Automatic Detection of Atrial Fibrillation Based on CNN-LSTM and Shortcut Connection

Atrial fibrillation (AF) is one of the most common persistent arrhythmias, which has a close connection to a large number of cardiovascular diseases. However, if spotted early, the diagnosis of AF can improve the effectiveness of clinical treatment and effectively prevent serious complications. In this paper, a combination of an 8-layer convolutional neural network (CNN) with a shortcut connection and 1-layer long short-term memory (LSTM), named 8CSL, was proposed for the Electrocardiogram (ECG) classification task. Compared with recurrent neural networks (RNN) and multi-scale convolution neural networks (MCNN), not only can 8CSL extract features skillfully, but also deal with long-term dependency between data. In particular, 8CSL includes eight shortcut connections that can improve the speed of the data transmission and processing as a result of the shortcut connections. The model was evaluated on the base of the test set of the Computing in Cardiology Challenge 2017 dataset with the F1 score. The ECG recordings were cropped or padded to the same length. After 10-fold cross-validation, the average test F1 score was 84.89%, 89.55%, and 85.64% when the segment length was 5, 10, 20 s, respectively. The experiment results demonstrate excellent performance with potential practical applications.

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Attention Mechanism-Based Convolutional Long Short-Term Memory Neural Networks to Electrocardiogram-Based Blood Pressure Estimation

Applied Sciences ◽

10.3390/app112412019 ◽

2021 ◽

Vol 11 (24) ◽

pp. 12019

Author(s):

Chia-Chun Chuang ◽

Chien-Ching Lee ◽

Chia-Hong Yeng ◽

Edmund-Cheung So ◽

Yeou-Jiunn Chen

Keyword(s):

Blood Pressure ◽

Neural Networks ◽

Short Term Memory ◽

Attention Mechanism ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Blood Pressure Estimation ◽

Fully Connected ◽

Electrocardiogram Ecg

Monitoring people’s blood pressure can effectively prevent blood pressure-related diseases. Therefore, providing a convenient and comfortable approach can effectively help patients in monitoring blood pressure. In this study, an attention mechanism-based convolutional long short-term memory (LSTM) neural network is proposed to easily estimate blood pressure. To easily and comfortably estimate blood pressure, electrocardiogram (ECG) and photoplethysmography (PPG) signals are acquired. To precisely represent the characteristics of ECG and PPG signals, the signals in the time and frequency domain are selected as the inputs of the proposed NN structure. To automatically extract the features, the convolutional neural networks (CNNs) are adopted as the first part of neural networks. To identify the meaningful features, the attention mechanism is used in the second part of neural networks. To model the characteristic of time series, the long short-term memory (LSTM) is adopted in the third part of neural networks. To integrate the information of previous neural networks, the fully connected networks are used to estimate blood pressure. The experimental results show that the proposed approach outperforms CNN and CNN-LSTM and complies with the Association for the Advancement of Medical Instrumentation standard.

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An Efficient Hybrid Mechanism with LSTM Neural Networks in Application to Stock Price Forecasting

Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques - Frontiers in Artificial Intelligence and Applications ◽

10.3233/faia200589 ◽

2020 ◽

Author(s):

Ngoc-An Nguyen-Pham ◽

Trung T. Nguyen

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Stock Price ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Practical Applications ◽

Hybrid Mechanism ◽

Dual Stage ◽

Long Short Term Memory

Recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks have shown some success with many practical applications in recent years such as machine translation, speech recognition, image processing and financial market forecasting. In recent years, a dual-stage attention-based recurrent neural network (DA-RNN) has shown some promising results on stock price prediction. We propose dual attention-dilated long short-term memory (DAD-LSTM) models combining DA-RNN and dilated recurrent neural networks (DRNN) to select the most relevant input features and capture the long-term temporal dependencies of a time series more efficiently. Numerical results from experiments on the NASDAQ 100, S&P 500, HSI and DJIA datasets show that DAD-LSTM models outperform the state-of-the-art and most recent approaches.

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Detection of Atrial Fibrillation Using a Machine Learning Approach

Information ◽

10.3390/info11120549 ◽

2020 ◽

Vol 11 (12) ◽

pp. 549

Author(s):

Sidrah Liaqat ◽

Kia Dashtipour ◽

Adnan Zahid ◽

Khaled Assaleh ◽

Kamran Arshad ◽

...

Keyword(s):

Machine Learning ◽

Atrial Fibrillation ◽

Deep Learning ◽

Short Term Memory ◽

Learning Algorithms ◽

Experimental Results ◽

Short Term ◽

Machine Learning Approach ◽

Long Short Term Memory ◽

Electrocardiogram Ecg

The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate.

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