Cuff-Less Blood Pressure Monitoring with a 3-Axis Accelerometer

Cardiorespiratory Sensors and Their Implications for Out-of-Hospital Cardiac Arrest Detection: A Systematic Review

Out-of-hospital cardiac arrest (OHCA) is a major health problem, with a poor survival rate of 2-11%. For the roughly 75% of OHCAs that are unwitnessed, survival is approximately 2-4.4%, as there are no bystanders present to provide life-saving interventions and alert Emergency Medical Services. Sensor technologies may reduce the number of unwitnessed OHCAs through automated detection of OHCA-associated physiological changes. However, no technologies are widely available for OHCA detection. This review identifies research and commercial technologies developed for cardiopulmonary monitoring that may be best suited for use in the context of OHCA, and provides recommendations for technology development, testing, and implementation. We conducted a systematic review of published studies along with a search of grey literature to identify technologies that were able to provide cardiopulmonary monitoring, and could be used to detect OHCA. We searched MEDLINE, EMBASE, Web of Science, and Engineering Village using MeSH keywords. Following inclusion, we summarized trends and findings from included studies. Our searches retrieved 6945 unique publications between January, 1950 and May, 2023. 90 studies met the inclusion criteria. In addition, our grey literature search identified 26 commercial technologies. Among included technologies, 52% utilized electrocardiography (ECG) and 40% utilized photoplethysmography (PPG) sensors. Most wearable devices were multi-modal (59%), utilizing more than one sensor simultaneously. Most included devices were wearable technologies (84%), with chest patches (22%), wrist-worn devices (18%), and garments (14%) being the most prevalent. ECG and PPG sensors are heavily utilized in devices for cardiopulmonary monitoring that could be adapted to OHCA detection. Developers seeking to rapidly develop methods for OHCA detection should focus on using ECG- and/or PPG-based multimodal systems as these are most prevalent in existing devices. However, novel sensor technology development could overcome limitations in existing sensors and could serve as potential additions to or replacements for ECG- and PPG-based devices.

Heart Rate Measurement Using the Built-In Triaxial Accelerometer from a Commercial Digital Writing Device

Currently, wearable technology is an emerging trend that offers remarkable access to our data through smart devices like smartphones, watches, fitness trackers and textiles. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor heart and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heart rate when they are attached to the chest. They can also help filter out some noise in ECG signals from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen) to standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is carried out with eight volunteers writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685 × 10-3 comparing the DigiPen's computed Δt (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates (HR =60Δt) from the pen accurately correlate with the reference ECG signals.

An Acute Kidney Injury Prediction Model for 24-hour Ultramarathon Runners

Acute kidney injury (AKI) is frequently seen in ultrarunners, and in this study, an AKI prediction model for 24-hour ultrarunners was built based on the runner’s prerace blood, urine, and body composition data. Twenty-two ultrarunners participated in the study. The risk of acquiring AKI was evaluated by a support vector machine (SVM) model, which is a statistical model commonly used for classification tasks. The inputs of the SVM model were the data collected 1 hour before the race, and the output of the SVM model was the decision of acquiring AKI. Our best AKI prediction model achieved accuracy of 96% in training and 90% in cross-validation tests. In addition, the sensitivity and specificity of the model were 90% and 100%, respectively. In accordance with the AKI prediction model components, ultra-runners are suggested to have high muscle mass and undergo regular ultra-endurance sports training to reduce the risk of acquiring AKI after participating in a 24-hour ultramarathon.

Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare

Acoustic signals are important markers to monitor physiological and pathological conditions, e.g., heart and respiratory sounds. The employment of traditional devices, such as stethoscopes, has been progressively superseded by new miniaturized devices, usually identified as microelectromechanical systems (MEMS). These tools are able to better detect the vibrational content of acoustic signals in order to provide a more reliable description of their features (e.g., amplitude, frequency bandwidth). Starting from the description of the structure and working principles of MEMS, we provide a review of their emerging applications in the healthcare field, discussing the advantages and limitations of each framework. Finally, we deliver a discussion on the lessons learned from the literature, and the open questions and challenges in the field that the scientific community must address in the near future.

Heart Rate and Respiratory Rate Monitoring Using Seismocardiography

Vital signs monitoring is critical for healthcare. Currently, at-home vital signs monitoring is obstructed by the complicated device, unaffordable cost, and inconvenience. In this study, we develop a simultaneous heart rate and respiratory rate monitoring technique that requires only one tri-axial accelerometer placing on the sternum. We devise a signal processing technique to generate seismocardiography and respiratory vibration from the raw acceleration data; furthermore, we formulate the algorithms to compute the heart rate and respiratory rate from the processed signals. We tested the methodology on 20 young healthy adults during pre-exercise and post-exercise sitting. The accuracy of 98.3% and 97.3% are achieved in heart rate monitoring during pre-exercise and post-exercise sitting. For respiratory rate, an accuracy of 96.8% is accomplished. Given the accuracy, affordable cost and convenience, the acceleration-based technique shows great promise for at-home vital signs monitoring.Clinical relevance- Portable heart rate and respiratory rate monitoring is substantial in elevating the quality of healthcare environment.

Motion Artifact Resilient Cuff-Less Blood Pressure Monitoring Using a Fusion of Multi-Dimensional Seismocardiograms

Blood pressure (BP) monitoring is critical to raise awareness of hypertension and hypotension, yet the commonly used techniques require the person staying still along with a cuff around the arm. Some cuff-less approaches have been researched, but all hinder the person from moving around. To address the challenge, we propose using a fusion of accelerometers to achieve motion artifact resilient blood pressure monitoring. Such technique is accomplished with the motion artifact removal process and feature extraction from multi-dimensional seismocardiograms. The efficacy of our BP monitoring models is validated in 19 young healthy adults. Both the diastolic and systolic BP monitoring models fulfill the AAMI standard and British Hypertension Society protocol. For sitting still BP monitoring, the mean and standard deviation of diastolic and systolic difference errors (DE) are 0.09 ± 4.10 and -0.25 ± 5.45 mmHg; moreover, the mean absolute difference errors (MADE) are 3.62 and 4.73 mmHg. In walking motions, the DE are 1.15 ±4.47 mmHg for diastolic BP and -0.38 ± 6.67 for systolic BP; furthermore, the MADE are 3.36 and 5.07 mmHg, respectively. The motion artifact resilient cuff-less BP monitoring reveals the potential of portable BP monitoring in healthcare environments.

Motion Artifact Resilient SCG-based Biometric Authentication Using Machine Learning

On account of privacy preserving issue and health-care monitoring, physiological signal biometric authentication system has gained popularity in recent years. Seismocardiogram (SCG) is now easily accessible owing to the advance of wearable sensor technology. However, SCG biometric has not been widely explored due to the challenging motion artifact removal. In this paper, we design placing the sensors at different body parts under different activities to determine the best sensor location. In addition, we develop SCG noise removal algorithm and utilize machine learning approach to perform biometric authentication tasks. We validate the proposed methods on 20 healthy young adults. The dataset contains acceleration data of sitting, standing, walking, and sitting post-exercise activities with the sensor placed at the wrists, neck, heart and sternum. We demonstrate that vertical and dorsal-ventral SCG near the heart and the sternum produce reliable SCG biometric evidenced by achieving the state-of-the-art performance. Moreover, we present the efficacy of the devised noise removal procedure in the authentication during walking motion.Clinical relevance- A seismocardiography-based biometric authentication system can help serve privacy preserving and reveal cardiovascular functioning information in clinics.