Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks

• Published in: IEEE transactions on biomedical engineering Vol. 63; no. 3; pp. 664 - 675
• Main Authors: Kiranyaz, Serkan, Ince, Turker, Gabbouj, Moncef
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Convolutional Neural Networks; Databases, Factual; Electrocardiography; Electrocardiography - methods; Feature extraction; Humans; Kernel; Monitoring; Neural networks; Neural Networks (Computer); Neurons; Patient-specific ECG classification; Precision Medicine; real-time heart monitoring; Signal Processing, Computer-Assisted; Training; real-time heart monitoring; Convolutional neural networks (CNNs); patient-specific ECG classification
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2015.2468589

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.