scholarly journals Deep learning consistently detects misplaced chest electrodes when recording the electrocardiogram: An error that is commonly undetected by physicians (Preprint)

2020 ◽  
Author(s):  
Khaled Rjoob ◽  
Raymond Bond ◽  
Dewar Finlay ◽  
Victoria McGilligan ◽  
Stephen J Leslie ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Deep Learning ◽  
Left Ventricular ◽  
Intercostal Space ◽  
Influential Factor ◽  
Body Surface Potential Maps ◽  
Erroneous Diagnosis ◽  
Body Surface Potential ◽  
Using Data ◽  
Electrocardiogram Ecg

BACKGROUND A 12-lead electrocardiogram (ECG) is the most common method to diagnose cardiovascular diseases such as acute myocardial infarction. However, there are a number of misinterpretations of the ECG caused by several different factors. One influential factor can take place during ECG acquisition where chest electrodes are misplaced. OBJECTIVE This research is the first experiment to build advanced algorithms to detect precordial (chest) electrode misplacement. METHODS in this article we used traditional machine learning (ML) and deep learning (DL) to auto-detect the misplacement of electrodes V1 and V2 using features from the resultant ECG. The algorithms were trained using data extracted from high resolution body surface potential maps consisting of patients who were diagnosed with myocardial infarction, left ventricular hypertrophy or normal. RESULTS DL achieved the highest accuracy in this study for detecting V1 and V2 electrode misplacement with an accuracy of 93.0% [95%CI=91.46,94.53] for misplacement in the second intercostal space. DL performance in the second intercostal space was benchmarked with physicians (n=11 and age=47.3±15.5) who are experienced in reading ECGs (mean number of ECGs read in the past year = 436.54±397.9). Physicians were poor at recognising chest electrode misplacement on the ECG and achieved a mean accuracy of 60% [95%CI=56.09,63.90] which was significantly poorer when compared to DL (P<.001). CONCLUSIONS DL provides the best performance for detecting chest electrode misplacement when compared to the ability of experienced physicians. Clinical Impact: DL and ML could be used to help flag ECGs that have been incorrectly recorded and that the data maybe be flawed, which could reduce an erroneous diagnosis.

scholarly journals Reliable Deep Learning–Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm Development and Validation

10.2196/25347 ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. e25347
Author(s):  
Khaled Rjoob ◽  
Raymond Bond ◽  
Dewar Finlay ◽  
Victoria McGilligan ◽  
Stephen J Leslie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ventricular Hypertrophy ◽  
Left Ventricular ◽  
Intercostal Space ◽  
The Past ◽  
Body Surface Potential Maps ◽  
Body Surface Potential ◽  
Using Data ◽  
Development And Validation ◽  
Electrocardiogram Ecg

Background A 12-lead electrocardiogram (ECG) is the most commonly used method to diagnose patients with cardiovascular diseases. However, there are a number of possible misinterpretations of the ECG that can be caused by several different factors, such as the misplacement of chest electrodes. Objective The aim of this study is to build advanced algorithms to detect precordial (chest) electrode misplacement. Methods In this study, we used traditional machine learning (ML) and deep learning (DL) to autodetect the misplacement of electrodes V1 and V2 using features from the resultant ECG. The algorithms were trained using data extracted from high-resolution body surface potential maps of patients who were diagnosed with myocardial infarction, diagnosed with left ventricular hypertrophy, or a normal ECG. Results DL achieved the highest accuracy in this study for detecting V1 and V2 electrode misplacement, with an accuracy of 93.0% (95% CI 91.46-94.53) for misplacement in the second intercostal space. The performance of DL in the second intercostal space was benchmarked with physicians (n=11 and age 47.3 years, SD 15.5) who were experienced in reading ECGs (mean number of ECGs read in the past year 436.54, SD 397.9). Physicians were poor at recognizing chest electrode misplacement on the ECG and achieved a mean accuracy of 60% (95% CI 56.09-63.90), which was significantly poorer than that of DL (P<.001). Conclusions DL provides the best performance for detecting chest electrode misplacement when compared with the ability of experienced physicians. DL and ML could be used to help flag ECGs that have been incorrectly recorded and flag that the data may be flawed, which could reduce the number of erroneous diagnoses.

scholarly journals The Opposite Polarity of the PQ Segment Compared to the P Wave Isointegral Maps

2011 ◽  
pp. 777-784
Author(s):  
K. KOZLÍKOVÁ ◽  
J. MARTINKA ◽  
J. MURÍN ◽  
J. BULAS
Keyword(s):  
Myocardial Infarction ◽  
Body Surface ◽  
Ventricular Hypertrophy ◽  
Opposite Polarity ◽  
Left Ventricular ◽  
P Wave ◽  
Healthy Controls ◽  
Body Surface Potential Maps ◽  
Body Surface Potential ◽  
The Right

The aim of our work was to study the opposite polarity of the PQ segment to the P wave body surface potential maps in different groups of patients. We constructed isointegral maps (IIM) in 26 healthy controls (C), 16 hypertensives (HT), 26 patients with arterial hypertension and left ventricular hypertrophy (LVH) and 15 patients with myocardial infarction (MI). We analyzed values and positions of map extrema and compared the polarity of maps using the correlation coefficient. The IIM P maxima appeared mainly over the precordium, the minima mainly in the right subclavicular area. The highest maxima were in the MI group, being significantly higher than in the HT and LVH groups. No differences concerning any values of other extrema were significant. The IIM PQ maxima were distributed over the upper half of the chest; the minima mainly over the middle sternum. A statistically significant opposite polarity between the IIM P and IIM PQ was found in 80 % of cases. The opposite polarity of the P wave and the PQ segment was proved in isointegral body surface maps. The extrema occurred in areas not examined by the standard chest leads. This has to be considered for diagnostic purposes.

Discriminant function analysis of body surface potential maps in acute myocardial infarction

1994 ◽  
Vol 27 ◽  
pp. 117-120 ◽  
Author(s):  
S.R. McMechan ◽  
C.M. Cullen ◽  
G. MacKenzie ◽  
G.J. Dempsey ◽  
G.T. Wright ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Surface Potential ◽  
Discriminant Function ◽  
Body Surface ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Body Surface Potential Maps ◽  
Body Surface Potential

Body surface potential maps in old inferior myocardial infarction. Assessment of diagnostic criteria

1986 ◽  
Vol 19 (3) ◽  
pp. 225-234 ◽  
Author(s):  
Luigi De Ambroggi ◽  
Tito Bertoni ◽  
Claudia Rabbia ◽  
Maurizio Landolina
Keyword(s):  
Myocardial Infarction ◽  
Diagnostic Criteria ◽  
Surface Potential ◽  
Body Surface ◽  
Inferior Myocardial Infarction ◽  
Body Surface Potential Maps ◽  
Body Surface Potential

Persistent ST-segment elevation following anterior myocardial infarction: Assessment of the role of ischemia by body-surface potential MAPS. (BSPM)

1978 ◽  
Vol 41 (2) ◽  
pp. 428
Author(s):  
B.Milan Horacek ◽  
Henry Gewirtz ◽  
Eldon R. Smith ◽  
Hermann K. Wolf ◽  
Pentti M. Rautaharju
Keyword(s):  
Myocardial Infarction ◽  
Surface Potential ◽  
Body Surface ◽  
Anterior Myocardial Infarction ◽  
St Segment Elevation ◽  
St Segment ◽  
Body Surface Potential Maps ◽  
Body Surface Potential

Selection of optimal electrocardiographic leads from body surface potential maps for the diagnosis of left ventricular hypertrophy

1989 ◽  
Vol 22 (3) ◽  
pp. 269 ◽  
Author(s):  
Fred Kornreich ◽  
Michaïl Kavadias ◽  
Terrence J. Montague ◽  
Pentti M. Rautaharju ◽  
Milan B. Horacek
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Surface Potential ◽  
Body Surface ◽  
Ventricular Hypertrophy ◽  
Left Ventricular ◽  
Body Surface Potential Maps ◽  
Body Surface Potential ◽  
Electrocardiographic Leads ◽  
Selection Of

scholarly journals STUDY OF RIGHT VENTRICULAR INFARCTION IN INFERIOR WALL MYOCARDIAL INFARCTION AT MEDICINE DEPARTMENT OF GMC, BETTIAH BIHAR

2020 ◽  
pp. 1-2
Author(s):  
Mahendra Kumar ◽  
Dharmendra Prasad ◽  
Parshuram Yugal ◽  
Debarshi Jana
Keyword(s):  
Myocardial Infarction ◽  
Right Ventricle ◽  
Right Ventricular ◽  
Left Ventricular ◽  
Inferior Wall ◽  
Medicine Department ◽  
Right Ventricular Infarction ◽  
St Elevation ◽  
Ventricular Infarction ◽  
Electrocardiogram Ecg

Background: Right ventricular infarction (RVI) is frequently associated with inferior wall myocardial infarction (MI). Methods: This study was designed to identify the burden of RVI in patientspresenting with inferior wall MI (n=50) byright precordial electrocardiogram (ECG) and comparing it with echocardiography (ECHO). Results: Their mean age was (54.5 ± 11.9 years); there were 42 males. ST elevation of greater than 1 mm in rightprecordial leads (RPL) suggestive of RVI was evident in 16 (32%) cases. Among the RPL (V3R - V6R) V4R and V5Rshowed sensitivity of 87.5%. The 12-lead ECG finding of ST-elevation greater than 1 mm in lead III and lead III/IIgreater than 1, had poor sensitivity (75%), specificity (88.2%) compared to ST- elevation of greater than 1 mm in any ofthe RPL (100%). Both the echocardiography criteria, namely right ventricular end-diastolic dimension (RVEDD) greaterthan 25 mm (92.3%) and the ratio of RVEDD to left ventricular end-diastolic dimension (RVEDD/LVEDD) greaterthan 0.7 (90%) indicating right ventricle (RV) dilatation was observed significantly more frequently in RVI group. Conclusions: RVI occurs in more than one-third of patients with acute inferior wall MI. All the patients with inferior wallMI should have RPL recorded as early as possible for evidence of RVI, of which V4R, V5R have the highest sensitivity.

scholarly journals Data Driven Computer Simulation to Analyse an ECG Limb Lead System Used in Connected Health Environments

2016 ◽  
Vol 55 (03) ◽  
pp. 258-265 ◽  
Author(s):  
Dewar Finlay ◽  
Daniel Guldenring ◽  
Cathal Breen ◽  
Raymond Bond
Keyword(s):  
Computer Simulation ◽  
Lead System ◽  
Ecg Signals ◽  
Connected Health ◽  
Body Surface Potential Maps ◽  
Body Surface Potential ◽  
The Difference ◽  
Electrocardiogram Ecg ◽  
Monitoring Devices ◽  
Computerised Analysis

SummaryBackground: Recently under the Connected Health initiative, researchers and small-medium engineering companies have developed Electrocardiogram (ECG) monitoring devices that incorporate non-standard limb electrode positions, which we have named the Central Einthoven (CE) configuration.Objectives: The main objective of this study is to compare ECG signals recorded from the CE configuration with those recorded from the recommended Mason-Likar (ML) configuration.Methods: This study involved extracting two different sets of ECG limb leads from each patient to compare the difference in the signals. This was done using computer simulation that is driven by body surface potential maps. This simulator was developed to facilitate this experiment but it can also be used to test similar hypotheses. This study included, (a) 176 ECGs derived using the ML electrode positions and (b) the 176 corresponding ECGs derived using the CE electrode positions. The signals from these ECGs were compared using root mean square error (RMSE), Pearson product-moment correlation coefficient (r) and similarity coefficient (SC). We also investigated whether the CE configuration influences the calculated mean cardiac axis. The top 10 cases where the ECGs were significantly different between the two configurations were visually compared by an ECG interpreter.Results: We found that the leads aVL, III and aVF are most affected when using the CE configuration. The absolute mean difference between the QRS axes from both configurations was 28° (SD = 37°). In addition, we found that in 82% of the QRS axes calculated from the CE configuration was more rightward in comparison to the QRS axes derived from the ML configuration. Also, we found that there is an 18% chance that a misleading axis will be located in the inferior right quadrant when using the CE approach. Thus, the CE configuration can emulate right axis deviation. The clinician visually identified 6 out of 10 cases where the CE based ECG yielded clinical differences that could result in false positives.Conclusions: The CE configuration will not yield the same diagnostic accuracy for diagnosing pathologies that rely on current amplitude criteria. Conversely, rhythm lead II was not significantly affected, which supports the use of the CE approach for assessing cardiac rhythm only. Any computerised analysis of the CE based ECG will need to take these findings into consideration.

Sign in / Sign up

Export Citation Format

Share Document