A flexible PCA-based ECG-reconstruction algorithm with confidenceestimation for ECG during exercise

Steffen A Mann1, Reinhold Orglmeister1

1 TU Berlin, Berlin, Germany

Abstract

Holter monitoring of the electrical activity of the heart isthe key for analysis of many intermittent cardiac irregular-ities. The measurements are however prone to movementartifacts or poor skin-electrode contact, often making someparts of the recorded signals useless since important infor-mation might be hidden by these artifacts. In this papera special ECG sensor node with active electrodes is usedto record the 12-channel ECG during movement. Severalmethods for detection of degraded signal quality are com-bined and adapted to identify leads that have been compro-mised on a beat-to-beat level. A PCA-based reconstructionalgorithm is then used to reconstruct leads with insufficientsignal quality using only undisturbed leads. Furthermorethis approach enables an estimation of the quality of re-construction even in absence of an undisturbed reference.

1. Introduction

24-hour ECG recordings are common practice whensuspected heart diseases are to be investigated. Theseholter ECGs are recorded in everyday situations wheremovement artifacts or deteriorated skin-electrode contactcan compromise parts of these signals. Leads can also belost due to electrodes that fell off during recordings some-times requiring patients to re-take these tests. The auto-matic identification of low signal quality and the recon-struction of corrupted or unrecorded ECG leads can vastlyimprove automated signal processing. This paper presentsa custom designed body-sensor-network (BSN) with 12-channel ECG and active electrodes and modifies existingapproaches for detection of low signal quality. A novelflexible PCA-based reconstruction algorithm [2] enablesdynamic, situation dependent reconstruction of whateverleads are currently not available or disturbed. In additionthis paper also focuses on even severe motion artefacts dur-ing exercise like jogging and climbing stairs. It also givesan estimation of the reliability of its reconstruction, eventhough no artefact-free reference signal can be recorded,e.g. during running. This is done using only prior knowl-

edge of the reconstructed lead and leads that were undis-turbed.Section 2 gives a brief overview over the developed hard-ware (BSN, sensor node & active electrodes). In section3 first the algorithm for signal quality evaluation will bedescribed, then the PCA-based reconstruction followed bythe reconstruction quality estimation. Section 4 presentsthe results and section 5 concludes with a discussion andoutlook.

2. Hardware

The sensor node for the acquisition of the 12-channelECG was designed as a part of a mobile body-sensor-network (BSN) with multiple synchronous operatingbiosensors. These nodes comprise a Bluetooth interfacefor synchronisation, control and data streaming, a SD-cardfor data storage, a MSP430 microcontroller as the heartof each sensor node and sensor specific components, likeanalog frontends. The key features for this BSN are syn-chronism of all sensors (20µs jitter between samples ondifferent nodes) and low energy consumption for continu-ous 24 hour data-acquisition.

Figure 1. 12-channel ECG sensor-node with active elec-trodes and accelerometers on each electrode.

ISSN 2325-8861 Computing in Cardiology 2014; 41:33-36. 33

2.1. 12 - channel ECG - Sensor - Node

Apart from the common BSN components for syn-chronisation, storage and control the ECG sensor-nodeimplements the 8-channel high-precision analog frontendADS1298 from Texas Instruments for ECG acquisition.It offers programmable gain amplification of each chan-nel and intern multiplexing for augmented leads, right-leg-drive while consuming only 0.75 mW

Channel . Further ADCsacquire accelerometer data from the active electrodes.

2.2. Active Electrodes

As the amplitudes of ECG leads are only several mV anearly amplification is desirable. The electrodes used withthe sensor node incorporate active filtering and amplifica-tion at the point of recording. They can be simply clippedto ordinary wet-gel ECG electrodes and possess additional3-axes accelerometers for detection of movement.

3. Methods

The sensor described above was used to record 12-leadECGs during everyday situations. These recordings in-clude motion intensive activities such as climbing stairsand jogging. Beat-to-beat quality evaluation of ECG-leads was realised using the electrodes’ accelerometricdata together with selected ECG-signal features from the’PhysioNet/CinC-Challenge-2011’ (CinCC2011) [1]. Asthese features originally were designed to work on 10s seg-ments they had to be adapted for beat-to-beat processing.Reconstruction is achieved by transforming the first PCsof leads with high quality into those leads with lower qual-ity using a reconstruction matrix A. A can be determinedby principal component analysis (PCA) for different situa-tions such as inhalation/exhalation, different heart-rates oreven postures or activities, as classified from the electrodesacceleration data.Lacking an undisturbed reference during exercise a confi-dence estimation was achieved by considering the recon-struction accuracy of the undisturbed leads in the samesegment by means of the Mean Square Error (MSE) andPearson Correlation, the overall segment quality as well asprior knowledge like R-wave amplitudes.The following paragraphs will detail the features usedfor ECG-quality estimation, the PCA-based reconstructionand the final confidence estimation for the reconstructedleads.

3.1. Signal Quality Evaluatuion

In order to define the signal quality different approachesout of the CinCC2011 were analysed and adapted. Thegoal was to identify disturbances in ECG-signals like mo-

Figure 2. PCA based approach for ECG reconstruction.

tion artefacts, insufficient skin-electrode contact as well aselectric disturbances. From the many criteria used in theframework of this competition, four have been selected forcloser analysis.• Overall range between 0.2 and 15mV [7]• ratio of power in frequency range 5-20Hz to total 0-62.5Hz [6]• self correlation, correlation to averaged beat [5]• inter lead correlation, correlation to other leads [5]

The above features were adapted to allow beat-to-beatanalysis. Additionally the variance of the acquired ac-celerometer data was used as a further parameter for signalquality. The lead quality was evaluated on a beat-to-beatbasis for all leads using a fixed length around all R-peaksdetected using a QRS-detector based on filter banks.

3.2. ECG - Reconstruction

After disturbed P-QRS-T segments have been identifiedthey have to be reconstructed. This is achieved by traininga general or many situation-, posture- or activity-specificreconstruction matrices A using PCA. This section willgive a short introduction to PCA, describe the selectionof leads for training of A and elaborate on some specialconsideration when using PCA reconstruction.

3.2.1. Principal Component Analysis

Principal Component Analysis is a linear transformation

y = Ax (1)

that transforms a set of input data x to reduce redundancyinduced by correlation. This technique is often used fordimension reduction, feature extraction, data-decorrelationand whitening [4]. It can be described as the problem offinding an orthogonal transformation matrix A that decor-relates the elements of a centered vector x. The Transfor-mation also maximises the variance of the principal com-ponents, which are the elements of y, and makes the co-variance matrix of the transformed dataset y diagonal. The

34

solution of maximizing the variance is given by the eigen-vectors of the covariance matrix Cx of x [3]. The principalcomponents were calculated using singular value decom-position.

3.2.2. Training

A linear transformation matrix ARec is used for recon-struction of disturbed segments. This matrix is obtained byPCA using all leads from undisturbed P-QRS-T segmentsor segments with minor disturbances. This training can bedone either for the full P-QRS-T segments or certain partsof the ECG like the P- or T-wave or QRS interval. DifferentARec can be used for different parts of P-QRS-T. As thesesegments have different origins within the heart this canincrease the total reconstruction quality if a reliable ECG-segmentation can be achieved. Another possibility is thetraining for different postures and activities. As changes inposture lead to shifting of the body and the relative posi-tion of the electrodes the waveform and therefore also theshape of ARec can change. Training and reconstructionbased on different posture and activity, derived from theaccelerometer data, can increase reconstruction accuracy.A general or patient specific approach is possible.

3.2.3. Reconstruction approach

Whenever a lead of a P-QRS-T segment is found to haveinsufficient quality it is automatically reconstructed usingother undisturbed leads in that same segment. First seg-ments with high quality are selected and used to calculatethe principal components. If too many leads have insuffi-cient quality the reconstruction attempt is aborted. Other-wise the first principal components are used to calculatethe reconstruction of the disturbed leads which are cal-culated by equation (2) using only the first n columns ofA−1

Rec and n PCs in y.

x = A−1Recy. (2)

This step assumes a high correlation between the recordedleads. Otherwise the PCs would change from their originalshape and the reconstruction would be off. As the sign ofthe PCs can change the best reconstruction has to be foundby changing the signs of every PC or some components ofthe ECG might be inverted. Also in segments where manyleads are disturbed the order of PCs can change. Thereforeall leads, the disturbed and undisturbed, are reconstructed,and the combination of PC signs and PC order is chosen,that gives the least mean square error for the undisturbedleads in the segment. The principal for the reconstructionis depicted in figure 2.

30 31 32 33 34 35 36 37 38 39 40−1

−0.5

0

0.5

1

time in s

Am

plit

ud

e

reconstructed ECG

recorded lead I

I from 4 PCs, 12 leads: ρ = 0.99942

I from 4 PCs, 10 leads: ρ = 0.99915

I from 4 PCs, 8 leads: ρ = 0.99253

I from 4 PCs, 6 leads: ρ = 0.9922

30 31 32 33 34 35 36 37 38 39 40−1

−0.5

0

0.5

1

time in s

Am

plit

ud

e

recorded lead II

II from 4 PCs, 12 leads: ρ = 0.9968

II from 4 PCs, 10 leads: ρ = 0.99902

II from 4 PCs, 8 leads: ρ = 0.99272

II from 4 PCs, 6 leads: ρ = 0.99268

30 31 32 33 34 35 36 37 38 39 40−1

−0.5

0

0.5

1

time in s

Am

plit

ud

e

recorded lead V1

V1 from 4 PCs, 12 leads: ρ = 0.9898

V1 from 4 PCs, 10 leads: ρ = 0.99059

V1 from 4 PCs, 8 leads: ρ = 0.98854

V1 from 4 PCs, 6 leads: ρ = 0.99373

30 31 32 33 34 35 36 37 38 39 40−1

−0.5

0

0.5

time in s

Am

plit

ud

e

recorded lead V3

V3 from 4 PCs, 12 leads: ρ = 0.9898

V3 from 4 PCs, 10 leads: ρ = 0.99059

V3 from 4 PCs, 8 leads: ρ = 0.98854

V3 from 4 PCs, 6 leads: ρ = 0.99373

Figure 3. ECG reconstruction using different numbers ofleads for reconstruction.

3.3. Reconstruction quality estimation

Common measures for reconstruction quality are meansquare error (MSE) and correlation between reconstructionand original signal. As it is difficult or impossible to get aclean reference during exercise these measures can only betested on artificially disturbed signals. On the other handa high correlation or mean square error doesn’t necessar-ily mean that all clinically relevant points are reconstructedcorrectly and that a diagnosis based on the reconstructionis equal to one based on the original signal. So on the onehand a substitute for the clean reference and on the othermore clinically relevant measures have to be found. As afirst step a confidence estimation was achieved by consid-ering MSE and correlation for undisturbed leads and theirreconstruction. The clinical equivalence was consideredby calculating the mean and variance of the R-peak ampli-tude and comparing the deviance.

4. Results

In this study four hours of 12-channel ECG contain-ing different activities including 96min of exercise and36min of controlled breathing from six healthy subjectswere evaluated, totalling in 17480 recorded heartbeats perlead. Table 1 gives an overview of the performance of thealgorithm by showing the percentage of beats classified asdisturbed, the percentage of disturbed beats that were re-constructed and the subsequent confidence estimation fordifferent activities.

5. Conclusion

The algorithm is able to automatically detect distur-bances in recorded ECGs on a beat-to-beat level. Leadswith the lowest disturbances in a segment are automati-cally detected and used for reconstruction of all leads inthat segment. The quality of the reconstruction can then

35

I

II

V1

V2

V3

V4

V5

V6

69,787,689,681,387,2 <50

Figure 4. ECGs with induced noise (beats 8-10) and clipping (beats 7&8) and movement artefacts from running (last beats).Beats with sightly reduced quality are marked in green, medium quality in blue and low quality in red. The reconstructedsignals are superimposed in black and the reconstruction confidence is noted on top of each reconstructed segment.

Table 1. Composition of test data and achieved results

Posture Time(min)

percentagedisturbed

percentagerecon-structable

confi-dence

Rest 90 0.20% 95.0% 0.90Controlledbreathing

36 2.94% 100% 0.88

Selectivedistur-bances

18 39.85% 91.5% 0.87

Minormovement

36 13.54% 99.4% 0.73

Majormovement

60 93.71% 76.3% 0.58

be assessed without the need for a clean reference. Goodreconstruction results can be achieved as long as there arestill multiple undisturbed leads available.The reconstruction performance could be improved byposture and activity dependent reconstruction matrices us-ing the accelerometer data for classification. Alternativelythe reconstruction matrix could be trained for differentparts of the ECG like Q- and T- wave or the QRS com-plex. More clinical measures for the confidence estimationshould also improve the general evaluation of the recon-struction in absence of a clean reference.

References

[1] Silva I, et al: Improving the Quality of ECGs CollectedUsing Mobile Phones. The PhysioNet /Computing in Car-diology Challenge 2011. In: Comput. Cardiol. 38 (2011),S. 1273-76.

[2] Mann S, et al.: PCA-based ECG lead reconstruction.Biomedical Engineering / Biomedizinische Technik. ISSN(Online) 1862-278X, September 2013

[3] Hyvrinen A, Karhunen J, Oja E, Independent ComponentAnalysis. Wiley, 2001

[4] Jolliffe I T, Principal Component Analysis, 2nd ed.,Springer, 2002.

[5] Xia H, et al.: Computer Algorithms for Evaluating theQuality of ECGs in Real Time. Computing in Cardiology2011;38:369372.

[6] Clifford GD, et al.:Signal Quality Indices and Data Fusionfor Determining Acceptability of Electrocardiograms Col-lected in Noisy Ambulatory Environments. Computing inCardiology 2011;38:285288.

[7] Moody B E, Rule-Based Methods for ECG Quality Control.Computing in Cardiology 2011;38:361363.

Address for correspondence:

Steffen A. MannSekretariat EN 3Einsteinufer 1710587 BerlinSteffen.Mann@tu-berlin.de

36