211 research outputs found
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
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/
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
In this paper have developed a novel hybrid hierarchical attention-based
bidirectional recurrent neural network with dilated CNN (HARDC) method for
arrhythmia classification. This solves problems that arise when traditional
dilated convolutional neural network (CNN) models disregard the correlation
between contexts and gradient dispersion. The proposed HARDC fully exploits the
dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM)
architecture to generate fusion features. As a result of incorporating both
local and global feature information and an attention mechanism, the model's
performance for prediction is improved.By combining the fusion features with a
dilated CNN and a hierarchical attention mechanism, the trained HARDC model
showed significantly improved classification results and interpretability of
feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score
normalization, filtering, denoising, and segmentation are used to prepare the
raw data for analysis. CGAN (Conditional Generative Adversarial Network) is
then used to generate synthetic signals from the processed data. The
experimental results demonstrate that the proposed HARDC model significantly
outperforms other existing models, achieving an accuracy of 99.60\%, F1 score
of 98.21\%, a precision of 97.66\%, and recall of 99.60\% using MIT-BIH
generated ECG. In addition, this approach substantially reduces run time when
using dilated CNN compared to normal convolution. Overall, this hybrid model
demonstrates an innovative and cost-effective strategy for ECG signal
compression and high-performance ECG recognition. Our results indicate that an
automated and highly computed method to classify multiple types of arrhythmia
signals holds considerable promise.Comment: 23 page
Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review
Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data
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