A Personalized Beat-to-Beat Heart Rate Detection System From Ballistocardiogram for Smart Home Applications

A non-invasive, concealed electrocardiogram and bioimpedance measurement system for captive primates

Captive housed non-human primates, specifically great apes such as chimpanzees (Pan troglodytes) are frequently reported to have died from or are diagnosed with potentially fatal heart conditions that require the monitoring of physiological signals such as electrocardiogram (ECG) or respiratory rate. ECG screening must be conducted after applying full anaesthesia, causing potential physical and emotional stress as well as risk for the animal. Here, we present an electronic system that simultaneously measures the ECG and the electrical bioimpedance for the early detection of abnormal cardiovascular activity. Modified gloves whose fingers are equipped with electrodes enable the caregiver to obtain three cardiovascular signals (ECG, pulse rate and respiratory rate) by placing the fingertips on specific parts of the non-human primate without needing any prior physical preparations. Validation (ECG and bioimpedance) was performed both on humans and on captive housed chimpanzees, where all the signals of interest were correctly acquired.

A 2.66 µW Clinician-Like Cardiac Arrhythmia Watchdog Based on P-QRS-T for Wearable Applications

A wearable electrocardiogram (ECG) device is an effective tool for managing cardiovascular diseases. This paper presents a low power clinician-like cardiac arrhythmia watchdog (CAW) for wearable ECG devices. The CAW is based on a novel P-QRS-T detection algorithm that makes use of clinical features to identify abnormalities. Implemented in 0.18 μm CMOS process, the CAW consumes 2.66 µW for 80 bpm heart rate at 1.2 V supply with an area of 0.578 mm². Verified on QT database, the average sensitivity/positive predictivity for P-wave, QRS complex and T-wave are over 93.39%/88.55%, 99.69%/99.48%, and 97.13%/93.18% respectively, across over 190000 beats. It shows over 99.8% arrhythmia detection accuracy for 43 subjects evaluated on MIT-BIH database.

Non-contact Heartbeat Detection Based on Ballistocardiogram Using UNet and Bidirectional Long Short-Term Memory

Benefiting from non-invasive sensing technologies, heartbeat detection from ballistocardiogram (BCG) signals is of great significance for home-care applications, such as risk prediction of cardiovascular disease (CVD) and sleep staging, etc. In this paper, we propose an effective deep learning model for automatic heartbeat detection from BCG signals based on UNet and bidirectional long short-term memory (Bi-LSTM). The developed deep learning model provides an effective solution to the existing challenges in BCG-aided heartbeat detection, especially for BCG in low signal-to-noise, in which the waveforms in BCG signals are irregular due to measured postures, rhythm and artifact motion. For validations, performance of the proposed detection is evaluated by BCG recordings from 24 subjects with different measured postures and heart rate ranges. The accuracy of the detected heartbeat intervals measured in different postures and signal qualities, in comparison with the R-R interval of ECG, is promising in terms of mean absolute error and mean relative error, respectively, which is superior to the state-of-the-art methods. Numerical results demonstrate that the proposed UNet-BiLSTM model performs robust to noise and perturbations (e.g. respiratory effort and artifact motion) in BCG signals, and provides a reliable solution to long term heart rate monitoring.

An Energy-Efficient ASK Demodulator Robust to Power-Carrier-Interference for Inductive Power and Data Telemetry

Wireless power and datatelemetry based on amplitude-shift keying (ASK) modulation over dual inductive links has been widely adopted in biomedical implants. Due to the mutual inductance between the power and data links, the large power-carrier-interference (PCI) will inevitably cause low signal-to-interference ratio (SIR) of the received signal, thereby increasing the bit-error-rate (BER) of the ASK demodulation. In this paper, an innovative high energy-efficient ASK demodulator robust to PCI has been proposed. Thanks to the proposed sampling-and-subtraction (SAS) architecture, the demodulator is capable of withstanding PCI with an amplitude up to 2.5 times as the data carrier without the need for any high-order filters. The prototype has been implemented with 180 nm standard CMOS process, occupying a core area of 0.51 mm ². The experimental results show that with 1 Mbps data rate and 13.56 MHz carrier frequency, the typical BER is less than 1.3×10 ^-3, while the energy efficiency is 280 pJ/bit, showing 7.5× improvement compared to the prior works. The energy-efficient robustness to PCI demonstrates the potential of the technique to be applied to retina prostheses as well as various kinds of ultra-low-power implantable biomedical devices.

Validation Study of a Contactless Monitoring Device for Vital Signs During Sleep and Sleep Architecture in Adults With Sleep-Disordered Breathing

Background and Objective Few clinical studies have investigated the accuracy of non-contact monitoring devices for vital signs during sleep and sleep architecture in adults with sleep-disordered breathing (SDB). The purpose of this study was to assess the accuracy of a contactless monitoring device for 1) heart rate, respiratory rate, and body temperature during sleep and 2) sleep architecture in adults with SDB.Methods Thirty-five consecutive adults, who visited a tertiary university hospital due to suspected SDB, underwent a complete physical examination and standard (level 1) polysomnography plus body temperature measurement with a contactless monitoring device (HoneyCube System).Results A total of 30 subjects (mean age = 46.43 ± 12.9 years; male: female = 22: 8) were finally included, and five subjects were excluded due to inadequate data in this study. The intraclass correlation coefficient values of heart rate, respiratory rate, and body temperature measured using the contactless monitoring device were 0.91 (95% confidence interval [CI]: 0.892, 0.928), 0.937 (95% CI: 0.919, 0.954), and 0.918 (95% CI: 0.895, 0.941), respectively. The mean kappa value for sleep architecture was 0.562 (95% CI: 0.529, 0.596).Conclusions The contactless monitoring device showed good (almost perfect) agreement in terms of heart rate, respiratory rate, and body temperature and moderate agreement in sleep architecture with contact measurements. These results suggest that the HoneyCube System is a good candidate device for sleep monitoring at home and in multiple accommodations.

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.

Measuring Sleep, Wakefulness, and Circadian Functions in Neurologic Disorders

Neurologic disorders impact the ability of the brain to regulate sleep, wake, and circadian functions, including state generation, components of state (such as rapid eye movement sleep muscle atonia, state transitions) and electroencephalographic microarchitecture. At its most extreme, extensive brain damage may even prevent differentiation of sleep stages from wakefulness (eg, status dissociatus). Given that comorbid sleep-wake-circadian disorders are common and can adversely impact the occurrence, evolution, and management of underlying neurologic conditions, new technologies for long-term monitoring of neurologic patients may potentially usher in new diagnostic strategies and optimization of clinical management.

The Latest Progress and Development Trend in the Research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the Field of Health Care

The current status of the research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the field of medical treatment, health care and nursing was analyzed systematically, and the important direction in the research was explored, to provide reference for the relevant researches. This study, based on two large databases, CNKI and PubMed, used the bibliometric analysis method to review the existing documents in the past 20 years, and made analyses on the literature of BCG and SCG for their annual changes, main countries/regions, types of research, frequently-used subject words, and important research subjects. The results show that the developed countries have taken a leading position in the researches in this field, and have made breakthroughs in some subjects, but their research results have been mainly gained in the area of research and development of the technologies, and very few have been actually industrialized into commodities. This means that in the future the researchers should focus on the transformation of BCG and SCG technologies into commercialized products, and set up quantitative health assessment models, so as to become the daily tools for people to monitor their health status and manage their own health, and as the main approaches of improving the quality of life and preventing diseases for individuals.

Non-Invasive Heart Rate Estimation From Ballistocardiograms Using Bidirectional LSTM Regression

Non-invasive heart rate estimation is of great importance in daily monitoring of cardiovascular diseases. In this paper, a bidirectional long short term memory (bi-LSTM) regression network is developed for non-invasive heart rate estimation from the ballistocardiograms (BCG) signals. The proposed deep regression model provides an effective solution to the existing challenges in BCG heart rate estimation, such as the mismatch between the BCG signals and ground-truth reference, multi-sensor fusion and effective time series feature learning. Allowing label uncertainty in the estimation can reduce the manual cost of data annotation while further improving the heart rate estimation performance. Compared with the state-of-the-art BCG heart rate estimation methods, the strong fitting and generalization ability of the proposed deep regression model maintains better robustness to noise (e.g., sensor noise) and perturbations (e.g., body movements) in the BCG signals and provides a more reliable solution for long term heart rate monitoring.