Classifying measured electrocardiogram signal quality using deep belief networks

There is a current trend towards wearable electrocardiogram (ECG) measurement systems, which enables measurement while the subject performs their normal activities of daily living (e.g., walking, driving, eating). This type of measurement is susceptible to higher levels of contaminants, compared to bedside measurements, due to subject movement and a measurement environment that is not well-controlled. Contaminants in the measured signal (e.g., motion artifact, power line interference) can cause incorrect interpretations, including misdiagnoses. Therefore, prior to ECG interpretations, it is important to have an algorithm capable of automatically classifying the measured ECG based on their signal quality. ECG of low signal quality can undergo additional pre-processing to mitigate the contaminants or the signal can be discarded. This can reduce misdiagnoses, including false-alarms which is a top medical technology hazard. In this paper, we propose an algorithm based on Deep Belief Networks (DBN) which can differentiate between noisy and clean signal measurements. Our algorithm is designed based on a three layer Restricted Boltzmann Machine (RBM) in which the first two RBMs are trained to extract the features and apply them to the third layer of RBM to classify the data. Results, using the MIT-BIH Arrhythmia database, demonstrate that our algorithm can successfully recognize a noisy ECG signal from a clean signal, with a classification accuracy between 75% and 99.5% depending on the level of contaminants. Our algorithm also correctly identifies clean arrhythmic signals and does not misidentify them as noisy. The proposed algorithm is applicable to any ECG measurement systems including wearable and bedside