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SIGNAL PROCESSING TECHNIQUES USED FOR THE ANALYSIS OF ACOUSTIC SIGNALS FROM HEART AND LUNGS TO DETECT PULMONARY EDEMA 1 Pratibha Sharma Electrical, Computer.

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Presentation on theme: "SIGNAL PROCESSING TECHNIQUES USED FOR THE ANALYSIS OF ACOUSTIC SIGNALS FROM HEART AND LUNGS TO DETECT PULMONARY EDEMA 1 Pratibha Sharma Electrical, Computer."— Presentation transcript:

1 SIGNAL PROCESSING TECHNIQUES USED FOR THE ANALYSIS OF ACOUSTIC SIGNALS FROM HEART AND LUNGS TO DETECT PULMONARY EDEMA 1 Pratibha Sharma Electrical, Computer and Energy Engineering Department, University of Colorado-Boulder

2 INTRODUCTION 2

3  Heart pumps out blood at slower rate than normal heart.  Pumping power/capacity is weaker than normal.  Cannot pump out enough oxygen and nutrients to meet the body’s needs. WHAT IS CONGESTIVE HEART FAILURE? Source: www.hearthealthyonline.com/

4 NEED FOR THE RESEARCH 4  Congestive heart failure is a one of the leading causes of death with nearly 250,000 deaths every year worldwide.

5 To help the physician monitor changes in the patient’s condition from decompensated to compensated or compensated to decompensated. 5 OBJECTIVE OF MY RESEARCH

6 6 EXPERIMENTAL DESIGN Capture acoustic signals of heart and lungs using digital stethoscope and measuring an ECG using Alivecore Heart monitoring machine.

7 EXPERIMENTAL SET UP DS32a Digital Stethoscope Alivecore Heart monitor Audacity Software

8 EQUIPMENT SPECIFICATION 1.Signal to noise ratio at zero gain for bell and diaphragm modes for ds32a stethoscope is in the range of 12db to 18 db. 2.Signal to noise ratio other than zero gain for different modes and locations for my stethoscope are in the range of 26 db to 45 db. 3.Minimum signal what stethoscope can measure is 50 mV, 20Hz-1000Hz(sensitivity). 8

9 SIGNAL PROCESSING TECHNIQUES USED FOR ANALYSIS OF ACOUSTIC HEART AND LUNG SIGNALS Fast Fourier Transform FFT Short Time Fourier Transform STFT Wavelet Transform 9

10 RESULTS FROM FOURIER TRANSFORM HEALTHY PERSONCOMPENSATED CHF PERSONDECOMPENSATED CHF PERSON 1 st Row - Signal in time domain for 10 seconds duration 2 nd Row – Power Spectra of time domain signal respective to its above figure

11 RESULTS FOR HEART SIGNALS AT LOCATION E FROM FFT 11 HEALTHY GROUPCOMPENSATED GROUP DECOMPENSATED GROUP X Axis- Frequency in Hz with 600Hz as maximum frequency Y Axis – Normalized Single Sided Amplitude Spectrum AVERAGE STANDARD DEVIATION

12 12 RESULTS FOR HEART SIGNALS AT LOCATION E ON 0.021 * NORMALIZED MAXIMUM POWER Fourier results at 2% Maximum normalized power of heart signal for healthy, compensated and decompensated group

13 DISCUSSION OF FOURIER TRANSFORM RESULTS The spectra is too wide so hard to conclude anything. Large variability in person to person results due to different factors like heart variability, body structures, lung capacity etc. Signals are non stationary therefore FFT is not a right tool to use. 13

14 ALTERNATIVE SIGNAL PROCESSING OPTIONS Time- Frequency Analysis is required. –STFT Narrow window leads to good time resolution and poor frequency resolution Wide window leads to good frequency resolution and poor time resolution –Wavelet (localized time and frequency resolution) 14

15 15 DIFFERENT PHONOCARDIOGRAMS

16 WAVELET ANALYSIS 16 High pass filter(g[n]) Low pass filter(g[n]) Signal convolved with filters Under sampling by 2

17 WAVELET ANALYSIS 17 1st level Decomposition 2nd level Decomposition 3rd level Decomposition X Axis- Time Y Axis- Frequency

18 18 WAVELET ANALYSIS DIA APPROXIMATE DETAILS DIAGONAL DETAILS DIAGONAL DETAILS DIAGONAL DETAILS DIAGONAL DETAILS VERTICAL DETAILS VERTICAL DETAILS HORIZONTAL DETAILS HORIZONTAL DETAILS X Y

19 RESULTS OF LUNG SIGNALS AT LOCATION E FROM WAVELET FOR SAME PATIENTS 19 1 st Row - First Measurement on 8 different individuals 2 nd Row - Second Measurement on the same individuals. This row corresponds to 1 st row respectively. 3 rd Row - measurement on healthy people.

20 RESULTS OF LUNG SIGNALS AT LOCATION E FROM WAVELET FOR SAME PATIENTS 20 1 st Row - First Measurement on 8 different individuals 2 nd Row - Second Measurement on the same individuals. This row corresponds to 1 st row respectively. 3 rd Row - measurement on healthy people.

21 RESULTS OF LUNG SIGNALS AT LOCATION E FROM WAVELET FOR SAME PATIENTS 21 1 st Row - First Measurement on 8 different individuals 2 nd Row - Second Measurement on the same individuals. This row corresponds to 1 st row respectively. 3 rd Row - measurement on healthy people.

22 RESULTS OF LUNG SIGNALS AT LOCATION E FROM WAVELET FOR SAME PATIENTS 22 1 st Row - First Measurement on 8 different individuals 2 nd Row - Second Measurement on the same individuals. This row corresponds to 1 st row respectively. 3 rd Row - measurement on healthy people.

23 RESULTS OF HEART SIGNALS AT LOCATION E FROM WAVELET FOR SAME PATIENTS 23 1 st Row - First Measurement on 8 different individuals 2 nd Row - Second Measurement on the same individuals. This row corresponds to 1 st row respectively. 3 rd Row - measurement on healthy people.

24 RESULTS OF HEART SIGNALS AT LOCATION E FROM WAVELET FOR SAME PATIENTS 24 1 st Row - First Measurement on 8 different individuals 2 nd Row - Second Measurement on the same individuals. This row corresponds to 1 st row respectively. 3 rd Row - measurement on healthy people.

25 RESULTS OF HEART SIGNALS AT LOCATION E FROM WAVELET FOR SAME PATIENTS 25 1 st Row - First Measurement on 8 different individuals 2 nd Row - Second Measurement on the same individuals. This row corresponds to 1 st row respectively. 3 rd Row - measurement on healthy people.

26 RESULTS OF HEART SIGNALS AT LOCATION E FROM WAVELET FOR SAME PATIENTS 26 1 st Row - First Measurement on 8 different individuals 2 nd Row - Second Measurement on the same individuals. This row corresponds to 1 st row respectively. 3 rd Row - measurement on healthy people.

27 C OMPARATIVE RESULTS OF SHORT DURATION HEART SIGNALS ( FROM ONE S 1 TO NEXT S 1) 27 X Axis- 8 different individuals with two measurements each Y Axis - Blue bar shows Decompensated condition and red bar shows compensated condition Note:- 1 and 6 patients are exception, 5 th patient is the one with both bars showing compensated condition.

28 CONCLUSIONS Wavelet analysis is better approach to analyze acoustic signals of heart and lungs ( non stationary signals). Wavelet analysis gave the promising results by showing the consistent difference in spectra within the same individual Wavelet decomposed coefficients narrowed in spectra for same duration of signals in between compensated and decompensated stage for the individual. Mean value of vertical coefficients taken for 1heart beat that is from one s1 sound to next s1 gave consistent differences in compensated and decompensated stages of the same patient. Variability between patients is so large, our analysis to date, does not give clear distinction between the patients. 28

29 RESULTS OF LUNG SIGNALS AT LOCATION E FROM WAVELET FOR SAME PATIENTS 29 1 st Row - First Measurement on 8 different individuals 2 nd Row - Second Measurement on the same individuals. This row corresponds to 1 st row respectively. 3 rd Row - measurement on healthy people.


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