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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.
Hypertension is a silent killer and one-third of its sufferers are unaware of its presence. Tonometric devices, like SphygmoCor, Compilor etc., represent the gold standard in pulse wave velocity (PWV) and augmentation index (AIx) measurements which are limited by their high cost and operational accuracy. Here, we present an alternative technology that is low cost and may be suitable for the ‘wearable’ setting. We undertook the comparisons of arterial waveforms obtained by photoplethysmogram (PPG) and finger ballistocardiogram (BPP) sensors which were then validated against a SphygmoCor tonometric device. Specifically, the agreement analysis of the augmentation, stiffness, reflection, elasticity, ejection elasticity and dicrotic reflection indexes showed that arterial distension waveform sensing using BPP sensor, has precision and accuracy similar to that of a SphygmoCor tonometric device whilst outperforming the volumetric arterial flow sensing using a PPG sensor, in every index. BPP indexes showed the r2 fit of up to 0.95 and Spearman’s rank correlation up to 0.91 when validated against the SphygmoCor tonometer. The estimated individual transfer functions for the BPP sensor, with reference to SphygmoCor, have accuracies of above 85% and 98% for 2 and 4-element windkessel (WK) models, respectively. The findings reported in this work may also be useful for the development of systems that are beneficial in the early and/or routine detection of hypertension.
The magnitude of the spatial ventricular gradient (MSVG) is an attractive parameter in electrocardiogram (ECG)monitoring applications. The MSVG is most commonlyobtained from150 Hz low-pass filtered resting ECGs. However, monitoring applications typically utilize 40 Hz low-pass filtered ECG data. The extend to which the value of the MSVG is affected by the utilization of 40 Hz low-pass monitoring ECG filters over the commonly used 150 Hz low-pass resting ECG filters has not previously been reported. The aim of this research was to quantify the differences between MSVG values computed using 40 Hz low-pass filtered ECG data (MSVG40) and 150 Hz low-pass filtered ECG data (MSVG150). The differences between the MSVG40 and the MSVG150 were quantified as systematic error (mean difference) and random error (span of Bland-Altman 95% limits of agreement) using a study population of 726 subjects. The systematic error was found to be 0.013 mV ms [95% confidence interval: 0.008 mV ms to 0.018 mV ms]. The random error was quantified as 0.282 mV ms [95% confidence interval: 0.266 mV ms to 0.298 mV ms]. Our findings suggest that it is possible to record accurate MSVG values using 40 Hz low-pass filtered ECG data.
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.
We perform a novel comparative analysis between optically and mechanically derived pulse transit time (PTT) that is universally employed technique for cuffless blood pressure (BP) estimation. For data collection two inline photoplethysmogram (PPG) sensors were mounted at the distal and proximal phalanxes of the index finger of each subject and top each PPG sensor fixture a finger ballistocardiogram (BPP) sensors were clamped. The clamped stacking of the BPP sensors over the PPG sensors provided vertical aligned acquisition of the blood flow waveform through the radial artery for both sensors. The analysis of variance (ANOVA) between PTT derived from the BPP and PPG sensors resulted in a statistically significant difference at p<0.05. The PTT derived from the BPP sensors showed higher values, 17.8 milliseconds on average, than the PTT derived from the PPG sensors. Higher accuracy PTT values will improve the estimation of cuffless BP and thus has the potential to revolutionize the technology.
Background: We have previously reported on the potential of patch-based ECG leads to observe changes typical during ischaemia. In this study we aim to assess the utility of patch-based leads in the detection of these changes.
Method: Body surface potential maps (BSPM) from subjects (n=45) undergoing elective percutaneous coronary angioplasty (PTCA) were used. The short spaced lead (SSL), that was previously identified as having the greatest ST-segment change between baseline and peak balloon inflation (PBI), was selected as the basis for a patch based lead system. A feature set of J-point amplitudes for all bipolar leads available within the same 100 mm region were included (n=6). Current 12-lead ECG criteria were applied to 12-lead ECGs for the same subjects to benchmark performance.
Results: The previously identified single SSL achieved sensitivity and specificity of 87% and 71% respectively using a Naive Bayes classifier. Adding other combinations of leads to this did not improve performance significantly. The 12-lead ECG performance was 62/93% (sensitivity/specificity).
Conclusion: This study suggests that short spaced leads can be sensitive to ischaemic ECG changes. However, due to the short distance between leads, they lack the specificity of the 12-lead ECG.