ABSTRACT This paper presents an automatic wearable electrocardiogram (ECG) classification and monitoring system with stacked denoising autoencoder (SDAE). We use a wearable device with wireless sensors to obtain the ECG data, and send these ECG data to a computer with Bluetooth 4.2. Then, these ECG data are classified by the automatic cardiac arrhythmia classification system. First, the ECG feature representation is learned by the SDAE with sparsity constraint. Then, the softmax regression is used to classify the ECG beats. In the fine-tuning phase, an active learning is added to improve the performance. In the active learning phase, we use the method that relies on the deep neural networks posterior probabilities to associate confidence measures to select the most informative samples. Breaking-ties and modified breaking-ties methods are used to select the most informative samples. We validate the proposed method on the well-known MIT-BIH arrhythmia database and ECG data obtained from the wearable device. We follow the recommendations of the Association for the Advancement of Medical Instrumentation for class labeling and results presentation. The results show that the classification performance of our proposed approach outperforms the most of the state-of-the-art methods.
The incorporation of DNNs in medicine, was also studied in REF , where an automatic wearable electrocardiogram (ECG) classification and monitoring system with stacked denoising autoencoder (SDAE) is presented for cardiac arrhythmia detection.
An Automatic Cardiac Arrhythmia Classification System With Wearable Electrocardiogram
IEEE Access
IEEE Access
Computer Science