Machine Learning Improves the Detection of Misplaced v1 and v2 Electrodes During 12-Lead Electrocardiogram Acquisition

Rjoob, Khaled; Bond, Raymond; Finlay, Dewar; McGilligan, Victoria; Leslie, Stephen J; Iftikhar, Aleeha; Guldenring, Daniel; Rababah, Ali; Knoery, Charles; Peace, Aaron

doi:10.22489/cinc.2019.035

Cited by 5 publications

(6 citation statements)

References 10 publications

(13 reference statements)

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“…Table 2 below shows mean sensitivity and mean specificity of each group. [25] 87.0% ±0.00 97.8% ±0.00 92.0% Han C. et al, [30] 56.1% ±27.7 99.9% ±0.04 71.8% Average =85.6% SD=±13.5 Average =98.5% SD=±2.5 B Rjoob K. et al, [23] 79.6% ±8.6 84.6% ±5.9 81.6% Rjoob K. et al, [24] 81.5% ±11.5 81.0% ±11.0 81.3% Average =80.5% SD=±0.95 Average =82.8% SD=±1.8 C Jekova I. et al, [26] 97.4% ±1.88 99.2% ±0.35 98.3% Heden B. et al, [18] 95.0% ±0.00 99.95% ±0.00 97.4% Jekova I. et al, [25] 96.8% ±0.00 97.8% ±0.00 97.3% Han C. et al, [30] 93.4% ±1.05 99.9% ±0.05 96.5% Gregg R. et al, [29] 88.1% ±3.90 99.7% ±0.20 93.5% Kors J. et al, [20] 83.7% ±21.31 98.5% ±2.07 90.4% Jan A. et al, [21] 81.5% ±31.8 99.8% ±0.18 89.7% Han C. et al, [22] 82.5% ±9.25 97.7% ±0.20 89.3% Heden B. et al, [17] 69.0% ±11.45 99.9% ±0.01 81.6% Heden B. et al, [19] 57.6% ±0.00 99.97% ±0.00 73.1% Bie J. et al, [28] 54.6% ±28.09 99.6% ±0.15 70.5% Average =81.7% SD=±14.5 Average =99.2% SD=±0.82…”

Section: Descriptive Summary Of Resultsmentioning

confidence: 99%

“…The fourteen studies used and evaluated ML based algorithms to detect electrode misplacement/interchange, and four ML models included artificial neural networks (ANNs) (n = 3) [17][18][19], decision trees (DT) (n=5) [20][21][22][23][24], correlation (n=3) [25][26][27], amplitude threshold (n=1) [28], haisty (n=1) [29]…”

Section: Study Characteristicsmentioning

confidence: 99%

“…and support vector machines (SVM) (n = 3)[23][24][30]. The plurality of the studies (n=7) focused on both limb and chest lead interchanges while other studies (n=4) focused on limb lead interchanges only, two studies considered vertical misplacement of chest electrodes and one study considered chest lead interchange only.…”

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confidence: 99%

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Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: A systematic review and meta-analysis

Rjoob

Bond

Finlay

et al. 2020

Journal of Electrocardiology

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Introduction: Electrode misplacement and interchange errors are known problems when recording the 12-lead electrocardiogram (ECG). Automatic detection of these errors could play an important role for improving clinical decision making and outcomes in cardiac care. The objectives of this systematic review and meta-analysis is to 1) study the impact of electrode misplacement on ECG signals and ECG interpretation, 2) to determine the most challenging electrode misplacements to detect using machine learning (ML), 3) to analyse the ML performance of algorithms that detect electrode misplacement or interchange according to sensitivity and specificity and 4) to identify the most commonly used ML technique for detecting electrode misplacement/interchange. This review analysed the current literature regarding electrode misplacement/interchange recognition accuracy using machine learning techniques. Method: A search of three online databases including IEEE, PubMed and ScienceDirect identified 228 articles, while 3 articles were included from additional sources from co-authors. According to the eligibility criteria, 14 articles were selected. The selected articles were considered for qualitative analysis and meta-analysis. Results: The articles showed the effect of lead interchange on ECG morphology and as a consequence on patient diagnoses. Statistical analysis of the included articles found that machine learning performance is high in detecting electrode misplacement/interchange except left arm/left leg interchange. Conclusion: This review emphasises the importance of detecting electrode misplacement detection in ECG diagnosis and the effects on decision making. Machine learning shows promise in detecting lead misplacement/interchange and highlights an opportunity for developing and operationalising deep learning algorithms such as convolutional neural network (CNN) to detect electrode misplacement/interchange.

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Section: Descriptive Summary Of Resultsmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

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Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: A systematic review and meta-analysis

Rjoob

Bond

Finlay

et al. 2020

Journal of Electrocardiology

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“…5%-99.9% and Sp.= 99.9% for decision trees [8,9,13,14]. Se = 56.5%-93.7%, Sp= 99% for Artificial neural Networks [10,15,16], Se = 68.1%-93.9%, Sp = 99.8% for Support Vector Machine [17,15] and Se = 74.9%-97.8% Sp = 91.0% for Amplitude Thresholds [18].…”

Section: Introductionmentioning

confidence: 99%

Wireless IoT Networks Security and Lightweight Encryption Schemes- Survey

T.¹,

Huisa²,

Espinoza³

et al. 2024

IJETAE

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The exchange of electrodes during ECG testing can lead to misdiagnosis in up to 24% of cases, some of which are severe medical conditions. To solve this problem, we develop an algorithm based on the exchanges of the elements of the 6x6 correlation coefficient score matrix for precordial leads (V1-V6) and maximum correlation with the V6 electrode for peripheral leads (I, II, III, -I, -II, -III, -aVR, -aVF and - aVL). The algorithm was validated using the k-fold crossvalidation technique (k=8) for 545 records from the PTB database. The algorithm analyzes specific correlation coefficients of simulated exchanges to minimize the computational cost. The results report average Sensitivity (Se) and Specificity (Sp) for the algorithm finding values of Se= 99.52% and Sp= 99.62% for one-, two- and three-hop precordial electrode exchanges, and Se= 92.10%, and Sp= 96.05% for exchanges in peripheral leads. The electrode exchange detection algorithm based on inter-lead correlation analysis provides accurate detection in both precordial and peripheral electrode exchanges in standard 12-lead ECGs, thus validating its implementation in biomedical devices such as the Think Health project.

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“…Morphological and statistical features, such as P-QRS-T amplitude (Hede et al 1995, de Bie et al 2014, Gregg et al 2017, QRS wave boundary and QRS-T wave pattern (Jekova et al 2013), and P-QRS-T vector direction (Han et al 2014), have been selected to identify lead misplacement. In Rjoob et al (2019aRjoob et al ( , 2019b, authors conducted V1 and V2 misplacement detection in three different positions: first, second and third intercostal spaces, which was based on morphological, frequency, and statistical features. Lead correlation features calculate correlation coefficients between leads and use machine learning models to detect misplacement based on coefficient trends (Jekova et al 2013, Jekova et al 2016, Jekova et al 2018.…”

Section: Introductionmentioning

confidence: 99%

A lightweight deep learning approach for detecting electrocardiographic lead misplacement

Huang,

Wang,

et al. 2024

Physiol. Meas.

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Objective: Electrocardiographic (ECG) lead misplacement can result in distorted waveforms and amplitudes, significantly impacting accurate interpretation. Although lead misplacement is a relatively low-probability event, with an incidence ranging from 0.4% to 4%, the large number of ECG records in clinical practice necessitates the development of an effective detection method. This paper aimed to address this gap by presenting a novel lead misplacement detection method based on deep learning models. Approach: We developed two novel lightweight deep learning model for limb and chest lead misplacement detection, respectively. For limb lead misplacement detection, two limb leads and V6 were used as inputs, while for chest lead misplacement detection, six chest leads were used as inputs. Our models were trained and validated using the Chapman database, with an 8:2 train-validation split, and evaluated on the PTB-XL, PTB, and LUDB databases. Additionally, we examined the model interpretability on the LUDB databases. Limb lead misplacement simulations were performed using mathematical transformations, while chest lead misplacement scenarios were simulated by interchanging pairs of leads. The detection performance was assessed using metrics such as accuracy, precision, sensitivity, specificity, and Macro F1-score. Main Results: Our experiments simulated three scenarios of limb lead misplacement and nine scenarios of chest lead misplacement. The proposed two models achieved Macro F1-scores ranging from 93.42% to 99.61% on two heterogeneous test sets, demonstrating their effectiveness in accurately detecting lead misplacement across various arrhythmias. Significance: The significance of this study lies in providing a reliable open-source algorithm for lead misplacement detection in ECG recordings. The source code is available at https://github.com/wjcai/ECG_lead_check.

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Machine Learning Improves the Detection of Misplaced v1 and v2 Electrodes During 12-Lead Electrocardiogram Acquisition

Cited by 5 publications

References 10 publications

Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: A systematic review and meta-analysis

Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: A systematic review and meta-analysis

Wireless IoT Networks Security and Lightweight Encryption Schemes- Survey

A lightweight deep learning approach for detecting electrocardiographic lead misplacement

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