LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices

Saadatnejad, Saeed; Oveisi, Mohammadhosein; Hashemi, Matin

doi:10.1109/jbhi.2019.2911367

Cited by 300 publications

(175 citation statements)

References 48 publications

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“…Saadatnejad et al [35] proposed ECG heartbeat classification for continuous monitoring. The work extracted raw ECG samples into heartbeat RR interval features and wavelet features.…”

Section: Traditional Machine Learning As Feature Extractor and Deep Lmentioning

confidence: 99%

Deep Learning in Physiological Signal Data: A Survey

Rim

Sung

Min

et al. 2020

Sensors

160

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Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.

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“…Saadatnejad et al [35] proposed ECG heartbeat classification for continuous monitoring. The work extracted raw ECG samples into heartbeat RR interval features and wavelet features.…”

Section: Traditional Machine Learning As Feature Extractor and Deep Lmentioning

confidence: 99%

Deep Learning in Physiological Signal Data: A Survey

Rim

Sung

Min

et al. 2020

Sensors

160

View full text Add to dashboard Cite

show abstract

“…As a comparison, results from RNN including those in [21], [23], [41] are listed in Table 7. The advantage of RNN modeling is that the circular network can store a certain length of context information, RNN can handle ECGs of any length of time.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…With the development of AI, more and more deep learning [7] methods are applied to address medical data [8]- [10], such as feedforward neural network [11]- [14], and the Recurrent Neural Networks (RNN) [15] ( Long Short-Term Memory (LSTM) [16], [17], Gate Recurrent Unit (GRU) [18]). However, most of previous studies used the MIT-BIH-AR database [19]- [21], which only has 48 patients. And they only used one-lead ECG data for model training.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Task Group Bi-LSTM Networks Application on Electrocardiogram Classification

Chen

Zhong

et al. 2020

IEEE J. Transl. Eng. Health Med.

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Background: Cardiovascular diseases (CVD) are the leading cause of death globally. Electrocardiogram (ECG) analysis can provide thoroughly assessment for different CVDs efficiently. We propose a multi-task group bidirectional long short-term memory (MTGBi-LSTM) framework to intelligent recognize multiple CVDs based on multi-lead ECG signals. Methods: This model employs a Group Bi-LSTM (GBi-LSTM) and Residual Group Convolutional Neural Network (Res-GCNN) to learn the dual feature representation of ECG space and time series. GBi-LSTM is divided into Global Bi-LSTM and Intra-Group Bi-LSTM, which can learn the features of each ECG lead and the relationship between leads. Then, through attention mechanism, the different lead information of ECG is integrated to make the model to possess the powerful feature discriminability. Through multi-task learning, the model can fully mine the association information between diseases and obtain more accurate diagnostic results. In addition, we propose a dynamic weighted loss function to better quantify the loss to overcome the imbalance between classes. Results: Based on more than 170,000 clinical 12-lead ECG analysis, the MTGBi-LSTM method achieved accuracy, precision, recall and F1 of 88.86%, 90.67%, 94.19% and 92.39%, respectively. The experimental results show that the proposed MTGBi-LSTM method can reliably realize ECG analysis and provide an effective tool for computer-aided diagnosis of CVD.

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“…Such features show significant variations among different individuals and under different conditions, and therefore, are not sufficient for accurate arrhythmia detection [16], [17]. Many solutions have been proposed based on artificial neural networks, and especially in recent years, deep convolutional neural networks (CNN) and recurrent neural networks (RNN) [17]- [22]. Neural network algorithms automatically extract the features from data, and hence, provide higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

ECG Classification Algorithm Based on STDP and R-STDP Neural Networks for Real-Time Monitoring on Ultra Low-Power Personal Wearable Devices

Amirshahi

Hashemi

2019

IEEE Trans. Biomed. Circuits Syst.

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This paper presents a novel ECG classification algorithm for real-time cardiac monitoring on ultra low-power wearable devices. The proposed solution is based on spiking neural networks which are the third generation of neural networks. In specific, we employ spike-timing dependent plasticity (STDP), and reward-modulated STDP (R-STDP), in which the model weights are trained according to the timings of spike signals, and reward or punishment signals. Experiments show that the proposed solution is suitable for real-time operation, achieves comparable accuracy with respect to previous methods, and more importantly, its energy consumption is significantly smaller than previous neural network based solutions.

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LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices

Cited by 300 publications

References 48 publications

Deep Learning in Physiological Signal Data: A Survey

Deep Learning in Physiological Signal Data: A Survey

A Multi-Task Group Bi-LSTM Networks Application on Electrocardiogram Classification

ECG Classification Algorithm Based on STDP and R-STDP Neural Networks for Real-Time Monitoring on Ultra Low-Power Personal Wearable Devices

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