Key result
A combined CNN and LSTM model for diagnosing eight ECG signals achieved an accuracy of 99.01%, specificity of 99.57%, and sensitivity of 97.67%.
Why the study?
Evaluating ECG signals externally is labor intensive due to their small amplitude, highlighting the need for automated detection and classification methods to assist in accurate, expeditious diagnosis.
Does a combined CNN and LSTM model accurately detect and classify arrhythmias from ECG signals?
Does a combined CNN and LSTM model accurately detect and classify arrhythmias from ECG signals?
A combined CNN and LSTM model can accurately detect and classify arrhythmias from ECG signals without requiring manual feature extraction or noise filtering.
May aid automated arrhythmia detection; leaves open prospective clinical validation before adoption.
Electrocardiogram (ECG) signal evaluation is routinely used in clinics as a significant diagnostic method for detecting arrhythmia. However, it is very labor intensive to externally evaluate ECG signals, due to their small amplitude. Using automated detection and classification methods in the clinic can assist doctors in making accurate and expeditious diagnoses of diseases. In this study, we developed a classification method for arrhythmia based on the combination of a convolutional neural network and long short-term memory, which was then used to diagnose eight ECG signals, including a normal sinus rhythm. The ECG data of the experiment were derived from the MIT-BIH arrhythmia database. The experimental method mainly consisted of two parts. The input data of the model were two-dimensional grayscale images converted from one-dimensional signals, and detection and classification of the input data was carried out using the combined model. The advantage of this method is that it does not require performing feature extraction or noise filtering on the ECG signal. The experimental results showed that the implemented method demonstrated high classification performance in terms of accuracy, specificity, and sensitivity equal to 99.01%, 99.57%, and 97.67%, respectively. Our proposed model can assist doctors in accurately detecting arrhythmia during routine ECG screening.
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Chen et al. (2020) studied Arrhythmia. Combined convolutional neural network (CNN) and long short-term memory (LSTM) model was evaluated on Classification performance (accuracy, specificity, and sensitivity). A combined CNN and LSTM model for diagnosing eight ECG signals achieved an accuracy of 99.01%, specificity of 99.57%, and sensitivity of 97.67%.
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