A new electrocardiographic concept: V1-V2-V3 are not only horizontal, but also frontal plane leads.

According to conventional teaching, the limb leads in the electrocardiogram (ECG) represent the frontal plane electrical vectors of the heart, whereas the chest leads signify the horizontal plane. The anterior chest leads V1-V2-V3, however, also have strong frontal plane representation which can result in morphological similarities in these leads to the augmented unipolar leads of the Einthoven triangle. This review highlights the significance of recognizing V1-V2-V3 as not only horizontal, but also as frontal plane leads. Appreciation of this phenomenon helps elucidate a colorful variety of clinically important but seemingly bizarre ECG manifestations that could not be explained otherwise.

A simple adaptation of a handheld ECG recorder to obtain chest lead equivalents.

Hand held ECG recorders are transforming the way we detect and diagnose heart rhythm disorders. The Kardia 6 L was launched in 2019 to detect and diagnose heart rhythm disorders recording a six lead (limb lead) ECG. Recording and analysis of precordial leads are currently not supported by the Kardia 6 L. In this study we aim to assess if reliable chest lead data can be obtained using a simple modification to the recording system.

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

INTRODUCTION: Electrode misplacement and interchange errors are known problems when recording the 12­lead 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.

Data driven feature selection and machine learning to detect misplaced V1 and V2 chest electrodes when recording the 12­lead electrocardiogram.

BACKGROUND: Electrocardiogram (ECG) lead misplacement can adversely affect ECG diagnosis and subsequent clinical decisions. V1 and V2 are commonly placed superior of their correct position. The aim of the current study was to use machine learning approaches to detect V1 and V2 lead misplacement to enhance ECG data quality. METHOD: ECGs for 453 patients, (normal n = 151, Left Ventricular Hypertrophy (LVH) n = 151, Myocardial Infarction n = 151) were extracted from body surface potential maps. These were used to extract both the correct and incorrectly placed V1 and V2 leads. The prevalence for correct and incorrect leads were 50%. Sixteen features were extracted in three different domains: time-based, statistical and time-frequency features using a wavelet transform. A hybrid feature selection approach was applied to select an optimal set of features. To ensure optimal model selection, five classifiers were used and compared. The aforementioned feature selection approach and classifiers were applied for V1 and V2 misplacement in three different positions: first, second and third intercostal spaces (ICS). RESULTS: The accuracy for V1 misplacement detection was 93.9%, 89.3%, 72.8% in the first, second and third ICS respectively. In V2, the accuracy was 93.6%, 86.6% and 68.1% in the first, second and third ICS respectively. There is a noticeable decline in accuracy when detecting misplacement in the third ICS which is expected.

Novel electrocardiographic criteria for the diagnosis of arrhythmogenic right ventricular cardiomyopathy.

AIMS: In order to improve the electrocardiographic (ECG) diagnosis of arrhythmogenic right ventricular cardiomyopathy (ARVC), we evaluated novel quantitative parameters of the QRS complex and the value of bipolar chest leads (CF leads) computed from the standard 12 leads. METHODS AND RESULTS: We analysed digital 12-lead ECGs in 44 patients with ARVC, 276 healthy subjects including 44 age and sex-matched with the patients and 36 genotyped members of ARVC families. The length and area of the terminal S wave in V1 to V3 were measured automatically using a common for all 12 leads QRS end. T wave negativity was assessed in V1 to V6 and in the bipolar CF leads computed from the standard 12 leads. The length and area of the terminal S wave were significantly shorter, whereas the S wave duration was significantly longer in ARVC patients compared with matched controls. Among members of ARVC families, those with mutations (n = 15) had shorter QRS length in V2 and V3 and smaller QRS area in lead V2 compared with those without mutations (n = 20). In ARVC patients, the CF leads were diagnostically superior to the standard unipolar precordial leads. Terminal S wave duration in V1 >48 ms or major T wave negativity in CF leads separated ARVC patients from matched controls with 90% sensitivity and 86% specificity. CONCLUSION: The terminal S wave length and area in the right precordial leads are diagnostically useful and suitable for automatic analysis in ARVC. The CF leads are diagnostically superior to the unipolar precordial leads.

The QRS morphology pattern in V5R is a novel and simple parameter for differentiating the origin of idiopathic outflow tract ventricular arrhythmias.

AIMS: There are many reports on the ECG characteristics of idiopathic outflow tract ventricular arrhythmias (OT-VAs) to predict their origin. However, differentiating near regions using 12-lead ECGs is still complicated. The synthesized 18-lead ECG derived from the 12-lead ECG can provide virtual waveforms of the right-sided chest leads (V3R, V4R, and V5R) and back leads (V7, V8, and V9). The aim of this study was to develop a simple and useful parameter for differentiating OT-VA origins using the 18-lead ECG. METHODS AND RESULTS: We studied 28 and 73 patients with idiopathic VAs in a pacemapping study and validation cohort, respectively. In the pacemapping study, several sites out of five different sites were paced in each patient: the anterior and posterior right ventricular OT (RVOT-ant and RVOT-post), right and left coronary cusps (RCC and LCC), and junction of both cusps (RLJ). The 18-lead ECGs during pacemapping among the five sites were compared for establishing a simple parameter to predict VA origins. A novel parameter using 18-lead ECGs was tested prospectively in 73 patients. In the pacemapping study, the dominant QRS morphology pattern in the synthesized V5R significantly differed among those sites (RVOT-ant:Rs, RVOT-post:rS, RCC:QS, RLJ:qR, and LCC:R). The patients in the validation cohort were divided into five groups depending on those QRS morphology patterns during VAs in the synthesized V5R. Each V5R QRS morphology pattern could predict a precise origin of the OT-VAs with an overall accuracy of 75%. CONCLUSION: The QRS morphology pattern in V5R was a simple and useful parameter for differentiating detailed OT-VA origins.

Electrocardiographic characteristics for the prediction of under-sensing in implantable loop recorders.

Background: Under-sensing (US) in implantable loop recorders (ILRs) interferes with the accurate diagnosis of arrhythmia, but there is little information available on the details of US of ILRs. The aim of this study was to clarify the frequency of US in patients with ILRs and to investigate the predictors of US in ILRs prior to implantation. Methods and Results: We studied 46 consecutive patients implanted ILR. During the mean follow-up period of 499 ± 363 days, 15 events of US were observed in five patients. There were no significant differences in patient characteristics between patients with and without US. In standard 12-lead electrocardiogram (ECG), QRS complex amplitude in anterolateral chest leads (V2 to V5) were significantly lower in patients with than without US (V2: 0.88 [0.66, 1.22] mV vs. 1.67 [1.23, 2.29] mV, p = .010 V3: 1.25 [1.00, 1.26] mV vs. 1.90 [1.41, 2.29] mV, p = .013; V4: 1.14 [0.96, 1.38] mV vs. 1.93 [1.65, 2.64] mV, p = .023; V5: 0.57 [0.50, 0.75] mV vs. 1.60 [1.20, 1.98] mV, p = .011, respectively). ROC curve analysis showed that cut-off values of 1.30 mV of QRS complex amplitude in V2, 1.26 mV of that in V3, and 0.75 mV of that in V5 had moderate accuracy for predicting US (V2: sensitivity 68%, specificity 100%, area under the curve [AUC] 0.86; V3: sensitivity 85%, specificity 80%, AUC 0.85; V5: sensitivity 98%, specificity 80%, AUC 0.85, respectively). Conclusions: US was observed in 10.9% patients with an ILR. QRS complex amplitude in anterolateral chest leads (V2 to V5) on ECG might be useful for predicting US in patients with ILRs.

Portable out-of-hospital electrocardiography: A review of current technologies.

Background: Availability of portable and home-based electrocardiography (ECG) is an important medical innovation, which has a potential to transform medical care. We performed this review to understand the current state of out-of-hospital portable ECG technologies with respect to their scope, ease of use, data transmission capabilities, and diagnostic accuracy. Methods: We conducted PubMed and Internet searches for "handheld" or "wearable" or "patch" electrocardiography devices to enlist available technologies. We also searched PubMed with names of individual devices to obtain additional citations. We classified available devices as a "single limb lead ECG recording devices" and chest-lead "ECG recording devices." If a device used more than three electrodes, it was defined as a conventional electrocardiography or Holter machine and was excluded from this review. Results: We identified a total of 15 devices. Overall, only six of these devices (five single lead and one chest lead) featured in published medical literature as identified from PubMed search. A total of 13 citations were available for the single limb lead ECG recording devices and 6 citations for the chest-lead ECG recording devices. Conclusions: Despite the increase in number of such devices, published biomedical literature regarding their diagnostic accuracy, reproducibility, or utility is scant.