Transcript “Analyze reaction of newborn to music ” Maslovsky Eugene Vainbrand Dmitri

“Analyze reaction of newborn to music ”
Maslovsky Eugene
Vainbrand Dmitri
Instructor: Kirshner Hagai
Winter 2005
Agenda
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Concept
Available Raw Data
Project Goals
Analysis Techniques
Project Flow
Results
Conclusions
Future research
Concept
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Observations of newborn show that music
influences their behavior.
Our project is part of a research on reactions of
newborn to different music styles.
Can Engineering Analysis methods add new views
and maybe resolve this issue?
Available Data
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Six bio-signals were recorded from newborn while
playing them music alternately:
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Respiratory.
ECG.
EEG from four sources.
There is an online recorded movie.
Project Goals
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Study characteristics of basic bio-signals.
Study different signal processing and statistical
methods.
Analyze given medical signals and define their
connection to music playing.
Analysis techniques
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Signal processing basic methods:
– DFT/FFT
– Filtering
– Window multiplying
– Parameter estimation
– AR model
– Spectrogram
Statistical and Math methods:
– Statistical hypothesis
– Histograms
Project Flow
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Analyzing ECG Signal
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Visual analyze in time and frequency
Basic Parameters analysis
Analyzing ECG in time domain
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Statistical analysis of Amplitude and periods
Typical period shape analysis
FECG
Analyzing Respiratory Signal
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Visual analyze in time and frequency
Basic Parameters analysis
Analyzing AR model
Definitions
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Time segments
First Second First
silent
silent music
First
Silent
Music
Third
silent
Second
music
Forth
silent
Third
music
Fifth
silent
Silent
Results
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ECG:
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Visual
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In time domain signal is periodical.
In frequency domain signal is modulated pulse train
with 50Hz bandwidth but there are no suitable
parameters to analyze.
Windowing didn’t give other visual information.
Result
Mean energy: Music
segments have slightly
lower energy
Hypothesis of equality Denied with 0.99
probability
Results
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Statistical analysis of R-Amplitude, HR and HRV
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HR
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HRV
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Similar histograms
Equality hypothesis not denied
Conclusion: No influence detected
Similar histograms except firsts 2 silent segments vs. all others
But Equality hypothesis not denied
Conclusion: No influence detected
R-Amplitude mean
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Similar histograms
Equality hypothesis not denied
Conclusion: No influence detected
Results
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Statistical analysis of R-Amplitude, HR and
HRV (cont)
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R-Amplitude deviation
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First two silent segments histogram is different
Equality hypothesis for First two silent segments vs. the
others was denied with 98% C.L.
Conclusion: R-peaks amplitude became more unstable
during the experiment. Can be related to music influence
Results
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Typical period
shape analysis
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Averaging on
all periods
shapes of
each segment
Bigger
difference
between 2 first
and rest
segments
Music and Silent
Music and First Silent
400
Silent and Fisrt Silent
400
Music
Silent
300
400
Music
First Silent
300
200
100
Silent
First Silent
300
200
200
100
100
0
0
-100
-100
-200
-200
-300
-300
-400
-400
0
-100
-200
-300
-400
0
30
50
100
150
Absolute Difference E tot=9.325
-500
0
60
50
100
150
Absolute Difference E tot=67.8805
-500
60
20
40
40
10
20
20
0
0
50
100
150
0
0
50
100
150
0
0
0
50
100
150
Absolute Difference E tot=52.6866
50
100
150
Results
Music
Silent
340
Silent and Fisrt Silent
Music and First Silent
Music and Silent
380
Music
First Silent
380
360
360
340
340
320
320
320
300
280
300
300
260
280
280
240
260
260
220
240
200
220
240
220
Silent
First Silent
200
180
200
36
30
44
42
40
38
Absolute Difference E tot=9.325
46
44
42
40
38
Absolute Difference E tot=67.8805
60
60
20
40
40
10
20
20
0
0
50
100
150
0
0
50
100
150
0
43
42
41
40
39
Absolute Difference E tot=52.6866
0
50
100
150
E  First Silent  101; E First Music  82; E Music Silent  14
Results
FECG
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The purpose: “edge influence” detection on HR
No edge influence was detected
FECG of serial peariods
0.7
Period size [Sec]
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FECG function
One Slice Bars
0.6
0.5
0.4
0.3
0
200
400
600
800
#Periods
FECG of serial peariods
1000
1200
3
Heart Freq. [Hz]
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FECG function
One Slice Bars
2.5
2
1.5
0
200
400
600
#Periods
800
1000
1200
Results
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Respiratory:
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Visual impression: noise, no typical cyclicality,
no typical amplitude.
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Mean Energy: No results that show connection.
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Signal processing:
 There is a typical picture in frequency domain
in 4Hz bandwidth but there no suitable
parameters to analyze.
 No effects from windowing and filtering. No
visual correlation found.
Results (cont.)
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Auto Regressive model fitting
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Goal – Estimate Frequency spectrum with auto
regressive model.
We couldn’t find suitable results.
Maximum peak frequency variation was totally
random
Results (cont.)
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Spectrogram analysis
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No good visual results was achieved by
spectrogram analysis
Thanks
Thanks for your attention!