Laser-Induced Graphene for Heartbeat Monitoring with HeartPy Analysis.

The HeartPy Python toolkit for analysis of noisy signals from heart rate measurements is an excellent tool to use in conjunction with novel wearable sensors. Nevertheless, most of the work to date has focused on applying the toolkit to data measured with commercially available sensors. We demonstrate the application of the HeartPy functions to data obtained with a novel graphene-based heartbeat sensor. We produce the sensor by laser-inducing graphene on a flexible polyimide substrate. Both graphene on the polyimide substrate and graphene transferred onto a PDMS substrate show piezoresistive behavior that can be utilized to measure human heartbeat by registering median cubital vein motion during blood pumping. We process electrical resistance data from the graphene sensor using HeartPy and demonstrate extraction of several heartbeat parameters, in agreement with measurements taken with independent reference sensors. We compare the quality of the heartbeat signal from graphene on different substrates, demonstrating that in all cases the device yields results consistent with reference sensors. Our work is a first demonstration of successful application of HeartPy to analysis of data from a sensor in development.

Mobile wireless monitoring system for prehospital emergency care.

BACKGROUND: Latest achievement technologies allow engineers to develop medical systems that medical doctors in the health care system could not imagine years ago. The development of signal theory, intelligent systems, biophysics and extensive collaboration between science and technology researchers and medical professionals, open up the potential for preventive, real-time monitoring of patients. With the recent developments of new methods in medicine, it is also possible to predict the trends of the disease development as well the systemic support in diagnose setting. Within the framework of the needs to track the patient health parameters in the hospital environment or in the case of road accidents, the researchers had to integrate the knowledge and experiences of medical specialists in emergency medicine who have participated in the development of a mobile wireless monitoring system designed for real-time monitoring of victim vital parameters. Emergency medicine responders are first point of care for trauma victim providing prehospital care, including triage and treatment at the scene of incident and transport from the scene to the hospital. Continuous monitoring of life functions allows immediate detection of a deterioration in health status and helps out in carrying out principle of continuous e-triage. In this study, a mobile wireless monitoring system for measuring and recording the vital parameters of the patient was presented and evaluated. Based on the measured values, the system is able to make triage and assign treatment priority for the patient. The system also provides the opportunity to take a picture of the injury, mark the injured body parts, calculate Glasgow Coma Score, or insert/record the medication given to the patient. Evaluation of the system was made using the Technology Acceptance Model (TAM). In particular we measured: perceived usefulness, perceived ease of use, attitude, intention to use, patient status and environmental status. METHODS: A functional prototype of a developed wireless sensor-based system was installed at the emergency medical (EM) department, and presented to the participants of this study. Thirty participants, paramedics and doctors from the emergency department participated in the study. Two scenarios common for the prehospital emergency routines were considered for the evaluation. Participants were asked to answer the questions referred to these scenarios by rating each of the items on a 5-point Likert scale. RESULTS: Path coefficients between each measured variable were calculated. All coefficients were positive, but the statistically significant were only the following: patient status and perceive usefulness (ß = 0.284, t = 2.097), environment (both urban a nd rural) and perceive usefulness (ß = 0.247, t = 2.570; ß = 0.329, t = 2.083, respectively), and perceive usefulness and behavioral intention (ß = 0.621 t = 7.269). The variance of intention is 47.9%. CONCLUSIONS: The study results show that the proposed system is well accepted by the EM personnel and can be used as a complementary system in EM department for continuous monitoring of patients' vital signs.

Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques.

BACKGROUND: Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. METHODS: Raw ECG data are filtered and segmented, and, following this, a complexity analysis is performed for feature extraction. Then, a machine-learning method is applied, combining a stacking-based classification module and a regression module for building systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) predictive models. In addition, the method allows a probability distribution-based calibration to adapt the models to a particular user. RESULTS: Using ECG recordings from 51 different subjects, 3129 30-s ECG segments are constructed, and seven features are extracted. Using a train-validation-test evaluation, the method achieves a mean absolute error (MAE) of 8.64 mmHg for SBP, 18.20 mmHg for DBP, and 13.52 mmHg for the MAP prediction. When models are calibrated, the MAE decreases to 7.72 mmHg for SBP, 9.45 mmHg for DBP and 8.13 mmHg for MAP. CONCLUSION: The experimental results indicate that, when a probability distribution-based calibration is used, the proposed method can achieve results close to those of a certified medical device for BP estimation.