Advanced Bio-Inspired System for Noninvasive Cuff-Less Blood Pressure Estimation from Physiological Signal Analysis

Abstract

Blood Pressure (BP) is one of the most important physiological indicators that provides useful information in the field of health-care monitoring. Blood pressure may be measured by both invasive and non-invasive methods. A novel algorithmic approach is presented to estimate systolic and diastolic blood pressure accurately in a way that does not require any explicit user calibration, i.e., it is non-invasive and cuff-less. The approach herein described can be applied in a medical device, as well as in commercial mobile smartphones by an ad hoc developed software based on the proposed algorithm. The authors propose a system suitable for blood pressure estimation based on the PhotoPlethysmoGraphy (PPG) physiological signal sampling time-series. Photoplethysmography is a simple optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is non-invasive since it takes measurements at the skin surface. In this paper, the authors present an easy and smart method to measure BP through careful neural and mathematical analysis of the PPG signals. The PPG data are processed with an ad hoc bio-inspired mathematical model that estimates systolic and diastolic pressure values through an innovative analysis of the collected physiological data. We compared our results with those measured using a classical cuff-based blood pressure measuring device with encouraging results of about 97% accuracy.

Bio-Inspired Deep-CNN Pipeline for Skin Cancer Early Diagnosis

“…We are extending the skin lesions image dataset in order to better validate the discrimination performance of the proposed approach. Moreover, we have been studying the use of physiological signals acquired in the area of the skin lesion to extract ad-hoc dynamic features to be used for nevus classification [20][21][22][23][24][25] as well as an ad-hoc chemotherapy approach, driven by features detected trough the proposed approach [26]. Specifically, through the innovative approaches used by the authors to characterize physiological signals related to blood-arterial flow activity (including the PhotoPlethysmoGraphic (PPG) signal analysis), we have been analyzing the underlying evolution of skin lesions for early detection of the lymphatic or hematic invasiveness of this lesion because vascular or lymphatic invasion is one of the most discriminating indicators of the malignancy or benignity.…”

Noninvasive Blood Pressure Classification Based on Photoplethysmography Using K-Nearest Neighbors Algorithm: A Feasibility Study

“…Kurylyak et al [18] proposed a non-invasive continuous BP estimation approach based on artificial neural networks (ANNs). Rundo et al [19] proposed a physiological ECG/PPG "combo" pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the convolution neural network (CNN) methodology, to filter PPG signal by assigning a recognition score to the wave forms in the time series. However, all these methodologies present the disadvantage that they are based on PTT calculation, which requires ECG/PPG hardware sensors, software, data extraction (PTT and PWV), etc., making them complicated when applied.…”

“…The system must be calibrated to regulate varying PPG waveform characteristics [27][28][29]. The quality of the PPG waveform is easily corrupted by poor blood circulation, and PPG waveform characteristics vary with fluctuations in peripheral vascular resistance, blood vessel wall elasticity, and blood viscosity [19]. PPG waveforms are easily affected; consequently, the connection between peripheral pulses and BP may not be optimal [21].…”

An Innovative Deep Learning Algorithm for Drowsiness Detection from EEG Signal

“…Robust and complex algorithms of analysis are required. In this context, the Machine Learning methodologies (i.e., Deep Learning) represent powerful methodologies [18][19][20] offering promising approaches for reliable results on signal analysis [21]. In this field, the Convolutional Neural Networks (CNNs) are the most appealing methods for this kind of analysis.…”