Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection

Abstract

Without employing a time-consuming feature engineering step, the ensemble classifier trained with this architecture provided a robust solution to the problem of detecting cardiac arrhythmia from noisy ECG signals. In addition, interpretation of the classifier by inspection of its network parameters and predictions revealed what aspects of the ECG signal the classifier considered most discriminating.

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

“…In the model, a LSTM block was used for sequence learning. Some studies on 1D signals such as EEG and ECG [52, 67, 68, 72] show that the combination of representation and sequence learning can yield a higher performance than by using representation learning alone. According to this information, we used a 128 unit LSTM block at the end of the representation learning layers.…”

“…LSTM is a practical approach to analyze time-series data [62] . In the last decade, the LSTM algorithm has been employed for arrhythmia detection [ [30] , [63] , [64] , [65] , [66] , [67] , [68] , [69] , [70] , [71] , [72] , [73] ]. Yildirim [65] proposed a wavelet sequence-based LSTM model to classify ECG signals.…”

“…Warric et al. [68] used a combined deep classifier, CNN and LSTM, in the 2017 PhysioNet/CinC Challenge [69] . Xiong et al.…”

“…We developed a DNN model to classify rhythms from each of 12-lead inputs. We preferred to use the representation and sequence learning structure together because of their salutary performance on both ECG [52, 67, 68, 72] and EEG signals [45] . The novelty of this paper can be summarized as follows: a useful single DNN model has been constructed to detect multiple rhythm classes on 12-lead signals.…”

Atrial Fibrillation Detection Using Deep Features and Convolutional Networks

“…Hybrid combinations of recurrent and convolutional networks have also been proposed to capture the unique sequential properties and morphological features of biomedical signals [9,10,11]. Techniques are also being introduced to capture both time and frequency domain characteristics of the signals as better inputs for machine learning models.…”