Inter-lead correlation analysis for automated detection of cable reversals in 12/16-lead ECG

Graph structure based data augmentation method

In this paper, we propose a novel graph-based data augmentation method that can generally be applied to medical waveform data with graph structures. In the process of recording medical waveform data, such as electrocardiogram (ECG), angular perturbations between the measurement leads exist due to imperfections in lead positions. The data samples with large angular perturbations often cause inaccuracy in algorithmic prediction tasks. We design a graph-based data augmentation technique that exploits the inherent graph structures within the medical waveform data to improve the F1 score by 1.44% over various tasks, models, and datasets. In addition, we show that Graph Augmentation improves model robustness by testing against adversarial attacks. Since Graph Augmentation is methodologically orthogonal to existing data augmentation techniques, they can be used in conjunction to further improve the final performance, resulting in a 2.47% gain of the F1 score. We believe that our Graph Augmentation method opens up new possibilities to explore in data augmentation.

A lightweight deep learning approach for detecting electrocardiographic lead misplacement

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 athttps://github.com/wjcai/ECG_lead_check.

Electrocardiographic lead reversals

Misplacement of cables during the recording of a 12-lead electrocardiogram [ECG] poses a non-negligible risk of creating panic and confusion at the bedside in daily clinical practice. Clinical awareness about the manifestations of commonly encountered electrode misplacements is imperative for avoiding misdiagnosis. A basic understanding of the electrophysiology behind these anomalous ECG tracings is likely to aid in prompt suspicion, accurate detection, and appropriate rectification in most cases. We discuss the abnormalities produced on 12-lead ECG tracings by the misplacement of electrode cables, with a focus on the clinical implications of the same. We suggest a mnemonic - 'SPIRAL' - as a quick screening criterion to detect commonly encountered lead reversals.

False Alarm Reduction in Self-Care by Personalized Automatic Detection of ECG Electrode Cable Interchanges

Introduction

False alarm reduction is an important challenge in self-care, whereas one of the most important false alarm causes in the cardiology domain is electrodes misplacements in ECG recordings, the main investigations to perform for early and pervasive detection of cardiovascular diseases. In this context, we present and assess a new method for electrode reversals identification for Mason-Likar based 3D ECG recording systems which are especially convenient to use in self-care and allow to achieve, as previously reported, high computerized ischemia detection accuracy.

Methods

We mathematically simulate the effect of the six pairwise reversals of the LA, RA, LL, and C2 electrodes on the three ECG leads I, II, and V2. Our approach then consists in performing serial comparisons of the newly recorded 3D ECG and of the six derived ECGs simulating an electrode reversal with a standard, 12-lead reference ECG by means of the CAVIAR software. We further use a scoring method to compare these analysis results and then apply a decision tree model to extract the most relevant measurements in a learning set of 121 patients recorded in ICU.

Results

The comparison of the seven sets of serial analysis results from the learning set resulted in the determination of a composite criteria involving four measurements of spatial orientation changes of QRS and T and providing a reversal identification accuracy of 100%. Almost the same results, with 99.99% of sensitivity and 100% of specificity, were obtained in two test sets from 90 patients, composed of 2098 and 2036 representative ECG beats respectively recorded during PTCA balloon inflation, a procedure which mimics ischemia, and before PTCA for control.

Conclusion

Personalized automatic detection of ECG electrode cable interchanges can reach almost the maximal accuracy of 100% in self-care, and can be performed in almost real time.

Simulating Arbitrary Electrode Reversals in Standard 12-lead ECG

Electrode reversal errors in standard 12-lead electrocardiograms (ECG) can produce significant ECG changes and, in turn, misleading diagnoses. Their detection is important but mostly limited to the design of criteria using ECG databases with simulated reversals, without Wilson's central terminal (WCT) potential change. This is, to the best of our knowledge, the first study that presents an algebraic transformation for simulation of all possible ECG cable reversals, including those with displaced WCT, where most of the leads appear with distorted morphology. The simulation model of ECG electrode swaps and the resultant WCT potential change is derived in the standard 12-lead ECG setup. The transformation formulas are theoretically compared to known limb lead reversals and experimentally proven for unknown limb-chest electrode swaps using a 12-lead ECG database from 25 healthy volunteers (recordings without electrode swaps and with 5 unicolor pairs swaps, including red (right arm-C1), yellow (left arm-C2), green (left leg (LL) -C3), black (right leg (RL)-C5), all unicolor pairs). Two applications of the transformation are shown to be feasible: 'Forward' (simulation of reordered leads from correct leads) and 'Inverse' (reconstruction of correct leads from an ECG recorded with known electrode reversals). Deficiencies are found only when the ground RL electrode is swapped as this case requires guessing the unknown RL electrode potential. We suggest assuming that potential to be equal to that of the LL electrode. The 'Forward' transformation is important for comprehensive training platforms of humans and machines to reliably recognize simulated electrode swaps using the available resources of correctly recorded ECG databases. The 'Inverse' transformation can save time and costs for repeated ECG recordings by reconstructing the correct lead set if a lead swap is detected after the end of the recording. In cases when the electrode reversal is unknown but a prior correct ECG recording of the same patient is available, the 'Inverse' transformation is tested to detect the exact swapping of the electrodes with an accuracy of (96% to 100%).