Automated Detection and Classification of Arrhythmia From ECG Signals Using Feature-Induced Long Short-Term Memory Network

Ganguly, Biswarup; Ghosal, Avishek; Das, Anirbed; Das, Debanjan; Chatterjee, Debanjan; Rakshit, Debmalya

doi:10.1109/lsens.2020.3006756

Cited by 47 publications

(24 citation statements)

References 18 publications

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“…Figure 10 shows the confusion matrix of the testing set using LSTM model. The average accuracy of our LSTM model is 97.11% which is comparable with the accuracy achieved from LSTM models used in literature [53,55,56]. Figure 12 shows the confusion matrix of the testing data using the hybrid CNN-LSTM model.…”

Section: The Results From the Cnn Modelsupporting

confidence: 68%

“…Moreover, to demonstrate the superiority of our proposed method for ECG beat classification, we compared the performance of our method with recently published works. As shown in Table 5, the accuracy of our hybrid model is higher than the accuracy achieved from CNN model [14,64], the LSTM model [56], and the CNN and LSTM models used in this work. Also, the accuracy of our hybrid model is higher than that achieved from hybrid CNN-LSTM [52,54,72] and is comparable to that achieved by Oh et al [71].…”

Section: The Results From the Cnn Modelmentioning

confidence: 65%

“…Many deep learning algorithms have been proposed in recent years [1,6,12,14,16,17,64]. Convolutional Neural Network (CNN) [32,43] and Long Short-Term Memory (LSTM) [25,33,56] are the most widely used, powerful, and efficient deep learning methods. The CNNs include large number of hidden layers that apply convolution and subsampling techniques on the input data to extract deep features [65].…”

Section: Deep Learning Modelsmentioning

confidence: 99%

“…These measures indicate how precisely the x-ray chest images are diagnosed [70]. To compute these measures, four different types of statistical values are computed which are True positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN) [56,70]. Then using these values, the above mentioned performance evaluation parameters have been computed as follows:…”

Section: Performance Evaluationmentioning

confidence: 99%

“…They have used A Convolutional Neural Network (CNN) structure to extract the morphological ECG features. They measured the extent of HRV by applying a composite structure based on Long Short-Term Memory (LSTM) [53][54][55][56] and a set of hand-crafted statistical features. Their model has a degraded accuracy since they have used binary classification only, and it requires more processing time because they have used manual feature extraction.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

Obeidat¹,

Alqudah²

2021

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In this paper we have utilized a hybrid lightweight 1D deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods for accurate, fast, and automated beat-wise ECG classification. The CNN and LSTM models were designed separately to compare with the hybrid CNN-LSTM model in terms of accuracy, number of parameters, and the time required for classification. The hybrid CNN-LSTM system provides an automated deep feature extraction and classification for six ECG beats classes including Normal Sinus Rhythm (NSR), atrial fibrillation (AFIB), atrial flutter (AFL), atrial premature beat (APB), left bundle branch block (LBBB), and right bundle branch block (RBBB). The hybrid model uses the CNN blocks for deep feature extraction and selection from the ECG beat. While the LSTM layer will learn how to extract contextual time information. The results show that the proposed hybrid CNN-LSTM model achieves high accuracy and sensitivity of 98.22% and 98.23% respectively. This model is light and fast in classifying ECG beats and superior to other previously used models which makes it very suitable for embedded systems designs that can be used in clinical applications for monitoring heart diseases in faster and more efficient manner.

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Section: The Results From the Cnn Modelsupporting

confidence: 68%