Model People Auscultation System Based on Capacitive Sensor

The medical teaching needs auscultation training, so a model people auscultation training system was designed based on capacitive sensing principle. The PIC32 CPU with charging time measuring unit was used as the system core. Capacitance sensors were set in different parts of the model, the sampled signal was digitalized and processed, the cancelling jitter algorithm and dynamic average filtering was used for improving signal, and then the simulation audio was played. At the same time, the acquisition data was sent to the workstation through Zigbee RF module for being processed. The experience results showed that the system could simulate the audio signal from the different model parts, and it’s useful for raising the training effect; the algorithms of dynamic average filtering and cancelling dithering played important role for keeping on the system stable

High-Performance Accelerometer Based On Asymmetric Gapped Cantilevers For Physiological Acoustic Sensing

Continuous or mobile monitoring of physiological sounds is expected to play important role in the emerging mobile healthcare field. Because of the miniature size, low cost, and easy installation, accelerometer is an excellent choice for continuous physiological acoustic signal monitoring. However, in order to capture the detailed information in the physiological signals for clinical diagnostic purpose, there are more demanding requirements on the sensitivity/noise performance of accelerometers. In this thesis, a unique piezoelectric accelerometer based on the asymmetric gapped cantilever which exhibits significantly improved sensitivity is extensively studied. A meso-scale prototype is developed for capturing the high quality cardio and respiratory sounds on healthy people as well as on heart failure patients. A cascaded gapped cantilever based accelerometer is also explored for low frequency vibration sensing applications such as ballistocardiogram monitoring. Finally, to address the power issues of wireless sensors such as wireless wearable health monitors, a wide band vibration energy harvester based on a folded gapped cantilever is developed and demonstrated on a ceiling air condition unit

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

Monitoring Cardiovascular Physiology using Bio-compatible AlN Piezoelectric Skin Sensors

Arterial pulse waves contain a wealth of parameters indicative of cardiovascular disease. As such, monitoring them continuously and unobtrusively can provide health professionals with a steady stream of cardiovascular health indices, allowing for the development of efficient, individualized treatments and early cardiovascular disease diagnosis solutions. Blood pulsations in superficial arteries cause skin surface deformations, typically undetectable to the human eye; therefore, Microelectromechanical systems (MEMS) can be used to measure these deformations and thus create unobtrusive pulse wave monitoring devices. Miniaturized ultrathin and flexible Aluminium Nitride (AlN) piezoelectric MEMS are highly sensitive to minute mechanical deformations, making them suitable for detecting the skin deformations caused by cardiac events and consequently providing multiple biomarkers useful for monitoring cardiovascular health and assessing cardiovascular disease risk. Conventional wearable continuous pulse wave monitoring solutions are typically large and based on technologies limiting their versatility. Therefore, we propose the adoption of 29.5 μm-thick biocompatible, skin-conforming devices on piezoelectric AlN to create versatile, multipurpose arterial pulse wave monitoring devices. In our initial trials, the devices are placed over arteries along the wrist (radial artery), neck (carotid artery), and suprasternal notch (on the chest wall and close to the ascending aorta). We also leverage the mechano-acoustic properties of the device to detect heart muscle vibrations corresponding to heart sounds S1 and S2 from the suprasternal notch measurement site. Finally, we characterize the piezoelectric device outputs observed with the cardiac cycle events using synchronized electrocardiogram (ECG) reference signals and provide information on heart rate, breathing rate, and heart sounds. The extracted parameters strongly agree with reference values as illustrated by minimum Pearson correlation coefficients (r) of 0.81 for pulse rate and 0.95 for breathing rate

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

LVAD Occlusion Condition Monitoring Using State Augmented Acoustic Spectral Images

Each year, thousands of people die from heart disease and related illnesses due to the lack of available donor organs. Left ventricular assist devices (LVADs) aim to mitigate that occurrence, serving as a bridge-to-surgery option. While short term survival rates of LVAD patients near that of orthotopic surgery they are not viable long term options due to varied reasons. This work examines one cause, outlet graft thrombosis, and develops an algorithm for increasingly robust classification of device condition as it pertains to thrombosis or more generally occlusion. In order to do so an in vitro heart simulator is developed so that varying degrees of signal non-stationarity can be simulated and tested over a wide range of physiological blood pressure and heart rate conditions. Using a seeded-fault methodology, acoustics are acquired at the LVAD outlet graft location and subsequent spectral images of the sounds are developed. Statistical parameters from the images are used as features for classification using a support vector machine (SVM) which yields promising results. Given a comprehensive training space classification can be performed to fair accuracies (roughly 80%) using only the spectral image parameters. However, when the training space is limited augmenting the image features with patient state parameters elicits more robust identification. The algorithm developed in this work offers non-invasive diagnostic potential for LVAD conditions otherwise requiring invasive means

Design and Implementation of an Integrated Biosensor Platform for Lab-on-a-Chip Diabetic Care Systems

Recent advances in semiconductor processing and microfabrication techniques allow the implementation of complex microstructures in a single platform or lab on chip. These devices require fewer samples, allow lightweight implementation, and offer high sensitivities. However, the use of these microstructures place stringent performance constraints on sensor readout architecture. In glucose sensing for diabetic patients, portable handheld devices are common, and have demonstrated significant performance improvement over the last decade. Fluctuations in glucose levels with patient physiological conditions are highly unpredictable and glucose monitors often require complex control algorithms along with dynamic physiological data. Recent research has focused on long term implantation of the sensor system. Glucose sensors combined with sensor readout, insulin bolus control algorithm, and insulin infusion devices can function as an artificial pancreas. However, challenges remain in integrated glucose sensing which include degradation of electrode sensitivity at the microscale, integration of the electrodes with low power low noise readout electronics, and correlation of fluctuations in glucose levels with other physiological data. This work develops 1) a low power and compact glucose monitoring system and 2) a low power single chip solution for real time physiological feedback in an artificial pancreas system. First, glucose sensor sensitivity and robustness is improved using robust vertically aligned carbon nanofiber (VACNF) microelectrodes. Electrode architectures have been optimized, modeled and verified with physiologically relevant glucose levels. Second, novel potentiostat topologies based on a difference-differential common gate input pair transimpedance amplifier and low-power voltage controlled oscillators have been proposed, mathematically modeled and implemented in a 0.18μm [micrometer] complementary metal oxide semiconductor (CMOS) process. Potentiostat circuits are widely used as the readout electronics in enzymatic electrochemical sensors. The integrated potentiostat with VACNF microelectrodes achieves competitive performance at low power and requires reduced chip space. Third, a low power instrumentation solution consisting of a programmable charge amplifier, an analog feature extractor and a control algorithm has been proposed and implemented to enable continuous physiological data extraction of bowel sounds using a single chip. Abdominal sounds can aid correlation of meal events to glucose levels. The developed integrated sensing systems represent a significant advancement in artificial pancreas systems