Automatic detection of ECG cable interchange by analyzing both morphology and interlead relations

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Automatic Detection of ECG Cable Interchange by Analyzing Both

Morphology and Interlead Relations

Chengzong Han, PhD, Richard E. Gregg, MS, Dirk Q. Feild, MA, Saeed Babaeizadeh, PhD

Advanced Algorithm Research Center, Philips Healthcare, Andover, MA, USA

Correspondence: Chengzong Han

Institution: Philips Healthcare

Address: 3000 Minuteman Rd

City: Andover

State: Massachusetts

Country: United States

Postal Code/Zip: 01810

Telephone: 1-978-659-2277

E-mail: [email protected]

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Abstract

Background: ECG cable interchange can generate erroneous diagnoses. For algorithms detecting

ECG cable interchange, high specificity is required to maintain a low total false positive rate because

the prevalence of interchange is low. In this study, we propose and evaluate an improved algorithm

for automatic detection and classification of ECG cable interchange.

Method: The algorithm was developed by using both ECG morphology information and redundancy

information. ECG morphology features included QRS and P-wave amplitude, frontal axis and

clockwise vector loop rotation. The redundancy features were derived based on the EASITM

lead

system transformation. The classification was implemented using linear support vector machine. The

development database came from multiple sources including both normal subjects and cardiac

patients. An independent database was used to test the algorithm performance. Common cable

interchanges were simulated by swapping either limb cables or precordial cables.

Results: For the whole validation database, the overall sensitivity and specificity for detecting

precordial cable interchange were 56.5% and 99.9%, the sensitivity and specificity for detecting limb

cable interchange (excluding left arm-left leg interchange) were 93.8% and 99.9%. Defining

precordial cable interchange or limb cable interchange as a single positive event, the total false

positive rate was 0.7%. When the algorithm was designed for higher sensitivity, the sensitivity for

detecting precordial cable interchange increased to 74.6% and the total false positive rate increased to

2.7%, while the sensitivity for detecting limb cable interchange was maintained at 93.8%. The low

total false positive rate was maintained at 0.6% for the more abnormal subset of the validation

database including only hypertrophy and infarction patients.

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Conclusion: The proposed algorithm can detect and classify ECG cable interchanges with high

specificity and low total false positive rate, at the cost of decreased sensitivity for certain precordial

cable interchanges. The algorithm could also be configured for higher sensitivity for different

applications where a lower specificity can be tolerated.

Keywords: Electrocardiograph; lead system; cable interchange; algorithm; classification

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Introduction

Forty million ECGs are recorded annually in the United States. Electrode placement is

considered one of the most important factors that determine ECG signal quality and misplacement can

produce incorrect ECG abnormalities and thus generate erroneous diagnostic reports (1). ECG cable

interchange is one of the most common electrode placement errors. A recent statement from the

American Heart Association recommends including cable interchange detection algorithms in ECG

devices (2). The magnitude of the cable misconnection problem has been measured between 0.4% and

4% in various clinical settings (3,4,5,6).

Different criteria and algorithms have been proposed to detect cable interchange based on ECG

analysis. Common criteria identify morphology features including P-QRS-T amplitude, area, R-wave

progression, and flat line on certain leads to detect cable interchanges (7,8,9,10). Vectorcardiogram

(VCG) analysis has also been proposed for the detection of cable interchanges (11,12). In terms of

automatic detection algorithms, most methods fall into two categories. The first category involves

morphology-based methods which extract a set of measurements from P, QRS, and T-waves and use

these measurements to derive detection criteria (13,3,14). The second category of algorithms

implements redundancy-based methods which use the redundant information contained in the eight

independent leads. Such methods use the transformation or reconstruction of ECGs from the original

lead system to an approximation and cable interchange is detected by comparing the original ECGs

with transformed or reconstructed ECGs (4,15). Batchvarov et al (1) reviewed the different types of

cable interchanges as well as criteria and algorithms for detecting cable interchanges. Most recently,

Xia et al (16) proposed a method combining morphology features from (13,3) and redundancy

features from (4) for the detection of just limb cable interchange.

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Different performance results were reported for the above-mentioned methods, depending on

the choice of database and algorithm configuration. From a clinical and practical point of view, a high

specificity algorithm for detecting cable interchange is preferred, in order to prevent generating many

false positive notifications, especially since the prevalence of cable interchange is low. To achieve this

goal, we propose a novel algorithm for automatic detection and classification of ECG cable

interchanges which uses both morphology and redundancy information. The algorithm performance

was evaluated using an independent population database for common limb cable interchanges and

precordial cable interchanges.

Methods

Database

The development database included two population sources. One source was body surface

potential maps recorded on a population including normal subjects, post-myocardial-infarction

patients, patients with a history of ventricular tachycardia but no evidence of a previous myocardial

infarction, and patients with a single-vessel coronary artery disease who underwent coronary

angioplasty (17). The second source was the Physionet PTB diagnostic ECG database (18,19) which

includes both healthy control subjects and patients with various cardiac diseases including myocardial

infarction, cardiomyopathy/heart failure, bundle branch block, dysrhythmia, myocardial hypertrophy,

valvular heart disease, myocarditis, and other miscellaneous conditions. Consecutive 10-second

snapshot ECGs were taken from each PTB recording. The validation database of 10-second records

came from the Common Standards for Electrocardiography (CSE) diagnostic database (20) which

includes healthy control subjects, ventricular hypertrophy patients, and myocardial infarction patients.

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For both the development and validation databases, the ECGs with suspected cable interchange were

excluded. In total, there were 6272 ECGs from 1125 patients in the development database and 1166

ECGs from 1166 patients in the validation database that were considered free of any cable

interchange. To further evaluate the algorithm performance, the validation database was divided into

two subsets. Subset 1 included 527 ECGs that were confirmed by independent experts as hypertrophy

and/or infarction, while subset 2 included the remaining 639 ECGs most of which were considered

normal. The ECGs from both development database and validation database all used standard limb

electrode placement.

We investigated 9 common cable interchanges (3,4) involving 3 limb cables, right arm (RA), left

arm (LA), and left leg (LL); and 6 precordial cables, V1 to V6. For each ECG, cable interchanges

were simulated by swapping either limb cables (LA-RA and RA-LL) or precordial cables (V1-V2,

V1-V3, V2-V3, V3-V4, V4-V5, V4-V6, and V5-V6), which generated additional 9 ECGs from each

original ECG with no cable interchange. The LA-LL interchange was not considered because experts

felt that serial ECGs are required for reliable detection of the LA-LL interchange; in other words,

even an expert cannot dependably detect LA-LL interchange using only a single 10-second snapshot.

Algorithm Development

Our detection and classification algorithm uses both morphology features and redundancy

features. The Philips DXL ECG algorithm was used to automatically extract morphology features

including P-wave frontal axis, P-wave clockwise vector loop rotation direction, QRS-wave frontal

axis, QRS-wave clockwise vector loop rotation direction, R-wave amplitude and T-wave amplitude

from lead I and lead II. The P-wave features were only used when the ECG had consistent beat-to-

beat PR interval and did not show atrial fibrillation or atrial flutter. The redundancy features were

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derived based on EASITM

transformation. In this context, redundancy means that many leads are

highly correlated. Each lead can be reconstructed from other leads with reasonable accuracy due to

this redundant information. For each ECG, after generating a separate ECG for all possible lead swaps

(including lead swaps for LA-RA, RA-LL, V1-V2, V1-V3, V2-V3, V3-V4, V4-V5, V4-V6, V5-V6,

and no lead swap), the inverse of EASITM

-lead conversion matrix (21,22) was used to transform each

swapped 12-lead ECG to the EASITM

-lead ECG and then the EASITM

-lead conversion matrix was

applied to convert back to a 12-lead ECG. The lead by lead root mean square errors (RMSE) between

swapped ECGs and the double EASITM

-transformed ECGs were calculated over the entire QRS-T

complex. The redundancy features for this study were the averaged RMSE among all 12 leads for

each lead swap.

To visualize the features (including both morphology features and redundancy features) used in

detection and classification, we used principal component analysis as the dimensionality reduction

technique to project the high-dimension feature space into a 2D feature space. The first two dominant

components with largest singular values were chosen as 2D features for visualization. After reducing

the dimensionality, we visualized the features for the development database on scatter plots. Figure

1.A shows the comparison of 2D features between LA-RA interchange and no cable interchange.

Figure 1.C shows the comparison of 2D features between V2-V3 interchange and no cable

interchange. For each comparison plot, the histograms of corresponding model output are shown in

Figure 1.B and Figure 1.D. The feature overlap between the V2-V3 interchange and no cable

interchange, shown in Figure.1.C, is larger than the feature overlap between LA-RA interchange and

no cable interchange, shown in Figure.1.A. This indicates that detecting V2-V3 interchange is more

challenging than detecting LA-RA interchange. A larger overlap in scatter plots was also observed

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when comparing features for the V4-V5 or V5-V6 interchanges with no cable interchange. Some

cable interchanges are difficult to detect even for expert electrocardiographers. Figure 2 shows a 12-

lead ECG example in which V5 and V6 are swapped. The difference between V5 and V6 is so small

that neither the algorithm nor the expert could detect the cable interchange with certainty. For this

particular example, the diagnostic algorithm generated the same interpretation with and without the

V5-V6 interchange.

The automatic algorithm was implemented based on a multi-class linear support vector machine

(SVM) in the form of C-support vector classification (23). As part of the standard method, binary

SVM classifiers were designed for every combination of two output classes. We used the LIBSVM

toolbox (23) to perform the training of the SVM models. A higher weight was assigned to the

parameter C in the binary SVM classifier for the output class with no cable interchange compared to

the other output classes with cable interchanges. This unequal weighting favored specificity over

sensitivity, which means fewer false positives at the cost of potentially more false negatives. To train

the SVM models, 5-fold cross validation and grid search based on overall detection accuracy were

performed to choose the optimal parameter C. Figure 1.B and Figure 1.D show the decision boundary

(vertical line) and the histogram of SVM score generated by the SVM model for the development

database. Figure 1.B shows the output of binary classifier for LA-RA interchange and no cable

interchange. Figure 1.D shows the output of binary classifier for V2-V3 interchange and no cable

interchange. The decision boundary was designed to reduce the false positive cases through the

unequal weighting of the C parameter mentioned above. There are more false negative cases in Figure

1.D than false negative cases in Figure 1.C. In order to perform multi-class classification, the “one-

against-one” approach was used, and then the final output class was chosen by comparing outputs

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from all binary classifiers using a voting strategy. The algorithm was configured in two ways. One

algorithm configuration aimed for low false positive rate (high specificity) and the other algorithm

configuration aimed for high sensitivity.

Statistical Analysis

Algorithm performance was evaluated in terms of sensitivity and specificity from a confusion

matrix. To calculate sensitivity and specificity for each cable interchange, that particular cable

interchange was defined as a positive event and all other cable interchanges as well as no cable

interchange were defined as a negative event thereby reducing the size of the confusion matrix to 2-

by-2. Considering that there were 9 types of individual cable interchanges, the algorithm performance

was also evaluated in terms of total false positive rate, which represents the ratio of cases that were

erroneously detected as any cable interchange within the group with no cable interchange. We

calculated the statistical significance based on the 95% confidence interval (CI) of the difference in

the performance ratios (sensitivity and specificity) as recommended by Altman et al (24).

Results

Table 1 and Table 2 summarize the performance of detecting each individual cable interchange

for two configurations of the algorithm on the whole validation database. For the low false positive

rate configuration, the total false positive rate was 0.7%. The sensitivity for interchanges of LA-RA,

RA-LL, V1-V2, V1-V3, V3-V4 and V4-V6 was much higher than the sensitivity for interchanges of

V2-V3, V4-V5, and V5-V6. For the high sensitivity algorithm configuration, the sensitivity was

significantly improved, especially for precordial cable interchanges V2-V3, V4-V5, and V5-V6.

However, the total false positive rate also increased. The change in specificity for each individual

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cable interchange was small between the two algorithm configurations.

The confusion matrix of the high sensitivity algorithm, shown in Figure 3.A, indicates that a

large number of the false positive cases (2.7% of all no cable interchange cases) came from the

erroneous detection of precordial cable interchange. In order to reduce this portion of false positive

cases (from 2.7% to 0.6%), as shown in Figure 3.B, the algorithm was configured to tradeoff true

cases of precordial cable interchange for false positive detections, which explains the decreased

sensitivity (from 74.6% to 56.5%) in the algorithm configured for low false positive rate. There were

only a small number of limb lead interchange false positive cases (0.1% of all no cable interchange

cases) for either algorithm configurations.

Table 3 and Table 4 summarize the performance of the low false positive algorithm for validation

database subset 1 and subset 2. Subset 1 included only hypertrophy and/or infarction patients, while

most of ECGs in subset 2 were normal. For these two subsets, false positive rates by the algorithm

were comparable (0.6% vs 0.8% with the difference confidence interval (CI) of [-1.0%, 1.3%]). The

sensitivity for most precordial cable interchanges was statistically different between these subsets,

except for the interchanges of V1-V2 (80.6% vs 76.2% with difference CI of [-0.3%, 9.1%]) and V3-

V4 (86.0% vs 82.6% with difference CI of [-0.9%, 7.5%]). Sensitivity was also significantly lower for

subset 1(hypertrophy and infarction) compared to subset 2 for limb cable interchanges of LA-RA

(from 98.0% to 90.3% with difference CI of [-10.6%, -5.0%]) and RA-LL (from 95.8% to 88.2% with

difference CI of [-10.8%, -4.4%]).

Discussion

In this study, we propose a new method for detecting ECG cable interchange which uses both

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morphology and redundancy information. In cardiology departments, an algorithm with high

specificity and low false positive rate is preferred because of the low prevalence of lead-wire

interchange (3,4,5,6). Therefore, the algorithm was configured to maintain a total false positive rate

less than 1%. Even with such configuration, the algorithm was still able to achieve good sensitivity

for certain types of cable interchange. We also showed that this algorithm could be configured with

different performance for other scenarios which may need higher sensitivity in detecting cable

interchanges. For the alternate algorithm configuration, the sensitivity was much improved especially

for those difficult cases such as cable interchanges of V2-V3, V4-V5, and V5-V6, at the cost of a

higher total false positive rate. These cable interchanges are difficult to detect because in many

patients there is little difference between the ECG waveforms recorded in those interchanged leads.

The sensitivity for precordial cable interchanges varied among different types of interchange. In

general, sensitivity of cable interchanges for V2-V3, V4-V5, and V5-V6 was lower than other

precordial cable interchanges. From an algorithm perspective, this is because there are larger feature

overlaps between cases for these types of cable interchanges and cases for no cable interchange (as

shown in the scatter plot of Figure 1). Particularly, when the algorithm was configured for low total

false positive rate, the sensitivity for cable interchanges for V4-V5 and V5-V6 was very low (less than

20%). Sometimes, the difference between V4 and V5 (or V5 and V6) is so small that both automated

algorithms and ECG experts may not be able to detect the cable interchange (an example is shown in

Figure 2). For such cases, detecting interchange may not be clinically important because the

diagnostic algorithm can usually generate the same diagnostic report due to the minor difference in

waveforms.

The sensitivity for limb cable interchanges of LA-RA and RA-LL was maintained at high level

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for both configurations of the algorithm. The specificity for two algorithm configurations was very

close and very high (larger than 99%), which however suggests the necessity of using total false

positive rate rather than specificity to compare the different algorithm configurations. The differences

between algorithms could be lost in the round off of the very high specificity numbers. Furthermore,

even under same configuration, specificity for each individual cable interchange was also very close,

which further emphasizes the importance of investigating the contribution of the individual false

positive rates for different interchanges to the total false positive rate.

During the development of the algorithm, we trained our model using databases including

various disease types and did not favor any particular disease types. To test the algorithm, we divided

the test database into two subsets where subset 1 includes ECGs from hypertrophy and/or infarction

patients, and subset 2 includes ECGs mostly from normal subjects. While the sensitivity varies

between subset 1 and subset 2, the total false positive rate and specificity are comparable and not

statistically different. Due to the limited number of test files (which require expert review and

annotation), we could not test the performance of our algorithm under every specific disease type.

This could be addressed in the future when such large databases including a variety of abnormalities

are available.

In this study, we did not investigate any cable interchange involving the right leg cable, or

interchange between limb and precordial cables. These cable interchanges cannot be simulated by

swapping the waveforms from 12-lead ECG and need specific databases for development and

validation. There are some commonly used criteria (e.g., flat line on limb lead) that could be used to

visually detect the cable interchange between arm cable and right leg cable (8,9). Compared to other

cases, an interchange between limb and precordial cables is rare since the limb cable is usually

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physically different than the precordial cable (e.g., a limb cable is usually longer than a precordial

cable). Future studies can be conducted when such specific databases are available.

Conclusion

The present study demonstrates that our novel algorithm can be configured to achieve high

specificity and low total false positive rate, and still detects a large number of limb cable interchanges

and precordial cable interchanges. The algorithm can also be configured for higher sensitivity. Our

results suggest that this ECG cable interchange detection algorithm could complement a 12-lead ECG

algorithm for more accurate diagnosis of cardiac diseases.

References

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Figure Legends

Figure 1. Visualization of the decision boundary for classification of lead wire misconnection in the

development database. (A) Scatter plot of 2D features after feature dimensionality reduction for LA-

RA cable interchange (black dot) and no cable interchange (gray dot). (B) Histogram of the SVM

scores for LA-RA cable interchange (black line) and no cable interchange (gray line). The x-axis of

the histogram is the SVM score which is calculated by linear combination of all features. The y-axis

of the histogram is the number of ECG cases that have same value of SVM score. The black vertical

dashed line indicates the decision boundary, the left of which is considered as LA-RA interchange and

the right of which is considered no interchange. (C) and (D) are the scatter plot and decision boundary

for V2-V3 cable interchange and no cable interchange.

Figure 2. A 12-lead ECG example with V5-V6 cable interchange. As seen, the waveforms for leads

V5 and V6 are very similar, making it extremely difficult to detect if a cable interchange has occurred.

Figure 3. Comparison of confusion matrices for algorithms configured for high sensitivity (A) and

low total false positive rate (B). All the individual types of cable interchange were summarized into

three categories: no cable interchange, precordial cable interchange and limb cable interchange. Each

column of the matrix represents the instances of the output predicted by the algorithm, while each row

represents the instances of the actual reference. The number in each matrix element is the percentage

of the number of certain algorithm-detected instances within the total number of one actual reference

instance.

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Tables

Table 1 Classification performance for algorithm configured for high sensitivity

V1-V2 V1-V3 V2-V3 V3-V4 V4-V5 V4-V6 V5-V6 LA-RA RA-LL

Sensitivity (%) 87.4 90.9 64.9 93.2 43.6 86.3 50.3 94.5 92.3

Specificity (%) 99.9 99.9 99.9 99.9 100.0 99.9 99.9 100.0 99.9

Note: LA: left arm; LL: left leg; RA: right arm; total false positive rate is 2.7%.

Table 2 Classification performance for algorithm configured for low total false positive rate


Sensitivity (%) 78.2 81.7 49.1 84.1 12.7 68.1 19.1 94.5 92.4

Specificity (%) 99.9 99.9 99.9 100.0 100.0 99.9 100.0 100.0 99.9


Table 3 Classification performance for algorithm configured for low total false positive rate using

subset 1 of hypertrophy/infarction patients


Sensitivity (%) 80.6 76.1 35.5 86.0 16.9 77.8 25.1 90.3 88.2

Specificity (%) 99.9 100.0 100.0 100.0 100.0 99.9 100.0 100.0 99.8


Table 4 Classification performance for algorithm configured for low total false positive rate using

subset 2 of validation database


Sensitivity (%) 76.2 86.2 60.3 82.6 9.2 60.1 14.2 98.0 95.8

Specificity (%) 99.9 99.9 100.0 100.0 100.0 99.9 99.9 100.0 100.0


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Figures

Figure 1

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Figure 2

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Figure 3

Automatic detection of ECG cable interchange by analyzing both morphology and interlead relations

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