Search

In this commentary paper, we discuss the use of the electrocardiogram to help clinicians make diagnostic and patient referral decisions in acute care settings. The paper discusses the factors that are likely to contribute to the variability and noise in the clinical decision making process for catheterization lab activation. These factors include the variable competence in reading ECGs, the intra/inter rater reliability, the lack of standard ECG training, the various ECG machine and filter settings, cognitive biases (such as automation bias which is the tendency to agree with the computer-aided diagnosis or AI diagnosis), the order of the information being received, tiredness or decision fatigue as well as ECG artefacts such as the signal noise or lead misplacement. We also discuss potential research questions and tools that could be used to mitigate this 'noise' and improve the quality of ECG based decision making.

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.

INTRODUCTION: Interpretation of the 12‑lead Electrocardiogram (ECG) is normally assisted with an automated diagnosis (AD), which can facilitate an 'automation bias' where interpreters can be anchored. In this paper, we studied, 1) the effect of an incorrect AD on interpretation accuracy and interpreter confidence (a proxy for uncertainty), and 2) whether confidence and other interpreter features can predict interpretation accuracy using machine learning. METHODS: This study analysed 9000 ECG interpretations from cardiology and non-cardiology fellows (CFs and non-CFs). One third of the ECGs involved no ADs, one third with ADs (half as incorrect) and one third had multiple ADs. Interpretations were scored and interpreter confidence was recorded for each interpretation and subsequently standardised using sigma scaling. Spearman coefficients were used for correlation analysis and C5.0 decision trees were used for predicting interpretation accuracy using basic interpreter features such as confidence, age, experience and designation. RESULTS: Interpretation accuracies achieved by CFs and non-CFs dropped by 43.20% and 58.95% respectively when an incorrect AD was presented (p

Background The 12-lead Electrocardiogram (ECG) has been used to detect cardiac abnormalities in the same format for more than 70 years. However, due to the complex nature of 12-lead ECG interpretation, there is a significant cognitive workload required from the interpreter. This complexity in ECG interpretation often leads to errors in diagnosis and subsequent treatment. We have previously reported on the development of an ECG interpretation support system designed to augment the human interpretation process. This computerised decision support system has been named ‘Interactive Progressive based Interpretation’ (IPI). In this study, a decision support algorithm was built into the IPI system to suggest potential diagnoses based on the interpreter's annotations of the 12-lead ECG. We hypothesise semi-automatic interpretation using a digital assistant can be an optimal man-machine model for ECG interpretation. Objectives To improve interpretation accuracy and reduce missed co-abnormalities. Methods The Differential Diagnoses Algorithm (DDA) was developed using web technologies where diagnostic ECG criteria are defined in an open storage format, Javascript Object Notation (JSON), which is queried using a rule-based reasoning algorithm to suggest diagnoses. To test our hypothesis, a counterbalanced trial was designed where subjects interpreted ECGs using the conventional approach and using the IPI + DDA approach. Results A total of 375 interpretations were collected. The IPI + DDA approach was shown to improve diagnostic accuracy by 8.7% (although not statistically significant, p-value = 0.1852), the IPI + DDA suggested the correct interpretation more often than the human interpreter in 7/10 cases (varying statistical significance). Human interpretation accuracy increased to 70% when seven suggestions were generated. Conclusion Although results were not found to be statistically significant, we found; 1) our decision support tool increased the number of correct interpretations, 2) the DDA algorithm suggested the correct interpretation more often than humans, and 3) as many as 7 computerised diagnostic suggestions augmented human decision making in ECG interpretation. Statistical significance may be achieved by expanding sample size.

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.

Computerised electrocardiogram (ECG) interpretation diagnostic algorithms have been developed to guide clinical decisions like with ST segment elevation myocardial infarction (STEMI) where time in decision making is critical. These computer-generated diagnoses have been proven to strongly influence the final ECG diagnosis by the clinician; often called automation bias. However, the computerised diagnosis may be inaccurate and could result in a wrong or delayed treatment harm to the patient. We hypothesise that an algorithmic certainty index alongside a computer-generated diagnosis might mitigate automation bias. The impact of reporting a certainty index on the final diagnosis is not known.To ascertain whether knowledge of the computer-generated ECG algorithmic certainty index influences operator diagnostic accuracy.Clinicians who regularly analyse ECGs such as cardiology or acute care doctors, cardiac nurses and ambulance staff were invited to complete an online anonymous survey between March and April 2019. The survey had 36 ECGs with a clinical vignette of a typical chest pain and which were either a STEMI, normal, or borderline (but do not fit the STEMI criteria) along with an artificially created certainty index that was either high, medium, low or none. Participants were asked whether the ECG showed a STEMI and their confidence in the diagnosis. The primary outcomes were whether a computer-generated certainty index influenced interpreter's diagnostic decisions and improved their diagnostic accuracy. Secondary outcomes were influence of certainty index between different types of clinicians and influence of certainty index on user's own-diagnostic confidence.A total of 91 participants undertook the survey and submitted 3262 ECG interpretations of which 75% of ECG interpretations were correct. Presence of a certainty index significantly increased the odds ratio of a correct ECG interpretation (OR 1.063, 95% CI 1.022-1.106, p = 0.004) but there was no significant difference between correct certainty index and incorrect certainty index (OR 1.028, 95% CI 0.923-1.145, p = 0.615). There was a trend for low certainty index to increase odds ratio compared to no certainty index (OR 1.153, 95% CI 0.898-1.482, p = 0.264) but a high certainty index significantly decreased the odds ratio of a correct ECG interpretation (OR 0.492, 95% CI 0.391-0.619, p 0.001). There was no impact of presence of a certainty index (p = 0.528) or correct certainty index (p = 0.812) on interpreters' confidence in their ECG interpretation.Our results show that the presence of an ECG certainty index improves the users ECG interpretation accuracy. This effect is not seen with differing levels of confidence within a certainty index, with reduced ECG interpretation success with a high certainty index compared with a trend for increased success with a low certainty index. This suggests that a certainty index improves interpretation when there is an increased element of doubt, possibly forcing the ECG user to spend more time and effort analysing the ECG. Further research is needed looking at time spent analysing differing certainty indices with alternate ECG diagnoses.

Background Acute Coronary Syndrome (ACS) is currently diagnosed using a 12‑lead Electrocardiogram (ECG). Our recent work however has shown that interpretation of the 12‑lead ECG is complex and that clinicians can be sub-optimal in their interpretation. Additionally, ECG does not always identify acute total occlusions in certain patients. Purpose The aim of the present study was to undertake an exploratory analysis to compare protein expression profiles of ACS patients that may in the future augment ECG diagnosis. Methods Patients were recruited consecutively at the cardiac catheterization laboratory at Altnagelvin Hospital over a period of 6 months. A low risk control group was recruited by advertisement. Blood samples were analysed using the multiplex proximity extension assays by OLINK proteomics. Support vector machine (SVM) learning was used as a classifier to distinguish between patient groups on training data. The ST segment elevation level was extracted from each ECG for a subset of patients and combined with the protein markers. Quadratic SVM (QSVM) learning was then used as a classifier to distinguish STEMI from NSTEMI on training and test data. Results Of the 344 participants recruited, 77 were initially diagnosed with NSTEMI, 7 with STEMI, and 81 were low risk controls. The other participants were those diagnosed with angina (stable and unstable) or non-cardiac patients. Of the 368 proteins analysed, 20 proteins together could significantly differentiate between patients with ACS and patients with stable angina (ROC-AUC = 0.96). Six proteins discriminated significantly between the stable angina and the low risk control groups (ROC-AUC = 1.0). Additionally, 16 proteins together perfectly discriminated between the STEMI and NSTEMI patients (ROC-AUC = 1). ECG comparisons with the protein biomarker data for a subset of patients (STEMI n = 6 and NSTEMI n = 6), demonstrated that 21 features (20 proteins + ST elevation) resulted in the highest classification accuracy 91.7% (ROC-AUC = 0.94). The 20 proteins without the ST elevation feature gave an accuracy of 80.6% (ROC-AUC 0.91), while the ST elevation feature without the protein biomarkers resulted in an accuracy of 69.3% (ROC-AUC = 0.81). Conclusions This preliminary study identifies panels of proteins that should be further explored in prospective studies as potential biomarkers that may augment ECG diagnosis and stratification of ACS. This work also highlights the importance for future studies to be designed to allow a comparison of blood biomarkers not only with ECG's but also with cardio angiograms.

The CardioQuick Patch® (CQP) has been developed to assist operators in accurately positioning precordial electrodes during 12-lead electrocardiogram (ECG) acquisition. This study describes the CQP design and assesses the device in comparison to conventional electrode application.Twenty ECG technicians were recruited and a total of 60 ECG acquisitions were performed on the same patient model over four phases: (1) all participants applied single electrodes to the patient; (2) all participants were then re-trained on electrode placement and on how to use the CQP; (3) participants were randomly divided into two groups, the standard group applied single electrodes and the CQP group used the CQP; (4) after a one day interval, the same participants returned to carry out the same procedure on the same patient (measuring intra-practitioner variability). Accuracy was measured with reference to pre-marked correct locations using ultra violet ink. NASA-TLK was used to measure cognitive workload and the Systematic Usability Scale (SUS) was used to quantify the usability of the CQP.There was a large difference between the minimum time taken to complete each approach (CQP=38.58s vs. 65.96s). The standard group exhibited significant levels of electrode placement error (V1=25.35mm±29.33, V2=18.1mm±24.49, V3=38.65mm±15.57, V4=37.73mm±12.14, V5=35.75mm±15.61, V6=44.15mm±14.32). The CQP group had statistically greater accuracy when placing five of the six electrodes (V1=6.68mm±8.53 [p0.001], V2=8.8mm±9.64 [p=0.122], V3=6.83mm±8.99 [p0.001], V4=14.90mm±11.76 [p0.001], V5=8.63mm±10.70 [p0.001], V6=18.13mm±14.37 [p0.001]). There was less intra-practitioner variability when using the CQP on the same patient model. NASA TLX revealed that the CQP did increase the cognitive workload (CQP group=16.51%±8.11 vs. 12.22%±8.07 [p=0.251]). The CQP also achieved a high SUS score of 91±7.28.The CQP significantly improved the reproducibility and accuracy of placing precordial electrodes V1, V3-V6 with little additional cognitive effort, and with a high degree of usability.

This study investigates the use of multivariate linear regression to estimate three bipolar ECG leads from the 12-lead ECG in order to improve P-wave signal strength. The study population consisted of body surface potential maps recorded from 229 healthy subjects. P-waves were then isolated and population based transformation weights developed. A derived P-lead (measured between the right sternoclavicular joint and midway along the costal margin in line with the seventh intercostal space) demonstrated significant improvement in median P-wave root mean square (RMS) signal strength when compared to lead II (94 μV vs. 76 μV, p < 0.001). A derived ES lead (from the EASI lead system) also showed small but significant improvement in median P-wave RMS (79 μV vs. 76 μV, p = 0.0054). Finally, a derived modified Lewis lead did not improve median P-wave RMS when compared to lead II. However, this derived lead improved atrioventricular RMS ratio. P-wave leads derived from the 12-lead ECG can improve signal-to-noise ratio of the P-wave; this may improve the performance of detection algorithms that rely on P-wave analysis.

Introduction: The electrocardiogram (ECG) is often interpreted incorrectly with up to 33% of interpretations containing a significant error. The difficulty in ECG interpretation is two-fold; 1) it demands an extensive knowledge of cardiac physiology, and 2) the ECG inflates cognitive workload due to the complex nature of its presentation. To make a diagnosis, the reader is required to measure ECG features in order to contrast these annotations with diagnostic criteria. Whilst signal processing algorithms can provide automated measurements, they are often imprecise. Based on these observations, a web-based system was developed to allow the interpreter to measure and input their own ECG annotations. These annotations are then processed by a rule-based algorithm, which presents a set of suggested diagnoses.However, imprecise and inconsistent human annotations would affect both the reader's diagnostic decision making and also the accuracy of the diagnoses suggested by the algorithm (junk in = junk out). Our study measures the variability of manual annotations collected using our web system. Clinical physiology students (n = 10) and medical practitioners (n = 11) participated in our study.Results: Annotations for the same ECG are as follows (all participants = A, students = S, medical practitioners = P):Heart Rate (A[mean = 89.39 bpm, SD = 9.95], S[mean = 88.7 bpm, SD = 4.27], P[mean = 91.4 bpm, SD = 14.68], p = 0.73), P-wave duration (A[mean = 0.08 s, SD = 0.02], S[mean = 0.09 s, SD = 0.03], P[mean = 0.08 s, SD = 0.01], p = 0.39), P-wave amplitude (A[mean = 0.18 mv, SD = 0.04], S[mean = 0.19 mv, SD = 0.05], P[mean = 0.18 mv, SD = 0.3], p = 0.38), P-R interval (A[mean = 0.16 s, SD = 0.04], S[mean = 0.18 s, SD = 0.05], P[mean = 0.16 s, SD = 0.03], p = 0.49), cardiac axis (A[mean = 58.11°, SD = 13.23], S[mean = 60°, SD = 0], P[mean = 51.5°, SD = 18.8], p = 0.46), Q-T interval (A[mean = 0.32 s, SD = 0.14], S[mean = 0.41 s, SD = 0.06], P[mean = 0.24 s, SD = 0.17], p < 0.01), R-R interval (A[mean = 0.63 s, SD = 0.21], S[mean = 0.72 s, SD = 0.13], P[mean = 0.53 s, SD = 0.27], p = 0.06) and QTc (A[mean = 0.4 s, SD = 0.15], S[mean = 0.48 s, SD = 0.09], P[mean = 0.33 s, SD = 0.2], p = 0.02).Discussion: Students annotated more features (5/8) with less variance. Students annotate interval measurements with 47% less variation than medical practitioners (Σ interval measurement; students SD = 0.36, practitioners SD = 0.68). Students also had less variation in measuring heart rate, P-wave amplitude and cardiac axis. Two of the annotated features (QT-interval and QTc) from both cohorts were statistically different (p ≤ 0.05).Conclusion: In order to make an accurate diagnosis precise ECG annotations are required. This study determined the variability of manual ECG annotations on a cohort containing both students and practitioners.

The 12-lead electrocardiogram (ECG) is a crucial diagnostic tool. However, the ideal method to assess competency in ECG interpretation remains unclear. We sought to evaluate whether keypad response technology provides a rapid, interactive way to assess ECG knowledge. 75 participants were enrolled [32 (43%) Primary Care Physicians, 24 (32%) Hospital Medical Staff and 19 (25%) Nurse Practitioners]. Nineteen ECGs with 4 possible answers were interpreted. Out of 1425 possible decisions 1054 (73.9%) responses were made. Only 570/1425 (40%) of the responses were correct. Diagnostic accuracy varied (0% to 78%, mean 42% ± 21%) across the entire cohort. Participation was high, (median 83%, IQR 50%–100%). Hospital Medical Staff had significantly higher diagnostic accuracy than nurse practitioners (50 ± 20% vs. 38 ± 19%, p = 0.04) and Primary Care Physicians (50 ± 20% vs. 40 ± 21%, p = 0.07) although not significant. Interactive voting systems can be rapidly and successfully used to assess ECG interpretation. Further education is necessary to improve diagnostic accuracy.

Introduction:This study analysed the utility of eye tracking technology for gaining insight into the decision making processes of nurses during their interpretation of patient scenarios and vital signs.Methods:Five patient monitoring scenarios (vignette, vital signs [ECG, BP etc.] and scoring criteria) were designed and validated by critical care experts. Participants were asked to interpret these scenarios whilst ‘thinking aloud’. Visual attention was measured using infrared light- based eye-tracking technology. Each interpretation was scored out of 10. Subjects comprised of students (n=36) and qualified nurses (n=11). Scores and self-rated confidence (where 1=low, 10=high) are presented using mean±SD. Significance testing was performed using a t-test and ANOVA where appropriate (α = 0.05). Multivariate regression was performed to determine if a machine could use eye gaze features to accurately predict competency (dependent variable=score). Independent eye gaze only variables were used in the regression models if they statistically significantly (p