An overview on state-of-the-art electrocardiogram signal processing methods: Traditional to AI-based approaches

Ardeti, Venkata Anuhya; Kolluru, Venkata Ratnam; Varghese, George Tom; Patjoshi, Rajesh Kumar

doi:10.1016/j.eswa.2023.119561

Cited by 12 publications

(9 citation statements)

References 171 publications

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“…The algorithm’s application to a cohort of 7200 samples demonstrated an overall effectiveness of 95.1%, with high sensitivity and specificity (94.7% and 95.4%). More sophisticated algorithms exist [ 18 ] but are undoubtedly more complex and costly.…”

Section: Discussionmentioning

confidence: 99%

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Validation of a New and Straightforward Algorithm to Evaluate Signal Quality during ECG Monitoring with Wearable Devices Used in a Clinical Setting

Neri,

Gallelli,

Dall’Olio

et al. 2024

Bioengineering

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Background: Wearable devices represent a new approach for monitoring key clinical parameters, such as ECG signals, for research and health purposes. These devices could outcompete medical devices in terms of affordability and use in out-clinic settings, allowing remote monitoring. The major limitation, especially when compared to implantable devices, is the presence of artifacts. Several authors reported a relevant percentage of recording time with poor/unusable traces for ECG, potentially hampering the use of these devices for this purpose. For this reason, it is of the utmost importance to develop a simple and inexpensive system enabling the user of the wearable devices to have immediate feedback on the quality of the acquired signal, allowing for real-time correction. Methods: A simple algorithm that can work in real time to verify the quality of the ECG signal (acceptable and unacceptable) was validated. Based on simple statistical parameters, the algorithm was blindly tested by comparison with ECG tracings previously classified by two expert cardiologists. Results: The classifications of 7200 10s-signal samples acquired on 20 patients with a commercial wearable ECG monitor were compared. The algorithm has an overall efficiency of approximately 95%, with a sensitivity of 94.7% and a specificity of 95.3%. Conclusions: The results demonstrate that even a simple algorithm can be used to classify signal coarseness, and this could allow real-time intervention by the subject or the technician.

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Section: Discussionmentioning

confidence: 99%

“…Today, dedicated signal acquisition software automatically classifies signal quality [ 18 ] with traditional and novel algorithms (for example, by implanting artificial intelligence—AI) [ 19 , 20 , 21 , 22 , 23 , 24 ]. These methods have variable sensitivity and specificity, with an overall accuracy usually >90% [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Validation of a New and Straightforward Algorithm to Evaluate Signal Quality during ECG Monitoring with Wearable Devices Used in a Clinical Setting

Neri,

Gallelli,

Dall’Olio

et al. 2024

Bioengineering

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“…For example, early detection and appropriate treatment of Atrial Fibrillation can reduce the risk of stroke and lead to a near-normal life expectancy [4]. The analysis of ECG signals is essential to detect arrhythmias, so many researchers use ECG datasets to design arrhythmias classification and prediction models based on machine and deep learning algorithms [5,6].…”

Section: Introductionmentioning

confidence: 99%

Developing Hybrid CNN-GRU Arrhythmia Prediction Models Using Fast Fourier Transform on Imbalanced ECG Datasets

Oleiwi,

AlShemmary,

Al-Augby

2024

MMEP

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There are many methods to diagnose heart disease; the most effective way is to analyze electrocardiogram (ECG) signals. Generally, the automatic classification techniques based on ECG analysis consist of three steps: data preprocessing, feature extraction, and classification. This study designed eight hybrid model architectures using several types of deep neural networks, including Convolution Neural Network (CNN), Gated Recurrent Unit (GRU), and Bidirectional GRU (Bi-GRU), four of them without Fast Fourier Transform (FFT) and the rest using FFT. Firstly, the MIT-BIH arrhythmia database is cleaned using the wavelet (WT) thresholding method that separates the combined noise and signal frequencies, making it ideal for processing nonstationary ECG signals. Additionally, the imbalance problem in this database was addressed using the synthetic minority over-sampling technique (SMOTE), which is more suitable for medical data than random synthesis methods. Secondly, hybrid models FFT-CNN, FFT-GRU, FFT-CNN-GRU, and FFT-CNN-Bi-GRU are constructed using the new proposed architecture by concatenating resultant features from two paths, the first path using ECG in the time domain and the second path using the resultant spectrum of ECG from FFT as input. A comparative study of the performance of all models was created in terms of accuracy, training time, number of trainable parameters, and robustness against noise. The results show that the proposed CNN, GRU, CNN-GRU, and CNN-Bi-GRU models without WT and FFT achieved 90%, 93%, 95%, and 96% accuracies, while the proposed FFT-CNN, FFT-GRU, FFT-CNN-GRU, and FFT-CNN-Bi-GRU models achieved 97%, 95%, 96%, and 97% accuracies with WT. So, the proposed FFT-CNN model was the best, with less training time and parameters than other models, which significantly impacts designing a high-efficiency model with less complexity for a practical medical diagnosis system. On the other hand, using FFT improved all models' performance, accuracy and robustness against noise.

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“…Electrocardiograms (ECGs) can be used to track the heart’s rhythmic activity [ 4 ]. The condition of the heart may be quickly diagnosed with an ECG, a non-invasive test.…”

Section: Introductionmentioning

confidence: 99%

A Residual-Dense-Based Convolutional Neural Network Architecture for Recognition of Cardiac Health Based on ECG Signals

Ahmed

Abbas

Daadaa

et al. 2023

Sensors

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Cardiovascular disorders are often diagnosed using an electrocardiogram (ECG). It is a painless method that mimics the cyclical contraction and relaxation of the heart’s muscles. By monitoring the heart’s electrical activity, an ECG can be used to identify irregular heartbeats, heart attacks, cardiac illnesses, or enlarged hearts. Numerous studies and analyses of ECG signals to identify cardiac problems have been conducted during the past few years. Although ECG heartbeat classification methods have been presented in the literature, especially for unbalanced datasets, they have not proven to be successful in recognizing some heartbeat categories with high performance. This study uses a convolutional neural network (CNN) model to combine the benefits of dense and residual blocks. The objective is to leverage the benefits of residual and dense connections to enhance information flow, gradient propagation, and feature reuse, ultimately improving the model’s performance. This proposed model consists of a series of residual-dense blocks interleaved with optional pooling layers for downsampling. A linear support vector machine (LSVM) classified heartbeats into five classes. This makes it easier to learn and represent features from ECG signals. We first denoised the gathered ECG data to correct issues such as baseline drift, power line interference, and motion noise. The impacts of the class imbalance are then offset by resampling techniques that denoise ECG signals. An RD-CNN algorithm is then used to categorize the ECG data for the various cardiac illnesses using the retrieved characteristics. On two benchmarked datasets, we conducted extensive simulations and assessed several performance measures. On average, we have achieved an accuracy of 98.5%, a sensitivity of 97.6%, a specificity of 96.8%, and an area under the receiver operating curve (AUC) of 0.99. The effectiveness of our suggested method for detecting heart disease from ECG data was compared with several recently presented algorithms. The results demonstrate that our method is lightweight and practical, qualifying it for continuous monitoring applications in clinical settings for automated ECG interpretation to support cardiologists.

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An overview on state-of-the-art electrocardiogram signal processing methods: Traditional to AI-based approaches

Cited by 12 publications

References 171 publications

Validation of a New and Straightforward Algorithm to Evaluate Signal Quality during ECG Monitoring with Wearable Devices Used in a Clinical Setting

Validation of a New and Straightforward Algorithm to Evaluate Signal Quality during ECG Monitoring with Wearable Devices Used in a Clinical Setting

Developing Hybrid CNN-GRU Arrhythmia Prediction Models Using Fast Fourier Transform on Imbalanced ECG Datasets

A Residual-Dense-Based Convolutional Neural Network Architecture for Recognition of Cardiac Health Based on ECG Signals

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