Real-time Physiological Signals Research Articles

Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study.

BackgroundWrist-worn activity monitors are often used to monitor heart rate (HR) and energy expenditure (EE) in a variety of settings including more recently in medical applications. The use of real-time physiological signals to inform medical systems including drug delivery systems and decision support systems will depend on the accuracy of the signals being measured, including accuracy of HR and EE. Prior studies assessed accuracy of wearables only during steady-state aerobic exercise.ObjectiveThe objective of this study was to validate the accuracy of both HR and EE for 2 common wrist-worn devices during a variety of dynamic activities that represent various physical activities associated with daily living including structured exercise.MethodsWe assessed the accuracy of both HR and EE for two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) during dynamic activities. Over a 2-day period, 20 healthy adults (age: mean 27.5 [SD 6.0] years; body mass index: mean 22.5 [SD 2.3] kg/m2; 11 females) performed a maximal oxygen uptake test, free-weight resistance circuit, interval training session, and activities of daily living. Validity was assessed using an HR chest strap (Polar) and portable indirect calorimetry (Cosmed). Accuracy of the commercial wearables versus research-grade standards was determined using Bland-Altman analysis, correlational analysis, and error bias.ResultsFitbit and Garmin were reasonably accurate at measuring HR but with an overall negative bias. There was more error observed during high-intensity activities when there was a lack of repetitive wrist motion and when the exercise mode indicator was not used. The Garmin estimated HR with a mean relative error (RE, %) of −3.3% (SD 16.7), whereas Fitbit estimated HR with an RE of −4.7% (SD 19.6) across all activities. The highest error was observed during high-intensity intervals on bike (Fitbit: −11.4% [SD 35.7]; Garmin: −14.3% [SD 20.5]) and lowest error during high-intensity intervals on treadmill (Fitbit: −1.7% [SD 11.5]; Garmin: −0.5% [SD 9.4]). Fitbit and Garmin EE estimates differed significantly, with Garmin having less negative bias (Fitbit: −19.3% [SD 28.9], Garmin: −1.6% [SD 30.6], P<.001) across all activities, and with both correlating poorly with indirect calorimetry measures.ConclusionsTwo common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) show good HR accuracy, with a small negative bias, and reasonable EE estimates during low to moderate-intensity exercise and during a variety of common daily activities and exercise. Accuracy was compromised markedly when the activity indicator was not used on the watch or when activities involving less wrist motion such as cycle ergometry were done.

Design and Evaluation of a Real Time Physiological Signals Acquisition System Implemented in Multi-Operating Rooms for Anesthesia.

With critical importance of medical healthcare, there exist urgent needs for in-depth medical studies that can access and analyze specific physiological signals to provide theoretical support for practical clinical care. As a consequence, obtaining the valuable medical data with minimal cost and impacts on hospital work comes as the first concern of researchers. Anesthesia plays a widely recognized role in surgeries, which attracts people to undertake relevant research. In this paper, a real-time physiological medical signal data acquisition system (PMSDA) for the multi-operating room applications is proposed with high universality of the hospital practical settings and research requirements. By utilizing a wireless communication approach, it provides an easily accessible network platform for collection of physiological medical signals such as photoplethysmogram (PPG), electrocardiograph (ECG) and electroencephalogram (EEG) during the surgery. In addition, the raw data is stored on a server for safe backup and further analysis of depth of anesthesia (DoA). Results show that the PMSDA exhibits robust, high quality performance and efficiently reduces costs compared to previously manual methods and allows seamless integration into hospital environment, independent of its routine work. Overall, it provides a pragmatic and flexible surgery-data acquisition system model with low impact and resource cost applicable to research in critical and practical medical circumstances.

Detection of epileptic dysfunctions in EEG signals using Hilbert vibration decomposition

Abstract Epilepsy is a neurological brain dysfunction that is manifested by recrudescent seizures. Due to high temporal resolution, brain activities recorded by electroencephalography (EEG) are commonly used for localization of seizures and identification of epileptic dysfunctions. However, it is often time consuming and challenging to detect EEG seizures using conventional Fourier-based methods and manual interpretation due to nonlinear and nonstationary dynamics of EEG. In this paper, we propose a new framework based on Hilbert vibration decomposition (HVD) for discriminating normal and epileptic EEG recordings. HVD exploits Hilbert transform presentation of instantaneous frequency and extracts monocomponents that have distinctive time-varying amplitudes and instantaneous frequencies from nonstationary signals. The proposed method employs estimated instantaneous frequencies of largest energy components as features given to least squares support vector machine (LS-SVM) for recognizing epileptic seizures and is shown to be appealing for real time physiological signal processing applications due to its reduced computational complexity. Test results on a benchmark EEG data set achieved 97.66% classification accuracy and area of 0.9914 under the receiver operating characteristics (ROC) curve using the delta, theta and alpha rhythms.

Sustainable Energy Generation from Piezoelectric Biomaterial for Noninvasive Physiological Signal Monitoring

Sustainable development of self-powered wearable electronics relies on bioinspired piezoelectric materials which could transduce minute deformation of human skin. In this work, fish-skin-based sustainable energy production is discussed in light of structure–property correlation. The developed energy harvester acts as a sensor that interacts with human body parts to monitor real-time physiological signals. Self-assembled collagen nanofibrils comprising fish skin show stable crystalline structure and possess nonlinear electrostriction effect without any electrical poling treatment. A fish-skin-based nanogenerator (FSKNG)/pressure sensor is ultrasensitive (sensitivity ∼27 mV N–1) and highly durable (over 75 000 cycles) and possesses very fast response time (∼4.9 ms). Importantly, in response to the external pressure (∼1.8 MPa), FSKNG generates open-circuit voltage, Voc ∼ 2 V, and short-circuit current, Isc ∼ 20 nA, due to inherent piezoelectric effect. The magnitude of the generated power (∼0.75 mW m–2) turn...

High Sensitivity Flexible Capacitive Pressure Sensor Using Polydimethylsiloxane Elastomer Dielectric Layer Micro-Structured by 3-D Printed Mold

3-D printing is used to fabricate mold for micro-structuring the polydimethylsiloxane (PDMS) elastomeric dielectric layer in capacitive flexible pressure sensors. It is shown that, despite of the limited resolution of the used commercial 3-D printer for producing the mold, the fabricated sensor with the micro-structured PDMS layers can achieve sensitivity higher than previous work using micro-fabricated silicon wafer molds. The devices also present very low detection limit, fast response/recovery speed, excellent durability and good tolerance to variations of ambient temperature and humidity, which enables to reliably monitor weak human physiological signals in real time. As an application example, the flexible pressure sensor is integrated in a wearable system to monitor wrist pulse.

Using demographic and time series physiological features to classify sepsis in the intensive care unit.

Sepsis, a systemic inflammatory response to infection, is a major health care problem that affects millions of patients every year in the intensive care units (ICUs) worldwide. Despite the fact that ICU patients are heavily instrumented with physiological sensors, early sepsis detection remains challenging, perhaps because clinicians identify sepsis by (i) using static scores derived from bed-side measurements individually, and (ii) deriving these scores at a much slower rate than the rate for which patient data is collected. In this study, we construct a generalized linear model (GLM) for the probability that an ICU patient has sepsis as a function of demographics and bedside measurements. Specifically, models were trained on 29 patient recordings from the MIMIC II database and evaluated on a different test set including 8 patient recordings. A classification accuracy of 62.5% was achieved using demographic measures as features. Adding physiological time series features to the model increased the classification accuracy to 75%. Although very preliminary, these results suggest that using generalized linear models incorporating real time physiological signals may be useful for an early detection of sepsis, thereby improving the chances of a successful treatment.

Variable-rate transmission method with coordinator election for wireless body area networks

Wearable computing technologies are becoming widely used in healthcare and for tracking various human work and leisure activities. The collection and transmission of sensor data are fundamental functions of these technologies. A wireless body area network (WBAN) helps these wearable devices to continuously acquire ambulatory and physiological signals in real time with multiple sensor nodes attached or implanted into a human body. Sensor nodes transmit data to a sink node with wireless connections can enable mobility of human bodies. Since the data transmission rate of each sensor node is different, we proposed a variable-rate transmission MAC protocol (VTMAC) with coordinator election based on the time division multiple access method. This adaptive coordinator election scheme can balance the sensor nodes' power consumption to extend the lifetime of WBAN as possible. It can also flexibly allocate time slots for each sensor node according to their different data transmission rates to enhance the efficiency of networks. The proposed method has been implemented and demonstrated on a hardware wireless communication platform with multiple wearable sensor nodes. Simulations show the VTMAC can achieve less power consumption and higher throughput performance compared with conventional protocols especially in high data transmission rate situation for the body area networks we simulated.

Mobile Nurse Information System with Efficient Physiological Monitoring for Patient

Current traditional physiological monitoring is accomplished by individual cardiogram monitor device attached to each patient’s bed. A nurse will have to frequently monitor the vital signs at patient’s bedside, which is inefficient and time consuming. Although some hospitals have deployed the central monitoring system, it still has space limit to the nurses and high cost. Our research work is to develop a mobile nurse information system with efficient physiological monitoring for patient based on iPad. Patient’s real-time physiological signals captured by the physiological monitoring system are integrated to the mobile nurse information system. Nurses could get just-in-time access to the data of the physiological monitoring system at anywhere through the clinical use of the mobile nurse information system. Trial version in our hospital showed that the system is feasible and efficient enough to apply in clinical settings.

Validation of Peripheral Pulse Wave Analysis Technique to Detect Aortic Valve State in Continuous-Flow LVADs1

Validation of Peripheral Pulse Wave Analysis Technique to Detect Aortic Valve State in Continuous-Flow LVADs1

Device Optimization Using Pulse Wave Analysis Techniques

Device Optimization Using Pulse Wave Analysis Techniques

A wearable mobihealth care system supporting real-time diagnosis and alarm

This paper describes a wearable mobihealth care system aiming at providing long-term continuous monitoring of vital signs for high-risk cardiovascular patients. We use a portable patient unit (PPU) and a wearable shirt (WS) to monitor electrocardiogram (ECG), respiration (acquired with respiratory inductive plethysmography, RIP), and activity. Owing to integrating fabric sensors and electrodes endowed with electro-physical properties into the WS, long-term continuous monitoring can be realized without making patients feel uncomfortable and restricting their mobility. The PPU analyzes physiological signals in real time and determines whether the patient is in danger or needs external help. The PPU will alert the patient and an emergency call will be automatically established with a medical service center (MSC) when life-threatening arrhythmias or falls are detected. With advanced gpsOne technology, the patient can be located and rescued immediately whether he/she is indoors or outdoors in case of emergency.

The real time processing of dynamic physiological signals

A signal processing system has been developed consisting of a microcomputer linked to a minicomputer. The system has been designed to allow ease of development of programs for processing physiological signals in real time. The system also allows extensive non real time processing to be carried out and the generation of graphical displays. Some of the problems involved in producing real time signal processing programs are discussed. The problem of computing the beat by beat mean pressure in real time is described as an example.