An open-source automated algorithm for removal of noisy beats for accurate impedance cardiogram analysis

Exaggerated Peripheral and Systemic Vasoconstriction During Trauma Recall in Posttraumatic Stress Disorder: A Co-Twin Control Study

BACKGROUND

Individuals with posttraumatic stress disorder (PTSD) face an increased risk of cardiovascular disease, but the mechanisms linking PTSD to cardiovascular disease remain incompletely understood. We used a co-twin control study design to test the hypothesis that individuals with PTSD exhibit augmented peripheral and systemic vasoconstriction during a personalized trauma recall task.

METHODS

In 179 older male twins from the Vietnam Era Twin Registry, lifetime history of PTSD and current (last month) PTSD symptoms were assessed. Participants listened to neutral and personalized trauma scripts while peripheral vascular tone (Peripheral Arterial Tone ratio) and systemic vascular tone (e.g., total vascular conductance) were measured. Linear mixed-effect models were used to assess the within-pair relationship between PTSD and vascular tone indices.

RESULTS

The mean age of participants was 68 years, and 19% had a history of PTSD. For the Peripheral Arterial Tone ratio analysis, 32 twins were discordant for a history of PTSD, and 46 were discordant for current PTSD symptoms. Compared with their brothers without PTSD, during trauma recall, participants with a history of PTSD had greater increases in peripheral (β = -1.01, 95% CI [-1.72, -0.30]) and systemic (total vascular conductance: β = -1.12, 95% CI [-1.97, -0.27]) vasoconstriction after adjusting for cardiovascular risk factors. Associations persisted after adjusting for antidepressant medication use and heart rate and blood pressure during the tasks. Analysis of current PTSD symptom severity showed consistent results.

CONCLUSIONS

PTSD is associated with exaggerated peripheral and systemic vasoconstrictor responses to traumatic stress reminders, which may contribute to elevated risk of cardiovascular disease.

Robust Arm Impedocardiography Signal Quality Enhancement Using Recursive Signal Averaging and Multi-Stage Wavelet Denoising Methods for Long-Term Cardiac Contractility Monitoring Armbands

Impedance cardiography (ICG) is a low-cost, non-invasive technique that enables the clinical assessment of haemodynamic parameters, such as cardiac output and stroke volume (SV). Conventional ICG recordings are taken from the patient's thorax. However, access to ICG vital signs from the upper-arm brachial artery (as an associated surrogate) can enable user-convenient wearable armband sensor devices to provide an attractive option for gathering ICG trend-based indicators of general health, which offers particular advantages in ambulatory long-term monitoring settings. This study considered the upper arm ICG and control Thorax-ICG recordings data from 15 healthy subject cases. A prefiltering stage included a third-order Savitzky-Golay finite impulse response (FIR) filter, which was applied to the raw ICG signals. Then, a multi-stage wavelet-based denoising strategy on a beat-by-beat (BbyB) basis, which was supported by a recursive signal-averaging optimal thresholding adaptation algorithm for Arm-ICG signals, was investigated for robust signal quality enhancement. The performance of the BbyB ICG denoising was evaluated for each case using a 700 ms frame centred on the heartbeat ICG pulse. This frame was extracted from a 600-beat ensemble signal-averaged ICG and was used as the noiseless signal reference vector (gold standard frame). Furthermore, in each subject case, enhanced Arm-ICG and Thorax-ICG above a threshold of correlation of 0.95 with the noiseless vector enabled the analysis of beat inclusion rate (BIR%), yielding an average of 80.9% for Arm-ICG and 100% for Thorax-ICG, and BbyB values of the ICG waveform feature metrics A, B, C and VET accuracy and precision, yielding respective error rates (ER%) of 0.83%, 11.1%, 3.99% and 5.2% for Arm-IG, and 0.41%, 3.82%, 1.66% and 1.25% for Thorax-ICG, respectively. Hence, the functional relationship between ICG metrics within and between the arm and thorax recording modes could be characterised and the linear regression (Arm-ICG vs. Thorax-ICG) trends could be analysed. Overall, it was found in this study that recursive averaging, set with a 36 ICG beats buffer size, was the best Arm-ICG BbyB denoising process, with an average of less than 3.3% in the Arm-ICG time metrics error rate. It was also found that the arm SV versus thorax SV had a linear regression coefficient of determination (R²) of 0.84.

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

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.

Validation of a new impedance cardiography analysis algorithm for clinical classification of stress states

Pre-ejection period (PEP) is an index of sympathetic nervous system activity that can be computed from electrocardiogram (ECG) and impedance cardiogram (ICG) signals, but sensitive to speech/motion artifact. We sought to validate an ICG noise removal method, three-stage ensemble-average algorithm (TEA), in data acquired from a clinical trial comparing active versus sham non-invasive vagal nerve stimulation (tcVNS) after standardized speech stress. We first compared TEA's performance versus the standard conventional ensemble-average algorithm (CEA) approach to classify noisy ICG segments. We then analyzed ECG and ICG data to measure PEP and compared group-level differences in stress states with each approach. We evaluated 45 individuals, of whom 23 had post-traumatic stress disorder (PTSD). We found that the TEA approach identified artifact-corrupted beats with intraclass correlation coefficient > 0.99 compared to expert adjudication. TEA also resulted in higher group-level differences in PEP between stress states than CEA. PEP values were lower in the speech stress (vs. baseline rest) group using both techniques, but the differences were greater using TEA (12.1 ms) than CEA (8.0 ms). PEP differences in groups divided by PTSD status and tcVNS (active vs. sham) were also greater when using the TEA versus CEA method, although the magnitude of the differences was lower. In conclusion, TEA helps to accurately identify noisy ICG beats during speaking stress, and this increased accuracy improves sensitivity to group-level differences in stress states compared to CEA, suggesting greater clinical utility.

Signal processing techniques applied to impedance cardiography ICG signals - a review

Over the last decade, Computer-Aided Diagnosis (CAD) systems have been provided significant research focus by researchers. CAD systems have been developed in order to minimise visual errors, to compensate manual interpretation, and to help medical staff to take decisions swiftly. These systems have been considered as powerful tools for a reliable, automatic, and low-cost monitoring and diagnosis. CAD systems are based on analysis and classification of several physiological signals for detecting and assessing different diseases related to the corresponding organ. The implementation of these systems requires the application of several advanced signal processing techniques. Specifically, in cardiology, CAD systems have achieved promising results in providing an accurate and rapid detection of cardiovascular diseases (CVDs). Particularly, the number of works on signal processing field for impedance cardiography (ICG) signals starts to grow slowly in recent years. This paper presents a review study of signal processing techniques applied to the ICG signal for the denoising, the analysis, the classification and the characterisation purposes. This review is intended to provide researchers with a broad overview of the currently used signal processing techniques for ICG signal analysis, as well as to improve future research by applying other recent advanced methods.

Pulse arrival time as a surrogate of blood pressure

Various models have been proposed for the estimation of blood pressure (BP) from pulse transit time (PTT). PTT is defined as the time delay of the pressure wave, produced by left ventricular contraction, measured between a proximal and a distal site along the arterial tree. Most researchers, when they measure the time difference between the peak of the R-wave in the electrocardiogram signal (corresponding to left ventricular depolarisation) and a fiducial point in the photoplethysmogram waveform (as measured by a pulse oximeter attached to the fingertip), describe this erroneously as the PTT. In fact, this is the pulse arrival time (PAT), which includes not only PTT, but also the time delay between the electrical depolarisation of the heart's left ventricle and the opening of the aortic valve, known as pre-ejection period (PEP). PEP has been suggested to present a significant limitation to BP estimation using PAT. This work investigates the impact of PEP on PAT, leading to a discussion on the best models for BP estimation using PAT or PTT. We conducted a clinical study involving 30 healthy volunteers (53.3% female, 30.9 ± 9.35 years old, with a body mass index of 22.7 ± 3.2 kg/m[Formula: see text]). Each session lasted on average 27.9 ± 0.6 min and BP was varied by an infusion of phenylephrine (a medication that causes venous and arterial vasoconstriction). We introduced new processing steps for the analysis of PAT and PEP signals. Various population-based models (Poon, Gesche and Fung) and a posteriori models (inverse linear, inverse squared and logarithm) for estimation of BP from PTT or PAT were evaluated. Across the cohort, PEP was found to increase by 5.5 ms ± 4.5 ms from its baseline value. Variations in PTT were significantly larger in amplitude, - 16.8 ms ± 7.5 ms. We suggest, therefore, that for infusions of phenylephrine, the contribution of PEP on PAT can be neglected. All population-based models produced large BP estimation errors, suggesting that they are insufficient for modelling the complex pathways relating changes in PTT or PAT to changes in BP. Although PAT is inversely correlated with systolic blood pressure (SBP), the gradient of this relationship varies significantly from individual to individual, from - 2946 to - 470.64 mmHg/s in our dataset. For the a posteriori inverse squared model, the root mean squared errors (RMSE) for systolic and diastolic blood pressure (DBP) estimation from PAT were 5.49 mmHg and 3.82 mmHg, respectively. The RMSEs for SBP and DBP estimation by PTT were 4.51 mmHg and 3.53 mmHg, respectively. These models take into account individual calibration curves required for accurate blood pressure estimation. The best performing population-based model (Poon) reported error values around double that of the a posteriori inverse squared model, and so the use of population-based models is not justified.

ReBeatICG: Real-time Low-Complexity Beat-to-beat Impedance Cardiogram Delineation Algorithm

This work presents ReBeatICG, a real-time, low-complexity beat-to-beat impedance cardiography (ICG) delineation algorithm that allows hemodynamic parameters monitoring. The proposed procedure relies only on the ICG signal compared to most algorithms found in the literature that rely on synchronous electrocardiogram signal (ECG) recordings. ReBeatICG was designed with implementation on an ultra-low-power microcontroller (MCU) in mind. The detection accuracy of the developed algorithm is tested against points manually labeled by cardiologists. It achieves a detection Gmean accuracy of 94.9%, 98.6%, 90.3%, and 84.3% for the B, C, X, and O characteristic points, respectively. Furthermore, several hemodynamic parameters were calculated based on annotated characteristic points and compared with values generated from the cardiologists' annotations. ReBeatICG achieved mean error rates of 0.11 ms, 9.72 ms, 8.32 ms, and 3.97% for HR, LVET, IVRT, and relative C-point amplitude, respectively.

Motion robust ICG measurements using a two-step spectrum denoising method

Objective. Impedance cardiography (ICG) is a noninvasive and continuous method for evaluating stroke volume and cardiac output. However, the ICG measurement is easily interfered due to respiration and body movements. Taking into consideration about the spectral correlations between the simultaneously collected ICG, electrocardiogram (ECG), and acceleration signals, this paper introduces a two-step spectrum denoising method to remove motion artifacts of ICG measurements in both resting and exercising scenarios.Approach. First, the major motion artifacts of ECG and ICG are separately suppressed by the spectral subtraction with respect to acceleration signals. The obtained ECG and ICG are further decomposed into two sets of intrinsic mode functions (IMFs) through the ensemble empirical mode decomposition. We then extract the shared spectral information between the two sets of IMFs using the canonical correlation analysis in a spectral domain. Finally, the ICG signal is reconstructed using those canonical variates with largest spectral correlations with ECG IMFs.Main results. The denoising method was evaluated for 30 subjects under both resting and cycling scenarios. Experimental results show that the beat contribution factor of ICG signals increases from its original 80.1%-97.4% after removing the motion artifacts.Significance. The proposed denoising scheme effectively improves the reliability of diagnosis and analysis on cardiovascular diseases relying on ICG signals.