Yih Chyun Jenq

Date of Award


Document Type


Degree Name

Master of Science (M.S.) in Electrical Engineering


Electrical Engineering

Physical Description

1 online resource (3, xi, 131 p.)


Cardiac pacemakers, Signal processing -- Digital techniques, Heart -- Sounds




Because the voltage amplitude of a heart beat is small compared to the amplitude of exponential noise, pacemakers have difficulty registering the responding heart beat immediately after a pacing pulse. This thesis investigates use of digital filters, an inverse filter and a lowpass filter, to eliminate the effects of exponential noise following a pace pulse. The goal was to create a filter which makes recognition of a haversine wave less dependent on natural subsidence of exponential noise. Research included the design of heart system, pacemaker, pulse generation, and D sensor system simulations. The simulation model includes the following components: \ • Signal source, A MA TLAB generated combination of a haversine signal, exponential noise, and myopotential noise. The haversine signal is a test signal used to simulate the QRS complex which is normally recorded on an ECG trace as a representa tion of heart function. The amplitude is approximately 10 mV. Simulated myopotential noise represents a uniformly distributed random noise which is generated by skeletal muscle tissue. The myopotential noise has a frequency spectrum extending from 70 to 1000Hz. The amplitude varies from 2 to 5 mV. Simulated exponential noise represents the depolarization effects of a pacing pulse as seen at the active cardiac lead. The amplitude is about -1 volt, large in comparison with the haversine signal. • AID converter, A combination of sample & hold and quantizer functions translate the analog signal into a digital signal. Additionally, random noise is created during quantization. • Digital filters, An inverse filter removes the exponential noise, and a lowpass filter removes myopotential noise. • Threshold level detector, A function which detects the strength and amplitude of the output signal was created for robustness and as a data sampling device. The simulation program is written for operation in a DOS environment. The program generates a haversine signal, myopotential noise (random noise), and exponential noise. The signals are amplified and sent to an AID converter stage. The resultant digital signal is sent to a series of digital filters, where exponential noise is removed by an inverse digital filter, and myopotential noise is removed by the Chebyshev type I lowpass digital filter. The output signal is "detected" if its waveform exceeds the noise threshold level. To determine what kind of digital filter would remove exponential noise, the spectrum of exponential noise relative to a haversine signal was examined. The spectrum of the exponential noise is continuous because the pace pulse is considered a non-periodic signal (assuming the haversine signal occurs immediately after a pace pulse). The spectrum of the haversine is also continuous, existing at every value of frequency co. The spectrum of the haversine is overlapped by the spectrum of and amplitude of the exponential, which is several orders of magnitude larger. The exponential cannot be removed by conventional filters. Therefore, an inverse filter approach is used to remove exponential noise. The transfer function of the inverse filter of the model has only zeros. This type of filter is called FIR, all-zero, non recursive, or moving average. Tests were run using the model to investigate the behavior of the inverse filter. It was found that the haversine signal could be clearly detected within a 5% change in the time constant of the exponential noise. Between 5% and 15% of change in the time constant, the filtered exponential amplitude swamps the haversine signal. The sensitivity of the inverse filter was also studied: when using a fixed exponential time constant but changing the location of the transfer function, the effect of the exponential noise on the haversine is minimal when zeros are located between 0.75 and 0.85 of the unit circle. After the source signal passes the inverse filter, the signal consists only of the haversine signal, myopotential noise, and some random noise introduced during quantization. To remove these noises, a Chebyshev type I lowpass filter is used.


If you are the rightful copyright holder of this dissertation or thesis and wish to have it removed from the Open Access Collection, please submit a request to and include clear identification of the work, preferably with URL

Persistent Identifier