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


CONCLUSIONS
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.


CONCLUSIONS
Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset.
Serkan
S
Kiranyaz
Turker
T
Ince
Moncef
M
Gabbouj
D000465:Algorithms; D016208:Databases, Factual; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D057285:Precision Medicine; D012815:Signal Processing, Computer-Assisted