Real Time Patient Specific ECG Classification by 1 D Convolutional Neural Networks
Goal: This paper presents a fast and accurate patient specific electrocardiogram (ECG) classification and monitoring system. Methods: An adaptive implementation of 1 D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient, an individual and simple CNN will be trained by using relatively small common and patient specific training data, and thus, such patient specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real time ECG monitoring and early alert system on a light weight wearable device. Results: The results over the MIT BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state of the art methods for the detection of ventricular ectopic beats and supraventricular ectopic beats. Conclusion: Besides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner. Significance: Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset.
IEEE Transactions on Biomedical Engineering