207 research outputs found
Automated Identication of Atrial Fibrillation from Single-lead ECGs Using Multi-branching ResNet
Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is
clinically identified with irregular and rapid heartbeat rhythm. AF puts a
patient at risk of forming blood clots, which can eventually lead to heart
failure, stroke, or even sudden death. It is of critical importance to develop
an advanced analytical model that can effectively interpret the
electrocardiography (ECG) signals and provide decision support for accurate AF
diagnostics. In this paper, we propose an innovative deep-learning method for
automated AF identification from single-lead ECGs. We first engage the
continuous wavelet transform (CWT) to extract time-frequency features from ECG
signals. Then, we develop a convolutional neural network (CNN) structure that
incorporates ResNet for effective network training and multi-branching
architectures for addressing the imbalanced data issue to process the 2D
time-frequency features for AF classification. We evaluate the proposed
methodology using two real-world ECG databases. The experimental results show a
superior performance of our method compared with traditional deep learning
models
Unidirectional-bidirectional recurrent networks for cardiac disorders classification
The deep learning approach of supervised recurrent network classifiers model, i.e., recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) are used in this study. The unidirectional and bidirectional for each cardiac disorder (CDs) class is also compared. Comparing both phases is needed to figure out the optimum phase and the best model performance for ECG using the Physionet dataset to classify five classes of CDs with 15 leads ECG signals. The result shows that the bidirectional RNNs method produces better results than the unidirectional method. In contrast to RNNs, the unidirectional LSTM and GRU outperformed the bidirectional phase. The best recurrent network classifier performance is unidirectional GRU with average accuracy, sensitivity, specificity, precision, and F1-score of 98.50%, 95.54%, 98.42%, 89.93% 92.31%, respectively. Overall, deep learning is a promising improved method for ECG classification
Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review
The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/
Automated ECG Waveform Annotation Based on Stacked Long Short-Term Memory
The classification of electrocardiogram (ECG) waveform segmentation techniques can be difficult due to physiological variation of heart rate and different characteristics of the different ECG waves in terms of shape, frequency, amplitude, and duration. The P-wave, PR-segment, QRS-complex, ST-segment, and T-wave are extracted as the feature for classification algorithm to diagnose specified cardiac disorders. This requires the implementation of algorithms that identify specific points within the ECG wave. Some previous computational algorithms for automatic classification of ECG segmentation are proposed to overcome limitations of manual inspection of the ECG. This study presents new insight into the ECG semantic segmentation problem is surmounted by a deep learning approach for automatic ECG wave-form. Long short-term memory (LSTM) is proposed for this task. This experimental study has been performed for six different waveforms of ECG signal that represents cardiac disorders obtained from the Physionet: QT database. Overall, LSTM performance achieved accuracy, sensitivity, specificity, precision, F1-score, is 93.36%, 86.85%, 95.78%, 81.79%, and 83.09%, respectively
An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification
An automatic system for heart arrhythmia classification can perform a substantial role inmanaging and treating cardiovascular diseases. In this paper, a deep learning-based multi-model system is proposed for the classification of electrocardiogram (ECG) signals. Two different deep learning bagging models are introduced to classify heartbeats into different arrhythmias types. The first model (CNN-LSTM) is based on a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) network to capture local features and temporal dynamics in the ECG data. The second model (RRHOS-LSTM) integrates some classical features, i.e. RR intervals and higher-order statistics (HOS), with LSTM model to effectively highlight abnormality heartbeats classes. We create a bagging model from the CNN-LSTM and RRHOS-LSTM networks by training each model on a different sub-sampling dataset to handle the high imbalance distribution of arrhythmias classes in the ECG data. Each model is also trained using a weighted loss function to provide high weight for not sufficiently represented classes. These models are then combined using a meta-classifier to form a strong coherent model. The meta-classifier is a feedforward fully connected neural network that takes the different predictions of bagging models as an input and combines them into a final prediction. The result of the meta-classifier is then verified by another CNN-LSTM model to decrease the false positive of the overall system. The experimental results are acquired by evaluating the proposed method on ECG data from the MIT-BIH arrhythmia database. The proposedmethod achieves an overall accuracy of 95.81% in the ‘‘subject-oriented’’ patient independent evaluation scheme. The averages of F1 score and positive predictive value are higher than all other methods by more than 3% and 8% respectively. The experimental results show the superiority of the proposed method for ECG heartbeats classification compared to many state-of-the-art methods
DENS-ECG: A Deep Learning Approach for ECG Signal Delineation
Objectives: With the technological advancements in the field of tele-health
monitoring, it is now possible to gather huge amounts of electro-physiological
signals such as electrocardiogram (ECG). It is therefore necessary to develop
models/algorithms that are capable of analysing these massive amounts of data
in real-time. This paper proposes a deep learning model for real-time
segmentation of heartbeats. Methods: The proposed algorithm, named as the
DENS-ECG algorithm, combines convolutional neural network (CNN) and long
short-term memory (LSTM) model to detect onset, peak, and offset of different
heartbeat waveforms such as the P-wave, QRS complex, T-wave, and No wave (NW).
Using ECG as the inputs, the model learns to extract high level features
through the training process, which, unlike other classical machine learning
based methods, eliminates the feature engineering step. Results: The proposed
DENS-ECG model was trained and validated on a dataset with 105 ECGs of length
15 minutes each and achieved an average sensitivity and precision of 97.95% and
95.68%, respectively, using a 5-fold cross validation. Additionally, the model
was evaluated on an unseen dataset to examine its robustness in QRS detection,
which resulted in a sensitivity of 99.61% and precision of 99.52%. Conclusion:
The empirical results show the flexibility and accuracy of the combined
CNN-LSTM model for ECG signal delineation. Significance: This paper proposes an
efficient and easy to use approach using deep learning for heartbeat
segmentation, which could potentially be used in real-time tele-health
monitoring systems
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