Application of Heartbeat-Attention Mechanism for Detection of Myocardial Infarction Using 12-Lead ECG Records

Abstract

Early detection and effective treatment of myocardial infarction can prevent the deterioration of ischemic heart disease and greatly reduce the possibility of sudden death. On the basis of standard 12-lead electrocardiogram (ECG) records, this paper proposes a bidirectional, long short-term memory (Bi-LSTM) network with a heartbeat-attention mechanism to effectively and automatically detect myocardial infarction (MI). First, we divide the standard 12-lead ECG records into sliding windows with the same number of heartbeats. Subsequently, we do not use any labels of heartbeats to train the Bi-LSTM network and the heartbeat-attention mechanism is applied to automatically weight the difference between unlabeled heartbeats. Finally, our method is validated by patients’ complete ECG records and real labels in the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database. When compared with the same network without the heartbeat-attention mechanism or other existing methods, our method achieves comparable or better performance. The accuracy, sensitivity, and specificity reach 94.77%, 95.58%, and 90.48%, respectively.

Hybrid Network with Attention Mechanism for Detection and Location of Myocardial Infarction Based on 12-Lead Electrocardiogram Signals

“…Deep fusional attention network was adopted to extract elaborate features from biological signals in seizure detection and sleep stage classification [16]. In MI diagnosis, the heartbeat-attention mechanism was introduced to automatically weight the difference between unlabeled heartbeats [22]. Furthermore, the attention mechanism has strong interpretability.…”

“…In processing biomedical signals, BiGRU has been successfully applied for human emotion classification through continuous electroencephalogram signals [41], and human identification through ECG based biometrics [42]. ECG signal is a typical kind of time series data, and LSTM has been effectively applied in MI diagnosis [21][22][23]. GRU architecture can achieve performance comparable to or even superior than LSTM [42], but its potential has been rarely investigated in MI diagnosis thus far.…”

“…Furthermore, CNN and GRU are two typical end-to-end learning paradigms with multiple levels of representation and especially suitable for discovering the spatial and temporal characteristics in high-dimensional data [18]. To alleviate the disadvantages of conventional frameworks, deep learning methods are exploited in MI diagnosis continuously and rapidly [19][20][21][22][23][24]. To a large extent, new research lays the foundations for the development of deep learning frameworks that make full use of 12-lead ECG signals.…”

“…Secondly, thus far, importance evaluation and weighted combination of each lead in MI diagnosis have been sparsely investigated. Even though the authors of [22][23][24][25] considered 12 leads simultaneously, each lead contained distinctive and complementary information that deserved different and separate processing rather than identical treatment. Thirdly, only a few researchers considered the inter-patient scheme on the Physikalisch-Technische Bundesanstalt (PTB) dataset.…”

Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives

“…A complete DL model can make its own intelligent decision without any human help even for complex problems with an unstructured, diverse, and interconnected dataset. Deep learning techniques are also being extensively used in the healthcare industry [132][133][134][135][136][137] for the timely detection of ECG anomalies, thereby facilitating the prediction and classification of CVDs. Table 4 highlights some of the commonly used DL algorithms, their ECG applications, and their short summary.…”