Advance Monitoring of Blood Pressure and Respiratory Rate Using De-Noising Auto Encoder

As the importance of continuous vital signs monitoring increases, the need for wearable devices to measure vital sign is increasing. In this study, the device is designed to measure blood pressure (BP), respiratory rate (RR), and heartrate (HR) with one sensor. The device is in earphone format and is manufactured as wireless type using Arduino-based bluetooth module. The device measures pulse signal in the Superficial temporal artery using Photoplethysmograghy (PPG) sensor. The device uses the Auto Encoder to remove noise caused by movement, etc., contained in the pulse signal. Extract the feature from the pulse signal and use them for the vital sign measurement. The device is measured using Slope transit time (STT) method for BP and Respiratory sinus arrhythmia (RSA) method for RR. Finally, the accuracy is determined by comparing the vital signs measured through the device with the reference vital signs measured simultaneously.

A Spectral-Based Approach for BCG Signal Content Classification

This paper has two objectives: the first is to generate two binary flags to indicate useful frames permitting the measurement of cardiac and respiratory rates from Ballistocardiogram (BCG) signals—in fact, human body activities during measurements can disturb the BCG signal content, leading to difficulties in vital sign measurement; the second objective is to achieve refined BCG signal segmentation according to these activities. The proposed framework makes use of two approaches: an unsupervised classification based on the Gaussian Mixture Model (GMM) and a supervised classification based on K-Nearest Neighbors (KNN). Both of these approaches consider two spectral features, namely the Spectral Flatness Measure (SFM) and Spectral Centroid (SC), determined during the feature extraction step. Unsupervised classification is used to explore the content of the BCG signals, justifying the existence of different classes and permitting the definition of useful hyper-parameters for effective segmentation. In contrast, the considered supervised classification approach aims to determine if the BCG signal content allows the measurement of the heart rate (HR) and the respiratory rate (RR) or not. Furthermore, two levels of supervised classification are used to classify human-body activities into many realistic classes from the BCG signal (e.g., coughing, holding breath, air expiration, movement, et al.). The first one considers frame-by-frame classification, while the second one, aiming to boost the segmentation performance, transforms the frame-by-frame SFM and SC features into temporal series which track the temporal variation of the measures of the BCG signal. The proposed approach constitutes a novelty in this field and represents a powerful method to segment BCG signals according to human body activities, resulting in an accuracy of 94.6%.

A High Sensitivity of Vital Signs Detector using Fiber Optic-based Fabry-Perot Interferometer

In this paper, a development of high sensitivity for vital sign detector based on the fiber optic-based Fabry-Perot interferometer (FFPI) has been proposed. Two interested parameters; heart rate (HR), and also blood pressure (BP) are measured as the vital sign parameters for investigating the performance of the FFPI. Particularly, the proposed sensor is exploited to detect human arterial pulse for indicating the number of interference signals (fringes). A fringe counting technique is, consequently, applied in associate with the deflection of material technique to demodulate the observed number of fringes into HR and BP. Additionally, the reflective thin film with reflectance of approximately 55% is utilized for attaching to the human wrist during the measurement. Furthermore, a digital sphygmomanometer model OMRON HEM-7130 is employed as a reference sensor. After 20 times of repeatability on the same human subject, the FFPI could indicate the systolic and diastolic BP, as well as HR, with average error of 0.94%, 1.64%, and 1.01%, respectively. Moreover, the FFPI could determine the mentioned parameters in decimal numbers, as opposed to the reference sensor. This could, thus, verified that the FFPI is a very sensitive and more precise instrument for applying to the vital sign measurement.