Since blood pressure is a significant parameter to examine people's physical attributes and it is useful to indicate cardiovascular diseases, the measurement/estimation of blood pressure has gained increasing attention. The continuous, cuff-less and non-invasive blood pressure estimation is required for the daily health monitoring. In recent years, studies have been focusing on the ways of blood pressure estimation based on other physiological parameters. It is widely accepted that the pulse transit time (PTT) is related to arterial stiffness, and can be used to estimate blood pressure.<p> A promising signal processing technology, Hilbert-Huang Transform (HHT), is introduced to analyze both ECG and PPG data, which are applied to calculate PTT. The relationship between blood pressure and PTT is illustrated, and the problems of calibration and re-calibration are also discussed. The proposed algorithm is tested based on the continuous data from MIMIC database. To verify the algorithm, the HHT algorithm is compared with other used processing technique (wavelet transform). The accuracy is calculated to validate the method. Furthermore, we collect data using our own developed system and test our algorithm.

Since blood pressure is a significant parameter to examine people's physical attributes and it is useful to indicate cardiovascular diseases, the measurement/estimation of blood pressure has gained increasing attention. The continuous, cuff-less and non-invasive blood pressure estimation is required for the daily health monitoring. In recent years, studies have been focusing on the ways of blood pressure estimation based on other physiological parameters. It is widely accepted that the pulse transit time (PTT) is related to arterial stiffness, and can be used to estimate blood pressure.<p> A promising signal processing technology, Hilbert-Huang Transform (HHT), is introduced to analyze both ECG and PPG data, which are applied to calculate PTT. The relationship between blood pressure and PTT is illustrated, and the problems of calibration and re-calibration are also discussed. The proposed algorithm is tested based on the continuous data from MIMIC database. To verify the algorithm, the HHT algorithm is compared with other used processing technique (wavelet transform). The accuracy is calculated to validate the method. Furthermore, we collect data using our own developed system and test our algorithm.

Sleep related breathing disorders in childhood are increasingly recognised as contributors to morbidity as well as suboptimal physical, mental and social development. The associated cost in social and economic terms is clearly significant. Detection and diagnosis of such disorders can be costly and technically difficult. Multi-parameter monitoring like polysomnography is expensive and inappropriate for mass screening or investigations remote from tertiary centres. A simple and non-invasive cardiovascular approach termed pulse transit time (PTT) is useful in determining the status of upper airways during sleep. This approach employs the measurement of the time delay in the arterial pulse pressure wave from the aortic valve of the heart to a peripheral site. PTT has shown promise to provide quantify inspiratory effort in adults with sleep disordered breathing (SDB) and can then be an effective monitoring tool in children. Initial investigations explore the parameters that can confound the PTT measurements of a child. This work provides an estimate of the expected PTT ranges from infancy to childhood. Thereafter, overnight PTT recordings were performed in conjunction with PSG studies to corroborate evidence for its capability in paediatric respiratory studies. The results show that PTT has the ability to identify abnormal respiratory events during sleep. Furthermore, the use of PTT can differentiate the nature of respiratory events as obstructive or central. Technological considerations to improve the robustness of PTT measure to be used not only in cardiorespiratory but also cardiovascular studies on children were also illustrated. Preliminary findings obtained here suggest that the properties of PTT show promise as a predictor for obstructive sleep apnoea. There is a need to bridge the gap of providing quality SDB diagnosis and yet accommodating requirements for a robust screening measure. PTT has demonstrated its suitability and utility as a measure to play a role in the investigation and monitoring of treatment in this area.

Smart watches in future will have smart wristband. This work analyses properties of new developed capacitive wristband sensor that measures ballistocardiogram (BCG) from single point on the wrist. In addition, it considers applications of this sensor to monitoring heart rate variability. Another application is in estimating changes (trend) in systolic blood pressure continuously when combined with lead one electrocardiogram (ECG). BP is one of the vital signs that indicates the health condition. It is commonly measured by cuff-based monitor using either auscultatory or oscillometric method. Cuff-based BP monitor is not portable and unable to measure BP continuously which means it is difficult to attach BP monitoring function on a wearable device. Significant research is conducted in estimating BP from pulse transit time (PTT) mathematically which would enable the cuffless BP measurement. In this work, a new time reference, RJ interval, which is the time delay between ECG and BCG signal peaks was tested whether it can be used as a surrogate of PTT in cuffless BP estimation. Based on the study done on 10 healthy people, it was shown that RJ intervals can be useful in evaluating trends of systolic blood pressure.

Abstract This thesis describes the development of pressure sensor arrays and a range of methods suitable for the long-term measurement of heart rate and blood pressure determination using a cuff and a pressure sensor array on the radial artery. This study also reviews the historical background of noninvasive blood pressure measurement methods, summarizes the accuracies achieved and explains the requirements for common national and international standards of accuracy. Two prototype series of pressure transducer arrays based on electro-mechanical film (EMFi) were designed and tested. By offering high (∼TΩ) resistance, EMFi is an excellent material for low-current long-term measurement applications. About 50 transducer arrays were built using different configurations and electrode materials to sense low-frequency pressure pulsations on the radial artery in the wrist. In addition to uniform quality, essential requirements included an adequate linear response in the desired temperature range. Transducer sensitivity was tested as a function of temperature in the range of 25–45 °C at varying static and alternating pressures. The average sensitivity of the EMFi used in the transducers proved adequate (∼2.2 mV/mmHg and ∼7 mV/mmHg for normal and high sensitive films) for the intended purpose. The thesis also evaluates blood pressure measurements by the electronic palpation method (EP) and compares the achieved accuracy to that of the oscillometric method (OSC) using average intra-arterial (IA) blood pressure as a reference. All of these three measurements were made simultaneously for each person. In one test group, measurements were conducted on healthy volunteers in sitting and supine position during increasing and decreasing cuff pressure. Another group, comprising elderly cardiac patients, was measured only in the supine position during cuff inflation. The results showed that the EP method was approximately as accurate as the OSC method with the healthy subjects and slightly more accurate with the cardiac patient group. The advantage of the EP method is that also the wave shape and velocity of arterial pressure pulses is available for further analysis, including the assessment of arterial stiffness.

No description available.

Oscillometry is the most common measurement method used in automatic blood pressure (BP) monitors. However, most of the oscillometric algorithms are without physiological and theoretical foundation, and rely on empirically derived coefficients for systolic and diastolic pressure evaluation which affects the reliability of the technique. In this thesis, the oscillometric BP estimation problem is addressed using a comprehensive modeling approach, based on which coefficient-free estimation of BP becomes possible. A feature-based neural network approach is developed to find an implicit relationship between BP and the oscillometric waveform (OMW). The modeling approach is then extended by developing a mathematical model for the OMW as a function of the arterial blood pressure, cuff pressure, and cuff-arm-artery system parameters. Based on the developed model, the explicit relationship between the OMW and the systolic and diastolic pressures is found and a new coefficient-free oscillometric BP estimation method using the trust region reflective algorithm is proposed. In order to improve the reliability of BP estimates, the electrocardiogram signal is recorded simultaneously with the OMW, as another independent source of information. The electrocardiogram signal is used to identify the true oscillometric pulses and calculate the pulse transit time (PTT). By combining our developed model of oscillomtery with an existing model of the pulse wave velocity, a new mathematical model is derived for the PTT during the cuff deflation. The derived model is incorporated to study the PTT-cuff pressure dependence, based on which a new coefficient-free BP estimation method is proposed. In order to obtain accurate and robust estimates of BP, the proposed model-based BP estimation method sare fused by computing the weighted arithmetic mean of their estimates. With fusion of the proposed methods, it is observed that the mean absolute error (MAE) in estimation of systolic and diastolic pressures is 4.40 and 3.00 mmHg, respectively, relative to the Food and Drug Administration-approved Omron monitor. In addition, the proposed feature-based neural network was compared with auscultatory measurements by trained observers giving MAE of 6.28 and 5.73 mmHg in estimation of systolic and diastolic pressures, respectively. The proposed models thus show promise toward developing robust BP estimation methods.

Abstract Multimodal measurements are increasingly being employed in the study of human physiology. Brain studies in particular can draw advantage of simultaneous measurements using different modalities to analyse correlations, mechanisms and relationships of physiological signals and their dynamics in relation to brain functions. Moreover, multimodal measurements help to identify components of physiological dynamics generated specifically by the brain. This thesis summarizes the study, design and development of non-invasive medical instruments that can be utilized in conjunction with magnetic resonance imaging (MRI). A key challenge in the development of measurement methods is posed by the extraordinary requirements that the MRI environment poses - all materials need to be MR-compatible and the selected instruments and devices must not be affected by the strong magnetic field generated by the MRI scanner nor the MRI by the instruments placed within its scanning volume. The presented methods allow simultaneous continuous measurement of heart rate (HR) and metabolism from the brain cortex as well as pulse wave velocity (PWV) and blood pressure measurements in synchrony with electroencephalography (EEG) and MRI. Furthermore, the thesis explored the reliability and accuracy of the responses gathered by the developed instruments and, using new experimental methods, estimated the propagation of near-infrared light in the human brain. The goal of the novel multimodal measurement environment is to provide more extensive tools for medical researchers, neurologists in particular, to acquire accurate information on the function of the brain and the human body. Measurements have been performed on more than 70 persons using the presented multimodal setup to study such factors as the correlation between blood oxygen level-dependent (BOLD) data and low-frequency oscillations (LFOs) during the resting state. / Tiivistelmä Multimodaalisia kuvantamismenetelmiä käytetään enenevässä määrin ihmisen fysiologian ja elintoimintojen tutkimisessa. Erityisesti aivotutkimuksessa samanaikaisesti useammalla modaliteetilla mittaaminen mahdollistaa erilaisten fysiologisten mekanismien ja niiden korrelaatioiden tutkimisen kehon ja aivotoimintojen välillä. Lisäksi multimodaaliset mittaukset auttavat yksilöimään fysiologiset komponentit toisistaan ja identifioimaan aivojen tuottamia fysiologisia signaaleja. Tämä väitöskirja kokoaa tutkimustyön sekä laite- ja instrumentointisuunnittelun ja sen kehittämistyön ei-invasiivisesti toteutettujen lääketieteen mittausmenetelmien käyttämiseksi magneettikuvauksen aikana. Erityishaasteena työssä on ollut magneettikuvausympäristö, joka asettaa erityisvaatimuksia mm. mittalaitteissa käytettäville materiaaleille sekä laitteiden häiriönsiedolle magneettikuvauslaitteen aiheuttaman voimakkaan magneettikentän takia. Kehitettävät mittausmenetelmät eivät myöskään saa aiheuttaa häiriöitä magneettikuvauslaitteen tuottamalle kuvainformaatiolle. Väitöskirjassa esitettävät mittausmenetelmät tekevät mahdolliseksi mitata magneettikuvausympäristössä ihmisen sydämen sykettä, veren virtauksen kulkunopeutta ja verenpaineen vaihteluja sekä aivokuoren metaboliaa - kaikki synkronissa aivosähkökäyrän mittaamisen ja magneettikuvantamisen kanssa. Lisäksi väitöskirjassa tutkitaan kehitettyjen mittausmenetelmien antamaa mittaustarkkuutta sekä arvioidaan lähi-infrapunavalon etenemistä ihmisen aivoissa uudenlaisella menetelmällä. Kehitetyllä multimodaalisella mittausympäristöllä on tavoitteena antaa lääketieteen alan tutkijoille, erityisesti neurologeille, käyttöön mittausmenetelmiä, joiden avulla voidaan tutkia ihmisen aivojen ja kehon välisiä toimintoja aiempaa kattavammin. Laitekokonaisuudella on tutkittu jo yli 70:tä henkilöä. Näissä mittauksissa on tutkittu mm. veren happitasojen hitaita vaihteluja ihmisen aivojen ollessa lepotilassa, ns. resting state -tilassa.

Blood pressure is one of the basic indicators of the health state of the cardiovascular system. High blood pressure is the main risk factor of ischemic heart disease, atherosclerosis and stroke. Therefore, it is important to monitor long-term changes in blood pressure and respond in time to these changes. Blood pressure meters are not standard household equipment, while a well-equipped smartphone is. Smartphones contain a large number of sensors capable of measuring biomedical signals. This thesis focuses on creating an application capable of determining blood pressure using data obtained from these sensors.