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

An open-access simultaneous electrocardiogram and phonocardiogram database

Objective.The EPHNOGRAM project aimed to develop a low-cost, low-power device for simultaneous electrocardiogram (ECG) and phonocardiogram (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.

Skin-interfacing wearable biosensors for smart health monitoring of infants and neonates

Health monitoring of infant patients in intensive care can be especially strenuous for both the patient and their caregiver, as testing setups involve a tangle of electrodes, probes, and catheters that keep the patient bedridden. This has typically involved expensive and imposing machines, to track physiological metrics such as heart rate, respiration rate, temperature, blood oxygen saturation, blood pressure, and ion concentrations. However, in the past couple of decades, research advancements have propelled a world of soft, wearable, and non-invasive systems to supersede current practices. This paper summarizes the latest advancements in neonatal wearable systems and the different approaches to each branch of physiological monitoring, with an emphasis on smart skin-interfaced wearables. Weaknesses and shortfalls are also addressed, with some guidelines provided to help drive the further research needed.

Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review

The rapid development of biosensing technologies together with the advent of deep learning has marked an era in healthcare and biomedical research where widespread devices like smartphones, smartwatches, and health-specific technologies have the potential to facilitate remote and accessible diagnosis, monitoring, and adaptive therapy in a naturalistic environment. This systematic review focuses on the impact of combining multiple biosensing techniques with deep learning algorithms and the application of these models to healthcare. We explore the key areas that researchers and engineers must consider when developing a deep learning model for biosensing: the data modality, the model architecture, and the real-world use case for the model. We also discuss key ongoing challenges and potential future directions for research in this field. We aim to provide useful insights for researchers who seek to use intelligent biosensing to advance precision healthcare.

A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography

Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at -15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of -19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.

Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review

This article presents a systematic review aimed at mapping the literature published in the last decade on the use of machine learning (ML) for clinical decision-making through wearable inertial sensors. The review aims to analyze the trends, perspectives, strengths, and limitations of current literature in integrating ML and inertial measurements for clinical applications. The review process involved defining four research questions and applying four relevance assessment indicators to filter the search results, providing insights into the pathologies studied, technologies and setups used, data processing schemes, ML techniques applied, and their clinical impact. When combined with ML techniques, inertial measurement units (IMUs) have primarily been utilized to detect and classify diseases and their associated motor symptoms. They have also been used to monitor changes in movement patterns associated with the presence, severity, and progression of pathology across a diverse range of clinical conditions. ML models trained with IMU data have shown potential in improving patient care by objectively classifying and predicting motor symptoms, often with a minimally encumbering setup. The findings contribute to understanding the current state of ML integration with wearable inertial sensors in clinical practice and identify future research directions. Despite the widespread adoption of these technologies and techniques in clinical applications, there is still a need to translate them into routine clinical practice. This underscores the importance of fostering a closer collaboration between technological experts and professionals in the medical field.

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.

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.

Current and Future Use of Artificial Intelligence in Electrocardiography

Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.

Wearable Biosensors in Congenital Heart Disease: Needs to Advance the Field

Traditional measures of clinical status and physiology have generally been based in health care settings, episodic, short in duration, and performed at rest. Wearable biosensors provide an opportunity to obtain continuous non-invasive physiologic data from patients with congenital heart disease (CHD) in the real-world setting, over longer durations, and across varying levels of activity. However, there are significant technical limitations to the use of wearable biosensors in CHD. Here, we review current applications of wearable biosensors in CHD; how clinical and research uses of wearable biosensors must consider various CHD physiologies; the technical challenges in developing wearable biosensors for CHD; and special considerations for digital biomarkers in CHD.

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.

Pain is reduced by transcutaneous cervical vagus nerve stimulation and correlated with cardiorespiratory variability measures in the context of opioid withdrawal

Over 100,000 individuals in the United States lost their lives secondary to drug overdose in 2021, with opioid use disorder (OUD) being a leading cause. Pain is an important component of opioid withdrawal, which can complicate recovery from OUD. This study's objectives were to assess the effects of transcutaneous cervical vagus nerve stimulation (tcVNS), a technique shown to reduce sympathetic arousal in other populations, on pain during acute opioid withdrawal and to study pain's relationships with objective cardiorespiratory markers. Twenty patients with OUD underwent opioid withdrawal while participating in a two-hour protocol. The protocol involved opioid cues to induce opioid craving and neutral conditions for control purposes. Adhering to a double-blind design, patients were randomly assigned to receive active tcVNS (n = 9) or sham stimulation (n = 11) throughout the protocol. At the beginning and end of the protocol, patients' pain levels were assessed using the numerical rating scale (0–10 scale) for pain (NRS Pain). During the protocol, electrocardiogram and respiratory effort signals were measured, from which heart rate variability (HRV) and respiration pattern variability (RPV) were extracted. Pre- to post- changes (denoted with a Δ) were computed for all measures. Δ NRS Pain scores were lower (P = 0.045) for the active group (mean ± standard deviation: −0.8 ± 2.4) compared to the sham group (0.9 ± 1.0). A positive correlation existed between Δ NRS pain scores and Δ RPV (Spearman's ρ = 0.46; P = 0.04). Following adjustment for device group, a negative correlation existed between Δ HRV and Δ NRS Pain (Spearman's ρ = −0.43; P = 0.04). This randomized, double-blind, sham-controlled pilot study provides the first evidence of tcVNS-induced reductions in pain in patients with OUD experiencing opioid withdrawal. This study also provides the first quantitative evidence of an association between breathing irregularity and pain. The correlations between changes in pain and changes in objective physiological markers add validity to the data. Given the clinical importance of reducing pain non-pharmacologically, the findings support the need for further investigation of tcVNS and wearable cardiorespiratory sensing for pain monitoring and management in patients with OUD.