Non-Invasive Wearable Patch Utilizing Seismocardiography for Peri-Operative Use in Surgical Patients

Cardiac Multi-Frequency Vibration Signal Sensor Module and Feature Extraction Method Based on Vibration Modeling

Cardiovascular diseases pose a long-term risk to human health. This study focuses on the rich-spectrum mechanical vibrations generated during cardiac activity. By combining Fourier series theory, we propose a multi-frequency vibration model for the heart, decomposing cardiac vibration into frequency bands and establishing a systematic interpretation for detecting multi-frequency cardiac vibrations. Based on this, we develop a small multi-frequency vibration sensor module based on flexible polyvinylidene fluoride (PVDF) films, which is capable of synchronously collecting ultra-low-frequency seismocardiography (ULF-SCG), seismocardiography (SCG), and phonocardiography (PCG) signals with high sensitivity. Comparative experiments validate the sensor's performance and we further develop an algorithm framework for feature extraction based on 1D-CNN models, achieving continuous recognition of multiple vibration features. Testing shows that the recognition coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the 8 features are 0.95, 2.18 ms, and 4.89 ms, respectively, with an average prediction speed of 60.18 us/point, meeting the re-quirements for online monitoring while ensuring accuracy in extracting multiple feature points. Finally, integrating the vibration model, sensor, and feature extraction algorithm, we propose a dynamic monitoring system for multi-frequency cardiac vibration, which can be applied to portable monitoring devices for daily dynamic cardiac monitoring, providing a new approach for the early diagnosis and prevention of cardiovascular diseases.

Adaptive convolutional dictionary learning for denoising seismocardiogram to enhance the classification performance of aortic stenosis

BACKGROUND

Aortic stenosis (AS) is the most prevalent type of valvular heart disease (VHD), traditionally diagnosed using echocardiogram or phonocardiogram. Seismocardiogram (SCG), an emerging wearable cardiac monitoring modality, is proved to be feasible in non-invasive and cost-effective AS diagnosis. However, SCG waveforms acquired from patients with heart diseases are typically weak, making them more susceptible to noise contamination. While most related researches focus on motion artifacts, sensor noise and quantization noise have been mostly overlooked. These noises pose additional challenges for extracting features from the SCG, especially impeding accurate AS classification.

METHOD

To address this challenge, we present a convolutional dictionary learning-based method. Based on sparse modeling of SCG, the proposed method generates a personalized adaptive-size dictionary from noisy measurements. The dictionary is used for sparse coding of the noisy SCG into a transform domain. Reconstruction from the domain removes the noise while preserving the individual waveform pattern of SCG.

RESULTS

Using two self-collected SCG datasets, we established optimal dictionary learning parameters and validated the denoising performance. Subsequently, the proposed method denoised SCG from 50 subjects (25 AS and 25 non-AS). Leave-one-subject-out cross-validation (LOOCV) was applied to 5 machine learning classifiers. Among the classifiers, a bi-layer neural network achieved a moderate accuracy of 90.2%, with an improvement of 13.8% from the denoising.

CONCLUSIONS

The proposed sparsity-based denoising technique effectively removes stochastic sensor noise and quantization noise from SCG, consequently improving AS classification performance. This approach shows promise for overcoming instrumentation constraints of SCG-based diagnosis.

Transcutaneous auricular Vagus Nerve Stimulation and Median Nerve Stimulation reduce acute stress in young healthy adults: a single-blind sham-controlled crossover study

Stress is a major determinant of health and wellbeing. Conventional stress management approaches do not account for the daily-living acute changes in stress that affect quality of life. The combination of physiological monitoring and non-invasive Peripheral Nerve Stimulation (PNS) represents a promising technological approach to quantify stress-induced physiological manifestations and reduce stress during everyday life. This study aimed to evaluate the effectiveness of three well-established transcutaneous PNS modalities in reducing physiological manifestations of stress compared to a sham: auricular and cervical Vagus Nerve Stimulation (taVNS and tcVNS), and Median Nerve Stimulation (tMNS). Using a single-blind sham-controlled crossover study with four visits, we compared the stress mitigation effectiveness of taVNS, tcVNS, and tMNS, quantified through physiological markers derived from five physiological signals peripherally measured on 19 young healthy volunteers. Participants underwent three acute mental and physiological stressors while receiving stimulation. Blinding effectiveness was assessed via subjective survey. taVNS and tMNS relative to sham resulted in significant changes that suggest a reduction in sympathetic outflow following the acute stressors: Left Ventricular Ejection Time Index (LVETI) shortening (tMNS: p = 0.007, taVNS: p = 0.015) and Pre-Ejection Period (PEP)-to-LVET ratio (PEP/LVET) increase (tMNS: p = 0.044, taVNS: p = 0.029). tMNS relative to sham also reduced Pulse Pressure (PP; p = 0.032) and tonic EDA activity (tonicMean; p = 0.025). The nonsignificant blinding survey results suggest these effects were not influenced by placebo. taVNS and tMNS effectively reduced stress-induced sympathetic arousal in wearable-compatible physiological signals, motivating their future use in novel personalized stress therapies to improve quality of life.

Clinical application of a body area network-based smart bracelet for pre-hospital trauma care

Objective

This study aims to explore the efficiency and effectiveness of a body area network-based smart bracelet for trauma care prior to hospitalization.

Methods

To test the efficacy of the bracelet, an observational cohort study was conducted on the clinical data of 140 trauma patients pre-admission to the hospital. This study was divided into an experimental group receiving smart bracelets and a control group receiving conventional treatment. Both groups were randomized using a random number table. The primary variables of this study were as follows: time to first administration of life-saving intervention, time to first administration of blood transfusion, time to first administration of hemostatic drugs, and mortality rates within 24 h and 28 days post-admission to the hospital. The secondary outcomes included the amount of time before trauma team activation and the overall length of patient stay in the emergency room.

Results

The measurement results for both the emergency smart bracelet as well as traditional equipment showed high levels of consistency and accuracy. In terms of pre-hospital emergency life-saving intervention, there was no significant statistical difference in the mortality rates between both groups within 224 h post-admission to the hospital or after 28-days of treatment in the emergency department. Furthermore, the treatment efficiency for the group of patients wearing smart bracelets was significantly better than that of the control group with regard to both the primary and secondary outcomes of this study. These results indicate that this smart bracelet has the potential to improve the efficiency and effectiveness of trauma care and treatment.

Conclusion

A body area network-based smart bracelet combined with remote 5G technology can assist the administration of emergency care to trauma patients prior to hospital admission, shorten the timeframe in which life-saving interventions are initiated, and allow for a quick trauma team response as well as increased efficiency upon administration of emergency care.

Towards Quantifying Stress in Patients with a History of Myocardial Infarction: Validating ECG-Derived Patch Features

Patients with prior myocardial infarction (MI) have an increased risk of experiencing a secondary event which is exacerbated by mental stress. Our team has developed a miniaturized patch with the capability to capture electrocardiogram (ECG), seismocardiogram (SCG) and photoplethysmogram (PPG) signals which may provide multimodal information to characterize stress responses within the post-MI population in ambulatory settings. As ECG-derived features have been shown to be informative in assessing the risk of MI, a critical first step is to ensure that the patch ECG features agree with gold-standard devices, such as the Biopac. However, this is yet to be done in this population. We, thus, performed a comparative analysis between ECG-derived features (heart rate (HR) and heart rate variability (HRV)) of the patch and Biopac in the context of stress. Our dataset contained post-MI and healthy control subjects who participated in a public speaking challenge. Regression analyses for patch and Biopac HR and HRV features (RMSSD, pNN50, SD1/SD2, and LF/HF) were all significant (p<0.001) and had strong positive correlations (r>0.9). Additionally, Bland-Altman analyses for most features showed tight limits of agreement: 0.999 bpm (HR), 11.341 ms (RMSSD), 0.07% (pNN50), 0.146 ratio difference (SD1/SD2), 0.750 ratio difference (LF/HF).Clinical relevance- This work demonstrates that ECG-derived features obtained from the patch and Biopac are in agreement, suggesting the clinical utility of the patch in deriving quantitative metrics of physiology during stress in post-MI patients. This has the potential to improve post-MI patients' outcomes, but needs to be further evaluated.

Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review

Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activity at the body's center of mass and on the chest, respectively. Since their inception, their potential for evaluating cardiovascular health has been studied. However, both BCG and SCG are impacted by respiration, leading to a periodic modulation of these signals. As a result, data processing algorithms have been developed to exclude the respiratory signals, or recording protocols have been designed to limit the respiratory bias. Reviewing the present status of the literature reveals an increasing interest in applying these techniques to extract respiratory information, as well as cardiac information. The possibility of simultaneous monitoring of respiratory and cardiovascular signals via BCG or SCG enables the monitoring of vital signs during activities that require considerable mental concentration, in extreme environments, or during sleep, where data acquisition must occur without introducing recording bias due to irritating monitoring equipment. This work aims to provide a theoretical and practical overview of cardiopulmonary interaction based on BCG and SCG signals. It covers the recent improvements in extracting respiratory signals, computing markers of the cardiorespiratory interaction with practical applications, and investigating sleep breathing disorders, as well as a comparison of different sensors used for these applications. According to the results of this review, recent studies have mainly concentrated on a few domains, especially sleep studies and heart rate variability computation. Even in those instances, the study population is not always large or diversified. Furthermore, BCG and SCG are prone to movement artifacts and are relatively subject dependent. However, the growing tendency toward artificial intelligence may help achieve a more accurate and efficient diagnosis. These encouraging results bring hope that, in the near future, such compact, lightweight BCG and SCG devices will offer a good proxy for the gold standard methods for assessing cardiorespiratory function, with the added benefit of being able to perform measurements in real-world situations, outside of the clinic, and thus decrease costs and time.

Assessment of a new KoMaWo electrode-patch configuration accuracy and review of the literature

INTRODUCTION

Standard 12‑lead electrocardiogram (ECG) is a basic element of routine everyday clinical practice. Traditional cardiac monitoring devices are associated with considerable limitations. Adhesive patches, novel digital solutions, may become a useful diagnostic tool for several cardiovascular diseases.

MATERIALS AND METHODS

We propose a new variation of ECG electrodes positioning called KoMaWo. 15 consecutive patients presenting with ST segment deviations due to coronary artery disease were enrolled. The accuracy and utility of the new configuration was assessed and compared with the Mason-Likar configuration, as well as with a standard 12‑lead ECG recording. The scans were blinded and interpreted by two independent cardiologists.

RESULTS

There were no statistically significant differences in morphology, as well as in the duration of individual waves, complexes, segments, and intervals between the scans obtained using all three methods. In a subgroup analysis, with regard to age, body mass and left ventricle ejection fraction (LVEF), KoMaWo was non-inferior to standard ECG with a 0.2 mm margin.

DISCUSSION

The role of traditional cardiac monitoring devices is recognized as the gold standard of patient management. However, certain limitations should be considered. Adhesive patches are light-weight, well-tolerated and do not interfere with daily activities of patients. These novel devices allow for extended monitoring, facilitating increased diagnostic accuracy, regarding cardiac arrhythmias.

CONCLUSIONS

The KoMaWo configuration is not inferior to standard electrode placement, nor to Mason-Likar configuration, including its ability to capture ST segment deviations. Adhesive patches may become a valid alternative for traditional cardiac monitoring methods.

Wearable Seismocardiography‐Based Assessment of Stroke Volume in Congenital Heart Disease

Background

Patients with congenital heart disease (CHD) are at risk for the development of low cardiac output and other physiologic derangements, which could be detected early through continuous stroke volume (SV) measurement. Unfortunately, existing SV measurement methods are limited in the clinic because of their invasiveness (eg, thermodilution), location (eg, cardiac magnetic resonance imaging), or unreliability (eg, bioimpedance). Multimodal wearable sensing, leveraging the seismocardiogram, a sternal vibration signal associated with cardiomechanical activity, offers a means to monitoring SV conveniently, affordably, and continuously. However, it has not been evaluated in a population with significant anatomical and physiological differences (ie, children with CHD) or compared against a true gold standard (ie, cardiac magnetic resonance). Here, we present the feasibility of wearable estimation of SV in a diverse CHD population (N=45 patients).

Methods and Results

We used our chest‐worn wearable biosensor to measure baseline ECG and seismocardiogram signals from patients with CHD before and after their routine cardiovascular magnetic resonance imaging, and derived features from the measured signals, predominantly systolic time intervals, to estimate SV using ridge regression. Wearable signal features achieved acceptable SV estimation (28% error with respect to cardiovascular magnetic resonance imaging) in a held‐out test set, per cardiac output measurement guidelines, with a root‐mean‐square error of 11.48 mL and R 2 of 0.76. Additionally, we observed that using a combination of electrical and cardiomechanical features surpassed the performance of either modality alone.

Conclusions

A convenient wearable biosensor that estimates SV enables remote monitoring of cardiac function and may potentially help identify decompensation in patients with CHD.

PySio: A New Python Toolbox for Physiological Signal Visualization and Feature Analysis

In physiological signal analysis, identifying meaningful relationships and inherent patterns in signals can provide valuable information regarding subjects' physiological state and changes. Although MATLAB has been widely used in signal processing and feature analysis, Python has recently dethroned MATLAB with the rise of data science, machine learning and artificial intelligence. Hence, there is a compelling need for a Python package for physiological feature analysis and extraction to achieve compatibility with downstream models often trained in Python. Thus, we present a novel visualization and feature analysis Python toolbox, PySio, to enable rapid, efficient and user-friendly analysis of physiological signals. First, the user should import the signal-of-interest with the corresponding sampling rate. After importing, the user can either analyze the signal as it is, or can choose a specific region for more detailed analysis. PySio enables the user to (i) visualize and analyze the physiological signals (or user-selected segments of the signals) in time domain, (ii) study the signals (or user-selected segments of the signals) in frequency domain through discrete Fourier transform and spectrogram representations, and (iii) investigate and extract the most common time (energy, entropy, zero crossing rate and peaks) and frequency (spectral entropy, rolloff, centroid, spread, peaks and bandpower) domain features, all with one click. Clinical relevance- As the physiological signals originate directly from the underlying physiological events, proper analysis of the signal patterns can provide valuable information in personalized treatment and wearable technology applications.

Quantifying Respiration Effects on Cardiac Vibrations using Teager Energy Operator and Gradient Boosted Trees

This work proposes a novel beat scoring system for quantifying the effects of exhalation and inhalation on the seismocardiogram (SCG) signals in rest and physiologically modulated conditions. Data from 19 subjects during rest, listening to classical music and recovery states were used. First, the SCG and electrocardiogram (ECG) signals were segmented into exhalation and inhalation phases using the respiration signal; and a representative SCG beat for each exhale and inhale phase was constructed using the ECG R-peak locations. Second, the significant differences across the exhalation- and inhalation-induced SCG beats were detected and extracted using the Teager- Kaiser energy operator. Finally, a gradient-based beat scoring system was developed using extreme gradient boosted trees and monotonic mapping. For the rest, classical music and recovery sessions, the area under the receiver operating characteristic curve was found to be 0.978, 0.874, 0.985, respectively. On the other hand, the kernel density estimation distributions of the inhalation and exhalation scores had an overlap of 14.2%, 41.2%, 10.6%, respectively. Overall, our results show that different physiological modulations directly change the effect of respiration on the SCG morphology, thus standardization across the beats should be studied for achieving more reliable and accurate investigation of cardiovascular parameters. Clinical relevance - Such a system can potentially allow for more informed and clinically useful SCG analysis by providing valuable insights regarding the intra-recording variability caused by the respiratory system.

Enabling Continuous Wearable Reflectance Pulse Oximetry at the Sternum

In light of the recent Coronavirus disease (COVID-19) pandemic, peripheral oxygen saturation (SpO₂) has shown to be amongst the vital signs most indicative of deterioration in persons with COVID-19. To allow for the continuous monitoring of SpO₂, we attempted to demonstrate accurate SpO₂ estimation using our custom chest-based wearable patch biosensor, capable of measuring electrocardiogram (ECG) and photoplethysmogram (PPG) signals with high fidelity. Through a breath-hold protocol, we collected physiological data with a wide dynamic range of SpO₂ from 20 subjects. The ratio of ratios (R) used in pulse oximetry to estimate SpO₂ was robustly extracted from the red and infrared PPG signals during the breath-hold segments using novel feature extraction and PPG_green-based outlier rejection algorithms. Through subject independent training, we achieved a low root-mean-square error (RMSE) of 2.64 ± 1.14% and a Pearson correlation coefficient (PCC) of 0.89. With subject-specific calibration, we further reduced the RMSE to 2.27 ± 0.76% and increased the PCC to 0.91. In addition, we showed that calibration is more efficiently accomplished by standardizing and focusing on the duration of breath-hold rather than the resulting range in SpO₂. The accurate SpO₂ estimation provided by our custom biosensor and the algorithms provide research opportunities for a wide range of disease and wellness monitoring applications.