1,417 research outputs found
Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring
Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference
Towards a cyber physical system for personalised and automatic OSA treatment
Obstructive sleep apnea (OSA) is a breathing disorder that takes place in the course of the sleep and is produced by a complete or a partial obstruction of the upper airway that manifests itself as frequent breathing stops and starts during the sleep. The real-time evaluation of whether or not a patient is undergoing OSA episode is a very important task in medicine in many scenarios, as for example for making instantaneous pressure adjustments that should take place when Automatic Positive Airway Pressure (APAP) devices are used during the treatment of OSA. In this paper the design of a possible Cyber Physical System (CPS) suited to real-time monitoring of OSA is described, and its software architecture and possible hardware sensing components are detailed. It should be emphasized here that this paper does not deal with a full CPS, rather with a software part of it under a set of assumptions on the environment. The paper also reports some preliminary experiments about the cognitive and learning capabilities of the designed CPS involving its use on a publicly available sleep apnea database
Acoustic sensing as a novel approach for cardiovascular monitoring at the wrist
Cardiovascular diseases are the number one cause of deaths globally. An increased cardiovascular risk can be detected by a regular monitoring of the vital signs including the heart rate, the heart rate variability (HRV) and the blood pressure. For a user to undergo continuous vital sign monitoring, wearable systems prove to be very useful as the device can be integrated into the user's lifestyle without affecting the daily activities. However, the main challenge associated with the monitoring of these cardiovascular parameters is the requirement of different sensing mechanisms at different measurement sites. There is not a single wearable device that can provide sufficient physiological information to track the vital signs from a single site on the body. This thesis proposes a novel concept of using acoustic sensing over the radial artery to extract cardiac parameters for vital sign monitoring. A wearable system consisting of a microphone is designed to allow the detection of the heart sounds together with the pulse wave, an attribute not possible with existing wrist-based sensing methods.
Methods: The acoustic signals recorded from the radial artery are a continuous reflection of the instantaneous cardiac activity. These signals are studied and characterised using different algorithms to extract cardiovascular parameters. The validity of the proposed principle is firstly demonstrated using a novel algorithm to extract the heart rate from these signals. The algorithm utilises the power spectral analysis of the acoustic pulse signal to detect the S1 sounds and additionally, the K-means method to remove motion artifacts for an accurate heartbeat detection. The HRV in the short-term acoustic recordings is found by extracting the S1 events using the relative information between the short- and long-term energies of the signal. The S1 events are localised using three different characteristic points and the best representation is found by comparing the instantaneous heart rate profiles. The possibility of measuring the blood pressure using the wearable device is shown by recording the acoustic signal under the influence of external pressure applied on the arterial branch. The temporal and spectral characteristics of the acoustic signal are utilised to extract the feature signals and obtain a relationship with the systolic blood pressure (SBP) and diastolic blood pressure (DBP) respectively.
Results: This thesis proposes three different algorithms to find the heart rate, the HRV and the SBP/ DBP readings from the acoustic signals recorded at the wrist. The results obtained by each algorithm are as follows:
1. The heart rate algorithm is validated on a dataset consisting of 12 subjects with a data length of 6 hours. The results demonstrate an accuracy of 98.78%, mean absolute error of 0.28 bpm, limits of agreement between -1.68 and 1.69 bpm, and a correlation coefficient of 0.998 with reference to a state-of-the-art PPG-based commercial device. A high statistical agreement between the heart rate obtained from the acoustic signal and the photoplethysmography (PPG) signal is observed.
2. The HRV algorithm is validated on the short-term acoustic signals of 5-minutes duration recorded from each of the 12 subjects. A comparison is established with the simultaneously recorded electrocardiography (ECG) and PPG signals respectively. The instantaneous heart rate for all the subjects combined together achieves an accuracy of 98.50% and 98.96% with respect to the ECG and PPG signals respectively. The results for the time-domain and frequency-domain HRV parameters also demonstrate high statistical agreement with the ECG and PPG signals respectively.
3. The algorithm proposed for the SBP/ DBP determination is validated on 104 acoustic signals recorded from 40 adult subjects. The experimental outputs when compared with the reference arm- and wrist-based monitors produce a mean error of less than 2 mmHg and a standard deviation of error around 6 mmHg.
Based on these results, this thesis shows the potential of this new sensing modality to be used as an alternative, or to complement existing methods, for the continuous monitoring of heart rate and HRV, and spot measurement of the blood pressure at the wrist.Open Acces
Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques
Hypertension is a potentially unsafe health ailment, which can be indicated
directly from the Blood pressure (BP). Hypertension always leads to other
health complications. Continuous monitoring of BP is very important; however,
cuff-based BP measurements are discrete and uncomfortable to the user. To
address this need, a cuff-less, continuous and a non-invasive BP measurement
system is proposed using Photoplethysmogram (PPG) signal and demographic
features using machine learning (ML) algorithms. PPG signals were acquired from
219 subjects, which undergo pre-processing and feature extraction steps. Time,
frequency and time-frequency domain features were extracted from the PPG and
their derivative signals. Feature selection techniques were used to reduce the
computational complexity and to decrease the chance of over-fitting the ML
algorithms. The features were then used to train and evaluate ML algorithms.
The best regression models were selected for Systolic BP (SBP) and Diastolic BP
(DBP) estimation individually. Gaussian Process Regression (GPR) along with
ReliefF feature selection algorithm outperforms other algorithms in estimating
SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively.
This ML model can be implemented in hardware systems to continuously monitor BP
and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table
The 2023 wearable photoplethysmography roadmap
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology
Acoustic sensing as a novel wearable approach for cardiac monitoring at the wrist
This paper introduces the concept of using acoustic sensing over the radial artery to extract cardiac parameters for continuous vital sign monitoring. It proposes a novel measurement principle that allows detection of the heart sounds together with the pulse wave, an attribute not possible with existing photoplethysmography (PPG)-based methods for monitoring at wrist. The validity of the proposed principle is demonstrated using a new miniature, battery-operated wearable device to sense the acoustic signals and a novel algorithm to extract the heart rate from these signals. The algorithm utilizes the power spectral analysis of the acoustic pulse signal to detect the S1 sounds and additionally, the K-means method to remove motion artifacts for an accurate heartbeat detection. It has been validated on a dataset consisting of 12 subjects with a data length of 6 hours. The results demonstrate an accuracy of 98.78\%, mean absolute error of 0.28 bpm, limits of agreement between -1.68 and 1.69 bpm, and a correlation coefficient of 0.998 with reference to a state-of-the-art PPG-based commercial device. The results in this proof of concept study demonstrate the potential of this new sensing modality to be used as an alternative, or to complement existing methods, for continuous monitoring of heart rate at wrist
Medical microprocessor systems
The practical classes and laboratory work in the discipline "Medical microprocessor systems", performed using software in the programming environment of microprocessors Texas Instruments (Code Composer Studio) and using of digital microprocessors of the Texas Instruments DSK6400 family, and models of electrical equipment in the environment of graphical programming LabVIEW 2010.ΠΠ°Π±ΠΎΡΠ°ΡΠΎΡΠ½ΠΈΠΉ ΠΏΡΠ°ΠΊΡΠΈΠΊΡΠΌ Π· ΠΏΡΠΎΠ³ΡΠ°ΠΌΡΠ²Π°Π½Π½Ρ ΡΠ° ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²ΠΈ ΠΌΠ΅Π΄ΠΈΡΠ½ΠΈΡ
ΠΌΡΠΊΡΠΎΠΏΡΠΎΡΠ΅ΡΠΎΡΠ½ΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ, ΡΠΊΠΈΠΉ Π²ΠΈΠΊΠ»Π°Π΄Π΅Π½ΠΎ Ρ Π½Π°Π²ΡΠ°Π»ΡΠ½ΠΎΠΌΡ ΠΏΠΎΡΡΠ±Π½ΠΈΠΊΡ Π΄ΠΎΠΏΠΎΠΌΠ°Π³Π°Ρ Π½Π°ΠΊΠΎΠΏΠΈΡΡΠ²Π°ΡΠΈ ΠΉ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎ Π²ΠΈΠΊΠΎΡΠΈΡΡΠΎΠ²ΡΠ²Π°ΡΠΈ ΠΎΡΡΠΈΠΌΠ°Π½Ρ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡ Π· ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ½ΠΎΠ³ΠΎ ΠΊΡΡΡΡ Π½Π° Π²ΡΡΡ
ΡΡΠ°Π΄ΡΡΡ
Π½Π°Π²ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡ, ΡΠΎ Ρ Π²Π°ΠΆΠ»ΠΈΠ²ΠΈΠΌ Π΄Π»Ρ ΠΏΡΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠΈ ΠΌΠ°Π³ΡΡΡΡΡΠ² ΡΠ° Π½Π΅ΠΎΠ±Ρ
ΡΠ΄Π½ΠΎΡ Π»Π°Π½ΠΊΠΎΡ Ρ Π½Π°ΡΠΊΠΎΠ²ΠΎΠΌΡ ΠΏΡΠ·Π½Π°Π½Π½Ρ ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΈΡ
ΠΎΡΠ½ΠΎΠ² Π±ΡΠΎΠΌΠ΅Π΄ΠΈΡΠ½ΠΎΡ Π΅Π»Π΅ΠΊΡΡΠΎΠ½ΡΠΊΠΈ.The laboratory workshop on the programming and construction of medical microprocessor systems, which is outlined in the tutorial, helps to accumulate and effectively use the information obtained from a theoretical course at all stages of the educational process, which is important for the preparation of masters and a necessary link in the scientific knowledge of the practical basics of biomedicine.ΠΠ°Π±ΠΎΡΠ°ΡΠΎΡΠ½ΡΠΉ ΠΏΡΠ°ΠΊΡΠΈΠΊΡΠΌ ΠΏΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΠΌΠΈΠΊΡΠΎΠΏΡΠΎΡΠ΅ΡΡΠΎΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ, ΠΊΠΎΡΠΎΡΡΠΉ ΠΈΠ·Π»ΠΎΠΆΠ΅Π½ Π² ΡΡΠ΅Π±Π½ΠΎΠΌ ΠΏΠΎΡΠΎΠ±ΠΈΠΈ ΠΏΠΎΠΌΠΎΠ³Π°Π΅Ρ Π½Π°ΠΊΠ°ΠΏΠ»ΠΈΠ²Π°ΡΡ ΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΈΠ· ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΡΡΡΠ° Π½Π° Π²ΡΠ΅Ρ
ΡΡΠ°Π΄ΠΈΡΡ
ΡΡΠ΅Π±Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ°, ΡΡΠΎ Π²Π°ΠΆΠ½ΠΎ Π΄Π»Ρ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠΈ ΠΌΠ°Π³ΠΈΡΡΡΠΎΠ² ΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΠΌ Π·Π²Π΅Π½ΠΎΠΌ Π² Π½Π°ΡΡΠ½ΠΎΠΌ ΠΏΠΎΠ·Π½Π°Π½ΠΈΠΈ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠ½ΠΎΠ² Π±ΠΈΠΎΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠΉ ΡΠ»Π΅ΠΊΡΡΠΎΠ½ΠΈΠΊΠΈ
A method for measuring blood pressure and cardiorespiratory oscillations
Studies with magnetoencephalography (MEG) are still quite rarely combined
simultaneously with methods that can provide a metabolic dimension to MEG
investigations. In addition, continuous blood pressure measurements which
comply with MEG compatibility requirements are lacking. For instance, by
combining methods reflecting neurovascular status one could obtain more
information on low frequency fluctuations that have recently gained increasing
interest as a mediator of functional connectivity within brain networks. This
paper presents a multimodal brain imaging setup, capable to non-invasively and
continuously measure cerebral hemodynamic, cardiorespiratory and blood
pressure oscillations simultaneously with MEG. In the setup, all methods apart
from MEG rely on the use of fibre optics. In particular, we present a method
for measuring of blood pressure and cardiorespiratory oscillations
continuously with MEG. The potential of this type of multimodal setup for
brain research is demonstrated by our preliminary studies on human, showing
effects of mild hypercapnia, gathered simultaneously with the presented
modalities
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