Date of Award
Doctor of Philosophy
Electrical and Computer Engineering
Dr. Massood Z. Atashbar
Dr. Bradley J. Bazuin
Dr. Paul D. Fleming
Stethograph system, sensor, sensory system, digital signal processing
Sensors have been receiving significant attention in the last decade and the demand for sensory systems has increased in recent years due to the rapid growth in the field of artificial intelligence (AI). Sensors can improve people’s awareness by providing them with real-time information on the environment and their immediate health conditions. This dissertation presents the fulfilment of three main projects and focuses on the development of a sensor, a sensory system, and a sensor signal recognition system for AI applications by employing printed electronics, analog circuit design, and digital signal processing techniques.
In the first project, a multi-channel stethograph system was designed and developed as an electronic auscultation system to non-invasively record heart, lung, and trachea (HLT) sounds through a set of 16 acoustic sensors. The multi-channel stethograph system was fabricated by placing 16 microphone-based transducers in CNC machined Delrin® housing cases that were covered using diaphragms. Among the 16 acoustic sensors, 14 were positioned in a memory foam pad, and two were placed directly on the heart and trachea to simultaneously acquire sounds from the lungs, heart and trachea. The sounds acquired from the 16 acoustic sensors were processed through a custom designed and fabricated 16-channel PCB for signal conditioning. A National Instruments (NI) 9205 data acquisition device (DAQ) along with a NI 9191 wireless chassis was used to acquire and wirelessly transmit the data from the 16-channel PCB to a Wi-Fi enabled device such as a PC/tablet. A LabVIEW and MATLAB program were developed to convert the recorded data from the acoustic sensors into 16 audio files (for audio playback) and the waveforms were plotted in both the time and frequency domains as well as a spectrogram for visual examination of any abnormal patterns during inhalation and exhalation. This provides critical information on the presence of wheezes, crackles and rhonchi sounds as well as abnormal heartbeat and respiration rate, which helps analyze the conditions of heart and lungs. The graphically displayed HLT sounds will help physicians in the clinical diagnoses and monitoring of lung and heart disorders, particularly chronic obstructive pulmonary disease (COPD), asthma, pneumonia, and congestive heart failure by providing objective evidence.
In the second project, a MATLAB based intelligent algorithm was developed for detecting the various adventitious sounds in the audio collected from the multi-channel stethograph system. Adventitious sounds are the key characteristics of cardiopulmonary diseases (CD) and assist the doctor/physician in the continuous diagnosis of lungs. The algorithm consists of breath pattern detection, candidate audio selection, breath pattern extraction and adventitious sound detection capabilities. Digital signal processing techniques such as filtering, windowing, enveloping, discrete Fourier transform (DFT) and thresholds were used for identifying and classifying the inhalation and exhalation patterns in the lung sound in an independent (automatic) and intelligent way. The auscultation diagnosis algorithm can identify and distinguish discontinuous adventitious sounds including wheeze, rhonchi, wheeze & rhonchi and squawk, with an accuracy of 96.9%, 95.3%, 90% and 100%, respectively. The algorithm has the potential to aid doctors/physicians in the early detection and monitoring of any lung disorders by providing objective evidence on adventitious sounds. This is specifically important for monitoring disease progression during the COVID-19 pandemic.
In the third project, a resistive flexible humidity sensor based on multi-walled carbon nanotubes (MWCNTs) was designed and fabricated. Screen and gravure printing processes were used for monolithically fabricating the humidity sensor containing interdigitated electrodes (IDE), a sensing layer and a meandering conductive heater. The sensing capability of the printed sensor, with heater, was investigated by subjecting it to relative humidity (RH) ranging from 10% to 90%. The response demonstrated an overall resistance change of 55% when the sensor was subjected to 90% RH, as compared to 10% RH. A maximum hysteresis of 5.1%, at 70% RH, was calculated for the resistive response of the sensor. The printed sensor can bend with a radius of curvature of up to 1.5 inches without causing any structural damage or effect on the sensor performance. In addition, due to the high sensitivity and rapid response, the developed sensor has the feasibility to be implemented in humidity monitoring applications.
Zhang, Xingzhe, "Development of Sensor, Sensory System and Signal Processing Algorithm for Intelligent Sensing Applications" (2021). Dissertations. 3814.