Title :
A simple method for detection and classification of ECG noises for wearable ECG monitoring devices
Author :
Satija, Udit ; Ramkumar, Barathram ; Manikandan, M. Sabarimalai
Author_Institution :
Sch. of Electr. Sci., Indian Inst. of Technol. Bhubaneswar, Bhubaneswar, India
Abstract :
An assessment of electrocardiogram (ECG) signal quality has become an unavoidable first step in most holter and ambulatory ECG signal analysis applications. In this paper, we present a simple method for automatically detection and classification of ECG noises. The proposed method consists of four major steps: moving average filter, blocking, feature extraction, and multistage decision-tree algorithm. In the proposed method, the dynamic amplitude range and autocorrelation maximum peak features are extracted for each block. In the first decision stage, a amplitude-dependent decision rule is used for detecting the presence of low-frequency (LF) noise (including, baseline wander (BW) and abrupt change (ABC) artifacts) and the high-frequency (HF) noise (including, power line interference (PLI) and muscle artifacts). In the second decision stage, the proposed method further classifies the LF noise into a BW noise or a ABC noise using the local dynamic amplitude range feature. The HF noise is classified into a PLI noise or a muscle noise using the local autocorrelation maximum peak feature. The proposed detection and classification method is tested and validated using a wide variety of clean and noisy ECG signals. Results show that the method can achieve an average sensitivity (Se) of 97.88%, positive productivity (+P) of 91.18% and accuracy of 89.06%.
Keywords :
body sensor networks; decision trees; electrocardiography; feature extraction; filtering theory; medical signal detection; muscle; signal classification; signal denoising; ABC noise; BW noise; ECG noise classification; ECG noise detection; HF noise; PLI noise; abrupt change artifacts; ambulatory ECG signal analysis applications; amplitude-dependent decision rule; autocorrelation maximum peak feature extraction; baseline wander; blocking algorithm; electrocardiogram signal quality assessment; first decision stage; high-frequency noise; local autocorrelation maximum peak feature; local dynamic amplitude range feature; low-frequency noise; moving average filter; multistage decision-tree algorithm; muscle artifacts; muscle noise; power line interference; second decision stage; wearable ECG monitoring devices; Correlation; Electrocardiography; Feature extraction; Low-frequency noise; Muscles; Noise measurement;
Conference_Titel :
Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on
Conference_Location :
Noida
Print_ISBN :
978-1-4799-5990-7
DOI :
10.1109/SPIN.2015.7095425