Cuffless Blood Pressure Estimation from only the Waveform of Photoplethysmography using CNN.

Although the pulse transit time is generally used for blood pressure estimation without a cuff, a method of estimating blood pressure only from photoplethysmography (PPG) based on the relationship between pulse waveform and blood pressure has been studied. This can eliminate the need for an electrocardiogram and allow more continuous and simpler blood pressure measurement. Previous studies have proposed methods of machine learning by extracting features such as wave height and time difference, or generating features with an auto-encoder.

Features Extraction for Cuffless Blood Pressure Estimation by Autoencoder from Photoplethysmography.

Several studies have been proposed to estimate blood pressure (BP) with cuffless devices using only a Photoplethysmograph (PPG) sensor on the basis of the physiological knowledge that the PPG changes depend on the state of the cardiovascular system. In these studies, machine learning algorithms were used to extract various features from the wave height and the elapsed time from the rising point of the pulse wave to feature points have been used to estimate the BP. However, the accuracy is still not adequate to be used as medical equipment because their features cannot express fully information of the pulse waveform which changes according to the BP.

Cuffless Blood Pressure Estimation Based on Data-Oriented Continuous Health Monitoring System.

Measuring blood pressure continuously helps monitor health and also prevent lifestyle related diseases to extend the expectancy of healthy life. Blood pressure, which is nowadays used for monitoring patient, is one of the most useful indexes for prevention of lifestyle related diseases such as hypertension. However, continuously monitoring the blood pressure is unrealistic because of discomfort caused by the tightening of a cuff belt.

Robust estimation of simulated urinary volume from camera images under bathroom illumination.

General uroflowmetry method involves the risk of nosocomial infections or time and effort of the recording. Medical institutions, therefore, need to measure voided volume simply and hygienically. Multiple cylindrical model that can estimate the fluid flow rate from the photographed image using camera has been proposed in an earlier study.

Estimating flow rate and total volume of simulated urine flow noninvasively from a monocular camera.

Urodynamics measurement tools in practical use carry the risk of nosocomial infection and invasiveness. Our study proposes a new method for urinary flow estimation. We propose multiple cylindrical model to estimate the amount of liquid volume from images taken by a monocular camera.

Separate estimation of long- and short-term systolic blood pressure variability from photoplethysmograph.

This paper proposed a method to monitor systolic blood pressure (BP) variability without using a cuff during the daytime. In this method, BP variability of long-term and short-term were separated and estimated respectively from features of phoplethysmograph (PPG) through the use of a frequency filter. Then, total variability was obtained from the combination of long-term and short-term.

Cuffless blood pressure estimation using only photoplethysmography based on cardiovascular parameters.

This study provides cuffless blood pressure estimation. In general, blood pressure changes when the subject's condition changes, and it is important to estimate it continuously and noninvasively. In many previous studies, they used PTT (Pulse Transmission Time) for estimating.

Measuring cognition delay caused by gaze movement toward the direction of depth.

It is difficult to judge from face images and/or from biological signal (such as brain waves) what the driver's state of cognition is. This paper reports experiments measuring the state of cognition accompanied with gaze movement in the direction of depth. While measuring the cognition time, the experimental setup was able to measure the effect of gaze movement in the direction of depth.

Estimating heart rate using wrist-type Photoplethysmography and acceleration sensor while running.

This study provides Heart Rate (HR) Estimation using wrist-type Photoplethysmogpraphy (PPG) sensor while the subject is running. We propose the algorithm to estimate heart rate for the wrist-type PPG sensor. Since body motion artifacts easily affect the arm portion, our method in this study also uses accelerometer built in the wrist-type sensor to improve the accuracy of heart rate estimation.

Estimation of drowsiness level based on eyelid closure and heart rate variability.

This paper presents a novel method that uses eyelid closure and heart rate variability to estimate the driver's drowsiness level. Laboratory experiments were conducted by using a proprietary driving simulator, which induced drowsiness among the test drivers. The purposes of these experiments were to obtain the electrocardiogram (ECG) and the eye-blink video sequences.

Cuffless blood pressure estimation by error-correcting output coding method based on an aggregation of AdaBoost with a photoplethysmograph sensor.

This paper presented a novel cuffless and non-invasive technique of Blood Pressure (BP) estimation with a pattern recognition method by using a Photoplethysmograph (PPG) sensor instead of a cuff. Error-Correcting Output Coding (ECOC) method was adopted as a multi-classifier machine based on an aggregation of general binary classifiers. AdaBoost was applied as binary classifier machine.

Cuffless and non-invasive Systolic Blood Pressure estimation for aged class by using a Photoplethysmograph.

This study provides cuffless and non-invasive technique of Systolic Blood Pressure (SBP) estimation by using only a Photoplethysmography (PPG) sensor. As people get older, cardiovascular peculiarities are changing more and more, so this study focuses on the presumption of SBP in old age. Ages 60 and over were defined as old age and grouped into an aged class in this study, ages fewer than 60 were grouped into a young-middle class.

Detecting motion artifact ECG noise during sleeping by means of a tri-axis accelerometer.

The electrocardiogram (ECG) monitoring has become a helpful tool for detecting a number of heart diseases. High quality ECG is utilized by the physicians for interpretation and identification of physiological and pathological phenomena. ECG recordings, however, are often corrupted by motion artifacts even when patient is sleeping due to his or her positional change.

Support vector machine based error filtering for holter electrocardiogram analysis.

Holter electrocardiogram data is analyzed by a computer, however, there is a detection of non-heartbeat as a heartbeat. This study dealt with reduction of the incorrect detection using support vector machine (SVM). By exploiting the power of SVM and human like information processing, the data was classified to heartbeat class or non-heartbeat class.