A method for automatic detection of atrial fibrillation: based on CNN combined with BLSTM (Preprint)

Mapping Intimacies ◽

10.2196/preprints.23326 ◽

2020 ◽

Author(s):

Wei Liang ◽

Liang Tao ◽

Baoning Liu

Keyword(s):

Atrial Fibrillation ◽

Short Term Memory ◽

Automatic Detection ◽

Effective Solution ◽

Short Term ◽

Accurate Analysis ◽

Ecg Signals ◽

Long Short Term Memory ◽

Fully Connected ◽

Deep Learning Model

BACKGROUND Atrial fibrillation (AF) is the most common arrhythmia harmful to human health. The morbidity and mortality of AF increase with age. However, due to its small amplitude and short duration, as well as its complexity and nonlinearity, it is very difficult to carry out accurate analysis only through expert experience. Therefore, the automatic detection of AF becomes extremely important. OBJECTIVE The purpose of this paper is to develop a method of automatic detection of AF, more accurate and efficient automatic detection of the occurrence of AF. METHODS In this paper, a deep learning model consisting of a convolutional neural network (CNN) and a bi-directional long short-term memory (BLSTM) network is proposed to detect AF automatically. The model is mainly composed of six convolutional layers, three pooling layers, a BLSTM layer and a fully connected layer. In addition, the data of MIT-BIH AFDB database is divided into different lengths of ECG segments, which are used as input to the network to verify the effect of different lengths of ECG segments on the final result. RESULTS The method proposed also achieved excellent results on ECG signals of 1 second. Most importantly, our proposed method achieved the best performances on ECG signals of 10 seconds, with an accuracy of 99.57%，a sensitivity of 99.65%,and a specificity of 99.49%. CONCLUSIONS This method requires no complex pretreatment and retains the characteristics of the original ECG to the greatest extent. Compared with the existing studies, the proposed method has higher accuracy and provides an effective solution for the automatic detection of AF.

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Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

Atmosphere ◽

10.3390/atmos12070924 ◽

2021 ◽

Vol 12 (7) ◽

pp. 924

Author(s):

Moslem Imani ◽

Hoda Fakour ◽

Wen-Hau Lan ◽

Huan-Chin Kao ◽

Chi Ming Lee ◽

...

Keyword(s):

Deep Learning ◽

Wind Speed ◽

Set Theory ◽

Rough Set ◽

Fuzzy Set Theory ◽

Short Term Memory ◽

Learning Model ◽

Short Term ◽

Long Short Term Memory ◽

Deep Learning Model

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

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Feasibility of Using Electrical Impedance Spectroscopy for Assessing Biological Cell Damage during Freezing and Thawing

Sensors ◽

10.3390/s21124129 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4129

Author(s):

Sisay Mebre Abie ◽

Ørjan Grøttem Martinsen ◽

Bjørg Egelandsdal ◽

Jie Hou ◽

Frøydis Bjerke ◽

...

Keyword(s):

Phase Angle ◽

Short Term Memory ◽

Cell Damage ◽

Electrical Impedance Spectroscopy ◽

Electric Impedance ◽

Freezing And Thawing ◽

Short Term ◽

Long Short Term Memory ◽

Impedance Parameters ◽

Deep Learning Model

This study was performed to test bioimpedance as a tool to detect the effect of different thawing methods on meat quality to aid in the eventual creation of an electric impedance-based food quality monitoring system. The electric impedance was measured for fresh pork, thawed pork, and during quick and slow thawing. A clear difference was observed between fresh and thawed samples for both impedance parameters. Impedance was different between the fresh and the frozen-thawed samples, but there were no impedance differences between frozen-thawed samples and the ones that were frozen-thawed and then stored at +3 °C for an additional 16 h after thawing. The phase angle was also different between fresh and the frozen-thawed samples. At high frequency, there were small, but clear phase angle differences between frozen-thawed samples and the samples that were frozen-thawed and subsequently stored for more than 16 h at +3 °C. Furthermore, the deep learning model LSTM-RNN (long short-term memory recurrent neural network) was found to be a promising way to classify the different methods of thawing.

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Compression of ECG Signals Using Long Short-Term Memory based Sequence-to-Sequence Autoencoder

2020 28th Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu49456.2020.9302503 ◽

2020 ◽

Author(s):

Gurkan Aydemir ◽

Tahir Bekiryazici ◽

Hakan Gurkan

Keyword(s):

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Ecg Signals ◽

Long Short Term Memory

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Pressure prediction and abnormal working conditions detection of water supply network based on LSTM

Water Science & Technology Water Supply ◽

10.2166/ws.2020.013 ◽

2020 ◽

Vol 20 (3) ◽

pp. 963-974 ◽

Cited By ~ 2

Author(s):

Zhe Xu ◽

Zhihao Ying ◽

Yuquan Li ◽

Bishi He ◽

Yun Chen

Keyword(s):

Water Supply ◽

Short Term Memory ◽

Learning Performance ◽

Support Vector ◽

Supply Network ◽

Short Term ◽

Water Supply Network ◽

Vector Machines ◽

Long Short Term Memory ◽

Deep Learning Model

Abstract In this study, a deep learning model based on LSTM (Long Short-Term Memory) is used to predict the state of a water supply network due to its highly complex nonlinearity. The inputs of the model include state information on the pressures at measuring points, as well as control information on the water supply pressure and flow at each entry point. In order to enhance the performance of the model in feature extraction and identification and improve prediction accuracy, a parallel LSTM tandem DNN deep neural network model (PLDNN) is proposed. The experimental results indicate that the model has better learning performance and accuracy compared with traditional prediction methods (artificial neural networks, support vector machines, etc.) and general LSTM models.

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