IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 46, NO. 3, MARCH 1999 311 SVD-Based On-Line Exercise ECG Signal Orthogonalization Burak Acar and Hayrettin K¨ oymen,* Senior Member, IEEE Abstract—An orthogonalization method to eliminate unwanted signal components in standard 12-lead exercise electrocardio- grams (ECG’s) is presented in this work. A singular-value- decomposition-based algorithm is proposed to decompose the signal into two time-orthogonal subspaces; one containing the ECG and the other containing artifacts like baseline wander and electromyogram. The method makes use of redundancy in 12- lead ECG. The same method is also tested for reconstruction of a completely lost channel. The online implementation of the method is given. It is observed that the first two decomposed channels with highest energy are sufficient to reconstruct the ST- segment and J-point. The dimension of the signal space, on the other hand, does not exceed three. Data from 23 patients, with duration ranging from 9 to 21 min, are used. Index Terms— Electromyogram (EMG) and baseline wander (BW) elimination, exercise electrocardiogram (ECG), online or- thogonalization, signal enhancement, singular value decomposi- tion (SVD). I. INTRODUCTION E XERCISE electrocardiogram (ECG) testing reveals very important clinical diagnostic information on cardiovascu- lar disorders. The test itself, however, sets up rather adverse conditions for ECG signal recording. Baseline wander (BW) and electromyogram (EMG) signal contaminates exercise ECG signals. Often the level of contamination is such that ECG signals are completely hidden by these unwanted signal com- ponents. Sophisticated signal processing techniques must be employed in order to recover the clinical data. The most important clinical information is contained in the variation of ST-segment level during the stress test. The ST-segment level is found in all phases of the test, namely the initial resting phase and every step of the exercise and the recovery phases. ST-segment level depression is sought for positive classification. Computerized systems calculate the average beats in successive time intervals, of length ranging from 6 to 20 s. This averaging is necessary since there exist large amounts of unwanted signal components in recordings taken during exercise. ST-level measurements are made on these average complexes. Various aspects of ECG signal enhancement had been discussed in literature [1]–[11]. Mertens et al. used a hybrid Manuscript received May 21, 1997; revised September 17, 1998. Asterisk indicates corresponding author. B. Acar is with the Electrical and Electronics Engineering Department, Bilkent University, Ankara 06533, Turkey. *H. K¨ oymen is with the Electrical and Electronics Engineering Department, Bilkent University, Ankara 06533, Turkey (e-mail: koymen@ee.bilkent.edu.tr). Publisher Item Identifier S 0018-9294(99)01847-9. approach, which is a combination of mean and median filtering [12]. Pinto proposed a BW filter and a time-varying filter to remove high-frequency EMG signal [13]. Sornmo estimated the BW from a QRS-free signal obtained by subtracting the average beat from the incoming noisy beat and used this to remove the BW [14]. Mortara proposed an approach to obtain a transfer function of the cardiac dipole, which he referred to as source consistency filtering [15]. Jane et al. used a cascade of two adaptive filters to remove BW and gave a comparison of this to cubic spline filter [16] whereas Meyer et al. used a cubic spline technique to estimate and remove BW from ECG [17]. Recently, Afonso et al. proposed a filter-bank-based approach for the processing of stress ECG [18]. They decomposed the signal into different subbands and processed each subband independently. The proposed methods provided enhancement of ECG data at varying levels. The efforts for enhancement were confined to processing of individual derivations, in all of these meth- ods. There is, however, a significant redundancy in 12-lead ECG, which can contribute to unwanted signal interference elimination when all derivations are processed simultaneously. Barr et al. addressed this redundancy while they investigated the optimum lead locations for complete representation of total body QRS surface potential distribution [19]. As far as standard leads are concerned, Barr et al. observed that this representation is rather poor. Lux et al. made use of the redundancy in a data set of 221 patients. This data consists of a set of approximately 600 body-surface potential maps recorded from 192 leads on every patient [20]. All of these maps are derived from measurements of potentials at every millisecond and within a single heartbeat. In their study, the spatial redundancy in body-surface potential maps is employed to obtain a set of orthonormal basis vectors to represent all the data on all the patients, using Karhunen–Loeve technique. Evans et al. employed the same technique on the same population to investigate the temporal redundancy in body surface recordings [21]. It is shown in [21] that the two sets of orthonormal basis vectors are necessary, one for QRS region and one for ST and T regions, to represent all the data accurately over the patient population. It must, of course, be noted that in [21], again, the analyzed data is confined to a period of a single heartbeat. In this paper, we propose a robust method particularly to obtain ST-segment level data during exercise test. The method makes use of the redundancy in standard leads to eliminate BW, EMG, and other interference in stress ECG. Presence of these unwanted signal components, originating 0018–9294/99$10.00  1999 IEEE