1
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Ng J, Christensen M. Registered nurses' knowledge and interpretation of ECG rhythms: A cross-sectional study. Nurs Crit Care 2023. [PMID: 38156358 DOI: 10.1111/nicc.13013] [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: 08/02/2023] [Revised: 11/01/2023] [Accepted: 11/03/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND Electrocardiographic (ECG) monitoring and recording are seen as the most commonly used non-invasive diagnostic tool to identify cardiac arrhythmia and myocardial damage in the clinical setting. There is an expectation that critical care nurses are ideally trained to interpret abnormalities and morphology in the ECG more proficiently than nurses from general ward areas. However, the ability to interpret and recognise ECG abnormalities is dependent on which critical care area nurses are currently working in and their level of experience. AIM The aim of this study was to investigate registered nurses' knowledge in being able to identify and interpret select electrocardiographic rhythms. STUDY DESIGN This was a cross-sectional study that evaluated registered nurses' knowledge of electrocardiogram rhythm identification and interpretation. A convenience sample of 105 registered nurses currently enrolled in a 2-year Master's programme leading to critical care specialism and advanced practice nurse award were recruited. A 20-item multiple choice questionnaire that provided examples of electrocardiogram rhythm (n=14) abnormalities and rhythm abnormalities caused by electrolyte disturbances (n=6) RESULTS: The study included registered nurses from critical care and general ward areas. The overall results were poor with only 55% of questions answered correctly. Coronary care nurses scored the highest in identifying ECG rhythms (12/20 ± 1.58; p < .001). When ECG abnormalities associated with electrolyte imbalances were analysed, both groups were unable to identify the effects of hypokalaemia and hypomagnesaemia effectively (p = .748). Length of time as a registered nurse (r = -0.304, p = .002) and length of time in current work environment were weakly correlated (r = -0.328, p = .001). Having a critical care background showed a positive relationship with nursing knowledge of ECG rhythm identification (r = 0.614, p < .001). CONCLUSION The results of this study demonstrate that nurses have a poor knowledge of ECG rhythm identification and interpretation, a consistent finding from other work. A possible solution is a revamp of education and training associated with ECG recognition and morphology. RELEVANCE TO CLINICAL PRACTICE Monitoring and assessing ECG morphology provide important details about cardio-electroconductive stability, especially with fluctuations in serum electrolyte levels seen in critical illness or trauma. For this, critical nurses must improve their proficiency through education/training or internal quality improvement activities in detecting abnormalities associated with ECG changes beyond those most easily recognizable rhythms such as atrial fibrillation or ventricular tachycardia.
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Affiliation(s)
- Jessie Ng
- School of Nursing, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
| | - Martin Christensen
- School of Nursing, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
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2
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Abd-Elbaki MKM, Ragab TM, Ismael NER, Khalil ASG. Robust, self-adhesive and anti-bacterial silk-based LIG electrodes for electrophysiological monitoring. RSC Adv 2023; 13:31704-31719. [PMID: 37908662 PMCID: PMC10613951 DOI: 10.1039/d3ra05730e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/23/2023] [Indexed: 11/02/2023] Open
Abstract
Flexible wearable electrodes have been extensively used for obtaining electrophysiological signals towards smart health monitoring and disease diagnosis. Here, low-cost, and non-conductive silk fabric (SF) have been processed into highly conductive laser induced graphene (LIG) electrodes while maintaining the original structure of SF. A CO2-pulsed laser was utilized to produce LIG-SF with controlled sheet resistance and mechanical properties. Laser processing of SFs under optimized conditions yielded LIG-SF electrodes with a high degree of homogeneity on both, top and bottom layers. Silk fibroin/Ca2+ adhesive layers effectively promoted the adhesive, anti-bacterial properties and provided a conformal contact of LIG-SF electrodes with human skin. Compared with conventional Ag/AgCl electrodes, LIG-SF electrodes possesses a much lower contact impedance in contact with human skin enabling highly stable electrophysiological signals recording. The applicability of adhesive LIG-SF electrodes to acquire electrocardiogram (ECG) signals was investigated. ECG signals recordings of adhesive LIG-SF electrodes showed excellent performance compared to conventional Ag/AgCl electrodes at intense body movements while running at different speeds for up to 9 km over a duration of 24 h. Therefore, our proposed adhesive LIG-SF electrodes can be applied for long-term personalized healthcare monitoring and sports management applications.
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Affiliation(s)
| | - Tamer Mosaad Ragab
- Department of Cardiology, Faculty of Medicine, Fayoum University 63514 Fayoum Egypt
| | - Naglaa E R Ismael
- Zoology Department, Faculty of Science, Fayoum University 63514 Fayoum Egypt
| | - Ahmed S G Khalil
- Physics Department, Environmental and Smart Technology Group, Faculty of Science, Fayoum University 63514 Fayoum Egypt
- Institute of Basic and Applied Sciences, Faculty of Engineering, Egypt-Japan University of Science and Technology (E-JUST) 179 New Borg El-Arab City Egypt
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3
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Elsheikh E, Alkhteeb N, Alamer A, Alarfaj MO, AlQarni G, Alsultan J. Medical Students' Competency and Confidence in Interpreting Electrocardiograms at King Faisal University, Al-Ahsa. Cureus 2023; 15:e46393. [PMID: 37927746 PMCID: PMC10620545 DOI: 10.7759/cureus.46393] [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] [Accepted: 10/02/2023] [Indexed: 11/07/2023] Open
Abstract
Introduction Electrocardiography is a crucial emergency tool in the pre-hospital situation. It is a useful non-invasive diagnostic technique for quickly identifying various heart disorders. The clinical value of the electrocardiogram (ECG) depends on the clinician's ability to interpret the result of the ECG accurately. Aims This study aims to assess the competency as well as the confidence in the interpretation of ECG among medical students at King Faisal University, Al Ahsa, Saudi Arabia. Methods This cross-sectional study was conducted among medical students enrolled at King Faisal University. Four hundred and ten (410) medical students from all five years completed an electronic self-administered pre-validated questionnaire. The questionnaire includes basic demographic data and ECG strips to assess medical students' competency and confidence levels in interpreting each case. Results More than half of the medical students were considered to have low competency (56.1%) and confidence (59%) levels. Increased competency and confidence scores were associated with fifth-year medical students and those who learned more about ECG interpretation through teaching during clinical rotations. The majority of medical students correctly interpreted anterior MI (69.3%), ventricular tachycardia (65.6%), and supraventricular tachycardia (61.2%). On the other hand, most students were unable to correctly identify pacemaker ECG (19.8%), long QT (21.2%) and left bundle branch block (33.4%). Conclusion Medical students' competency and confidence in ECG interpretation seems to be lacking. Fifth-year medical students who learned more ECG skills through teaching during clinical rotations tended to be more competent and confident with their ECG interpretation skills.
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Affiliation(s)
- Eman Elsheikh
- Internal Medicine, College of Medicine, King Faisal University, Al-Ahsa, SAU
- Cardiology, College of Medicine, Tanta University, Tanta, EGY
| | | | - Aisha Alamer
- Internal Medicine, King Faisal University, Al-Ahsa, SAU
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4
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Zeng W, Yuan C. Myocardial infarction detection using ITD, DWT and deterministic learning based on ECG signals. Cogn Neurodyn 2023; 17:941-964. [PMID: 37522048 PMCID: PMC10374507 DOI: 10.1007/s11571-022-09870-7] [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: 02/09/2022] [Revised: 07/16/2022] [Accepted: 08/05/2022] [Indexed: 11/03/2022] Open
Abstract
Nowadays, cardiovascular diseases (CVD) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention issue. Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgement and several electrocardiographic (ECG) or vectorcardiographic (VCG) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study, we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector, which is decomposed into a series of proper rotation components (PRCs) by using the intrinsic time-scale decomposition (ITD) method. The novel cardiac vector may fully reflect the pathological alterations provoked by MI and may be correlated to the disparity of cardiac system dynamics between healthy and MI subjects. ITD is employed to measure the variability of cardiac vector and the first PRCs are extracted as predominant PRCs which contain most of the cardiac vector's energy. Second, four levels discrete wavelet transform with third-order Daubechies (db3) wavelet function is employed to decompose the predominant PRCs into different frequency bands, which combines with three-dimensional phase space reconstruction to derive features. The properties associated with the cardiac system dynamics are preserved. Since the frequency components above 40 Hz are lack of use in ECG analysis, in order to reduce the feature dimension, the advisable sub-band (D4) is selected for feature acquisition. Third, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the tenfold cross-validation style, the achieved average classification accuracy is reported to be 98.20%. Results verify the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.
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Affiliation(s)
- Wei Zeng
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012 People’s Republic of China
| | - Chengzhi Yuan
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881 USA
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5
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Choi KH, Kim SJ, Kim H, Jang HW, Yi H, Park MC, Choi C, Ju H, Lim JA. Fibriform Organic Electrochemical Diodes with Rectifying, Complementary Logic and Transient Voltage Suppression Functions for Wearable E-Textile Embedded Circuits. ACS NANO 2023; 17:5821-5833. [PMID: 36881690 DOI: 10.1021/acsnano.2c12418] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
In this study, a fibriform electrochemical diode capable of performing rectifying, complementary logic and device protection functions for future e-textile circuit systems is fabricated. The diode was fabricated using a simple twisted assembly of metal/polymer semiconductor/ion gel coaxial microfibers and conducting microfiber electrodes. The fibriform diode exhibited a prominent asymmetrical current flow with a rectification ratio of over 102, and its performance was retained after repeated bending deformations and washings. Fundamental studies on the electrochemical interactions of polymer semiconductors with ions reveal that the Faradaic current generated in polymer semiconductors by electrochemical reactions results in an abrupt current increase under a forward bias, in which the threshold voltages of the device are determined by the oxidation or reduction potential of the polymer semiconductor. Textile-embedded full-wave rectifiers and logic gate circuits were implemented by simply integrating the fibriform diodes, exhibiting AC-to-DC signal conversion and logic operation functions, respectively. It was also confirmed that the proposed fibriform diode can suppress transient voltages and thus protect a low-voltage operational wearable e-textile circuit.
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Affiliation(s)
- Kwang-Hun Choi
- Center for Optoelectronic Materials and Devices, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
| | - Soo Jin Kim
- Center for Optoelectronic Materials and Devices, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
| | - Hyoungjun Kim
- Center for Optoelectronic Materials and Devices, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Division of Nano and Information Technology, KIST School, Korea University of Science and Technology of Korea (UST), Seoul 02792, Republic of Korea
| | - Ho Won Jang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
- Advanced Institute of Convergence Technology, Seoul National University, Suwon 16229, Republic of Korea
| | - Hyunjung Yi
- Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Department of Materials Science and Engineering, YU-KIST Institute, Yonsei University, Seoul 03722, Republic of Korea
| | - Min-Chul Park
- Center for Optoelectronic Materials and Devices, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Changsoon Choi
- Center for Optoelectronic Materials and Devices, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Hyunsu Ju
- Center for Optoelectronic Materials and Devices, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Jung Ah Lim
- Center for Optoelectronic Materials and Devices, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Division of Nano and Information Technology, KIST School, Korea University of Science and Technology of Korea (UST), Seoul 02792, Republic of Korea
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6
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Noise ECG generation method based on generative adversarial network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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7
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Evaluation of handcrafted features and learned representations for the classification of arrhythmia and congestive heart failure in ECG. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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8
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Competency in ECG Interpretation and Arrhythmias Management among Critical Care Nurses in Saudi Arabia: A Cross Sectional Study. Healthcare (Basel) 2022; 10:healthcare10122576. [PMID: 36554100 PMCID: PMC9777912 DOI: 10.3390/healthcare10122576] [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: 11/13/2022] [Revised: 12/14/2022] [Accepted: 12/16/2022] [Indexed: 12/23/2022] Open
Abstract
Background: Electrographic interpretation skills are important for healthcare practitioners caring for patients in need of cardiac assessment. Competency in ECG interpretation skills is critical to determine any abnormalities and initiate the appropriate care required. The purpose of the study was to determine the level of competence in electrocardiographic interpretation and knowledge in arrhythmia management of nurses in critical care settings. Methods: A descriptive cross-sectional design was used. A convenience sample of 255 critical care nurses from 4 hospitals in the Al-Madinah Region in Saudi Arabia was used. A questionnaire was designed containing a participant’s characteristics and 10 questions with electrocardiographic strips. A pilot test was carried out to evaluate the validity and reliability of the questionnaire. Descriptive and bivariate analyses were conducted using an independent t-test, one-way ANOVA, or bi-variate correlation tests, as appropriate. A statistical significance of p < 0.05 was assumed. Results: Females comprised 87.5% of the sample, and the mean age of the sample was 32.1 (SD = 5.37) years. The majority of the participants (94.9%) had taken electrocardiographic interpretation training courses. The mean total score of correct answers of all 10 ECG strips was 6.45 (±2.54) for ECG interpretation and 4.76 (±2.52) for arrhythmia management. No significant differences were observed between ECG competency level and nursing experience or previous training. Nurses working in the ICU and CCU scored significantly higher than those working in ED. Conclusions: The electrocardiographic knowledge in ECG interpretation and arrhythmia management of critical care nurses is low. Therefore, improving critical care nurses’ knowledge of ECGs, identification, and management of cardiac arrhythmias is essential.
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Pagano TP, dos Santos LL, Santos VR, Sá PHM, Bonfim YDS, Paranhos JVD, Ortega LL, Nascimento LFS, Santos A, Rönnau MM, Winkler I, Nascimento EGS. Remote Heart Rate Prediction in Virtual Reality Head-Mounted Displays Using Machine Learning Techniques. SENSORS (BASEL, SWITZERLAND) 2022; 22:9486. [PMID: 36502188 PMCID: PMC9738680 DOI: 10.3390/s22239486] [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: 10/27/2022] [Revised: 11/22/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
Head-mounted displays are virtual reality devices that may be equipped with sensors and cameras to measure a patient's heart rate through facial regions. Heart rate is an essential body signal that can be used to remotely monitor users in a variety of situations. There is currently no study that predicts heart rate using only highlighted facial regions; thus, an adaptation is required for beats per minute predictions. Likewise, there are no datasets containing only the eye and lower face regions, necessitating the development of a simulation mechanism. This work aims to remotely estimate heart rate from facial regions that can be captured by the cameras of a head-mounted display using state-of-the-art EVM-CNN and Meta-rPPG techniques. We developed a region of interest extractor to simulate a dataset from a head-mounted display device using stabilizer and video magnification techniques. Then, we combined support vector machine and FaceMash to determine the regions of interest and adapted photoplethysmography and beats per minute signal predictions to work with the other techniques. We observed an improvement of 188.88% for the EVM and 55.93% for the Meta-rPPG. In addition, both models were able to predict heart rate using only facial regions as input. Moreover, the adapted technique Meta-rPPG outperformed the original work, whereas the EVM adaptation produced comparable results for the photoplethysmography signal.
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Affiliation(s)
- Tiago Palma Pagano
- Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
| | - Lucas Lisboa dos Santos
- Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
| | - Victor Rocha Santos
- Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
| | - Paulo H. Miranda Sá
- Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
| | - Yasmin da Silva Bonfim
- Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
| | | | - Lucas Lemos Ortega
- Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
| | | | - Alexandre Santos
- HP Inc. Brazil R&D, Porto Alegre 90619-900, Rio Grande do Sul, Brazil
| | | | - Ingrid Winkler
- Department of Management and Industrial Technology, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
| | - Erick G. Sperandio Nascimento
- Department of Management and Industrial Technology, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
- Faculty of Engineering and Physical Sciences, School of Computer Science and Electronic Engineering, Surrey Institute for People-Centred AI, University of Surrey, Guildford GU2 7XH, UK
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10
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Marzog HA, Abd HJ. Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2022; 2022:1-8. [DOI: 10.1155/2022/9884076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023] Open
Abstract
The heart’s electrical activity is registered by an electrocardiogram (ECG), which consists of a wealth of pathological data on heart diseases such as arrhythmia. However, with increasing complexity and nonlinearity, direct observation of ECG signals and analysis is very tough. The highest accuracy of classification performance for machine learning approaches are 99.7 for neural network with wavelet scattering features extraction and 99.92 for SVM also with wavelet scattering features extraction. Through wavelet cascades with a neural network, the wavelet scattering transform can yield a translation invariant and deflection depictions of ECG signals. We suggested a new wavelet scattering transform-based method for automatically classifying three types of ECG heart diseases as follows: arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). The bandwidth of the scaling function is used to critically downsample the wavelet scattering transform in time. As a result, each of the scattering paths has 16-time windows. Beat classification performance is classified by utilizing the MIT-BIH arrhythmia dataset. The suggested method is able to conduct high accuracy arrhythmia classification, with a 99.7% and 99.92% accuracy rate of the neural network (NN) and support vector machine (SVM), respectively, and will aid physicians in ECG explanation.
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Affiliation(s)
- Heyam A. Marzog
- Electrical Engineering Department, College of Engineering, University of Babylon, Hilla, Babil, Iraq
- Engineering Technical College/Najaf, Al-Furat Al-Awsat Technical University, Al Najaf 31001, Iraq
| | - Haider. J. Abd
- Electrical Engineering Department, College of Engineering, University of Babylon, Hilla, Babil, Iraq
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11
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A novel technique for the detection of myocardial dysfunction using ECG signals based on CEEMD, DWT, PSR and neural networks. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10262-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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12
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A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection. J Clin Med 2022; 11:jcm11174935. [PMID: 36078865 PMCID: PMC9456488 DOI: 10.3390/jcm11174935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 11/17/2022] Open
Abstract
An automatic electrocardiogram (ECG) myocardial infarction detection system needs to satisfy several requirements to be efficient in real-world practice. These requirements, such as reliability, less complexity, and high performance in decision-making, remain very important in a realistic clinical environment. In this study, we investigated an automatic ECG myocardial infarction detection system and presented a new approach to evaluate its robustness and durability performance in classifying the myocardial infarction (with no feature extraction) under different noise types. We employed three well-known supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF), and tested the performance and robustness of these techniques in classifying normal (NOR) and myocardial infarction (MI) using real ECG records from the PTB database after normalization and segmentation of the data, with a suggested inter-patient paradigm separation as well as noise from the MIT-BIH noise stress test database (NSTDB). Finally, we measured four metrics: accuracy, precision, recall, and F1-score. The simulation revealed that all of the models performed well, with values of over 0.50 at lower SNR levels, in terms of all the metrics investigated against different types of noise, indicating that they are encouraging and acceptable under extreme noise situations are are thus considered sustainable and robust models for specific forms of noise. All of the methods tested could be used as ECG myocardial infarction detection tools in real-world practice under challenging circumstances.
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Liu J, Li Z, Jin Y, Liu Y, Liu C, Zhao L, Chen X. A review of arrhythmia detection based on electrocardiogram with artificial intelligence. Expert Rev Med Devices 2022; 19:549-560. [DOI: 10.1080/17434440.2022.2115887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Jinlei Liu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Zhiyuan Li
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Yanrui Jin
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Yunqing Liu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Chengliang Liu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China
| | - Liqun Zhao
- Department of Cardiology, Shanghai First People’s Hospital Affiliated to Shanghai Jiao Tong University, 100 Haining Road, Shanghai 200080, China
| | - Xiaojun Chen
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
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14
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Irfan S, Anjum N, Althobaiti T, Alotaibi AA, Siddiqui AB, Ramzan N. Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22155606. [PMID: 35957162 PMCID: PMC9370835 DOI: 10.3390/s22155606] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 05/25/2023]
Abstract
Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost.
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Affiliation(s)
- Saad Irfan
- Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan; (S.I.); (A.B.S.)
| | - Nadeem Anjum
- Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan; (S.I.); (A.B.S.)
| | - Turke Althobaiti
- Faculty of Science, Northern Border University, Arar 1321, Saudi Arabia;
| | | | - Abdul Basit Siddiqui
- Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan; (S.I.); (A.B.S.)
| | - Naeem Ramzan
- School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK;
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15
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Tao Y, Li Z, Gu C, Jiang B, Zhang Y. ECG-based expert-knowledge attention network to tachyarrhythmia recognition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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Class-specific weighted broad learning system for imbalanced heartbeat classification. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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17
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Domazetoski V, Gligoric G, Marinkovic M, Shvilkin A, Krsic J, Kocarev L, Ivanovic MD. The influence of atrial flutter in automated detection of atrial arrhythmias - are we ready to go into clinical practice?". COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106901. [PMID: 35636359 DOI: 10.1016/j.cmpb.2022.106901] [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: 11/21/2021] [Revised: 05/13/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To investigate the impact of atrial flutter (Afl) in the atrial arrhythmias classification task. We additionally advocate the use of a subject-based split for future studies in the field in order to avoid within-subject correlation which may lead to over-optimistic inferences. Finally, we demonstrate the effectiveness of the classifiers outside of the initially studied circumstances, by performing an inter-dataset model evaluation of the classifiers in data from different sources. METHODS ECG signals of two private and three public (two MIT-BIH and Chapman ecgdb) databases were preprocessed and divided into 10s segments which were then subject to feature extraction. The created datasets were divided into a training and test set in two ways, based on a random split and a patient split. Classification was performed using the XGBoost classifier, as well as two benchmark classification models using both data splits. The trained models were then used to make predictions on the test data of the remaining datasets. RESULTS The XGBoost model yielded the best performance across all datasets compared to the remaining benchmark models, however variability in model performance was seen across datasets, with accuracy ranging from 70.6% to 89.4%, sensitivity ranging from 61.4% to 76.8%, and specificity ranging from 87.3% to 95.5%. When comparing the results between the patient and the random split, no significant difference was seen in the two private datasets and the Chapman dataset, where the number of samples per patient is low. Nonetheless, in the MIT-BIH dataset, where the average number of samples per patient is approximately 1300, a noticeable disparity was identified. The accuracy, sensitivity, and specificity of the random split in this dataset of 93.6%, 86.4%, and 95.9% respectively, were decreased to 88%, 61.4%, and 89.8% in the patient split, with the largest drop being in Afl sensitivity, from 71% to 5.4%. The inter-dataset scores were also significantly lower than their intra-dataset counterparts across all datasets. CONCLUSIONS CAD systems have great potential in the assistance of physicians in reliable, precise and efficient detection of arrhythmias. However, although compelling research has been done in the field, yielding models with excellent performances on their datasets, we show that these results may be over-optimistic. In our study, we give insight into the difficulty of detection of Afl on several datasets and show the need for a higher representation of Afl in public datasets. Furthermore, we show the necessity of a more structured evaluation of model performance through the use of a patient-based split and inter-dataset testing scheme to avoid the problem of within-subject correlation which may lead to misleadingly high scores. Finally, we stress the need for the creation and use of datasets with a higher number of patients and a more balanced representation of classes if we are to progress in this mission.
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Affiliation(s)
- Viktor Domazetoski
- Research Center for Computer Science and Information Technologies, Macedonian Academy of Sciences and Arts, Skopje, Macedonia.
| | - Goran Gligoric
- Vinca Institute of Nuclear Sciences - National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
| | - Milan Marinkovic
- Cardiology clinic, Clinical center of Serbia, School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Alexei Shvilkin
- Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, USA
| | - Jelena Krsic
- Vinca Institute of Nuclear Sciences - National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
| | - Ljupco Kocarev
- Research Center for Computer Science and Information Technologies, Macedonian Academy of Sciences and Arts, Skopje, Macedonia; Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, Macedonia
| | - Marija D Ivanovic
- Vinca Institute of Nuclear Sciences - National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
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18
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Machine Learning Models and Videos of Facial Regions for Estimating Heart Rate: A Review on Patents, Datasets, and Literature. ELECTRONICS 2022. [DOI: 10.3390/electronics11091473] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
Estimating heart rate is important for monitoring users in various situations. Estimates based on facial videos are increasingly being researched because they allow the monitoring of cardiac information in a non-invasive way and because the devices are simpler, as they require only cameras that capture the user’s face. From these videos of the user’s face, machine learning can estimate heart rate. This study investigates the benefits and challenges of using machine learning models to estimate heart rate from facial videos through patents, datasets, and article review. We have searched the Derwent Innovation, IEEE Xplore, Scopus, and Web of Science knowledge bases and identified seven patent filings, eleven datasets, and twenty articles on heart rate, photoplethysmography, or electrocardiogram data. In terms of patents, we note the advantages of inventions related to heart rate estimation, as described by the authors. In terms of datasets, we have discovered that most of them are for academic purposes and with different signs and annotations that allow coverage for subjects other than heartbeat estimation. In terms of articles, we have discovered techniques, such as extracting regions of interest for heart rate reading and using video magnification for small motion extraction, and models, such as EVM-CNN and VGG-16, that extract the observed individual’s heart rate, the best regions of interest for signal extraction, and ways to process them.
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19
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Conception and realization of an IoT-enabled deep CNN decision support system for automated arrhythmia classification. JOURNAL OF INTELLIGENT SYSTEMS 2022. [DOI: 10.1515/jisys-2022-0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Arrhythmias are irregular heartbeats that may be life-threatening. Proper monitoring and the right care at the right time are necessary to keep the heart healthy. Monitoring electrocardiogram (ECG) patterns on continuous monitoring devices is time-consuming. An intense manual inspection by caregivers is not an option. In addition, such an inspection could result in errors and inter-variability. This article proposes an automated ECG beat classification method based on deep neural networks (DNN) to aid in the detection of cardiac arrhythmias. The data collected by an Internet of Things enabled ECG monitoring device are transferred to a server. They are analysed by a deep learning model, and the results are shared with the primary caregiver. The proposed model is trained using the MIT-BIH ECG arrhythmia database to classify into four classes: normal beat (N), left bundle branch block beat (L), right bundle branch block beat (R), and premature ventricular contraction (V). The received data are sampled with an overlapping sliding window and divided into an 80:20 ratio for training and testing, with tenfold cross-validation. The proposed method achieves higher accuracy with a simple model without any preprocessing when compared with previous works. For the train and test sets, we achieved accuracy rates of 99.09 and 99.03%, respectively. A precision, recall, and F1 scores of 0.99 is obtained. The proposed model achieves its goal of developing a simple and accurate ECG monitoring system with improved performance. This simple and efficient deep learning approach for heartbeat classification could be applied in real-time telehealth monitoring systems.
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20
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Cardiac arrhythmia detection using dual-tree wavelet transform and convolutional neural network. Soft comput 2022. [DOI: 10.1007/s00500-021-06653-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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21
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Li N, Zhu L, Ma W, Wang Y, He F, Zheng A, Zhang X. The Identification of ECG Signals Using WT-UKF and IPSO-SVM. SENSORS (BASEL, SWITZERLAND) 2022; 22:1962. [PMID: 35271105 PMCID: PMC8915117 DOI: 10.3390/s22051962] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/17/2022] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
The biometric identification method is a current research hotspot in the pattern recognition field. Due to the advantages of electrocardiogram (ECG) signals, which are difficult to replicate and easy to obtain, ECG-based identity identification has become a new direction in biometric recognition research. In order to improve the accuracy of ECG signal identification, this paper proposes an ECG identification method based on a multi-scale wavelet transform combined with the unscented Kalman filter (WT-UKF) algorithm and the improved particle swarm optimization-support vector machine (IPSO-SVM). First, the WT-UKF algorithm can effectively eliminate the noise components and preserve the characteristics of ECG signals when denoising the ECG data. Then, the wavelet positioning method is used to detect the feature points of the denoised signals, and the obtained feature points are combined with multiple feature vectors to characterize the ECG signals, thus reducing the data dimension in identity identification. Finally, SVM is used for ECG signal identification, and the improved particle swarm optimization (IPSO) algorithm is used for parameter optimization in SVM. According to the analysis of simulation experiments, compared with the traditional WT denoising, the WT-UKF method proposed in this paper improves the accuracy of feature point detection and increases the final recognition rate by 1.5%. The highest recognition accuracy of a single individual in the entire ECG identification system achieves 100%, and the average recognition accuracy can reach 95.17%.
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Affiliation(s)
- Ning Li
- School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China; (L.Z.); (W.M.); (Y.W.); (F.H.)
| | - Longhui Zhu
- School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China; (L.Z.); (W.M.); (Y.W.); (F.H.)
| | - Wentao Ma
- School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China; (L.Z.); (W.M.); (Y.W.); (F.H.)
| | - Yelin Wang
- School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China; (L.Z.); (W.M.); (Y.W.); (F.H.)
| | - Fuxing He
- School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China; (L.Z.); (W.M.); (Y.W.); (F.H.)
| | - Aixiang Zheng
- School of Humanities and Foreign Languages, Xi’an University of Technology, Xi’an 710048, China;
| | - Xiaoping Zhang
- Department of Electronic, Electrical, and Systems Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK;
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22
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Kim JK, Jung S, Park J, Han SW. Arrhythmia detection model using modified DenseNet for comprehensible Grad-CAM visualization. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103408] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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23
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Guess M, Zavanelli N, Yeo WH. Recent Advances in Materials and Flexible Sensors for Arrhythmia Detection. MATERIALS 2022; 15:ma15030724. [PMID: 35160670 PMCID: PMC8836661 DOI: 10.3390/ma15030724] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/06/2022] [Accepted: 01/16/2022] [Indexed: 12/24/2022]
Abstract
Arrhythmias are one of the leading causes of death in the United States, and their early detection is essential for patient wellness. However, traditional arrhythmia diagnosis by expert evaluation from intermittent clinical examinations is time-consuming and often lacks quantitative data. Modern wearable sensors and machine learning algorithms have attempted to alleviate this problem by providing continuous monitoring and real-time arrhythmia detection. However, current devices are still largely limited by the fundamental mismatch between skin and sensor, giving way to motion artifacts. Additionally, the desirable qualities of flexibility, robustness, breathability, adhesiveness, stretchability, and durability cannot all be met at once. Flexible sensors have improved upon the current clinical arrhythmia detection methods by following the topography of skin and reducing the natural interface mismatch between cardiac monitoring sensors and human skin. Flexible bioelectric, optoelectronic, ultrasonic, and mechanoelectrical sensors have been demonstrated to provide essential information about heart-rate variability, which is crucial in detecting and classifying arrhythmias. In this review, we analyze the current trends in flexible wearable sensors for cardiac monitoring and the efficacy of these devices for arrhythmia detection.
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Affiliation(s)
- Matthew Guess
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.G.); (N.Z.)
- Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Nathan Zavanelli
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.G.); (N.Z.)
- Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Woon-Hong Yeo
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.G.); (N.Z.)
- Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Neural Engineering Center, Institute for Materials, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Correspondence: ; Tel.: +1-404-385-5710
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24
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Dey N, V. R. Introduction to image-assisted disease screening. Magn Reson Imaging 2022. [DOI: 10.1016/b978-0-12-823401-3.00001-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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25
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Tadesse GA, Javed H, Weldemariam K, Liu Y, Liu J, Chen J, Zhu T. DeepMI: Deep multi-lead ECG fusion for identifying myocardial infarction and its occurrence-time. Artif Intell Med 2021; 121:102192. [PMID: 34763807 DOI: 10.1016/j.artmed.2021.102192] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 07/07/2021] [Accepted: 10/05/2021] [Indexed: 11/16/2022]
Abstract
Myocardial Infarction (MI) has the highest mortality of all cardiovascular diseases (CVDs). Detection of MI and information regarding its occurrence-time in particular, would enable timely interventions that may improve patient outcomes, thereby reducing the global rise in CVD deaths. Electrocardiogram (ECG) recordings are currently used to screen MI patients. However, manual inspection of ECGs is time-consuming and prone to subjective bias. Machine learning methods have been adopted for automated ECG diagnosis, but most approaches require extraction of ECG beats or consider leads independently of one another. We propose an end-to-end deep learning approach, DeepMI, to classify MI from Normal cases as well as identifying the time-occurrence of MI (defined as Acute, Recent and Old), using a collection of fusion strategies on 12 ECG leads at data-, feature-, and decision-level. In order to minimise computational overhead, we employ transfer learning using existing computer vision networks. Moreover, we use recurrent neural networks to encode the longitudinal information inherent in ECGs. We validated DeepMI on a dataset collected from 17,381 patients, in which over 323,000 samples were extracted per ECG lead. We were able to classify Normal cases as well as Acute, Recent and Old onset cases of MI, with AUROCs of 96.7%, 82.9%, 68.6% and 73.8%, respectively. We have demonstrated a multi-lead fusion approach to detect the presence and occurrence-time of MI. Our end-to-end framework provides flexibility for different levels of multi-lead ECG fusion and performs feature extraction via transfer learning.
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Affiliation(s)
- Girmaw Abebe Tadesse
- Department of Engineering, University of Oxford, Oxford, United Kingdom; IBM Research, Kenya.
| | - Hamza Javed
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| | | | - Yong Liu
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Disease, Guangdong Provincial People's Hospital, University of Technology, Guangzhou, China
| | - Jin Liu
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Disease, Guangdong Provincial People's Hospital, University of Technology, Guangzhou, China
| | - Jiyan Chen
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Disease, Guangdong Provincial People's Hospital, University of Technology, Guangzhou, China
| | - Tingting Zhu
- Department of Engineering, University of Oxford, Oxford, United Kingdom
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Abstract
In this paper, we introduce VPNet, a novel model-driven neural network architecture based on variable projection (VP). Applying VP operators to neural networks results in learnable features, interpretable parameters, and compact network structures. This paper discusses the motivation and mathematical background of VPNet and presents experiments. The VPNet approach was evaluated in the context of signal processing, where we classified a synthetic dataset and real electrocardiogram (ECG) signals. Compared to fully connected and one-dimensional convolutional networks, VPNet offers fast learning ability and good accuracy at a low computational cost of both training and inference. Based on these advantages and the promising results obtained, we anticipate a profound impact on the broader field of signal processing, in particular on classification, regression and clustering problems.
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Affiliation(s)
- Péter Kovács
- Department of Numerical Analysis, Eötvös Loránd University, Pázmány Péter stny. 1/C, Budapest 1117, Hungary
| | - Gergő Bognár
- Department of Numerical Analysis, Eötvös Loránd University, Pázmány Péter stny. 1/C, Budapest 1117, Hungary.,Institute of Signal Processing, Johannes Kepler University Linz, Altenberger str. 69, Linz 4040, Austria.,JKU LIT SAL eSPML Lab, Silicon Austria Labs, Altenberger str. 69, Linz 4040, Austria
| | - Christian Huber
- Embedded AI Research Group, Silicon Austria Labs GmbH, Altenberger str. 69, Linz 4040, Austria
| | - Mario Huemer
- Institute of Signal Processing, Johannes Kepler University Linz, Altenberger str. 69, Linz 4040, Austria.,JKU LIT SAL eSPML Lab, Silicon Austria Labs, Altenberger str. 69, Linz 4040, Austria
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27
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Seo HC, Oh S, Kim H, Joo S. ECG data dependency for atrial fibrillation detection based on residual networks. Sci Rep 2021; 11:18256. [PMID: 34521892 PMCID: PMC8440762 DOI: 10.1038/s41598-021-97308-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/09/2021] [Indexed: 12/05/2022] Open
Abstract
Atrial fibrillation (AF) is an arrhythmia that can cause blood clot and may lead to stroke and heart failure. To detect AF, deep learning-based detection algorithms have recently been developed. However, deep learning models were often trained with limited datasets and were evaluated within the same datasets, which makes their performance generally drops on the external datasets, known as data dependency. For this study, three different databases from PhysioNet were used to investigate the data dependency of deep learning-based AF detection algorithm using the residual neural network (Resnet). Resnet 18, 34, 50 and 152 model were trained with raw electrocardiogram (ECG) signal extracted from independent database. The highest accuracy was about 98–99% which is evaluation results of test dataset from the own database. On the other hand, the lowest accuracy was about 53–92% which was evaluation results of the external dataset extracted from different source. There are data dependency according to the train dataset and the test dataset. However, the data dependency decreased as a large amount of train data.
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Affiliation(s)
- Hyo-Chang Seo
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seok Oh
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyunbin Kim
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Segyeong Joo
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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28
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Jahmunah V, Ng EYK, San TR, Acharya UR. Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals. Comput Biol Med 2021; 134:104457. [PMID: 33991857 DOI: 10.1016/j.compbiomed.2021.104457] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/22/2021] [Accepted: 04/26/2021] [Indexed: 01/02/2023]
Abstract
Cardiovascular diseases (CVDs) are main causes of death globally with coronary artery disease (CAD) being the most important. Timely diagnosis and treatment of CAD is crucial to reduce the incidence of CAD complications like myocardial infarction (MI) and ischemia-induced congestive heart failure (CHF). Electrocardiogram (ECG) signals are most commonly employed as the diagnostic screening tool to detect CAD. In this study, an automated system (AS) was developed for the automated categorization of electrocardiogram signals into normal, CAD, myocardial infarction (MI) and congestive heart failure (CHF) classes using convolutional neural network (CNN) and unique GaborCNN models. Weight balancing was used to balance the imbalanced dataset. High classification accuracies of more than 98.5% were obtained by the CNN and GaborCNN models respectively, for the 4-class classification of normal, coronary artery disease, myocardial infarction and congestive heart failure classes. GaborCNN is a more preferred model due to its good performance and reduced computational complexity as compared to the CNN model. To the best of our knowledge, this is the first study to propose GaborCNN model for automated categorizing of normal, coronary artery disease, myocardial infarction and congestive heart failure classes using ECG signals. Our proposed system is equipped to be validated with bigger database and has the potential to aid the clinicians to screen for CVDs using ECG signals.
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Affiliation(s)
- V Jahmunah
- Department of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| | - E Y K Ng
- Department of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
| | | | - U Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore; Biomedical Engineering, School of Social Science and Technology, Singapore University of Social Sciences, Singapore; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan; Department Bioinformatics and Medical Engineering, Asia University, Taiwan; School of Management and Enterprise, University of Southern Queensland, Australia.
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29
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Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate. SENSORS 2021; 21:s21051906. [PMID: 33803265 PMCID: PMC7967244 DOI: 10.3390/s21051906] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/04/2021] [Accepted: 03/04/2021] [Indexed: 12/14/2022]
Abstract
Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.
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30
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Zeng W, Yuan J, Yuan C, Wang Q, Liu F, Wang Y. A novel technique for the detection of myocardial dysfunction using ECG signals based on hybrid signal processing and neural networks. Soft comput 2021. [DOI: 10.1007/s00500-020-05465-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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31
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Preliminary study in the analysis of the severity of cardiac pathologies using the higher-order spectra on the heart-beats signals. POLISH JOURNAL OF MEDICAL PHYSICS AND ENGINEERING 2021. [DOI: 10.2478/pjmpe-2021-0010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Abstract
Phonocardiography is a technique for recording and interpreting the mechanical activity of the heart. The recordings generated by such a technique are called phonocardiograms (PCG). The PCG signals are acoustic waves revealing a wealth of clinical information about cardiac health. They enable doctors to better understand heart sounds when presented visually. Hence, multiple approaches have been proposed to analyze heart sounds based on PCG recordings. Due to the complexity and the high nonlinear nature of these signals, a computer-aided technique based on higher-order statistics (HOS) is employed, it is known to be an important tool since it takes into account the non-linearity of the PCG signals. This method also known as the bispectrum technique, can provide significant information to enhance the diagnosis for an accurate and objective interpretation of heart condition.
The objective expected by this paper is to test in a preliminary way the parameters which can make it possible to establish a discrimination between the various signals of different pathologies and to characterize the cardiac abnormalities.
This preliminary study will be done on a reduced sample (nine signals) before applying it subsequently to a larger sample. This work examines the effectiveness of using the bispectrum technique in the analysis of the pathological severity of different PCG signals. The presented approach showed that HOS technique has a good potential for pathological discrimination of various PCG signals.
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32
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Murugappan M, Murugesan L, Jerritta S, Adeli H. Sudden Cardiac Arrest (SCA) Prediction Using ECG Morphological Features. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-020-04765-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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33
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Van Manh H, Nguyen NV, Thang PM. An innovative method based on Shannon energy envelope and summit navigation for detecting R peaks of noise stress test signals. J Electrocardiol 2021; 65:8-17. [PMID: 33460861 DOI: 10.1016/j.jelectrocard.2020.12.012] [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: 09/23/2020] [Revised: 12/24/2020] [Accepted: 12/27/2020] [Indexed: 10/22/2022]
Abstract
In recent decades, there has been an increased demand for the processing of electrocardiogram (ECG) signals because of its significant role in diagnosing cardiac diseases. The QRS complex is the dominant feature of the ECG signal. The detection of QRS complexes is thus an essential part of almost any ECG signal processing systems. This paper presents a developed QRS complex detection method using dominant peak extraction and Shannon energy envelope for useful ECG signal analysis. The algorithm is divided into three main stages: pre-processing, searching for dominant peaks, and removing false R peaks. The proposed algorithm is validated in static ECG recordings from the MIT-BIH Arrhythmia Database (MITDB) and noise-contaminated ECG stress tests from the Glasgow University Database (GUDB), separately. The method compares favorably with conventional and recently published results of many QRS detection algorithms on the same MITDB. Subsequently, valuable performance coefficients are also found on the GUDB. The average detection accuracy of finding R peaks exceed 99% on both the databases, especially for cardiac stress test records with high interference levels. The method enables a highly effective ECG signal processing tool under various noises, artifacts, abnormalities, and morphologies.
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Affiliation(s)
- Hoang Van Manh
- Faculty of Engineering Mechanics and Automation, University of Engineering and Technology, Vietnam National University, Hanoi 10000, Viet Nam
| | - Ngoc-Viet Nguyen
- Faculty of Electrical and Electronic Engineering, Phenikaa University, Hanoi 12116, Viet Nam; Phenikaa Research and Technology Institute (PRATI), A&A Green Phoenix Group JSC, No.167 Hoang Ngan, Trung Hoa, Cau Giay, Hanoi 11313, Viet Nam.
| | - Pham Manh Thang
- Faculty of Engineering Mechanics and Automation, University of Engineering and Technology, Vietnam National University, Hanoi 10000, Viet Nam
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Pathological discrimination of the phonocardiogram signal using the bispectral technique. Phys Eng Sci Med 2020; 43:1371-1385. [PMID: 33165819 DOI: 10.1007/s13246-020-00943-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 10/27/2020] [Indexed: 10/23/2022]
Abstract
Phonocardiography is a dynamic non-invasive and relatively low-cost technique used to monitor the state of the mechanical activity of the heart. The recordings generated by such a technique is called phonocardiogram (PCG) signals. When shown visually, PCG signals can provide more insights of heart sounds for medical doctors. Thus, several approaches have been proposed to analyse these sounds through PCG recordings. However, due to the complexity and the high nonlinear nature of these recordings, a computer-assisted technique based on higher-order statistics HOS is shown to be, among these techniques, an important tool in PCG signal processing. The third-order spectra technique is one of these techniques; known as bispectrum, it can provide significant information to support physicians with an accurate and objective interpretation of heart condition. This technique is implemented and discussed in this paper. The implemented technique is used for the analysis of heart severity on nine different PCG recordings. These are normal, innocent murmur, coarctation of the aorta, ejection click, atrial gallop, opening snap, aortic stenosis, drum rumble, and aortic regurgitation. A unique bispectrum representation is generated for each type of heart sounds signal. Then, based on the bispectrum analysis, fifteen higher-order spectra HOS features such as the bispectral amplitude, the entropies, the moments, and the weighted center are extracted from each PCG record. The obtained HOS-features showed a well-correlated evolution with the increasing importance of heart severity leading therefore to a high potential in discriminating pathological PCG signals. One should know that, generally, classification of pathological PCG signals refers to the distinction between the presence of a pathology from its absence (binary response) while the discrimination considered in this paper provides an analogue response (value) which can vary from one pathology to another in an increasing or decreasing way.
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Li Y, Qu Q, Wang M, Yu L, Wang J, Shen L, He K. Deep learning for digitizing highly noisy paper-based ECG records. Comput Biol Med 2020; 127:104077. [PMID: 33171291 DOI: 10.1016/j.compbiomed.2020.104077] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 10/19/2020] [Accepted: 10/20/2020] [Indexed: 10/23/2022]
Abstract
Electrocardiography (ECG) is essential in many heart diseases. However, some ECGs are recorded by paper, which can be highly noisy. Digitizing the paper-based ECG records into a high-quality signal is critical for further analysis. We formulated the digitization problem as a segmentation problem and proposed a deep learning method to digitize highly noisy ECG scans. Our method extracts the ECG signal in an end-to-end manner and can handle different paper record layouts. In the experiment, our model clearly extracted the ECG waveform with a Dice coefficient of 0.85 and accurately measured the common ECG parameters with more than 0.90 Pearson's correlation. We showed that the end-to-end approach with deep learning can be powerful in ECG digitization. To the best of our knowledge, we provide the first approach to digitize the least informative noisy binary ECG scans and potentially be generalized to digitize various ECG records.
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Affiliation(s)
- Yao Li
- Medicine School of Chinese PLA, Beijing, 100853, China; Core Laboratory of Translational Medicine, Chinese PLA General Hospital, Beijing, 100853, China; Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, 100853, China
| | - Qixun Qu
- Shenzhen Digital Life Institute, Shenzhen, Guangdong, 518000, China
| | - Meng Wang
- Shenzhen Digital Life Institute, Shenzhen, Guangdong, 518000, China
| | - Liheng Yu
- The 980th Hospital of PLA Joint Logistical Support Force (Bethune International Peace Hospital), Shijiazhuang, Hebei, 050082, China
| | - Jun Wang
- Shenzhen Digital Life Institute, Shenzhen, Guangdong, 518000, China; ICarbonX, Zhuhai, Guangdong, 519000, China; Macau University of Science and Technology, Macau
| | - Linghao Shen
- Shenzhen Digital Life Institute, Shenzhen, Guangdong, 518000, China.
| | - Kunlun He
- Core Laboratory of Translational Medicine, Chinese PLA General Hospital, Beijing, 100853, China; Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, 100853, China.
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Wavelet Scattering Transform for ECG Beat Classification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:3215681. [PMID: 33133225 PMCID: PMC7568798 DOI: 10.1155/2020/3215681] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 08/09/2020] [Accepted: 09/20/2020] [Indexed: 01/14/2023]
Abstract
An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats. In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat. Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows. These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and k-nearest neighbour (KNN) classifiers for classification. The 4th time window in combination with KNN (k = 4) has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation. Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation.
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Hsu PY, Cheng CK. Arrhythmia Classification using Deep Learning and Machine Learning with Features Extracted from Waveform-based Signal Processing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:292-295. [PMID: 33017986 DOI: 10.1109/embc44109.2020.9176679] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Arrhythmia is a serious cardiovascular disease, and early diagnosis of arrhythmia is critical. In this study, we present a waveform-based signal processing (WBSP) method to produce state-of-the-art performance in arrhythmia classification. When performing WBSP, we first filtered ECG signals, searched local minima, and removed baseline wandering. Subsequently, we fit the processed ECG signals with Gaussians and extracted the parameters. Afterwards, we exploited the products of WBSP to accomplish arrhythmia classification with our proposed machine learning-based and deep learning-based classifiers. We utilized MIT-BIH Arrhythmia Database to validate WBSP. Our best classifier achieved 98.8% accuracy. Moreover, it reached 96.3% sensitivity in class V and 98.6% sensitivity in class Q, which both share one of the best among the related works. In addition, our machine learning-based classifier accomplished identifying four waveform components essential for automated arrhythmia classification: the similarity of QRS complex to a Gaussian curve, the sharpness of the QRS complex, the duration of and the area enclosed by P-wave.Clinical relevance- Early diagnosis and automated classification of arrhythmia is clinically essential.
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Sun R, Chen JR, Wang YX, Zhou YR, Luo YY. An ERP Experimental Study About the Effect of Authorization Cue Characteristics on the Privacy Behavior of Recommended Users. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2020. [DOI: 10.20965/jaciii.2020.p0509] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
At the present time, consumers often disclose their privacy when using online platforms to receive personalized recommendation information and services, but they are also highly concerned whether their privacy is being violated. “Privacy paradox” is becoming a hot topic of research. What are the potential impacts of individual cognitive differences and situational cues on privacy decision-making? How to balance the internal causes of the “privacy paradox” so that consumers are more willing to accept personalized recommendation services based on users’ privacy data? Can the transparency of privacy rights ease user conflict perceptions and promote disclosure intentions? These questions are inconclusive. Therefore, the purpose of this our research was to explore consumer privacy paradoxical behaviors from a novel perspective of the characteristics of authorization cues, and to clarify the internal relationship between individual cognitive processing and privacy authorization cues. This study suggests that the big data platform, when collecting or using user information, should try to reduce the behaviors that induce users’ resistance. It also provides a reference for how to better achieve benign interaction in personalized recommendation between Internet companies and users. The event-related potential technique is adopted to explore the matching relationship between individual cognitive processing and privacy authorization cues and to analyze the internal neural mechanism of the personalized recommendation user in the authorization decision. The experiment simulated the privacy authorization situation, and adopted a 2 × 2 × 2 hybrid experimental design: authority sensitivity (high/low) * authorization transparency (with/without permission) * cognitive style (field dependent/field independent). The experimental results show that: (1) Authorization transparency, authority sensitivity and their interactions will affect the user’s privacy authorization behaviors, and the interaction of the two cues has a greater impact on the behavior than the role of a single cue; (2) The cognitive style will affect the individual’s attention resource allocation in the authorization scenario, which, limited by cognitive resources, will result in selective attention to contextual cues: Compared with the field-independent group with self-characterization as a reference, the field-dependent group induced a greater P2 amplitude; (3) When the two-cue valences in the authoritative scenario are inconsistent, the amplitude of the N2 component is greater than that when the valences are consistent, and the amplitude of the N2 induced by the field-dependent group is more affected by the scenario cue valence; (4) Regardless of whether it is a field-dependent group or a field-independent group, there is no salient difference in the amplitude of LPP components induced in each scenario. According to the results of this study, even if privacy authorization involves high risks, individuals tend to selectively seek supportive cues or avoid obtaining information that is inconsistent with their cognition. This research reveals the differences of neural mechanisms in users’ actual decision-making, provides the possibility for further exploration of the black box behind users’ attitudes and behaviors, and opens up new ideas for the study of the “privacy paradox.”
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Zeng W, Yuan J, Yuan C, Wang Q, Liu F, Wang Y. Classification of myocardial infarction based on hybrid feature extraction and artificial intelligence tools by adopting tunable-Q wavelet transform (TQWT), variational mode decomposition (VMD) and neural networks. Artif Intell Med 2020; 106:101848. [PMID: 32593387 DOI: 10.1016/j.artmed.2020.101848] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 02/16/2020] [Accepted: 03/20/2020] [Indexed: 12/18/2022]
Abstract
Cardiovascular diseases (CVD) is the leading cause of human mortality and morbidity around the world, in which myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. Currently, electrocardiogram (ECG) is widely used by the clinicians to diagnose MI patients due to its inexpensiveness and non-invasive nature. Pathological alterations provoked by MI cause slow conduction by increasing axial resistance on coupling between cells. This issue may cause abnormal patterns in the dynamics of the tip of the cardiac vector in the ECG signals. However, manual interpretation of the pathological alternations induced by MI is a time-consuming, tedious and subjective task. To overcome such disadvantages, computer-aided diagnosis techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on hybrid feature extraction and artificial intelligence tools. Tunable quality factor (Q-factor) wavelet transform (TQWT), variational mode decomposition (VMD) and phase space reconstruction (PSR) are utilized to extract representative features to form cardiac vectors with synthesis of the standard 12-lead and Frank XYZ leads. They are combined with neural networks to model, identify and detect abnormal patterns in the dynamics of cardiac system caused by MI. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. Second, this vector is decomposed into a set of frequency subbands with a number of decomposition levels by using the TQWT method. Third, VMD is employed to decompose the subband of the 4-dimensional cardiac vector into different intrinsic modes, in which the first intrinsic mode contains the majority of the cardiac vector's energy and is considered to be the predominant intrinsic mode. It is selected to construct the reference variable for analysis. Fourth, phase space of the reference variable is reconstructed, in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Fifth, cardiac system dynamics can be modeled and identified using neural networks, which employ the ED of 3D PSR of the reference variable as the input features. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, data sets, which include conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls from PTB diagnostic ECG database, are used for evaluation. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 97.98%. Currently, ST segment evaluation is one of the major and traditional ways for the MI detection. However, there exist weak or even undetectable ST segments in many ECG signals. Since the proposed method does not rely on the information of ST waves, it can serve as a complementary MI detection algorithm in the intensive care unit (ICU) of hospitals to assist the clinicians in confirming their diagnosis. Overall, our results verify that the proposed features may satisfactorily reflect cardiac system dynamics, and are complementary to the existing ECG features for automatic cardiac function analysis.
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Affiliation(s)
- Wei Zeng
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China.
| | - Jian Yuan
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Chengzhi Yuan
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA
| | - Qinghui Wang
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Fenglin Liu
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Ying Wang
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China
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Faust O, Ciaccio EJ, Acharya UR. A Review of Atrial Fibrillation Detection Methods as a Service. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E3093. [PMID: 32365521 PMCID: PMC7246533 DOI: 10.3390/ijerph17093093] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 04/19/2020] [Accepted: 04/24/2020] [Indexed: 12/28/2022]
Abstract
Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals.
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Affiliation(s)
- Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK
| | - Edward J. Ciaccio
- Department of Medicine—Cardiology, Columbia University, New York, NY 10027, USA;
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Electronic & Computer Engineering, Singapore 599489, Singapore;
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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Salah IB, De la Rosa R, Ouni K, Salah RB. Automatic diagnosis of valvular heart diseases by impedance cardiography signal processing. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101758] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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42
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Heo J, Lee JJ, Kwon S, Kim B, Hwang SO, Yoon YR. A novel method for detecting ST segment elevation myocardial infarction on a 12-lead electrocardiogram with a three-dimensional display. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101700] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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43
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Liu J, Zhang C, Zhu Y, Ristaniemi T, Parviainen T, Cong F. Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 184:105120. [PMID: 31627147 DOI: 10.1016/j.cmpb.2019.105120] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 10/03/2019] [Accepted: 10/03/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE It is challenging to conduct real-time identification of myocardial infarction (MI) due to artifact corruption and high dimensionality of multi-lead electrocardiogram (ECG). In the present study, we proposed an automated single-beat MI detection and localization system using dual-Q tunable Q-factor wavelet transformation (Dual-Q TQWT) denoising algorithm. METHODS After denoising and segmentation of ECG, a fourth-order wavelet tensor (leads × subbands × samples × beats) was constructed based on the discrete wavelet packet transform (DWPT), to represent the features considering the information of inter-beat, intra-beat, inter-frequency, and inter-lead. To reduce the tensor dimension and preserve the intrinsic information, the multilinear principal component analysis (MPCA) was employed. Afterward, 84 discriminate features were fed into a classifier of bootstrap-aggregated decision trees (Treebagger). A total of 78 healthy and 328 MI (6 types) records including 57557 beats were chosen from PTB diagnostic ECG database for evaluation. RESULTS The validation results demonstrated that our proposed MI detection and localization system embedded with Dual-Q TQWT and wavelet packet tensor decomposition outperformed commonly used discrete wavelet transform (DWT), empirical mode decomposition (EMD) denoising methods and vector-based PCA method. With the Treebagger classifier, we obtained an accuracy of 99.98% in beat level and an accuracy of 97.46% in record level training/testing for MI detection. We also achieved an accuracy of 99.87% in beat level and an accuracy of 90.39% in record level for MI localization. CONCLUSION Altogether, the automated system brings potential improvement in automated detection and localization of MI in clinical practice.
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Affiliation(s)
- Jia Liu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China; Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, 40014, Finland.
| | - Chi Zhang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Yongjie Zhu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China; Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, 40014, Finland
| | - Tapani Ristaniemi
- Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, 40014, Finland
| | - Tiina Parviainen
- Centre for Interdisciplinary Brain Research, Department of Psychology, Faculty of Education and Psychology, University of Jyvaskyla, Jyvaskyla, 40014, Finland
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China; Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, 40014, Finland.
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Lih OS, Jahmunah V, San TR, Ciaccio EJ, Yamakawa T, Tanabe M, Kobayashi M, Faust O, Acharya UR. Comprehensive electrocardiographic diagnosis based on deep learning. Artif Intell Med 2020; 103:101789. [PMID: 32143796 DOI: 10.1016/j.artmed.2019.101789] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 11/06/2019] [Accepted: 12/31/2019] [Indexed: 11/15/2022]
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. For the latter especially, the ECG perturbations can be subtle and potentially misclassified during manual interpretation and/or when analyzed by traditional algorithms found in ECG instrumentation. For automated diagnostic systems (ADS), deep learning techniques are favored over conventional machine learning techniques, due to the automatic feature extraction and selection processes involved. This paper highlights various deep learning algorithms exploited for the classification of ECG signals into CAD, MI, and CHF conditions. The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. A 16-layer LSTM model was developed in our study and validated using 10-fold cross-validation. A classification accuracy of 98.5% was achieved. Our proposed model has the potential to be a useful diagnostic tool in hospitals for the classification of abnormal ECG signals.
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Affiliation(s)
- Oh Shu Lih
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - V Jahmunah
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | | | | | - Toshitaka Yamakawa
- Department of Computer Science and Electrical Engineering, Kumamoto University, Japan
| | - Masayuki Tanabe
- Department of Computer Science and Electrical Engineering, Kumamoto University, Japan; International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan
| | - Makiko Kobayashi
- Department of Computer Science and Electrical Engineering, Kumamoto University, Japan
| | - Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan.
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Park JH, Dehaini D, Zhou J, Holay M, Fang RH, Zhang L. Biomimetic nanoparticle technology for cardiovascular disease detection and treatment. NANOSCALE HORIZONS 2020; 5:25-42. [PMID: 32133150 PMCID: PMC7055493 DOI: 10.1039/c9nh00291j] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Cardiovascular disease (CVD), which encompasses a number of conditions that can affect the heart and blood vessels, presents a major challenge for modern-day healthcare. Nearly one in three people has some form of CVD, with many suffering from multiple or intertwined conditions that can ultimately lead to traumatic events such as a heart attack or stroke. While the knowledge obtained in the past century regarding the cardiovascular system has paved the way for the development of life-prolonging drugs and treatment modalities, CVD remains one of the leading causes of death in developed countries. More recently, researchers have explored the application of nanotechnology to improve upon current clinical paradigms for the management of CVD. Nanoscale delivery systems have many advantages, including the ability to target diseased sites, improve drug bioavailability, and carry various functional payloads. In this review, we cover the different ways in which nanoparticle technology can be applied towards CVD diagnostics and treatments. The development of novel biomimetic platforms with enhanced functionalities is discussed in detail.
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Affiliation(s)
| | | | - Jiarong Zhou
- Department of NanoEngineering, Chemical Engineering Program, and Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Maya Holay
- Department of NanoEngineering, Chemical Engineering Program, and Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Ronnie H. Fang
- Department of NanoEngineering, Chemical Engineering Program, and Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Liangfang Zhang
- Department of NanoEngineering, Chemical Engineering Program, and Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
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Savostin AA, Ritter DV, Savostina GV. Using the K-Nearest Neighbors Algorithm for Automated Detection of Myocardial Infarction by Electrocardiogram Data Entries. PATTERN RECOGNITION AND IMAGE ANALYSIS 2019. [DOI: 10.1134/s1054661819040151] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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47
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Goovaerts G, Padhy S, Vandenberk B, Varon C, Willems R, Van Huffel S. A Machine-Learning Approach for Detection and Quantification of QRS Fragmentation. IEEE J Biomed Health Inform 2019; 23:1980-1989. [DOI: 10.1109/jbhi.2018.2878492] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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48
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Shahid AH, Singh M. Computational intelligence techniques for medical diagnosis and prognosis: Problems and current developments. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.05.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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49
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Jahmunah V, Oh SL, Wei JKE, Ciaccio EJ, Chua K, San TR, Acharya UR. Computer-aided diagnosis of congestive heart failure using ECG signals - A review. Phys Med 2019; 62:95-104. [PMID: 31153403 DOI: 10.1016/j.ejmp.2019.05.004] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 05/02/2019] [Accepted: 05/04/2019] [Indexed: 12/16/2022] Open
Abstract
The heart muscle pumps blood to vital organs, which is indispensable for human life. Congestive heart failure (CHF) is characterized by the inability of the heart to pump blood adequately throughout the body without an increase in intracardiac pressure. The symptoms include lung and peripheral congestion, leading to breathing difficulty and swollen limbs, dizziness from reduced delivery of blood to the brain, as well as arrhythmia. Coronary artery disease, myocardial infarction, and medical co-morbidities such as kidney disease, diabetes, and high blood pressure all take a toll on the heart and can impair myocardial function. CHF prevalence is growing worldwide. It afflicts millions of people globally, and is a leading cause of death. Hence, proper diagnosis, monitoring and management are imperative. The importance of an objective CHF diagnostic tool cannot be overemphasized. Standard diagnostic tests for CHF include chest X-ray, magnetic resonance imaging (MRI), nuclear imaging, echocardiography, and invasive angiography. However, these methods are costly, time-consuming, and they can be operator-dependent. Electrocardiography (ECG) is inexpensive and widely accessible, but ECG changes are typically not specific for CHF diagnosis. A properly designed computer-aided detection (CAD) system for CHF, based on the ECG, would potentially reduce subjectivity and provide quantitative assessment for informed decision-making. Herein, we review existing CAD for automatic CHF diagnosis, and highlight the development of an ECG-based CAD diagnostic system that employs deep learning algorithms to automatically detect CHF.
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Affiliation(s)
- V Jahmunah
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
| | - Shu Lih Oh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Joel Koh En Wei
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | | | - Kuang Chua
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | | | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500 Subang Jaya, Malaysia.
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MOHANTY MONALISA, BISWAL PRADYUT, SABUT SUKANTA. VENTRICULAR TACHYCARDIA AND FIBRILLATION DETECTION USING DWT AND DECISION TREE CLASSIFIER. J MECH MED BIOL 2019. [DOI: 10.1142/s0219519419500088] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the life-threatening ventricular arrhythmias that require treatment in an emergency. Detection of VT and VF at an early stage is crucial for achieving the success of the defibrillation treatment. Hence an automatic system using computer-aided diagnosis tool is helpful in detecting the ventricular arrhythmias in electrocardiogram (ECG) signal. In this paper, a discrete wavelet transform (DWT) was used to denoise and decompose the ECG signals into different consecutive frequency bands to reduce noise. The methodology was tested using ECG data from standard CU ventricular tachyarrhythmia database (CUDB) and MIT-BIH malignant ventricular ectopy database (VFDB) datasets of PhysioNet databases. A set of time-frequency features consists of temporal, spectral, and statistical were extracted and ranked by the correlation attribute evaluation with ranker search method in order to improve the accuracy of detection. The ranked features were classified for VT and VF conditions using support vector machine (SVM) and decision tree (C4.5) classifier. The proposed DWT based features yielded the average sensitivity of 98%, specificity of 99.32%, and accuracy of 99.23% using a decision tree (C4.5) classifier. These results were better than the SVM classifier having an average accuracy of 92.43%. The obtained results prove that using DWT based time-frequency features with decision tree (C4.5) classifier can be one of the best choices for clinicians for precise detection of ventricular arrhythmias.
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Affiliation(s)
- MONALISA MOHANTY
- Department of Electronics & Communication Engineering, ITER, SOA Deemed to be University, Odisha, India
| | - PRADYUT BISWAL
- Department of Electronics and Telecommunication Engineering, IIIT Bhubaneswar, Odisha, India
| | - SUKANTA SABUT
- School of Electronics Engineering, KIIT Deemed to be University, Odisha, India
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