Pre processing the abdominal ECG signal using combination of FIR filter and principal component analysis

Ramasamy, Savitha; Breesha, S. R.; Joseph, X. Felix

doi:10.1109/iccpct.2015.7159460

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…The noise of ECG signal mainly includes power frequency noise, EMG interference and baseline drift. The power frequency noise is the electromagnetic interference caused by the power supply environment around the acquisition equipment when the ECG signal is collected [27]. Its amplitude is low, and the noise frequency is maintained at about 50Hz, EMG interference is a signal caused by muscle trembling during human movement, with a range of 0-2000Hz and energy concentration of 30-300Hz.The frequency of baseline drift is low, and the frequency distribution is within 0.15-0.3Hz.…”

Section: Ecg Signal Preprocessingmentioning

confidence: 99%

Arrhythmia Classification Method Based on SECNN-LSTM

Sun,

Wei,

Zhang

et al. 2023

Preprint

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In order to improve the recognition and prediction accuracy of automatic classification of ECG signals, this paper proposes an arrhythmia classification method based on SECNN-LSTM. First, ECG signal is preprocessed, and the data is resampled for the problem of data imbalance, then the SECNN-LSTM network model is built. The spatial features of the signal are extracted by SECNN model and the front and back dependencies of the feature information are captured by LSTM model. The method has been tested and verified on the MIT-BIH Arrhythmia Database, and compared with other traditional arrhythmia classification method. The accuracy, precision, sensitivity, specificity and F1 value of the model have been improved to different degrees, and the average accuracy of the model has reached 98.70%. The experimental results show that this method can efficiently and accurately identify normal beats and four common types of arrhythmia diseases.

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Section: Ecg Signal Preprocessingmentioning

confidence: 99%

Arrhythmia Classification Method Based on SECNN-LSTM

Sun,

Wei,

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A need for data pre-processing arises due to the existence of noisy readings, which limits the efficiency of deep learning architectures [53]. A multitude of solutions have been proposed, including combining finite impulse response (FIR) filters and principal component analysis (PCA) [54], wavelet transform approaches [55]- [57], modified empirical mode decomposition [58], and iterative denoising [59].…”

Section: Data Pre-processingmentioning

confidence: 99%

Robust Multimodal Heartbeat Detection Using Hybrid Neural Networks

et al. 2020

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Many arrhythmia datasets are multimodal due to the simultaneous collection of physiological signals of a subject. These datasets frequently have missing modalities or missing block-wise data, a characteristic that various recent applications of neural networks fail to consider. Most arrhythmic detection models only use electrocardiogram and blood pressure recordings. Unconsidered physiological signals may be strongly correlated with other modalities despite having missing data. To improve robustness and accuracy of heartbeat detection, all available modalities should be considered in multimodal arrhythmia datasets. Several hybrid neural networks are proposed to robustly analyze heartbeats by considering every available physiological signal. These networks combine elements from convolutional neural networks, recurrent neural networks, and a deep learning architecture. This enables researchers to analyze every signal of subjects while the set of signals collected among subjects may differ. The proposed hybrid neural networks provide more robust results in heartbeat detection when utilizing missing data modalities. INDEX TERMS Multimodal, heartbeat detection, deep learning, neural networks.

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Detection of Epilepsy patients using coot optimization based feed forward multilayer neural network

Nagwanshi,

Potnis

2023

Journal of Experimental & Theoretical Artificial Intelligen

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Pre processing the abdominal ECG signal using combination of FIR filter and principal component analysis

Cited by 9 publications

References 5 publications

Arrhythmia Classification Method Based on SECNN-LSTM

Arrhythmia Classification Method Based on SECNN-LSTM

Robust Multimodal Heartbeat Detection Using Hybrid Neural Networks

Detection of Epilepsy patients using coot optimization based feed forward multilayer neural network

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