Arab J Sci Eng DOI 10.1007/s13369-013-0801-0
RESEARCH ARTICLE - ELECTRICAL ENGINEERING
Automated Noise Removal and Feature Extraction in ECGs Rabya B. K. Hoti · Shahid Khattak
Received: 30 April 2012 / Accepted: 13 July 2013 © King Fahd University of Petroleum and Minerals 2013
Abstract In this study, a new viewpoint of electrocardiogram (ECG) denoising and feature extraction is presented. A filter bank is designed to target diverse form of noise such as power line interference, electromyographic artifacts, base line wander and different motion artifacts. A collection of denoising techniques like principal component analysis, fast Fourier transform filters and segmentation filters has been utilized to target different kind of noise present in the offline ECGs. To extract morphological details from the ECG c-mean clustering and derivatives are exploited. The algorithm has been applied on real offline ECG signals from St.Petersburg Institute of Cardiological Techniques database as well as MIT-BIH databases. The SNR gained using the denoising filter bank is 38.3437 dB. For MIT-BIH database, the sensitivity of the QRS detector is 99.79 % while its predictivity if 99.92 %. The QRS detector’s sensitivity estimated for St. Petersburg database is 99.87 % and its predictivity is 99.90 %. The sensitivity and predictivity of P- and T-wave detection for both the databases are almost the same, i.e., 99.87 and 99.98 %, respectively. 1 Introduction Keywords Principal component analysis · ECG · Artifacts · Baseline wander · Power interference · White Gaussian noise
R. B. K. Hoti (B) · S. Khattak Department of Electrical Engineering, COMSATS Institute of Information Technology (COMSATS IIT), University Road, Tobe Camp, Abbottabad, Khyber Pukhtoonkhwa, Pakistan e-mail:
[email protected] S. Khattak e-mail:
[email protected]
Medical sciences have shown remarkable improvements in recent years, presenting tremendous results in area like cancer, tumor, venereal diseases, etc. One of the most focused areas of Medical science is cardiac syndrome. Despite many achievements, the area remains an open threat to human life causing almost 17 million deaths every year [1]. Therefore, computer-aided cardiac disease analysis has been an active research area for the past 40 years. Different cardiac analysis tools have been developed. Most effective being the electrocardiogram (ECG). An ECG provides a detailed profile of the electrical impulses which causes the cardiac fibers to contract and relax. It is used for the detection of different heart diseases and sudden death syndromes [2].
123
Arab J Sci Eng
ECG is a composite signal whose components are generated as a result of different heart activities. Factors which complicate the analysis of ECG are the artifacts and noises introduced during its extraction [3]. Examples of such noises are power line interference (PLI), base-line wander (BLW) [4], electromyographic (EMG) interference [5], electrode pope noise, electrode skin motion artifacts, electro surgical noise, etc. [6]. Using signal processing tools, we can mitigate the effect of these artifacts and extract different attributes from the ECGs, such as: morphological information [7,8], heart rate variability [9], frequency domain features [10], and various transform coefficients [11,12]. The ECG noises reside in different frequency bands which either overlap or lay very close to the desired information signal. Since an ECG is a non-symmetric and non-stationary signal, using a traditional linear low-pass filter results in the distortion and loss of information [11]. In [12], a moving average filter has been iteratively used to smooth out the ECG signal. However, the repeated use of a moving average filter results in loss of essential information, especially in the QRS regions where a relatively high frequency is observed. In [13], two filters (median filter and morphological filter) have been used to remove power-line interference, EMG noises and baseline drift. However, the proposed scheme only treats noises that prevail for long duration without considering noises that occur for a short span of time. This paper focuses on the removal of both burst and persistent noises and therefore making it easy to detect the morphological features of an offline ECG more accurately. An average SNR gain of 38.3437 dB is achieved after the mitigation of all targeted impairments. In present work, the overall gain is achieved using multiple cascaded stages, each designed to target different types of impairment. The resulting signal is smoother in areas having low-frequency components and completely keeping regions of high-frequency components QRS intact. In [14], the SNR improvement has been calculated for different artifacts present in ECG in isolation using adaptive filtering techniques. However, they do not discuss the overall average SNR gain. A part from signal denoising this paper also deals with the morphological features of an ECG, providing a sensitivity of 99.79 % for QRS detection as compared to 99.7 % claimed by [15], while the sensitivity of P and T waves is 99.87 %. The paper is organized as follows: ECG signal morphology is discussed in Sect. 2. In Sect. 3, improved denoising techniques are presented. Section 4 deals with the morphological detection of P, QRS and T waves. Results are discussed in Sect. 5. Finally conclusions are drawn in Sect. 6.
123
2 ECG Signal An ECG signal reflects the electrical activity of the heart in time domain which initiates muscle contraction. One normal sinus cycle of the ECG corresponds to a single heart beat. An ECG signal is conventionally labeled with the letters P, Q, R, S, and T on each of its critical points as illustrated in Fig. 1. Its frequency ranges from 0.67 to 120 Hz, where 0.67 Hz is the minimum frequency observed in Bradycardia when the pulse rate is 40 beats/min [2]. The P and T waves are lowfrequency components, i.e., 5–9 Hz [16,17], while the QRS complex resides at higher frequency. To record non-invasive ECGs, multiple electrodes (also called leads) are attached to different parts of the body. These recordings are subjected to different kind of noises which lie in different frequency ranges. Thus, a single filter cannot be used for noise removal. These noises are mainly divided into two categories: persistent and burst noises.
2.1 Persistent Noises These noises are correlated in signals coming from all the leads having a similar temporal distribution but with different intensity level. These noises reside in a variety of frequency bands, such as the low-frequency range (BLW), medium frequency (PLI) and high frequency (EMG) signals [18].
2.2 Burst Noise Burst noises are a kind of white Gaussian noise (WGN) that appear on a subset of leads for a very short duration, example for these noises are electrode pop noise, electrode motion artifact, electro surgical noise, etc. [19]. The frequency ranges for these noises are not well defined.
Fig. 1 ECG signal (I01) from St.-Petersburg Institute of Cardiological Techniques (12-lead) Arrhythmia Database. Demonstrating different components of an ECG signal
Arab J Sci Eng
3 Denoising Technique ECG denoising requires a collection of filters targeting different frequency bands. The proposed filtering model is illustrated in Fig. 2, where at first the burst noises are mitigated which is followed by the removal of the persistent noises. The details of these filter modules are given below.
3.1 Burst Noise Removal The burst noise is like an impulse or set of impulses that exist for a very short time interval as shown in Fig. 3. It may occur in any one of the leads attached to body for ECG recording. Multiple lead ECG recorded signals of the same body are correlated differing mainly due to burst noises. Therefore, a mechanism is required to extract the information common to all leads, while the signal that is restricted to only a few leads for a short duration is treated as burst noise. In other words,
a tool is required to de-correlate the information present in all leads of an ECG signal. One of the most efficient de-correlation tools is principal component analysis (PCA). It is a well-known technique used in image and signal processing for multivariate statistical analysis and is used not only as a dimension reduction tool but also as a data-driven temporal filter [20]. As burst noise is not correlated with the ECG signals therefore PCA can play an important role in its removal. PCA filters out uncorrelated random noise without introducing any new artifacts in the data. It decomposes the data into a set of orthogonal vectors called principal components (PCs). The first few components cater most of the low-frequency information while the last ones present higher frequencies, i.e., WGN. The PCA algorithm is provided in Table 1. St. Petersburg database has a 12-lead ECG signal illustrated in Fig. 4. Therefore, applying PCA yields 12 PCs as shown in Fig. 5, on the basis of spectral analysis of these PCs as presented in Fig. 6, it can be easily deduced that the
Fig. 2 Proposed denoising model based on two modules, initially a burst noise removal unit followed by persistent noise removal
0.3 Burst Noise
0.2
Amplitude
0.1
0
-0.1
-0.2
-0.3 200
220
240
260
280
300
320
340
360
380
400
time
Fig. 3 The figure demonstrates burst noise occurrence in one lead of an ECG signal. The occurrence can be due to electrode artifact or electro surgical equipments
123
Arab J Sci Eng Table 1 PCA algorithm Input: A matrix X ∈ [N ×M] with N representing number of a samples and M presents the number of leads Output: A vector xfilt ∈ [N ×1] free of temporal burst noise 1. u[n] =
1 N
M
m=1 X[n, m]
/*Mean along each row dimension*/
2. A = X − uk /*Calculating zero mean matrix*/ where k = 1[1×M] 3. C = A.AT /*(.)T transpose of a matrix*/ 4. D = VT CV /*Eigen value decomposition*/where, ‘V’ is a matrix formed from Eigen vectors and ‘D’is the diagonal matrix of eigen values of C matrix arranged in descending order. The Eigen vectors form the basis matrix for the data (referred as PCs). 5. T ∈ [N ×L] /*Form a matrix by choosing the first ‘L number of PCs where 1≤ L
