Data‐driven approach for automatic detection of aortic valve opening: B point detection from impedance cardiogram

Sheikh, Shafa-at Ali; Gurel, Nil Z.; Gupta, Shishir Kumar; Chukwu, Ikenna V; Levantsevych, Oleksiy; Alkhalaf, Mhmtjamil; Soudan, Majd; Abdulbaki, Rami; Haffar, Ammer; Vaccarino, Viola; Inan, Omer T.; Shah, Amit; Clifford, Gari D.; Rad, Ali Bahrami

doi:10.1111/psyp.14128

Cited by 3 publications

(3 citation statements)

References 63 publications

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“…However, researchers reported that they have not found any significant changes in the morphology with NICO100C-MRI (Cieslak et al, 2015(Cieslak et al, , 2018, which is different from the NICO100C used in this work. Furthermore, the B point on the −dZ/dt (representing aortic valve opening) can reveal the beta-adrenergic influences in its inflection shape (interested readers are referred to Arbol (2017) and Sheikh et al, (2022b) for different B point shapes). Laboratory experiments with dogs have shown that high beta-adrenergic influences on the myocardium are represented by a single inflection point (B point), whereas low beta-adrenergic influences are characterized by two detectable inflection points (later inflection point is marked as B point) on an −dZ/dt waveform (Sherwood et al, 1990).…”

Section: Discussionmentioning

confidence: 99%

“…Feature importance analysis was performed to identify the features playing a substantial role in PTSD classification (Sheikh et al., 2022b). The threshold for selecting features was also selected using grid‐search with different threshold values (mean, median, 0.75 × mean, 0.75 × median) of feature importance computed as mean decrease in impurity.…”

Section: Methodsmentioning

confidence: 99%

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Impedance cardiogram based exploration of cardiac mechanisms in post‐traumatic stress disorder during trauma recall

Sheikh,

Shah,

Bremner

et al. 2023

Psychophysiology

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Post‐traumatic stress disorder (PTSD) is an independent risk factor for developing heart failure; however, the underlying cardiac mechanisms are still elusive. This study aims to evaluate the real‐time effects of experimentally induced PTSD symptom activation on various cardiac contractility and autonomic measures. We recorded synchronized electrocardiogram and impedance cardiogram from 137 male veterans (17 PTSD, 120 non‐PTSD; 48 twin pairs, 41 unpaired singles) during a laboratory‐based traumatic reminder stressor. To identify the parameters describing the cardiac mechanisms by which trauma reminders can create stress on the heart, we utilized a feature selection mechanism along with a random forest classifier distinguishing PTSD and non‐PTSD. We extracted 99 parameters, including 76 biosignal‐based and 23 sociodemographic, medical history, and psychiatric diagnosis features. A subject/twin‐wise stratified nested cross‐validation procedure was used for parameter tuning and model assessment to identify the important parameters. The identified parameters included biomarkers such as pre‐ejection period, acceleration index, velocity index, Heather index, and several physiology‐agnostic features. These identified parameters during trauma recall suggested a combination of increased sympathetic nervous system (SNS) activity and deteriorated cardiac contractility that may increase the heart failure risk for PTSD. This indicates that the PTSD symptom activation associates with real‐time reductions in several cardiac contractility measures despite SNS activation. This finding may be useful in future cardiac prevention efforts.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Impedance cardiogram based exploration of cardiac mechanisms in post‐traumatic stress disorder during trauma recall

Sheikh,

Shah,

Bremner

et al. 2023

Psychophysiology

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“…Nemati et al used multimodality for respiratory rate estimation from multiple time-series including the ECG (Nemati et al 2010). Sheikh et al developed a data-driven approach for automatic detection of aortic valve opening from impedance cardiography (ICG) data (Sheikh et al 2022). Dia et al used ECG R-peaks as reference for denoising synchronously recorded PCG data (Dia et al 2018).…”

Section: Introductionmentioning

confidence: 99%

An open-access simultaneous electrocardiogram and phonocardiogram database

Kazemnejad,

Karimi,

Gordany

et al. 2024

Physiol. Meas.

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Objective: The EPHNOGRAM project aimed to develop a low-cost, low-power device for simultaneous ECG and PCG recording, with additional channels for environmental audio to enhance PCG through active noise cancellation. The objective was to study multimodal electro-mechanical activities of the heart, offering insights into the differences and synergies between these modalities during various cardiac activity levels.Approach: We developed and tested several hardware prototypes of a simultaneous ECG-PCG acquisition device. Using this technology, we collected simultaneous ECG and PCG data from 24 healthy adults during different physical activities, including resting, walking, running, and stationary biking, in an indoor fitness center. The data were annotated using a robust software that we developed for detecting ECG R-peaks and PCG S1 and S2 components, and overseen by a human expert. We also developed machine learning models using ECG-based, PCG-based, and joint ECG-PCG features, like R-R and S1-S2 intervals, to classify physical activities and analyze electro-mechanical dynamics.Main Results: The results show a significant coupling between ECG and PCG components, especially during high-intensity exercise. Notable micro-variations in S2-based heart rate show differences in the heart's electrical and mechanical functions. The Lomb-Scargle periodogram and approximate entropy analyses confirm the higher volatility of S2-based heart rate compared to ECG-based heart rate. Correlation analysis shows stronger coupling between R-R and R-S1 intervals during high-intensity activities. Hybrid ECG-PCG features, like the R-S2 interval, were identified as more informative for physical activity classification through mRMR feature selection and SHAP value analysis.Significance: The EPHNOGRAM database, is available on PhysioNet. The database enhances our understanding of cardiac function, enabling future studies on the heart's mechanical and electrical interrelationships. The results of this study can contribute to improved cardiac condition diagnoses. Additionally, the designed hardware has the potential for integration into wearable devices and the development of multimodal stress test technologies.

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An Effective Method of Detecting Characteristic Points of Impedance Cardiogram Verified in the Clinical Pilot Study

Karpiel

Richter

Feige

et al. 2022

Sensors

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Accurate and reliable determination of the characteristic points of the impedance cardiogram (ICG) is an important research problem with a growing range of applications in the cardiological diagnostics of patients with heart failure (HF). The shapes of the characteristic waves of the ICG signal and the temporal location of the characteristic points B, C, and X provide significant diagnostic information. On this basis, essential diagnostic cardiological parameters can be determined, such as, e.g., cardiac output (CO) or stroke volume (SV). Although the importance of this problem is obvious, we face many challenges, including noisy signals and the big variability in the morphology, which altogether make the accurate identification of the characteristic points quite difficult. The paper presents an effective method of ICG points identification intended for conducting experimental research in the field of impedance cardiography. Its effectiveness is confirmed in clinical pilot studies.

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Data‐driven approach for automatic detection of aortic valve opening: B point detection from impedance cardiogram

Cited by 3 publications

References 63 publications

Impedance cardiogram based exploration of cardiac mechanisms in post‐traumatic stress disorder during trauma recall

Impedance cardiogram based exploration of cardiac mechanisms in post‐traumatic stress disorder during trauma recall

An open-access simultaneous electrocardiogram and phonocardiogram database

An Effective Method of Detecting Characteristic Points of Impedance Cardiogram Verified in the Clinical Pilot Study

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