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In this paper, a convolutional neural network (CNN) with long short-term memory (LSTM) is designed to detect QRS complexes in noisy electrocardiogram (ECG) signals. The CNN performs feature extraction while the LSTM determines the QRS complex timings. A multi-layer perception (MLP) after the LSTM is added to format the QRS complex detection predictions. With a unique data preparation procedure that includes proper design of training dataset, the proposed CNN-LSTM can achieve superior inter-patient testing performance, which means the testing and training datasets do not share any same patient ECG records. This generalization ability characteristic is critical to automated ECG analysis in an age of big data collected from noisy wearable ECG devices. The MIT-BIH and the European ST-T noise stress test databases are used to validate the effectiveness of the proposed algorithm in terms of sensitivity (recall), positive predictive value (precision), F1 score and timing root mean square error of R peak positions.

Inter-Patient CNN-LSTM for QRS Complex Detection in Noisy ECG Signals