Automatic Classification of ECG Data Quality for Each Channel - Qi Wang; Yu Wei Mao; Liqun Ren; Zhe Li; Hongxing Liu (IEEE Access, 2020)
In the analysis of ECG data, it is necessary to first automatically classify the quality of ECG data to facilitate the subsequent processing. PhysioNet/Computing in Cardiology Challenge 2011 and existing researches involve the quality of the entire record of ECG, which fails to maximize utilization of ECG data. In fact, single-lead ECG has been widely used in many fields, such as wearable devices, sleep apnea monitoring, and deriving respiratory rate. However, the present methods for evaluating the data quality by each channel only divide them into two categories: acceptable and unacceptable, which is relatively coarse. This paper proposes a new method for the automatic classification of ECG data quality by channel. This method divides them into four categories: (1) electrode shedding, marked as C3; (2) serious noise interference, under which it is difficult to detect R wave, marked as C2; (3) partial noise interference, under which part of R waves may not be detected correctly, marked as C1; (4) high quality signal, marked as C0. The 2011 competition data was re-marked according to the channel with the help of our designed auxiliary program. This paper defined some features and designed a tree classifier using One-Class Support Vector Machine(OCSVM). The test results of our method show that the detection accuracy of electrode shedding is 93.22%, serious noise interference is 90%, partial noise interference is 89.22%, and high quality signal is 97.19%. It shows that the method has a broad prospect in the automatic preprocessing of ECG data.