Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

Awan, Muhammad Tayyib; Amir, Muhammad; Maqsood, Sarmad; Yousufi, Musyyab; Abdullah, Suheel; Irfan, Muhammad

doi:10.5755/j01.itc.50.1.24145

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…To begin, we use a converted storage service and the expand information helper procedure to divide up the information into manageable chunks. To create the scalograms, 1-D ECG signals were transformed using Continuous Wavelet Transformation (CWT), which was then used to create the final 2-D color images [5,40,16,41,18,37].…”

Section: Datasetmentioning

confidence: 99%

HybDeepNet: ECG Signal Based Cardiac Arrhythmia Diagnosis Using a Hybrid Deep Learning Model

Pandian

Kalpana²

2023

ITC

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To monitor electrical indications from the heart and assess its performance, the electrocardiogram (ECG) is the most common and routine diagnostic instrument employed. Cardiac arrhythmias are only one example of the many heart conditions people might have. ECG records are used to diagnose an arrhythmia, an abnormal cardiac beat that can cause a stroke in extreme circumstances. However, due to the extensive data that an ECG contains, it is quite difficult to glean the necessary information through visual analysis. Therefore, it is crucial to develop an effective (automatic) method to analyze the vast amounts of data available from ECG. For decades, researchers have focused on developing methods to automatically and computationally categorize and identify cardiac arrhythmias. However, monitoring for arrhythmias in real-time is challenging. To streamline the detection and classification process, this research presents a hybrid deep learning-based technique. There are two major contributions to this study. To automate the noise reduction and feature extraction, 1D ECG data are first transformed into 2D Scalogram images. Following this, a combined approach called the Residual attention-based 2D-CNN-LSTM-CNN (RACLC) is recommended by merging multiple learning models, specifically the 2D convolutional neural network (CNN) and the Long Short-Term Memory (LSTM) system, based on research findings. The name of this model comes from a combination of the two deep learning. Both the beats themselves, which provide morphological information, and the beats paired with neighboring segments, which provide temporal information, are essential. Our suggested model simultaneously collects time-domain and morphological ECG signal data and combines them. The application of the attention block to the network helps to strengthen the valuable information, acquire the confidential message in the ECG signal, and boost the efficiency of the model when it comes to categorization. To evaluate the efficacy of the proposed RACLC method, we carried out a complete experimental investigation making use of the MIT-BIH arrhythmia database, which is used by a large number of researchers. The results of our experiments show that the automated detection method we propose is effective.

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Section: Datasetmentioning

confidence: 99%

HybDeepNet: ECG Signal Based Cardiac Arrhythmia Diagnosis Using a Hybrid Deep Learning Model

Pandian

Kalpana²

2023

ITC

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“…In addition, abnormal ECG signals, such as arrhythmia and premature ventricular contraction [22], have been investigated. There have also been studies conducted that deal with separating fetal ECG from mother ECG [23]. These studies were conducted in various experimental settings, and multiple algorithms have been proposed for detecting R-peaks, generating trigger signals, and classifying normal and abnormal ECG signals.…”

Section: Introductionmentioning

confidence: 99%

Self-Attention MHDNet: A Novel Deep Learning Model for the Detection of R-Peaks in the Electrocardiogram Signals Corrupted with Magnetohydrodynamic Effect

Chowdhury

Khan

et al. 2023

Bioengineering

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Magnetic resonance imaging (MRI) is commonly used in medical diagnosis and minimally invasive image-guided operations. During an MRI scan, the patient’s electrocardiogram (ECG) may be required for either gating or patient monitoring. However, the challenging environment of an MRI scanner, with its several types of magnetic fields, creates significant distortions of the collected ECG data due to the Magnetohydrodynamic (MHD) effect. These changes can be seen as irregular heartbeats. These distortions and abnormalities hamper the detection of QRS complexes, and a more in-depth diagnosis based on the ECG. This study aims to reliably detect R-peaks in the ECG waveforms in 3 Tesla (T) and 7T magnetic fields. A novel model, Self-Attention MHDNet, is proposed to detect R peaks from the MHD corrupted ECG signal through 1D-segmentation. The proposed model achieves a recall and precision of 99.83% and 99.68%, respectively, for the ECG data acquired in a 3T setting, while 99.87% and 99.78%, respectively, in a 7T setting. This model can thus be used in accurately gating the trigger pulse for the cardiovascular functional MRI.

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An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal

et al. 2022

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The development of automatic monitoring and diagnosis systems for cardiac patients over the internet has been facilitated by recent advancements in wearable sensor devices from electrocardiographs (ECGs), which need the use of patient-specific approaches. Premature ventricular contraction (PVC) is a common chronic cardiovascular disease that can cause conditions that are potentially fatal. Therefore, for the diagnosis of likely heart failure, precise PVC detection from ECGs is crucial. In the clinical settings, cardiologists typically employ long-term ECGs as a tool to identify PVCs, where a cardiologist must put in a lot of time and effort to appropriately assess the long-term ECGs which is time consuming and cumbersome. By addressing these issues, we have investigated a deep learning method with a pre-trained deep residual network, ResNet-18, to identify PVCs automatically using transfer learning mechanism. Herein, features are extracted by the inner layers of the network automatically compared to hand-crafted feature extraction methods. Transfer learning mechanism handles the difficulties of required large volume of training data for a deep model. The pre-trained model is evaluated on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia and Institute of Cardiological Technics (INCART) datasets. First, we used the Pan–Tompkins algorithm to segment 44,103 normal and 6423 PVC beats, as well as 106,239 normal and 9987 PVC beats from the MIT-BIH Arrhythmia and IN-CART datasets, respectively. The pre-trained model employed the segmented beats as input after being converted into 2D (two-dimensional) images. The method is optimized with the using of weighted random samples, on-the-fly augmentation, Adam optimizer, and call back feature. The results from the proposed method demonstrate the satisfactory findings without the using of any complex pre-processing and feature extraction technique as well as design complexity of model. Using LOSOCV (leave one subject out cross-validation), the received accuracies on MIT-BIH and INCART are 99.93% and 99.77%, respectively, suppressing the state-of-the-art methods for PVC recognition on unseen data. This demonstrates the efficacy and generalizability of the proposed method on the imbalanced datasets. Due to the absence of device-specific (patient-specific) information at the evaluating stage on the target datasets in this study, the method might be used as a general approach to handle the situations in which ECG signals are obtained from different patients utilizing a variety of smart sensor devices.

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Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

Cited by 3 publications

References 24 publications

HybDeepNet: ECG Signal Based Cardiac Arrhythmia Diagnosis Using a Hybrid Deep Learning Model

HybDeepNet: ECG Signal Based Cardiac Arrhythmia Diagnosis Using a Hybrid Deep Learning Model

Self-Attention MHDNet: A Novel Deep Learning Model for the Detection of R-Peaks in the Electrocardiogram Signals Corrupted with Magnetohydrodynamic Effect

An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal

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