Human factors analysis of the CardioQuick Patch®: A novel engineering solution to the problem of electrode misplacement during 12-lead electrocardiogram acquisition

Modified precordial lead ECG SafOne on electrocardiography recordings

Adaptability in precordial lead placement is one of the sources of electrocardiography inaccuracy. The present experimental study aimed to investigate the modified precordial lead ECG SafOne on electrocardiography recordings. Fourteen subjects were selected using purposive sampling. All the artefacts that emerged in the ECG recording results from the subjects using both the modified precordial lead ECG SafOne and precordial lead standard ECG were measured and identified. Data were analysed using a t test to examine the difference in the artefacts from all ECG recordings. The electrocardiography recordings of males aged 21–25 years using modified precordial lead ECG SafOne showed that out of 168 precordial leads from 14 subjects, two indicated artefact images in lead II (1.19%) and three in lead III (1.79%). The statistical test showed no significant difference in terms of artefacts that emerged in the electrocardiography recording results from both standard ECG and modified precordial lead ECG SafOne (p = 0.096). The modified precordial lead ECG SafOne showed no significant effect on ECG recordings related to artefacts. Additionally, the precordial lead ECG SafOne had no substantial difference in the presence of artefacts compared to the standard ECG. Therefore, ECG SafOne was usable as an ECG precordial lead for electrocardiography recording.

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

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.

A PHONOCARDIOGRAM-BASED NOISE-ROBUST REAL-TIME HEART RATE MONITORING ALGORITHM FOR OUTPATIENTS DURING NORMAL ACTIVITIES

For outpatients who need continuous monitoring of heart rate (HR) variation, it is important that HR can be monitored during normal activities such as speaking and walking. In this study, a noise-robust real-time HR monitoring algorithm based on phonocardiogram (PCG) signals is proposed. PCG signals were recorded using an electronic stethoscope; electrocardiogram (ECG) signals were recorded simultaneously with HR references. The proposed algorithm consisted of pre-processing, peak/nonpeak classification, voice noise processing, walking noise processing, and HR calculation. The performance of the algorithm was evaluated using PCG/ECG signals from 11 healthy participants. For comparison, the absolute errors between manually extracted ECG-based HR values and automatically calculated PCG-based HR values were calculated for the proposed algorithm and the comparison algorithm in two different test protocols. Experimental results showed that the average absolute errors of the proposed algorithm were 72.03%, 22.92%, and 36.39% of the values of the comparison algorithm for resting-state, speaking-state, and walking-state data, respectively, in protocol-1. In protocol-2, the average absolute error was 36.99% of that of the comparison algorithm. A total of 1102 cases in protocol-1 and 783 in protocol-2 had an absolute error [Formula: see text] beats per minute (BPM) using the comparison algorithm and an absolute error [Formula: see text] BPM using the proposed algorithm. On the basis of these results, we anticipate that the proposed algorithm can potentially improve the performance of continuous real-time HR monitoring during activities of normal life, thereby improving the safety of outpatients with cardiovascular diseases.

[Electrocardiograms: derived derivations!]

Simple and non-invasive, the electrocardiogram is a basic examination for studying how the cardiac muscle functions. Very commonly used, it nonetheless requires great rigour when applying the electrodes to avoid false results that can be harmful to appropriate care for the patient. A reminder of good practices.

Main artifacts in electrocardiography

Electrocardiographic artifacts are defined as electrocardiographic alterations, not related to cardiac electrical activity. As a result of artifacts, the components of the electrocardiogram (ECG) such as the baseline and waves can be distorted. Motion artifacts are due to shaking with rhythmic movement. Examples of motion artifacts include tremors with no evident cause, Parkinson's disease, cerebellar or intention tremor, anxiety, hyperthyroidism, multiple sclerosis, and drugs such as amphetamines, xanthines, lithium, benzodiazepines, or shivering (due to hypothermia, fever (rigor due to shaking), cardiopulmonary resuscitation by chest compression (oscillations of great amplitude) and patients who move their limbs during the test, causing sudden irregularities in the ECG baseline that may resemble premature contractions or interfere with ECG wave shapes, or other supraventricular and ventricular arrhythmias. When the skeletal muscles experience shaking, the ECG is "bombarded" by apparently random electrical activity.