Supervised ECG wave segmentation using convolutional LSTM

Malali, Aman; Hiriyannaiah, Srinidhi; Siddesh, G M; Srinivasa, K. G.; Sanjay, N T

doi:10.1016/j.icte.2020.04.004

Cited by 35 publications

(25 citation statements)

References 6 publications

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“…Malali et al [28] proposed convolutional LSTM (ConvLSTM) to segment the ECG waves. The input model of a 700 x 4 was fed into the input and convolution layers.…”

Section: Related Workmentioning

confidence: 99%

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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“…Malali et al [28] proposed convolutional LSTM (ConvLSTM) to segment the ECG waves. The input model of a 700 x 4 was fed into the input and convolution layers.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2021

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“…We developed and tested an algorithm for segmentation and classification of 12-lead ECGs. Even though our approach for segmentation did not achieve a state of the art result in beat detection, its performance is comparable to other deep learning based approaches [23][24][25].…”

Section: Discussionmentioning

confidence: 63%

ECG Segmentation using a Neural Network as the Basis for Detection of Cardiac Pathologies

Sodmann¹,

Vollmer²

2020

2020 Computing in Cardiology Conference (CinC)

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Electrocardiography allows fast and noninvasive diagnosis and screening of a wide range of cardiac diseases. The interpretation of ECGs is difficult and depends on the levels of training of the physician. In consequence, pathologies can remain undiagnosed or norm-variations are interpreted as pathological. The PhysioNet/Computing in Cardiology Challenge 2020 aims to classify various cardiac pathologies in 12lead ECGs, data was collected across a variety of different clinics and countries to pave the way for a common evaluation of ECGs. Our Team Heartly-AI proposes a two-step algorithm using a UNet and XGBoost for the 2020 Phys-ioNet Computing in Cardiology Challenge "Classification of 12 lead ECGs". The algorithm achieved a 5-fold crossvalidation metric of 0.113 and scored 0.136 on the official test set, therefore placing us 28th out of the 41 teams in the official ranking.

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“…Signal segmentation was performed using a Convolutional Long Short-Term Memory (ConvLSTM) neural network as a machine learning model to detect exhaled sounds within each 1-minute sound recording. The structure of the model was determined with reference to the model used in a study on the segmentation of an electrocardiogram waveform [11].…”

Section: Convolutional Lstm Networkmentioning

confidence: 99%

Identification of Respiratory Sounds Collected from Microphones Embedded in Mobile Phones

Fukuyama

Sugiyama

Chin

et al. 2022

ABE

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Sudden deterioration of condition in patients with various diseases, such as cardiopulmonary arrest, may result in poor outcome even after resuscitation. Early detection of deterioration is important in medical and long-term care settings, regardless of the acute or chronic phase of disease. Early detection and appropriate interventions are essential before resuscitating measures are required. Among the vital signs that indicate the general condition of a patient, respiratory rate has a greater ability to predict serious events such as thromboembolism and sepsis than heart rate and blood pressure, even in early stages. Despite its importance, however, respiratory rate is frequently overlooked and not measured, making it a neglected vital sign. To facilitate the measurement of respiratory rate, a non-invasive method of detecting respiratory sounds was developed based on deep learning technology, using a built-in microphone in a smartphone. Smartphones attached to the bed headboards of 20 participants undergoing polysomnography (PSG) at Kyoto University Hospital recorded respiratory sounds. Sound data were synchronized with overnight respiratory information. After excluding periods of abnormal breathing on the PSG report, sound data were processed for each 1-minute period. Expiration sound was determined using the pressure ow sensor signal on PSG. Finally, a model to identify the expiration section from the sound information was created using a deep learning algorithm from the convolutional Long Short Term Memory network. The accuracy of the learning model in identifying the expiratory section was 0.791, indicating that respiratory rate can be determined using the microphone in a smartphone. By collecting data from more patients and improving the accuracy of this method, respiratory rates could be more easily monitored in all situations, both inside and outside the hospital.

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Supervised ECG wave segmentation using convolutional LSTM

Cited by 35 publications

References 6 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

ECG Segmentation using a Neural Network as the Basis for Detection of Cardiac Pathologies

Identification of Respiratory Sounds Collected from Microphones Embedded in Mobile Phones

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