Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals.

Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments. Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers. Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ≤ 49 % ). Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.

Approximation of Influenza-like illness rates using sleep and cardiorespiratory data from a smart bed

Introduction: Pathophysiologic responses to viral infections affect sleep duration, quality, and concomitant cardiorespiratory function. Real-world, longitudinal monitoring of sleep metrics using a Smart Bed could prove to be invaluable for infectious disease detection. Previously we leveraged sleep metrics from a smart bed to build a COVID-19 symptom detection model. Analysis of pre-pandemic data with this model indicated that our results may generalize to detecting symptoms of other influenza-like illnesses (ILI). Here we investigated whether seasonal ILI trends reported by US Center for Disease Control and Prevention (CDC) can be approximated from aggregation of individual ILI symptom predictions. Materials and Methods: An IRB approved survey with COVID-19-specific questions was presented to opting-in Sleep Number customers from August to November 2020 in the USA. COVID-19 test results were reported by 3546/9370 respondents (249 positive;3297 negative). Sleep duration, sleep quality, duration of restful sleep, time to fall asleep, respiration rate, heart rate, and motion level were obtained using ballistocardiography signals from the smart bed. Longitudinal seep data from January 2020 to December 2020 from 122 of the positive and 1603 of the negative respondents were used to develop an individual-level COVID-19 symptom detection model. The model produces a probability of experiencing COVID-19 symptoms for each sleep session. Pre-pandemic sleep data from January 2017 to December 2019 from 4187 responders (1820 sleep sessions per night on average) were used to assess the ability of the developed model to generalize to ILI symptom detection. Weekly rates of high-scoring sleep sessions between January 2017 and June 2018 were fitted to the weekly ILI rates as reported by CDC using a negative binomial model. Subsequently, Pearson correlation coefficients were calculated for the predicted and reported rates between July 2018 and December 2019. Results: Correlation between the predicted and CDC reference was 0.91 (+0.04 compared to the baseline model). Correlation restricted to the influenza season (week 40 of 2018 to week 20 of 2019) was 0.87 (+0.13 compared to the baseline model). Conclusions: The sleep metrics measured with a smart bed platform are a unique source of longitudinal data, collected in a real-world, unobtrusive manner. This system may serve as a valuable asset in predicting and tracking the development of symptoms associated with a wide variety of respiratory illnesses, including influenza and COVID-19. Acknowledgements: This study was funded by Sleep Number Corporation.

CONTINUES REMOTE CARDIAC MONITORING IN CRITICALLY ILL COVID-19 PATIENTS (C3 STUDY)

Objective: 1. To evaluate the use of remote cardiac monitoring of critically ill COVID-19 patients. 2. To correlate DOZEE early warning score(DEWS) with severity and outcome Design and method: Ballistocardiography (BCG)Ballistocardiography is a noninvasive method based on the measurement of the body motion generated by the ejection of the blood at each cardiac cycle. It also contains motion arising from breathing, snoring and body movements. Dozee Early Warning System (DEWS): DEWS is an overall score for risk assessment of the physiological status of a person. It is a cumulative score of risk levels of physiological parameters like HR,RR and SPo2, which acts as an early predictor for possible physiological decline. Assessment of severity of of Acute-illness Detection of clinical deterioration Initiation of a timely and competent clinical response Total 39 subjects were observed where 24 of the subjects were Male and 15 Female and the average duration of stay at the hospital was 5 days. There were 20 patients who had comorbid conditions like HYPOTHYROID, NHL,ASTHMA etc. 19 patients did not present with any co morbidities. The outcome of 10 patients was death and 29 patients were discharged after recovery, as reported by the healthcare professionals at the ward. The vitals of the subjects were continuously monitored by Dozee, a contactless remote patient monitoring system enabled with Dozee Early Warning System (DEWS) which reflects the overall patient condition based on the Respiration, Heart Rate and Spo2 of the patients. Results: The data from the continuous monitoring of the respiration rate, heart rate and oxygen saturation of the 39 patients were analysed for their duration of stay at the hospital. The DEWS score of the patients were also analysed Conclusions: It was concluded that continuous vitals monitoring of the patients and the resulting Dews scores were an indicator of the improving or deteriorating condition of the patients. The discharged patients showed a decrease in the DEWS score, especially Breathing DEWS before they recovered. However, the expired patients showed steady increase or a stagnant high Breathing dews until time of death.

Effects of 4-Day Online Yoga on Ballistocardiography Based Sleep & HRV in Indian Medical Professionals vs Randomized Waitlist Controls During COVID-19

Methods: In this ongoing randomized waitlist-controlled trial, we assessed changes in sleep,HRV & vitals, recorded overnight using a Ballistocardiography based health monitoring device. Outcomes were measured before (Day 0) & after (Day 4) a four-day online breath meditation workshop (OBMW) involving Sudarshan Kriya Yoga. 90 MPs from a tertiary care hospital in northern India were randomized equally (1:1) (45 participants each) to experimental (mean age 27.4±3.6) & waitlist-control (28.8±3.48) groups using computer-generated sequentially numbered opaque sealed envelopes. Results: All outcomes were found comparable at baseline. The between-groups analysis showed a highly significant increase in total sleep duration (p=0.000), duration of deep sleep (p=0.034), light sleep (p=0.000) & rapid eye movement sleep (p=0.000) with a significant reduction in respiration rate (p=0.015) for the Experimental group when compared to Controls. Within-group analysis showed highly significant improvements in HRV outcomes of SDNN (p=0.000) & RMSSD (p=0.000) & reduction in heart rate (p=0.006) for the experimental group alone. Background: Medical professionals (MPs) are facing tremendous stress, sleep deprivation & burnout due to COVID related high patient inflow& continuouswork shifts. Lowheart rate variability (HRV) & poor sleep regimes are associated with cardiomyopathy & diabetes in the long run. Yoga has strong evidence for its multifold mental & physical health benefits, yet no previous study has determined its acute effects on objective sleep measures & HRV among MPs during a pandemic. Conclusion: Maintaining a good sleep routine & high HRV result in greater cardiovascular fitness & vagal tone. Four days of OBMW might help induce psycho-physical relaxation & prove to be a feasible, cost-effective, & well-accepted tool to help build stress resilience. As the stakeholders in patient care i.e., MPs are healthy, it might further improve patient care & reduce the chance of medical errors. Further research is warranted to determine long-term effects in this regard.

A Noncontact Ballistocardiography-Based IoMT System for Cardiopulmonary Health Monitoring of Discharged COVID-19 Patients.

We developed a ballistocardiography (BCG)-based Internet-of-Medical-Things (IoMT) system for remote monitoring of cardiopulmonary health. The system composes of BCG sensor, edge node, and cloud platform. To improve computational efficiency and system stability, the system adopted collaborative computing between edge nodes and cloud platforms. Edge nodes undertake signal processing tasks, namely approximate entropy for signal quality assessment, a lifting wavelet scheme for separating the BCG and respiration signal, and the lightweight BCG and respiration signal peaks detection. Heart rate variability (HRV), respiratory rate variability (RRV) analysis and other intelligent computing are performed on cloud platform. In experiments with 25 participants, the proposed method achieved a mean absolute error (MAE)±standard deviation of absolute error (SDAE) of 9.6±8.2 ms for heartbeat intervals detection, and a MAE±SDAE of 22.4±31.1 ms for respiration intervals detection. To study the recovery of cardiopulmonary function in patients with coronavirus disease 2019 (COVID-19), this study recruited 186 discharged patients with COVID-19 and 186 control volunteers. The results indicate that the recovery performance of the respiratory rhythm is better than the heart rhythm among discharged patients with COVID-19. This reminds the patients to be aware of the risk of cardiovascular disease after recovering from COVID-19. Therefore, our remote monitoring system has the ability to play a major role in the follow up and management of discharged patients with COVID-19.

Kinocardiography Derived from Ballistocardiography and Seismocardiography Shows High Repeatability in Healthy Subjects.

Recent years have witnessed an upsurge in the usage of ballistocardiography (BCG) and seismocardiography (SCG) to record myocardial function both in normal and pathological populations. Kinocardiography (KCG) combines these techniques by measuring 12 degrees-of-freedom of body motion produced by myocardial contraction and blood flow through the cardiac chambers and major vessels. The integral of kinetic energy (iK) obtained from the linear and rotational SCG/BCG signals, and automatically computed over the cardiac cycle, is used as a marker of cardiac mechanical function. The present work systematically evaluated the test-retest (TRT) reliability of KCG iK derived from BCG/SCG signals in the short term (<15 min) and long term (3-6 h) on 60 healthy volunteers. Additionally, we investigated the difference of repeatability with different body positions. First, we found high short-term TRT reliability for KCG metrics derived from SCG and BCG recordings. Exceptions to this finding were limited to metrics computed in left lateral decubitus position where the TRT reliability was moderate-to-high. Second, we found low-to-moderate long-term TRT reliability for KCG metrics as expected and confirmed by blood pressure measurements. In summary, KCG parameters derived from BCG/SCG signals show high repeatability and should be further investigated to confirm their use for cardiac condition longitudinal monitoring.