liu PY, Lin C, Lin CS, Fang WH, Lee CC, Wang CH, Tsai DJ. Artificial Intelligence-Enabled Electrocardiography Detects B-Type Natriuretic Peptide and N-Terminal Pro-Brain Natriuretic Peptide. Diagnostics (Basel) 2023; 13:2723. [PMID: 37685262 PMCID: PMC10487184 DOI: 10.3390/diagnostics13172723] [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: 07/25/2023] [Revised: 08/18/2023] [Accepted: 08/20/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND: The B-type natriuretic peptide (BNP) and N-terminal pro-brain natriuretic peptide (pBNP) are predictors of cardiovascular morbidity and mortality. Since the artificial intelligence (AI)-enabled electrocardiogram (ECG) system is widely used in the management of many cardiovascular diseases (CVDs), patients requiring intensive monitoring may benefit from an AI-ECG with BNP/pBNP predictions. This study aimed to develop an AI-ECG to predict BNP/pBNP and compare their values for future mortality. METHODS: The development, tuning, internal validation, and external validation sets included 47,709, 16,249, 4001, and 6042 ECGs, respectively. Deep learning models (DLMs) were trained using a development set for estimating ECG-based BNP/pBNP (ECG-BNP/ECG-pBNP), and the tuning set was used to guide the training process. The ECGs in internal and external validation sets belonging to nonrepeating patients were used to validate the DLMs. We also followed-up all-cause mortality to explore the prognostic value. RESULTS: The DLMs accurately distinguished mild (≥500 pg/mL) and severe (≥1000 pg/mL) an abnormal BNP/pBNP with AUCs of ≥0.85 in the internal and external validation sets, which provided sensitivities of 68.0-85.0% and specificities of 77.9-86.2%. In continuous predictions, the Pearson correlation coefficient between ECG-BNP and ECG-pBNP was 0.93, and they were both associated with similar ECG features, such as the T wave axis and correct QT interval. ECG-pBNP provided a higher all-cause mortality predictive value than ECG-BNP. CONCLUSIONS: The AI-ECG can accurately estimate BNP/pBNP and may be useful for monitoring the risk of CVDs. Moreover, ECG-pBNP may be a better indicator to manage the risk of future mortality.
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Affiliation(s)
- Pang-Yen Liu
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (P.-Y.L.); (C.-S.L.)
| | - Chin Lin
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei 114, Taiwan;
- School of Public Health, National Defense Medical Center, Taipei 114, Taiwan
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (P.-Y.L.); (C.-S.L.)
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei 114, Taiwan;
| | - Wen-Hui Fang
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
- Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Chia-Cheng Lee
- Medical Informatics Office, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Chih-Hung Wang
- Department of Otolaryngology–Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei 114, Taiwan
| | - Dung-Jang Tsai
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei 114, Taiwan;
- School of Public Health, National Defense Medical Center, Taipei 114, Taiwan
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
- Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City 242, Taiwan
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Chen YHJ, Lin CS, Lin C, Tsai DJ, Fang WH, Lee CC, Wang CH, Chen SJ. An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration. J Med Syst 2023; 47:81. [PMID: 37523102 DOI: 10.1007/s10916-023-01980-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 07/21/2023] [Indexed: 08/01/2023]
Abstract
Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from the initial assessment is limited, hindering the risk identification accuracy of triage. Therefore, we sought to develop a 'dynamic' triage system as secondary screening, using artificial intelligence (AI) techniques to integrate information from initial assessment data and subsequent examinations. This retrospective cohort study included 134,112 ED visits with at least one electrocardiography (ECG) and chest X-ray (CXR) in a medical center from 2012 to 2022. Additionally, an independent community hospital provided 45,614 ED visits as an external validation set. We trained an eXtreme gradient boosting (XGB) model using initial assessment data to predict all-cause mortality in 7 days. Two deep learning models (DLMs) using ECG and CXR were trained to stratify mortality risks. The dynamic triage levels were based on output from the XGB-triage and DLMs from ECG and CXR. During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of the XGB-triage model was >0.866; furthermore, the AUCs of DLMs using ECG and CXR were >0.862 and >0.886, respectively. The dynamic triage scale provided a higher C-index (0.914-0.920 vs. 0.827-0.843) than the original one and demonstrated better predictive ability for 5-year mortality, 30-day ED revisit, and 30-day discharge. The AI-based risk scale provides a more accurate and dynamic stratification of mortality risk in ED patients, particularly in identifying patients who tend to be overlooked due to atypical symptoms.
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Affiliation(s)
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center Taipei, Taipei, Taiwan
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Chin Lin
- School of Public Health, National Defense Medical Center, Taipei, Taiwan
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
| | - Dung-Jang Tsai
- Center for Artificial Intelligence and Internet of Things, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Wen-Hui Fang
- Center for Artificial Intelligence and Internet of Things, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Cheng Lee
- Medical Informatics Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chih-Hung Wang
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Sy-Jou Chen
- Department of Emergency Medicine, Tri-Service General Hospital, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490, Taiwan.
- Graduate Institute of Injury Prevention and Control, College of Public Health and Nutrition, Taipei Medical University, Taipei, Taiwan.
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Lou YS, Lin CS, Fang WH, Lee CC, Lin C. Extensive deep learning model to enhance electrocardiogram application via latent cardiovascular feature extraction from identity identification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107359. [PMID: 36738606 DOI: 10.1016/j.cmpb.2023.107359] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 12/22/2022] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Deep learning models (DLMs) have been successfully applied in biomedicine primarily using supervised learning with large, annotated databases. However, scarce training resources limit the potential of DLMs for electrocardiogram (ECG) analysis. METHODS We have developed a novel pre-training strategy for unsupervised identity identification with an area under the receiver operating characteristic curve (AUC) >0.98. Accordingly, a DLM pre-trained with identity identification can be applied to 70 patient characteristic predictions using transfer learning (TL). These ECG-based patient characteristics were then used for cardiovascular disease (CVD) risk prediction. The DLMs were trained using 507,729 ECGs from 222,473 patients and validated using two independent validation sets (n = 27,824/31,925). RESULTS The DLMs using our method exhibited better performance than directly trained DLMs. Additionally, our DLM performed better than those of previous studies in terms of gender (AUC [internal/external] = 0.982/0.968), age (correlation = 0.886/0.892), low ejection fraction (AUC = 0.942/0.951), and critical markers not addressed previously, including high B-type natriuretic peptide (AUC = 0.921/0.899). Additionally, approximately 50% of the ECG-based characteristics provided significantly more prediction information for cardiovascular risk than real characteristics. CONCLUSIONS This is the first study to use identity identification as a pre-training task for TL in ECG analysis. An extensive exploration of the relationship between ECG and 70 patient characteristics was conducted. Our DLM-enhanced ECG interpretation system extensively advanced ECG-related patient characteristic prediction and mortality risk management for cardiovascular diseases.
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Affiliation(s)
- Yu-Sheng Lou
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan; School of Public Health, National Defense Medical Center, Taipei, Taiwan
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C.; Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Wen-Hui Fang
- Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chia-Cheng Lee
- Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C.; Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chin Lin
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan; School of Public Health, National Defense Medical Center, Taipei, Taiwan; Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C.; School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C..
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Yoon T, Kang D. Multi-Modal Stacking Ensemble for the Diagnosis of Cardiovascular Diseases. J Pers Med 2023; 13:jpm13020373. [PMID: 36836607 PMCID: PMC9967487 DOI: 10.3390/jpm13020373] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/18/2023] [Accepted: 02/18/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Deep learning methods have been widely used in the field of medical image analysis and have shown promising results in the diagnosis of CVDs. METHODS Experiments were performed on 12-lead electrocardiogram (ECG) databases collected by Chapman University and Shaoxing People's Hospital. The ECG signal of each lead was converted into a scalogram image and an ECG grayscale image and used to fine-tune the pretrained ResNet-50 model of each lead. The ResNet-50 model was used as a base learner for the stacking ensemble method. Logistic regression, support vector machine, random forest, and XGBoost were used as a meta learner by combining the predictions of the base learner. The study introduced a method called multi-modal stacking ensemble, which involves training a meta learner through a stacking ensemble that combines predictions from two modalities: scalogram images and ECG grayscale images. RESULTS The multi-modal stacking ensemble with a combination of ResNet-50 and logistic regression achieved an AUC of 0.995, an accuracy of 93.97%, a sensitivity of 0.940, a precision of 0.937, and an F1-score of 0.936, which are higher than those of LSTM, BiLSTM, individual base learners, simple averaging ensemble, and single-modal stacking ensemble methods. CONCLUSION The proposed multi-modal stacking ensemble approach showed effectiveness for diagnosing CVDs.
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Wu Z, Li X, Feng Z, Wan C, Li Y, Li T, Yang Q, Liu X, Ren M, Li J, Shang X, Zhang X, Huang X. Stable and Dynamic Multiparameter Monitoring on Chests Using Flexible Skin Patches with Self-Adhesive Electrodes and a Synchronous Correlation Peak Extraction Algorithm. Adv Healthc Mater 2023; 12:e2202629. [PMID: 36604167 DOI: 10.1002/adhm.202202629] [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: 10/12/2022] [Revised: 12/26/2022] [Indexed: 01/07/2023]
Abstract
Advances in wearable bioelectronics interfacing directly with skin offer important tools for non-invasive measurements of physiological parameters. However, wearable monitoring devices majorly conduct static sensing to avoid signal disturbance and unreliable contact with the skin. Dynamic multiparameter sensing is challenging even with the advanced flexible skin patches. This epidermal electronics system with self-adhesive conductive electrodes to supply stable skin contact and a unique synchronous correlation peak extraction (SCPE) algorithm to minimize motion artifacts in the photoplethysmogram (PPG) signals. The skin patch system can simultaneously and precisely monitor electrocardiogram (ECG), PPG, body temperature, and acceleration on chests undergoing daily activities. The low latency between the ECG and the PPG signals enables the SCPE algorithm that leads to reduced errors in deduced heart rates and improved performance in oxygen level determination than conventional adaptive filtering and wavelet transformation approaches. Dynamic multiparameter recording over 24 h by the system can reflect the circadian patterns of the wearers with low disturbance from motion artifacts. This demonstrated system may be applied for health monitoring in large populations to alleviate pressure on medical systems and assist management of public health crisis.
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Affiliation(s)
- Ziyue Wu
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Xueting Li
- Institute of Wearable Technology and Bioelectronics, Qiantang Science and Technology Innovation Center, 1002 23rd Street, Hangzhou, Zhejiang, 310018, China
| | - Zhijie Feng
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Chunxue Wan
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Ya Li
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Tianyu Li
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Qing Yang
- Center of Flexible Wearable Technology, Institute of Flexible Electronic Technology of Tsinghua, 906 Asia-Pacific Road, Jiaxing, Zhejiang, 314006, China
| | - Xinyu Liu
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Miaoning Ren
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Jiameng Li
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Xue Shang
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Xiangyu Zhang
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Xian Huang
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China.,Center of Flexible Wearable Technology, Institute of Flexible Electronic Technology of Tsinghua, 906 Asia-Pacific Road, Jiaxing, Zhejiang, 314006, China
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Safdar MF, Nowak RM, Pałka P. A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network. SENSORS (BASEL, SWITZERLAND) 2022; 22:9576. [PMID: 36559944 PMCID: PMC9780813 DOI: 10.3390/s22249576] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/18/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
The non-invasive electrocardiogram (ECG) signals are useful in heart condition assessment and are found helpful in diagnosing cardiac diseases. However, traditional ways, i.e., a medical consultation required effort, knowledge, and time to interpret the ECG signals due to the large amount of data and complexity. Neural networks have been shown to be efficient recently in interpreting the biomedical signals including ECG and EEG. The novelty of the proposed work is using spectrograms instead of raw signals. Spectrograms could be easily reduced by eliminating frequencies with no ECG information. Moreover, spectrogram calculation is time-efficient through short-time Fourier transformation (STFT) which allowed to present reduced data with well-distinguishable form to convolutional neural network (CNN). The data reduction was performed through frequency filtration by taking a specific cutoff value. These steps makes architecture of the CNN model simple which showed high accuracy. The proposed approach reduced memory usage and computational power through not using complex CNN models. A large publicly available PTB-XL dataset was utilized, and two datasets were prepared, i.e., spectrograms and raw signals for binary classification. The highest accuracy of 99.06% was achieved by the proposed approach, which reflects spectrograms are better than the raw signals for ECG classification. Further, up- and down-sampling of the signals were also performed at various sampling rates and accuracies were attained.
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Affiliation(s)
- Muhammad Farhan Safdar
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
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Lin C, Chen CC, Chau T, Lin CS, Tsai SH, Lee DJ, Lee CC, Shang HS, Lin SH. Artificial intelligence-enabled electrocardiography identifies severe dyscalcemias and has prognostic value. Clin Chim Acta 2022; 536:126-134. [PMID: 36167147 DOI: 10.1016/j.cca.2022.09.021] [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: 07/13/2022] [Revised: 09/15/2022] [Accepted: 09/20/2022] [Indexed: 11/03/2022]
Abstract
CONTEXT Abnormal serum calcium concentrations affect the heart and may alter the electrocardiogram (ECG), but the detection of hypocalcemia and hypercalcemia (collectively dyscalcemia) relies on blood laboratory tests requiring turnaround time. OBJECTIVE The study aimed to develop a bloodless artificial intelligence (AI)-enabled (ECG) method to rapidly detect dyscalcemia and analyze its possible utility for outcome prediction. METHODS This study collected 86,731 development, 15,611 tuning, 11,105 internal validation, and 8401 external validation ECGs from electronic medical records with at least 1 ECG associated with an albumin-adjusted calcium (aCa) value within 4 h. The main outcomes were to assess the accuracy of AI-ECG to predict aCa and follow up these patients for all-cause mortality, new-onset acute myocardial infraction (AMI), and new-onset heart failure (HF) to validate the ability of AI-ECG-aCa for previvor identification. RESULTS ECG-aCa had mean absolute errors (MAE) of 0.78/0.98 mg/dL and achieved an area under receiver operating characteristic curves (AUCs) 0.9219/0.8447 and 0.8948/0.7723 to detect severe hypercalcemia and hypocalcemia in the internal/external validation sets, respectively. Although < 20 % variance of ECG-aCa could be explained by traditional ECG features, the ECG-aCa was found to be associated with more complications. Patients with ECG-hypercalcemia but initially normal aCa were found to have a higher risk of subsequent all-cause mortality [hazard ratio (HR): 2.05, 95 % conference interval (CI): 1.55-2.70], new-onset AMI (HR: 2.88, 95 % CI: 1.72-4.83), and new-onset HF (HR: 2.02, 95 % CI: 1.38-2.97) in the internal validation set, which were also seen in external validation. CONCLUSION The AI-ECG-aCa may help detecting severe dyscalcemia for early diagnosis and ECG-hypercalcemia also has prognostic value for clinical outcomes (all-cause mortality and new-onset AMI and HF).
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Affiliation(s)
- Chin Lin
- School of Medicine, National Defense Medical Center, Taipei, Taiwan, ROC; School of Public Health, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Chien-Chou Chen
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Tom Chau
- Department of Medicine, Providence St. Vincent Medical Center, Portland, OR, USA
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Shi-Hung Tsai
- Department of Emergency Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Ding-Jie Lee
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Chia-Cheng Lee
- Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC; Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Hung-Sheng Shang
- Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Shih-Hua Lin
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.
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