Semantic segmentation of ECG waves using hybrid channel-mix convolutional and bidirectional LSTM

Londhe, Aboli N.; Atulkar, Mithilesh

doi:10.1016/j.bspc.2020.102162

Cited by 51 publications

(16 citation statements)

References 42 publications

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“…Some intelligent processing methods have been proposed for DL algorithms, such as autoencoder (encoder-decoder), CNNs, and LSTM framework. Londhe et al [1] conducted a concept of image segmentation for ECG wave segmentation, called semantic segmentation. They proposed a hybrid model based on ConvBiLSTM to attend the semantic segmentation of ECG waveforms.…”

Section: Related Workmentioning

confidence: 99%

“…A convolution layer, as a part of CNNs, is an automatic extraction of the input model, which can extract deep features from ECG signal data points [1]. The convolution process can be expressed as follows [30]:…”

Section: A Convolutional Layersmentioning

confidence: 99%

“…Each heart cycle of one normal beat of ECG contains different three main waveforms, i.e., P-wave, QRS complex, and Twave. These waveforms have a standard amplitude and time duration, indicating various heart conditions [1]. Relevant information from the ECG waveform must be extracted from the physiological signal to diagnose various heart abnormalities [2].…”

Section: Introductionmentioning

confidence: 99%

“…Relevant information from the ECG waveform must be extracted from the physiological signal to diagnose various heart abnormalities [2]. To achieve high diagnostic accuracies, ECG analysis requires the knowledge to extract the morphology of the ECG waves or/and segments (delineation) [1][3] [4]. For example, to atrial fibrillation (AF) detection, at least we have to know P-wave absence, which it is one of the ECG signal important features.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Beat-to-Beat Electrocardiogram Waveform Classification Based on a Stacked Convolutional and Bidirectional Long Short-Term Memory

et al. 2021

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Delineating the electrocardiogram (ECG) waveform is an important step with high significance in cardiology diagnosis. It refers to extract the ECG morphology in start, peak, end points of waveform. Due to various shapes and abnormalities presented in ECG signals, several conventional computer algorithms always fail to extract the essential feature of heart information. Thus, it is critical to investigate an automated ECG signal delineation with its result accuracy. In this study, we propose the delineation process by using bidirectional long short-term memory (BiLSTM) classifier. Such process was conducted as one beat to the next (beat-to-beat), that means the ECG waveform classification is start of P-wave1 to start of P-wave2. However, such classifier lack of feature extraction process, reducing the classification accuracy result. To improve the classifier performance, convolutional layers as facture extraction are stacked with BiLSTM named ConvBiLSTM. We conducted the experimental based on seven-class ECG waveform using a publicly available QT Database with annotation of the main waveforms to produce high accurate classifier, i.e., P start -Pend, Pend -QRSstart, QRSstart -Rpeak, Rpeak -QRSend, QRSend -Tstart, Tstart -Tend, and Tend -Pstart. It was found that the proposed model showed remarkable results with overall average performances of 99.83% accuracy, 98.82% sensitivity, 99.90% specificity, 98.86% precision, and 98.84% F1 score. Based on these promising results, the efficacy of the proposed stacked ConvBiLSTM model in classifying ECG waveform provides a great opportunity to help cardiologists in diagnosis decision-making for faster assessment.

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Section: Related Workmentioning

confidence: 99%

Section: A Convolutional Layersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Beat-to-Beat Electrocardiogram Waveform Classification Based on a Stacked Convolutional and Bidirectional Long Short-Term Memory

et al. 2021

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“…Complete delineation of ECG can be performed by the CNN–LSTM model with a sensitivity of 97.95% ( Peimankar and Puthusserypady, 2021 ). Another study reports use of sample-wise, so-called semantic segmentation of the raw ECG via the CNN–biLSTM network, with an overall accuracy of 95.54% ( Londhe and Atulkar, 2021 ). Bidirectional LSTM (biLSTM) layers are the layers, where the input sequence is analyzed in the forward and backward directions resulting in better learning of important patterns.…”

Section: Ecg Analysismentioning

confidence: 99%

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

et al. 2022

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Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

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Prediction of total volatile basic nitrogen (TVB‐N) in fish meal using a metal‐oxide semiconductor electronic nose based on the VMD‐SSA‐LSTM algorithm

Li,

et al. 2024

J Sci Food Agric

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BACKGROUNDThe total volatile basic nitrogen (TVB‐N) is the main indicator for evaluating the freshness of fish meal, and accurate detection and monitoring of TVB‐N is of great significance for the health of animals and humans. Here, to realize fast and accurate identification of TVB‐N, in this article, a self‐developed electronic nose (e‐nose) was used, and the mapping relationship between the gas sensor response characteristic information and TVB‐N value was established to complete the freshness detection.RESULTSThe TVB‐N variation curve was decomposed into seven subsequences with different frequency scales by means of variational mode decomposition (VMD). Each subsequence was modelled using different long short‐term memory (LSTM) models, and finally, the final TVB‐N prediction result was obtained by adding the prediction results based on different frequency components. To improve the performance of the LSTM, the sparrow search algorithm (SSA) was used to optimize the number of hidden units, learning rate and regularization coefficient of LSTM. The prediction results indicated that the high accuracy was obtained by the VMD‐LSTM model optimized by SSA in predicting TVB‐N. The coefficient of determination (R2), the root‐mean‐squared error (RMSE) and relative standard deviation (RSD) between the predicted value and the actual value of TVBN were 0.91, 0.115 and 6.39%, respectively.CONCLUSIONSThis method improves the performance of e‐nose in detecting the freshness of fish meal and provides a reference for the quality detection of e‐nose in other materials. © 2024 Society of Chemical Industry.

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Semantic segmentation of ECG waves using hybrid channel-mix convolutional and bidirectional LSTM

Cited by 51 publications

References 42 publications

Beat-to-Beat Electrocardiogram Waveform Classification Based on a Stacked Convolutional and Bidirectional Long Short-Term Memory

Beat-to-Beat Electrocardiogram Waveform Classification Based on a Stacked Convolutional and Bidirectional Long Short-Term Memory

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

Prediction of total volatile basic nitrogen (TVB‐N) in fish meal using a metal‐oxide semiconductor electronic nose based on the VMD‐SSA‐LSTM algorithm

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