Biosignals PsychoPhysio Signal Processin
Biosignals: PsychoPhysio-Signal Processing Concept
J. Rafiee * I ,M.A. Rafiee I ,N. Prause 2
• Corresponding author: I Multidisciplinary Design Lab, MANE, Rensselaer Polytechnic Institute, Troy, NY, USA. (rafiee@rpLedu)
2 Sexual Psychophysiology and Affective Neuroscience Lab, Dept. of Psychology, Idaho State University, Pocatello, ID, USA.
Abstract- This paper introduces the concept of PsychoPhysioSignal Processing (PPSP), which may partially combine
psychology (e.g. psycho-physiology, neuro-psychology), urology,
bio- engineering, applied mathematics (e.g. data analysis), and
signal processing techniques to be helpful for design, model,
manufacture, and analyze of theoretical and experimental
systems for (human) sexual behaviors. For example, a real-time
system is introduced to automatically detect movement artifacts
existing in vaginal pulse amplitude (VPA) measures to eliminate
time-consuming handy artifact removing.
I. CONCEPT
Clinical experiments for analysis of (human) sexual
behaviors have been extensively developing [1-3]. Psychophysiologists are the rare individuals who experimentally
attempt to understand sex-related issues. Numerous people are
challenged with sex problems and attention to this issue is
scarce. Their ongoing research on sex issues consists of
measuring and recording typical biological signals such as
neurological and physiological signals (e.g. EEG, ERP, VPA,
temperature, and so on) by means of biomedical devices
following by mathematical analyses on them. This paper
introduces a new concept called PPSP, one which partially
combines psychology (e.g. psycho-physiology, neuropsychology), urology [1], bio-engineering, applied math (e.g.
data analysis), and signal processing techniques for better
understanding of these types of biosignals. These signals
require a broad knowledge of science (psychology and
urology) for understanding and interpreting of such a
sophisticated term as human sexual behaviors. Although it
might be doable having not in-depth knowledge on the
physics of the problem in a few areas of signal processing,
psychophysio-signals can not be easily interpreted without
psychological and biological understanding of the problem.
Therefore, in order to facilitate, automate, computerize, and
mechanize the process of sexual data capturing and analysis,
PPSP would be helpful for clinical psychologists to analyze
(human) sexual behaviors.
significant problem for subsequent data analysis. Furthermore,
there are no standard criteria for what constitutes an artifact,
and errors in an experimenter's judgment may increase the
rate of false positives by removing data that is incorrectly
classified as an artifact or false negatives by failing to remove
true artifacts. These fundamental limitations in VP A signal
processing suggest that the reliability and generalizability of
VPA, within and across individuals, is potentially reduced in
even the most methodologically sound studies. This paper
introduces an original technique to automatically detect
movement artifacts using advanced signal processing
methods.
B. Experimental VPA measures
The vaginal photoplethysmograph system monitors genital
responses in women thought to reflect sexual arousal. Backscattered light from an embedded light source is received by a
photocell. The signal pulses with heartbeats, which typically
are around 60 BPM in the laboratory. In this research AC
VPA signals, which are thought to reflect phasic changes in
the vascular walls that result from pressure changes within the
vessels, were collected with a Biopac (Model MPI00)
amplifier and transducer with infrared light source. Next, a
band pass filter between 0.5 to 30 Hz was applied. The data
was collected during an examination of the influence of
alcohol on sexual response [4]. Women attended a laboratory
session in a private testing room. First, they watched a 10minute neutral video followed by a 3-minute erotic video [4].
Data from the last three-minutes of the neutral video viewing
are taken into consideration for processing in this research.
Then, the subjects consumed alcohol to reach 0.025 blood
alcohol level (BAL), followed by 0.08 BAL and tests were
repeated at each stage. Therefore, six classes are abbreviated
as N 0.0, N 0.025, and N 0.08 for neutral videos and E 0.0, E
0.025, and E 0.08 for erotic videos.
C. Algorithm for automatic artifact detection
II. PSYCHOPHYSIOSIGNALS (CASE STUDY)
A. Example ofsex-related issues
VP A is likely the most common method used to measure
female sexual response by means of vaginal photoplethysmography, but it has been criticized for theoretical and
practical assumptions underlying its use. At a practical level,
VP A signals are notoriously sensitive to movement artifacts
[4], which are manually removed from the raw data, currently.
The potential for interpretation bias in such cases poses a
Movement artifacts existing in VP A measures are divided into
three categories: small-, medium-, and high-impact artifacts
(see Figure 1). The algorithm for automatic artifact detection
of medium and high impact artifacts is as follows:
1. Segmentation: in signal analysis, depending on that the
process is off- or on-line, segmentation can be implemented.
VPA signals were segmented into smaller one-second
segments to also make the computations easier. Hence, 180
segmented signals were ended up for each of six classes.
5
o
-5
>
0.025 (Neutral)
\l
12
6
18
24
(sec)
0.08 (Erotic)
60
40
>
IV.
Medium-impact artifact
\U
-10
data from artifacts for a 180-seconds time signal requires 182
seconds.
I-In addition to automatic artifact detection, the number of
movement artifacts is increased by feeding the subjects with
alcohol.
2- There was not any tangible change in VP A signals for
those recorded from subjects while watching neutral videos
from amplitude point of view.
High-impact artifact
20
CONCLUSION
This paper introduces the concept of PPSP to assist the
clinical psychologists for design, analysis, modeling,
simulation, manufacturing, automation of electro-mechanical
systems to interpret sexual behaviors. For example, an
automatic real-time system for detection of movement
artifacts was briefly explained. One surprising result is that it
can tremendously save the time using automatic systems. For
example, it would be the possibilities to extensively study
several subjects to get more clinical information in such a
complicated subjective process.
sca le = (5,30) - seg me ntati on tim e= 12 sec
90
Figure I. Movement artifacts in VPA signals (in a sample subject)
80
2. Wavelet transform and autocorrelation function were
applied for analysis of the signals in this research [5].
continuous wavelet coefficients of the segmented signals
(CWC-SS) was calculated for each class using Daubechies 44
ｾ＠
!B
u
"'ｾ＠
function and in fifth decomposition level that led to 2 5 scales
60
50
'"
'ij;
(2 5 series of CWC-SS).
ｾ＠
3. Autocorrelation of CWC-SS was calculated. Achieved lags
based on trial-and-error set to 40 to reduce the data size and
CPU time.
4. Mean of absolute value of CWC-SS was defined as
"weight" to measure the strength of the time-domain signal.
5. Power spectral density (PSD) of autocorrelated CWC-SS
times by "weight" are computed for 2 5 CWC-SS (see Figure
2 for one of the scales). In order to make the Figure more
obvious, segmentation time was increased to 12 seconds). As
shown, natural and dominant frequencies of VP A are
appeared in the second lag (In PSD, the first half lags are
plotted because of the symmetry).
III.
70
V>
DISCUSSION
As shown in Figure 2, significant frequency information of
our VPA can be found in the second lag, where high- and
medium-impact artifacts are easily distinguishable.
Summarizing the results, we have:
1- Running the computer algorithm takes less than one second
for our case study. Also, for one segment signal capturing, we
need another one-second time. Therefore, the system is able to
detect the artifacts in 2 seconds from recording to saving data,
while subject is watching the video. In clear words, cleaned
;;;
40
=
ｾ＠ 30
.!eO>
Ｎｾ＠
20
10
5
10
l ags
15
20
Figure 2. PSD of autocorrelated CWC in six classes -18 segmented signals,
Lag=40, scale=(5,30)
V.
ACKNOWLEDGEMENT
This research is supported by RPI, NY, USA and SPAN
lab, Dept. of Psychology, ISU, Pocatello, ID, USA.
REFERENCES
[I]
[2]
[3]
[4]
[5]
Goldstein, I. Current Urology Reports 9 (6) 2008, pp. 475-482
Rosen R., Brown C., Heiman 1., Leiblum S., Meston C., Shabsigh R.,
Ferguson D., D'Agostino Jr.R., J Sex Marital Ther 26 (2) 2000,191-208
Derogatis L.R, Rosen R., Leiblum S., Burnett A., Heiman J., J Sex
Marital Ther. 28 (4) 2002, pp. 317-330.
Prause, N., Cerny, 1., Janssen, E., J Sex Med, 2 (2005) 58-65.
Rafiee J., Arvani F., Harifi A., Sadeghi M.H., Mech. Syst. Signal Pro 21
(2007) 1746-1754.
J. Rafiee * I ,M.A. Rafiee I ,N. Prause 2
• Corresponding author: I Multidisciplinary Design Lab, MANE, Rensselaer Polytechnic Institute, Troy, NY, USA. (rafiee@rpLedu)
2 Sexual Psychophysiology and Affective Neuroscience Lab, Dept. of Psychology, Idaho State University, Pocatello, ID, USA.
Abstract- This paper introduces the concept of PsychoPhysioSignal Processing (PPSP), which may partially combine
psychology (e.g. psycho-physiology, neuro-psychology), urology,
bio- engineering, applied mathematics (e.g. data analysis), and
signal processing techniques to be helpful for design, model,
manufacture, and analyze of theoretical and experimental
systems for (human) sexual behaviors. For example, a real-time
system is introduced to automatically detect movement artifacts
existing in vaginal pulse amplitude (VPA) measures to eliminate
time-consuming handy artifact removing.
I. CONCEPT
Clinical experiments for analysis of (human) sexual
behaviors have been extensively developing [1-3]. Psychophysiologists are the rare individuals who experimentally
attempt to understand sex-related issues. Numerous people are
challenged with sex problems and attention to this issue is
scarce. Their ongoing research on sex issues consists of
measuring and recording typical biological signals such as
neurological and physiological signals (e.g. EEG, ERP, VPA,
temperature, and so on) by means of biomedical devices
following by mathematical analyses on them. This paper
introduces a new concept called PPSP, one which partially
combines psychology (e.g. psycho-physiology, neuropsychology), urology [1], bio-engineering, applied math (e.g.
data analysis), and signal processing techniques for better
understanding of these types of biosignals. These signals
require a broad knowledge of science (psychology and
urology) for understanding and interpreting of such a
sophisticated term as human sexual behaviors. Although it
might be doable having not in-depth knowledge on the
physics of the problem in a few areas of signal processing,
psychophysio-signals can not be easily interpreted without
psychological and biological understanding of the problem.
Therefore, in order to facilitate, automate, computerize, and
mechanize the process of sexual data capturing and analysis,
PPSP would be helpful for clinical psychologists to analyze
(human) sexual behaviors.
significant problem for subsequent data analysis. Furthermore,
there are no standard criteria for what constitutes an artifact,
and errors in an experimenter's judgment may increase the
rate of false positives by removing data that is incorrectly
classified as an artifact or false negatives by failing to remove
true artifacts. These fundamental limitations in VP A signal
processing suggest that the reliability and generalizability of
VPA, within and across individuals, is potentially reduced in
even the most methodologically sound studies. This paper
introduces an original technique to automatically detect
movement artifacts using advanced signal processing
methods.
B. Experimental VPA measures
The vaginal photoplethysmograph system monitors genital
responses in women thought to reflect sexual arousal. Backscattered light from an embedded light source is received by a
photocell. The signal pulses with heartbeats, which typically
are around 60 BPM in the laboratory. In this research AC
VPA signals, which are thought to reflect phasic changes in
the vascular walls that result from pressure changes within the
vessels, were collected with a Biopac (Model MPI00)
amplifier and transducer with infrared light source. Next, a
band pass filter between 0.5 to 30 Hz was applied. The data
was collected during an examination of the influence of
alcohol on sexual response [4]. Women attended a laboratory
session in a private testing room. First, they watched a 10minute neutral video followed by a 3-minute erotic video [4].
Data from the last three-minutes of the neutral video viewing
are taken into consideration for processing in this research.
Then, the subjects consumed alcohol to reach 0.025 blood
alcohol level (BAL), followed by 0.08 BAL and tests were
repeated at each stage. Therefore, six classes are abbreviated
as N 0.0, N 0.025, and N 0.08 for neutral videos and E 0.0, E
0.025, and E 0.08 for erotic videos.
C. Algorithm for automatic artifact detection
II. PSYCHOPHYSIOSIGNALS (CASE STUDY)
A. Example ofsex-related issues
VP A is likely the most common method used to measure
female sexual response by means of vaginal photoplethysmography, but it has been criticized for theoretical and
practical assumptions underlying its use. At a practical level,
VP A signals are notoriously sensitive to movement artifacts
[4], which are manually removed from the raw data, currently.
The potential for interpretation bias in such cases poses a
Movement artifacts existing in VP A measures are divided into
three categories: small-, medium-, and high-impact artifacts
(see Figure 1). The algorithm for automatic artifact detection
of medium and high impact artifacts is as follows:
1. Segmentation: in signal analysis, depending on that the
process is off- or on-line, segmentation can be implemented.
VPA signals were segmented into smaller one-second
segments to also make the computations easier. Hence, 180
segmented signals were ended up for each of six classes.
5
o
-5
>
0.025 (Neutral)
\l
12
6
18
24
(sec)
0.08 (Erotic)
60
40
>
IV.
Medium-impact artifact
\U
-10
data from artifacts for a 180-seconds time signal requires 182
seconds.
I-In addition to automatic artifact detection, the number of
movement artifacts is increased by feeding the subjects with
alcohol.
2- There was not any tangible change in VP A signals for
those recorded from subjects while watching neutral videos
from amplitude point of view.
High-impact artifact
20
CONCLUSION
This paper introduces the concept of PPSP to assist the
clinical psychologists for design, analysis, modeling,
simulation, manufacturing, automation of electro-mechanical
systems to interpret sexual behaviors. For example, an
automatic real-time system for detection of movement
artifacts was briefly explained. One surprising result is that it
can tremendously save the time using automatic systems. For
example, it would be the possibilities to extensively study
several subjects to get more clinical information in such a
complicated subjective process.
sca le = (5,30) - seg me ntati on tim e= 12 sec
90
Figure I. Movement artifacts in VPA signals (in a sample subject)
80
2. Wavelet transform and autocorrelation function were
applied for analysis of the signals in this research [5].
continuous wavelet coefficients of the segmented signals
(CWC-SS) was calculated for each class using Daubechies 44
ｾ＠
!B
u
"'ｾ＠
function and in fifth decomposition level that led to 2 5 scales
60
50
'"
'ij;
(2 5 series of CWC-SS).
ｾ＠
3. Autocorrelation of CWC-SS was calculated. Achieved lags
based on trial-and-error set to 40 to reduce the data size and
CPU time.
4. Mean of absolute value of CWC-SS was defined as
"weight" to measure the strength of the time-domain signal.
5. Power spectral density (PSD) of autocorrelated CWC-SS
times by "weight" are computed for 2 5 CWC-SS (see Figure
2 for one of the scales). In order to make the Figure more
obvious, segmentation time was increased to 12 seconds). As
shown, natural and dominant frequencies of VP A are
appeared in the second lag (In PSD, the first half lags are
plotted because of the symmetry).
III.
70
V>
DISCUSSION
As shown in Figure 2, significant frequency information of
our VPA can be found in the second lag, where high- and
medium-impact artifacts are easily distinguishable.
Summarizing the results, we have:
1- Running the computer algorithm takes less than one second
for our case study. Also, for one segment signal capturing, we
need another one-second time. Therefore, the system is able to
detect the artifacts in 2 seconds from recording to saving data,
while subject is watching the video. In clear words, cleaned
;;;
40
=
ｾ＠ 30
.!eO>
Ｎｾ＠
20
10
5
10
l ags
15
20
Figure 2. PSD of autocorrelated CWC in six classes -18 segmented signals,
Lag=40, scale=(5,30)
V.
ACKNOWLEDGEMENT
This research is supported by RPI, NY, USA and SPAN
lab, Dept. of Psychology, ISU, Pocatello, ID, USA.
REFERENCES
[I]
[2]
[3]
[4]
[5]
Goldstein, I. Current Urology Reports 9 (6) 2008, pp. 475-482
Rosen R., Brown C., Heiman 1., Leiblum S., Meston C., Shabsigh R.,
Ferguson D., D'Agostino Jr.R., J Sex Marital Ther 26 (2) 2000,191-208
Derogatis L.R, Rosen R., Leiblum S., Burnett A., Heiman J., J Sex
Marital Ther. 28 (4) 2002, pp. 317-330.
Prause, N., Cerny, 1., Janssen, E., J Sex Med, 2 (2005) 58-65.
Rafiee J., Arvani F., Harifi A., Sadeghi M.H., Mech. Syst. Signal Pro 21
(2007) 1746-1754.