ECG Heartbeat Classification Using Multimodal Fusion

Ahmad, Zeeshan; Tabassum, Anika; Guan, Ling; Khan, Naimul

doi:10.1109/access.2021.3097614

Cited by 63 publications

(13 citation statements)

References 67 publications

(52 reference statements)

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“…For ease of annotation, each beat is marked with a special symbol. Arrhythmias are abnormalities in the frequency, rhythm, origin, conduction velocity, and sequence of excitation of the electrical impulses of the heart, with the direct consequence of sudden cardiac death and heart failure [ 19 ]. It is estimated that there are hundreds of millions of cardiovascular patients worldwide, of whom 26.8% suffer from arrhythmias, and the prevalence and mortality rates are still on the rise.…”

Section: Mit-bih Cardiac Database For Online Automatic Diagnosis Of Cardiac Arrhythmiasmentioning

confidence: 99%

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Yan

Zhang

2021

Journal of Healthcare Engineering

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Arrhythmias are a relatively common type of cardiovascular disease. Most cardiovascular diseases are often accompanied by arrhythmias. In clinical practice, an electrocardiogram (ECG) can be used as a primary diagnostic tool for cardiac activity and is commonly used to detect arrhythmias. Based on the hidden and sudden nature of the MIT-BIH ECG database signal and the small-signal amplitude, this paper constructs a hybrid model for the temporal correlation characteristics of the MIT-BIH ECG database data, to learn the deep-seated essential features of the target data, combine the characteristics of the information processing mechanism of the arrhythmia online automatic diagnosis system, and automatically extract the spatial features and temporal characteristics of the diagnostic data. First, a combination of median filter and bandstop filter is used to preprocess the data in the ECG database with individual differences in ECG waveforms, and there are problems of feature inaccuracy and useful feature omission which cannot effectively extract the features implied behind the massive ECG signals. Its diagnostic algorithm integrates feature extraction and classification into one, which avoids some bias in the feature extraction process and provides a new idea for the automatic diagnosis of cardiovascular diseases. To address the problem of feature importance variability in the temporal data of the MIT-BIH ECG database, a hybrid model is constructed by introducing algorithms in deep neural networks, which can enhance its diagnostic efficiency.

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Section: Mit-bih Cardiac Database For Online Automatic Diagnosis Of Cardiac Arrhythmiasmentioning

confidence: 99%

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Yan

Zhang

2021

Journal of Healthcare Engineering

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“…Essa et al [30], following arrhythmia detection, proposed the bagging model of a CNN-LSTM (long short-term memory) and RRHOS-LSTM (RR Interval and higher-order statistics) classifiers, each trained with different sub-samples, being both connected with an ANN meta-classifier and verified with a final CNN-LSTM model, allowing a general performance of 95.81%. Ahmad et al [31] proposed the Multimodal Image Fusion (MIF) and the Multimodal Feature Fusion (MFF) frameworks for arrhythmia and Myocardial Infarction, each receives the ECG signal transformed into Gramian Angular Field, Recurrence Plot, and Markov Transition Field images. MIF creates a new image fusing its image input for CNN classification, while MFF generates the fusion of the extracted features of the CNN penultimate layer for training a Support Vector Machine (SVM) for heartbeat classification.…”

Section: Related Workmentioning

confidence: 99%

Sex Recognition through ECG Signals aiming toward Smartphone Authentication

et al. 2022

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Physiological signals are strongly related to a person’s state of health and carry information about the human body. For example, by ECG, it is possible to obtain information about cardiac disease, emotions, personal identification, and the sex of a person, among others. This paper proposes the study of the heartbeat from a soft-biometric perspective to be applied to smartphone unlocking services. We employ the user heartbeat to classify the individual by sex (male, female) with the use of Deep Learning, reaching an accuracy of 94.4% ± 2.0%. This result was obtained with the RGB representation of the union of the time-frequency transformation from the pseudo-orthogonal X, Y, and Z bipolar signals. Evaluating each bipolar contribution, we found that the XYZ combination provides the best category distinction using GoogLeNet. The 24-h Holter database of the study contains 202 subjects with a female size of 49.5%. We propose an architecture for managing this signal that allows the use of a few samples to train the network. Due to the hidden nature of ECG, it does not present vulnerabilities like public trait exposition, light/noise sensibility, or learnability compared to fingerprint, facial, voice, or password verification methods. ECG may complement those gaps en route to a cooperative authentication ecosystem.

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“…As the input of this fusion, the ECG is converted into different images by using Gramian Angular Field (GAF), Recurrence Plot (RP), and Markov Transition Field (MTF). e experiment was done on the MIT-BIH dataset for five arrhythmia conditions and achieved the required accuracy [9]. e current methods are not considered to work at a satisfactory level.…”

Section: Literature Reviewmentioning

confidence: 99%

ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches

Rahman

Asif

Sultan

et al. 2022

Computational Intelligence and Neuroscience

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According to the World Health Organization (WHO) report, heart disease is spreading throughout the world very rapidly and the situation is becoming alarming in people aged 40 or above (Xu, 2020). Different methods and procedures are adopted to detect and diagnose heart abnormalities. Data scientists are working on finding the different methods with the required accuracy (Strodthoff et al., 2021). Electrocardiogram (ECG) is the procedure to find the heart condition in the waveform. For ages, the machine learning techniques, which are feature based, played a vital role in the medical sciences and centralized the data in cloud computing and having access throughout the world. Furthermore, deep learning or transfer learning widens the vision and introduces different transfer learning methods to ensure accuracy and time management to detect the ECG in a better way in comparison to the previous and machine learning methods. Hence, it is said that transfer learning has turned world research into more appropriate and innovative research. Here, the proposed comparison and accuracy analysis of different transfer learning methods by using ECG classification for detecting ECG Arrhythmia (CAA-TL). The CAA-TL model has the multiclassification of the ECG dataset, which has been taken from Kaggle. Some of the healthy and unhealthy datasets have been taken in real-time, augmented, and fused with the Kaggle dataset, i.e., Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH dataset). The CAA-TL worked on the accuracy of heart problem detection by using different methods like ResNet50, AlexNet, and SqueezeNet. All three deep learning methods showed remarkable accuracy, which is improved from the previous research. The comparison of different deep learning approaches with respect to layers widens the research and gives the more clarity and accuracy and at the same time finds it time-consuming while working with multiclassification with massive dataset of ECG. The implementation of the proposed method showed an accuracy of 98.8%, 90.08%, and 91% for AlexNet, SqueezeNet, and ResNet50, respectively.

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ECG Heartbeat Classification Using Multimodal Fusion

Cited by 63 publications

References 67 publications

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Sex Recognition through ECG Signals aiming toward Smartphone Authentication

ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches

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