An ECG Signal Prediction Method Based on ARIMA Model and DWT

Fu-ying, Huang; Qin, Tuanfa; Wang, Limei; Wan, Haibin; Ren, Junyu

doi:10.1109/iaeac47372.2019.8997620

Cited by 17 publications

(10 citation statements)

References 12 publications

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“…An ECG signal prediction method based on phase space reconstruction (PSR) and BPNN was proposed in [19], with accuracy close to that of the previously mentioned method. An ECG signal prediction method based on autoregressive integrated moving average (ARIMA) model and discrete wavelet transform (DWT) was proposed in [20]. Although the prediction accuracy of the method is relatively high, it needs to smooth the ECG signal in advance, and there is a certain signal distortion.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for ECG Signal Prediction Based on VMD, Cao Method, and LSTM Neural Network

Fu-ying

Qin

Wang

et al. 2021

Preprint

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Background: In body area network (BAN), accurate prediction of ECG signal can not only let doctors know the patient's condition in advance, but also help to reduce the energy consumption of sensors. In order to improve the accuracy of ECG signal prediction, this paper proposes a deep learning method for ECG signal prediction. Methods: The proposed prediction method combines variational mode decomposition (VMD), Cao method and a long short-term memory (LSTM) neural network. In the method, VMD decomposes ECG data into a series of intrinsic mode functions (IMFs), which reduces the non-stationary character of ECG signals and helps to improve the prediction accuracy. Cao method is used to determine the input dimension of LSTM input layer, namely, the minimum embedding dimension of each IMF is the input dimension of LSTM input layer. Each IMF is predicted by a LSTM neural network which adopts Adam optimizer. All IMFs predictions are aggregated to get the final prediction result. Results: To evaluate the prediction accuracy of the proposed method, simulation experiments are carried out on ECG data from the MIT-BIH Arrhythmia Database. Experimental results show that the RMSE (root mean square error) and MAE (mean absolute error) of the proposed model are 0.001326 and 0.001044 respectively, which are more than 10 percent lower than the traditional prediction methods.Conclusions: Compared with some traditional prediction methods, the proposed prediction method improves the prediction accuracy obviously.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for ECG Signal Prediction Based on VMD, Cao Method, and LSTM Neural Network

Fu-ying

Qin

Wang

et al. 2021

Preprint

Self Cite

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“…Sun et al [20] used an ARIMA model to analyze ECG data streams, but they did not use one to predict an ECG signal. An ECG signal prediction method based on an ARIMA model and a discrete wavelet transform (DWT) was proposed in [21]. This method obtained a good prediction effect, but it needed a significant amount of calculation because it used many high-order autoregressive (AR) models.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Prediction Method for ECG Signals Based on VMD, PSR, and RBF Neural Network

Fu-ying

Qin

Wang³

et al. 2021

BioMed Research International

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To explore a method to predict ECG signals in body area networks (BANs), we propose a hybrid prediction method for ECG signals in this paper. The proposed method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a radial basis function (RBF) neural network to predict an ECG signal. To reduce the nonstationarity and randomness of the ECG signal, we use VMD to decompose the ECG signal into several intrinsic mode functions (IMFs) with finite bandwidth, which is helpful to improve the prediction accuracy. The input parameters of the RBF neural network affect the prediction accuracy and computational burden. We employ PSR to optimize input parameters of the RBF neural network. To evaluate the prediction performance of the proposed method, we carry out many simulation experiments on ECG data from the MIT-BIH Arrhythmia Database. The experimental results show that the root mean square error (RMSE) and mean absolute error (MAE) of the proposed method are of 10-3 magnitude, while the RMSE and MAE of some competitive prediction methods are of 10-2 magnitude. Compared with other several prediction methods, our method obviously improves the prediction accuracy of ECG signals.

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“…Dai et al proposed a lossless compression method for periodic signals based on an adaptive dictionary model which can predict the current data value according to the history [19]. Huang et al proposed a novel ECG signal prediction model that uses an autoregressive integrated moving average (ARIMA) model and discrete wavelet transform (DWT) [20]. Nonetheless, these compressors are all designed for specific signals.…”

Section: Introductionmentioning

confidence: 99%

Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor

Chen

Luo

2021

Applied Sciences

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The use of sensor applications has been steadily increasing, leading to an urgent need for efficient data compression techniques to facilitate the storage, transmission, and processing of digital signals generated by sensors. Unlike other sequential data such as text sequences, sensor signals have more complex statistical characteristics. Specifically, in every signal point, each bit, which corresponds to a specific precision scale, follows its own conditional distribution depending on its history and even other bits. Therefore, applying existing general-purpose data compressors usually leads to a relatively low compression ratio, since these compressors do not fully exploit such internal features. What is worse, partitioning a bit stream into groups with a preset size will sometimes break the integrity of each signal point. In this paper, we present a lossless data compressor dedicated to compressing sensor signals which is built upon a novel recurrent neural architecture named multi-channel recurrent unit (MCRU). Each channel in the proposed MCRU models a specific precision range of each signal point without breaking data integrity. During compressing and decompressing, the mirrored network will be trained on observed data; thus, no pre-training is needed. The superiority of our approach over other compressors is demonstrated experimentally on various types of sensor signals.

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An ECG Signal Prediction Method Based on ARIMA Model and DWT

Cited by 17 publications

References 12 publications

A Deep Learning Method for ECG Signal Prediction Based on VMD, Cao Method, and LSTM Neural Network

A Deep Learning Method for ECG Signal Prediction Based on VMD, Cao Method, and LSTM Neural Network

Hybrid Prediction Method for ECG Signals Based on VMD, PSR, and RBF Neural Network

Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor

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