Impact of the high-frequency cutoff of bandpass filtering on ECG quality and clinical interpretation: A comparison between 40Hz and 150Hz cutoff in a surgical preoperative adult outpatient population

Evaluating the impacts of digital ECG denoising on the interpretive capabilities of healthcare professionals

Aims

Electrocardiogram (ECG) interpretation is an essential skill across multiple medical disciplines; yet, studies have consistently identified deficiencies in the interpretive performance of healthcare professionals linked to a variety of educational and technological factors. Despite the established correlation between noise interference and erroneous diagnoses, research evaluating the impacts of digital denoising software on clinical ECG interpretation proficiency is lacking.

Methods and results

Forty-eight participants from a variety of medical professions and experience levels were prospectively recruited for this study. Participants' capabilities in classifying common cardiac rhythms were evaluated using a sequential blinded and semi-blinded interpretation protocol on a challenging set of single-lead ECG signals (42 × 10 s) pre- and post-denoising with robust, cloud-based ECG processing software. Participants' ECG rhythm interpretation performance was greatest when raw and denoised signals were viewed in a combined format that enabled comparative evaluation. The combined view resulted in a 4.9% increase in mean rhythm classification accuracy (raw: 75.7% ± 14.5% vs. combined: 80.6% ± 12.5%, P = 0.0087), a 6.2% improvement in mean five-point graded confidence score (raw: 4.05 ± 0.58 vs. combined: 4.30 ± 0.48, P < 0.001), and 9.7% reduction in the mean proportion of undiagnosable data (raw: 14.2% ± 8.2% vs. combined: 4.5% ± 2.4%, P < 0.001), relative to raw signals alone. Participants also had a predominantly positive perception of denoising as it related to revealing previously unseen pathologies, improving ECG readability, and reducing time to diagnosis.

Conclusion

Our findings have demonstrated that digital denoising software improves the efficacy of rhythm interpretation on single-lead ECGs, particularly when raw and denoised signals are provided in a combined viewing format, warranting further investigation into the impact of such technology on clinical decision-making and patient outcomes.

Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques

Current research focuses on improving electrocardiogram (ECG) monitoring systems to enable real-time and long-term usage, with a specific focus on facilitating remote monitoring of ECG data. This advancement is crucial for improving cardiovascular health by facilitating early detection and management of cardiovascular disease (CVD). To efficiently meet these demands, user-friendly and comfortable ECG sensors that surpass wet electrodes are essential. This has led to increased interest in ECG capacitive electrodes, which facilitate signal detection without requiring gel preparation or direct conductive contact with the body. This feature makes them suitable for wearables or integrated measurement devices. However, ongoing research is essential as the signals they measure often lack sufficient clinical accuracy due to susceptibility to interferences, particularly Motion Artifacts (MAs). While our primary focus is on studying MAs, we also address other limitations crucial for designing a high Signal-to-Noise Ratio (SNR) circuit and effectively mitigating MAs. The literature on the origins and models of MAs in capacitive electrodes is insufficient, which we aim to address alongside discussing mitigation methods. We bring attention to digital signal processing approaches, especially those using reference signals like Electrode-Tissue Impedance (ETI), as highly promising. Finally, we discuss its challenges, proposed solutions, and offer insights into future research directions.

Stress influence on real-world driving identified by monitoring heart rate variability and morphologic variability of electrocardiogram signals: the case of intercity roads

Objectives. This study examines which of the heart rate variability (HRV) and morphologic variability (MV) metrics may have the highest accuracy in different stress detection during real-world driving. Methods. The cross-sectional study was carried out among 93 intercity mini-bus male drivers aged 22-67 years. The Trillium 5000 Holter Recorder and GARMIN Virb Elite camera were used to determine heart rate and vehicle speed measurements along the path, respectively. We considered the HRV and MV metrics of electrocardiogram (ECG) signals including the mean RR interval (mRR), mean heart rate (mHR), normalized low-frequency spectrum (nLF), normalized high-frequency spectrum (nHF), normalized very low-frequency spectrum (nVLF), difference of normalized low-frequency spectrum and normalized high-frequency spectrum (dLFHF), and sympathovagal balance index (SVI). Results. The analysis showed that the HRV metrics mHR, mRR, nVLF, nLF, nHF, dLFHF and SVI are effective in mental stress detection while driving as compared to rest time. We obtained a high accuracy of stress detection for MV metrics as compared to the traditional HRV analysis, of approximately 92%. Conclusions. Our findings indicate that driver stress could be detected with an accuracy of 92% using MV metrics as an accurate physiological index of the driver's state.

Importance of Electrode Selection and Number in Reconstructing Standard Twelve Lead Electrocardiograms

Many clinical and consumer electrocardiogram (ECG) devices collect fewer electrodes than the standard twelve-lead ECG and either report less information or employ algorithms to reconstruct a full twelve-lead signal. We assessed the optimal electrode selection and number that minimizes redundant information collection while maximizing reconstruction accuracy. We employed a validated deep learning model to reconstruct ECG signals from 250 different patients in the PTB database. Different numbers and combinations of electrodes were removed from the ECG before reconstruction to measure the effect of electrode inclusion on reconstruction accuracy. The Left Leg (LL) electrode registered the largest drop in average reconstruction accuracy, from an R2 of 0.836 when the LL was included to 0.737 when excluded. Additionally, we conducted a correlation analysis to identify leads that behave similarly. We demonstrate that there exists a high correlation between leads I, II, aVL, aVF, V4, V5, and V6, which all occupy the bottom right quadrant in an ECG axis interpretation, and likely contain redundant information. Based on our analysis, we recommend the prioritization of electrodes RA, LA, LL, and V3 in any future lead collection devices, as they appear most important for full ECG reconstruction.

A virtual reality study investigating the train illusion

The feeling of self-movement that occurs in the absence of physical motion is often referred to as vection, which is commonly exemplified using the train illusion analogy (TIA). Limited research exists on whether the TIA accurately exemplifies the experience of vection in virtual environments (VEs). Few studies complemented their vection research with participants' qualitative feedback or by recording physiological responses, and most studies used stimuli that contextually differed from the TIA. We investigated whether vection is experienced differently in a VE replicating the TIA compared to a VE depicting optic flow by recording subjective and physiological responses. Additionally, we explored participants' experience through an open question survey. We expected the TIA environment to induce enhanced vection compared to the optic flow environment. Twenty-nine participants were visually and audibly immersed in VEs that either depicted optic flow or replicated the TIA. Results showed optic flow elicited more compelling vection than the TIA environment and no consistent physiological correlates to vection were identified. The post-experiment survey revealed discrepancies between participants' quantitative and qualitative feedback. Although the dynamic content may outweigh the ecological relevance of the stimuli, it was concluded that more qualitative research is needed to understand participants' vection experience in VEs.

Dependence of Skin-Electrode Contact Impedance on Material and Skin Hydration

Dry electrodes offer an accessible continuous acquisition of biopotential signals as part of current in-home monitoring systems but often face challenges of high-contact impedance that results in poor signal quality. The performance of dry electrodes could be affected by electrode material and skin hydration. Herein, we investigate these dependencies using a circuit skin-electrode interface model, varying material and hydration in controlled benchtop experiments on a biomimetic skin phantom simulating dry and hydrated skin. Results of the model demonstrate the contribution of the individual components in the circuit to total impedance and assist in understanding the role of electrode material in the mechanistic principle of dry electrodes. Validation was performed by conducting in vivo skin-electrode contact impedance measurements across ten normative human subjects. Further, the impact of the electrode on biopotential signal quality was evaluated by demonstrating an ability to capture clinically relevant electrocardiogram signals by using dry electrodes integrated into a toilet seat cardiovascular monitoring system. Titanium electrodes resulted in better signal quality than stainless steel electrodes. Results suggest that relative permittivity of native oxide of electrode material come into contact with the skin contributes to the interface impedance, and can lead to enhancement in the capacitive coupling of biopotential signals, especially in dry skin individuals.

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

Background: Myocardial ischemia is a common early symptom of cardiovascular disease (CVD). Reliable detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. The vectorcardiogram (VCG) could improve the performance of ECG-based myocardial ischemia detection by affording temporal-spatial characteristics related to myocardial ischemia and capturing subtle changes in ST-T segment in continuous cardiac cycles. We aim to investigate if the combination of ECG and VCG could improve the performance of machine learning algorithms in automatic myocardial ischemia detection. Methods: The ST-T segments of 20-second, 12-lead ECGs, and VCGs were extracted from 377 patients with myocardial ischemia and 52 healthy controls. Then, sample entropy (SampEn, of 12 ECG leads and of three VCG leads), spatial heterogeneity index (SHI, of VCG) and temporal heterogeneity index (THI, of VCG) are calculated. Using a grid search, four SampEn and two features are selected as input signal features for ECG-only and VCG-only models based on support vector machine (SVM), respectively. Similarly, three features (S I , THI, and SHI, where S I is the SampEn of lead I) are further selected for the ECG + VCG model. 5-fold cross validation was used to assess the performance of ECG-only, VCG-only, and ECG + VCG models. To fully evaluate the algorithmic generalization ability, the model with the best performance was selected and tested on a third independent dataset of 148 patients with myocardial ischemia and 52 healthy controls. Results: The ECG + VCG model with three features (S I ,THI, and SHI) yields better classifying results than ECG-only and VCG-only models with the average accuracy of 0.903, sensitivity of 0.903, specificity of 0.905, F1 score of 0.942, and AUC of 0.904, which shows better performance with fewer features compared with existing works. On the third independent dataset, the testing showed an AUC of 0.814. Conclusion: The SVM algorithm based on the ECG + VCG model could reliably detect myocardial ischemia, providing a potential tool to assist cardiologists in the early diagnosis of CVD in routine screening during primary care services.

Epidermal Electrodes with Ferrimagnetic/Conductive Properties for Biopotential Recordings

Interfacing ultrathin functional films for epidermal applications with external recording instruments or readout electronics still represents one of the biggest challenges in the field of tattoo electronics. With the aim of providing a convenient solution to this ever-present limitation, in this work we propose an innovative free-standing electrode made of a composite thin film based on the combination of the conductive polymer PEDOT:PSS and ferrimagnetic powder. The proposed epidermal electrode can be directly transferred onto the skin and is structured in two parts, namely a conformal conductive part with a thickness of 3 μm and a ferrimagnetic-conductive part that can be conveniently connected using magnetic connections. The films were characterized for ECG recordings, revealing a performance comparable to that of commercial pre-gelled electrodes in terms of cross-spectral coherence, signal-to-noise ratio, and baseline wandering. These new, conductive, magnetically interfaceable, and free-standing conformal films introduce a novel concept in the domain of tattoo electronics and can set the basis for the development of a future family of epidermal devices and electrodes.

ECGAssess: A Python-Based Toolbox to Assess ECG Lead Signal Quality

Electrocardiography (ECG) is the method most often used to diagnose cardiovascular diseases. To obtain a high-quality recording, the person conducting an ECG must be a trained expert. When these experts are not available, this important diagnostic tool cannot be used, consequently impacting the quality of healthcare. To avoid this problem, it must be possible for untrained healthcare professionals to record diagnostically useful ECGs so they can send the recordings to experts for diagnosis. The ECGAssess Python-based toolbox developed in this study provides feedback regarding whether ECG signals are of adequate quality. Each lead of the 12-lead recordings was classified as acceptable or unacceptable. This feedback allows people to identify and correct errors in the use of the ECG device. The toolbox classifies the signals according to stationary, heart rate, and signal-to-noise ratio. If the limits of these three criteria are exceeded, this is indicated to the user. To develop and optimize the toolbox, two annotators reviewed a data set of 1,200 ECG leads to assess their quality, and each lead was classified as acceptable or unacceptable. The evaluation of the toolbox was done with a new data set of 4,200 leads, which were annotated the same way. This evaluation shows that the ECGAssess toolbox correctly classified over 94% of the 4,200 ECG leads as either acceptable or unacceptable in comparison to the annotations.

ECG simulator with configurable skin-electrode impedance and artifacts emulation

Electrocardiograms (ECG) recorded from everyday objects, such as wearables, fitness machines or smart steering wheels are becoming increasingly common. Applications are diverse and include health monitoring, athletic performance optimization, identification, authentication, and entertainment. In this study we report the design and implementation of an innovative ECG simulator, providing simulation of signal related artifacts and a dynamically adjustable skin-electrode interface model. The ECG simulator includes a unique combination of features: emulation of time dependent skin-electrode impedance, adjustable differential and common-mode interference, generation of lead-off events and analog front-end output digitalization. The skin-electrode capacitance range is 1 nF-255 nF and the resistance span is 4 kΩ-996 kΩ. System's functionality is demonstrated using a commercially available ECG front-end. The simulated SNR degradation introduced by the ECG simulator is under 0.1 dB. Results show that the skin-electrode interface can have a significant impact in the acquired waveforms. Impedance electrode imbalance, specifically of the resistive component, can generate artifacts which can be misinterpreted has arrhythmias. The proposed device can be useful for hardware and software ECG development and for training physicians and nurses to readily recognize skin-electrode impedance related artifacts.

Estimation of the Respiratory Rate from Localised ECG at Different Auscultation Sites

The respiratory rate (RR) is a vital physiological parameter in prediagnosis and daily monitoring. It can be obtained indirectly from Electrocardiogram (ECG) signals using ECG-derived respiration (EDR) techniques. As part of the study in designing an early cardiac screening system, this work aimed to study whether the accuracy of ECG derived RR depends on the auscultation sites. Experiments were conducted on 12 healthy subjects to obtain simultaneous ECG (at auscultation sites and Lead I as reference) and respiration signals from a microphone close to the nostril. Four EDR algorithms were tested on the data to estimate RR in both the time and frequency domain. Results reveal that: (1) The location of the ECG electrodes between auscultation sites does not impact the estimation of RR, (2) baseline wander and amplitude modulation algorithms outperformed the frequency modulation and band-pass filter algorithms, (3) using frequency domain features to estimate RR can provide more accurate RR except when using the band-pass filter algorithm. These results pave the way for ECG-based RR estimation in miniaturised integrated cardiac screening device.

Computational approaches for detection of cardiac rhythm abnormalities: Are we there yet?

The analysis of an electrocardiogram (ECG) is able to provide vital information on the electrical activity of the heart and is crucial for the accurate diagnosis of cardiac arrhythmias. Due to the nature of some arrhythmias, this might be a time-consuming and difficult to accomplish process. The advent of novel machine learning technologies in this field has a potential to revolutionise the use of the ECG. In this review, we outline key advances in ECG analysis for atrial, ventricular and complex multiform arrhythmias, as well as discuss the current limitations of the technology and the barriers that must be overcome before clinical integration is feasible.

An Efficient Teager Energy Operator-Based Automated QRS Complex Detection

Database

The efficiency and robustness of the proposed method has been tested on Fantasia Database (FTD), MIT-BIH Arrhythmia Database (MIT-AD), and MIT-BIH Normal Sinus Rhythm Database (MIT-NSD).

Aim

Because of the importance of QRS complex in the diagnosis of cardiovascular diseases, improvement in accuracy of its measurement has been set as a target. The present study provides an algorithm for automatic detection of QRS complex on the ECG signal, with the benefit of energy and reduced impact of noise on the ECG signal.

Method

The method is basically based on the Teager energy operator (TEO), which facilitates the detection of the baseline threshold and extracts QRS complex from the ECG signal.

Results

The testing of the undertaken method on the Fanatasia Database showed the following results: sensitivity (Se) = 99.971%, positive prediction (P+) = 99.973%, detection error rate (DER) = 0.056%, and accuracy (Acc) = 99.944%. On MIT-AD involvement, Se = 99.74%, P+ = 99.97%, DER = 0.291%, and Acc = 99.71%. On MIT-NSD involvement, Se = 99.878%, P+ = 99.989%, DER = 0.134%, and Acc = 99.867%.

Conclusion

Despite the closeness of the recorded peaks which inflicts a constraint in detection of the two consecutive QRS complexes, the proposed method, by applying 4 simple and quick steps, has effectively and reliably detected the QRS complexes which make it suitable for practical purposes and applications.

Prevalence and incidence of Q-wave unrecognized myocardial infarction in general population: Diagnostic value of the electrocardiogram. The REGICOR study

BACKGROUND

Diagnosis of unrecognized myocardial infarction (UMI) remains an open question in epidemiological and clinical studies, inhibiting effective secondary prevention of myocardial infarction. We aimed to determine the prevalence and incidence of Q-wave UMI in asymptomatic individuals aged 35 to 74years, and to ascertain the positive predictive value (PPV) of asymptomatic Q-wave to diagnose UMI.

METHODS

Two population-based cross-sectional studies were conducted, in 2000 (with 10-year follow-up) and in 2005. A baseline electrocardiogram was obtained for each participant. Imaging techniques (echocardiography, cardiac magnetic resonance imaging, and myocardial perfusion single-photon emission computerized tomography) were used to confirm UMI in patients with asymptomatic Q-wave.

RESULTS

The prevalence of confirmed Q-wave UMI in the 5580 participants was 0.18% (95% confidence interval [CI]: 0.10-0.33) and the incidence rate was 27.1 Q-wave UMI per 100,000person-years. The proportion of confirmed Q-wave UMI with respect to all prevalent MI was 8.1% (95% CI: 4.4-14.2). The PPV of asymptomatic Q-wave to diagnose Q-wave UMI was 29.2% (95% CI: 18.2-43.2%) overall, but much higher (75%, 95% CI: 40.9-92.9%) in participants with 10-year CHD risk ≥10%, compared to lower-risk participants.

CONCLUSION

Opportunistic identification of asymptomatic Q-waves by routine electrocardiogram overestimates actual Q-wave UMI, which represents 8% to 13% of all myocardial infarction in the population aged 35 to 74years. This overestimation is particularly high in the population at low cardiovascular risk. In epidemiological studies and in clinical practice, diagnosis of a pathologic Q-wave in asymptomatic patients requires detailed analysis of imaging tests to confirm or rule out myocardial necrosis.