1. VEERMATA JIJABAI TECHNOLOGICAL
INSTITUTE
Estimation of Respiration Rate from EDR signal
and
Automatic discrimination between NSR, VT and
VF signals
M.Tech Thesis Presentation
Date : 02-07-2014
Guided by :
Dr. A. N. Cheeran
Presented by :
Nidhi Sharma
122151021
In collaboration with :
A3 RMT
2. 2
Estimation of Respiration Rate
from ECG Derived Respiration
(EDR) signal
Automatic discrimination between Normal
Sinus Rhythm (NSR), Ventricular Tachycardia
(VT) and Ventricular Fibrillation (VF) signals
This presentation is divided into 2 parts
3. Respiration information is very important in
heart diagnosis
Monitoring respiration rate is needed in Post-operative Care,
Understanding several arrhythmias and synchronisation of MRI
scans of Heart and Thorax
4. Obtaining respiratory signal needs extra
sensors in addition to those needed for ECG
This can interfere with natural breathing process and also
cannot be used for ambulatory or long-term monitoring
5. Respiration mainly manifests itself on ECG
in three ways
Respiratory Sinus Arrhythmia
R-S Amplitude Modulation
Baseline Wander
6. EDR is derivation of a patient’s respiratory signal
(non-calibrated) by digitally processing the ECG
Changes the electrical
impedance, which
modifies the ECG
Movement of electrodes with
respect to the heart during
respiration
7. ECG Signal Processing
R peak Detection
RR interval calculation
and EDR signal
Peak detection
Band Pass
Filter
Differentiator
Squarer
Moving Window
Integration
Baseline Wander
Filter
Wavelet
based
denoising
Smoothing using S-Golay
filter
Overview of the Algorithm:
Calculate the
distances between
consecutive R
peaks
Plot these values
using cubic
spline
interpoation
Find the peaks
above
threshold t
Check if the no
of peaks > 30
If yes, check if
distance
between
successive
peaks > 1211
If yes,
discard
invalid
peaks
If no of peaks <
30, find the
peaks above
threshold 2t
Discard the peaks which are
at a distance < 1500
8. Different data are used during the training and
testing phase of the algorithm
Training Phase
Testing Phase
Fantasia Database provides both the ECG and
simultaneously recorded respiration signal
Recording of ECG and
noting the breath count at
A3 RMT and VJTI
14. Input Raw Signal
Pre process the ECG
R-peak Detection
RR-interval calculation and EDR Signal
15. Input Raw Signal
Pre process the ECG
R-peak Detection
RR-interval calculation and EDR Signal
16. Input Raw Signal
Pre process the ECG
R-peak Detection
RR-interval calculation and EDR Signal
Peak Detection
17. Input Raw Signal
Pre process the ECG
R-peak Detection
RR-interval calculation and EDR Signal
Peak Detection
18. Reference during training is the respiratory
waveform from the database
Respiration rate calculated on database waveform = 17
Respiration rate calculated from the algorithm = 17
20. 20
The proposed algorithm shows a tolerance
limit of ± 2 and takes very less execution time
Thus, proposed algorithm is computationally inexpensive and
fast implying its usage in real time
Tolerance
Limit
± 2
Total
samples
50
Within
tolerance
limit
45
Outside
tolerance
limit
5
21. Automatic discrimination between Normal Sinus Rhythm
(NSR), Ventricular Tachycardia (VT) and Ventricular
Fibrillation (VF) signals
22. Doctors have to take a life-saving decision of
delivering an electric counter-shock to the
patient
23. Ventricular Tachycardia (VT) originates from
a single site within the ventricles at a rate
greater than 100 bpm
Ventricles do not have the opportunity to completely
empty and refill. Hence, cardiac output is decreased
24. Ventricular Tachycardia (VT) originates from
a single site within the ventricles at a rate
greater than 100 bpm
The QRS complex is wide, bizarre and >0.12 seconds
No P wave
26. 26
Heart’s electrical activity becomes disordered
In Ventricular Fibrillation(VF) many impulses
are initiated from many locations in
ventricles
Cardiac output is non-existent
27. In Ventricular Fibrillation(VF) many impulses
are initiated from many locations in
ventricles
Bizarre, irregular and random waveform
No identifiable QRS complexes or P wave
28. 28
Stochastic components lead to
deviations and variation in shape
ECG signal has both regular and stochastic
aspects which needs to be modeled by
stochastically driven model
Two different cycles of same ECG signal are aligned
29. 29
Delay reconstruction method reveals both
regular and stochastic components of ECG
Delay reconstruction method takes nearby point information
and preserves most of the wave information
Maps time series to phase space trajectory using time
delay method
Delineates the nonlinear behavior
41. Input Raw Signal
Pre process the ECG
Calculate the 2nd difference of negative peaks (T)
42. Input Raw Signal
Pre process the ECG
Calculate the 2nd difference of negative peaks (T)
43. Input Raw Signal
Pre process the ECG
Calculate the 2nd difference of negative peaks (T)
Plot the trajectory and convert it into image
Divide this image into 40x40 boxes
Calculate the no. of boxes traced by trajectory (n1)
44. Input Raw Signal
Pre process the ECG
Calculate the 2nd difference of negative peaks (T)
Plot the trajectory and convert it into image
Divide this image into 40x40 boxes
Calculate the no. of boxes traced by trajectory (n1)
45. Input Raw Signal
Pre process the ECG
Calculate the 2nd difference of negative peaks (T)
Plot the trajectory and convert it into image
Divide this image into 40x40 boxes
Calculate the no. of boxes traced by trajectory (n1)
Multiply the input image with three masks
Calculate the resulting no. of boxes (n45,n135,nT)
46. Input Raw Signal
Pre process the ECG
Calculate the 2nd difference of negative peaks (T)
Plot the trajectory and convert it into image
Divide this image into 40x40 boxes
Calculate the no. of boxes traced by trajectory (n1)
Multiply the input image with three masks
Calculate the resulting no. of boxes (n45,n135,nT)
47. Input Raw Signal
Pre process the ECG
Calculate the 2nd difference of negative peaks (T)
Plot the trajectory and convert it into image
Divide this image into 40x40 boxes
Calculate the no. of boxes traced by trajectory (n1)
Multiply the input image with three masks
Calculate the resulting no. of boxes (n45,n135,nT)
A
51. A
Is T within
limit of 30
points ?
Yes
Is nT >
100?
The signal is
VT
Yes
No
Other
Abnormality
52. A
Is T within
limit of 30
points ?
Yes
Is nT >
100?
The signal is
VT
Yes
No
Other
Abnormality
No
Is n1 > 500
or
(n135 +n45 )
> 200 ?
53. A
Is T within
limit of 30
points ?
Yes
Is nT >
100?
The signal is
VT
Yes
No
Other
Abnormality
No
Is n1 > 500
or
(n135 +n45 )
> 200 ?
Yes
The signal is VF
54. A
Is T within
limit of 30
points ?
Yes
Is nT >
100?
The signal is
VT
Yes
No
Other
Abnormality
No
Is n1 > 500
or
(n135 +n45 )
> 200 ?
Yes
The signal is VF
No
The signal is NSR
55.
56. The proposed algorithm shows an overall
accuracy of 97%
Actual
Signal
Classified As
NSR VT VF Other
abnormality
Sensitivity
80 NSR 78 0 2 0 97.5%
25 VT 0 23 0 2 92%
70 VF 0 2 68 0 97.14%
Confusion Matrix
57. 57
Phase space reconstruction
phenomenon along with
masks and combined with
peak detection algorithm can
classify NSR, VT and VF much
more efficiently
In summary, this is an efficient, fast and simple
discrimination algorithm which can be useful at the time of
manual defibrillation
58. Combination of parameters
like R wave amplitude
modulation and area under the
QRS curve
Developing a respiratory
signal from a database of
exercise and heart rate
recovery ECGs, a study
can be performed to identify
the types of respiratory
abnormalities that correlate to
different kinds of autonomic
dysfunction
FUTURE SCOPE….
Combination of spatial filling
index along with the current
algorithm
Further work can be done to
develop simple indices based
on phase space reconstruction
method for classification of
other cardiac disorders
59. References
Journal Papers:
•Othman M A., et al., (2012), "Characterization of Ventricular Tachycardia and Fibrillation Using
Semantic Mining." Computer & Information Science 5, no. 5.
•Sarvestani R., et al., (2009),"VT and VF classification using trajectory analysis." Nonlinear Analysis:
Theory, Methods & Applications71.12: e55-e61.
•Mane R., et al., (2013),"Cardiac Arrhythmia Detection By ECG Feature Extraction." International Journal
of Engineering Research and Applications 3, no. 2: 327-332.
•Anton A., et al., (2007) "Detecting ventricular fibrillation by time-delay methods." IEEE Trans. Biomed.
Eng. 54 (1) 174_177.
Proceedings Papers:
•Rocha, T., et al., (2008),"Phase space reconstruction approach for ventricular arrhythmias
characterization." In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual
International Conference of the IEEE, pp. 5470-5473. IEEE.
Websites:
•CU Database, Massachusetts Institute of Technology, [online] available :
http://www.physionet.org/physiobank/database/cudb
•The MIT-BIH Normal Sinus Rhythm Database, [online] available :
http://www.physionet.org/physiobank/database/nsrdb
•The MIT-BIH Malignant Ventricular Arrhythmia Database, [online] available :
http://www.physionet.org/physiobank/database/vfdb
Book
RPEAK DETECTION :
ECG signal is passed through Band-pass filter which has been implemented as cascaded low-pass filter and high-pass filter to achieve a 3dB passband from 5 – 12 Hz.
After filtering the signal is differentiated to provide the QRS complex slope information. The five point differentiator is used to get approximately linear region of operation between DC and 30 Hz.
The signal is then squared point by point. This makes all data points positive and does non linear amplification of higher frequencies. This is to determine the maximum amplitudes in the ECG signal those can be termed as R peaks.
The purpose of moving window integration is to obtain waveform feature information in addition to the slope of R.
RR INTERVAL CALCULATION:
The RR-Interval is defined as the time period between subsequent R-Peaks. After proper detection of R peaks, the interval between consecutive R peaks is calculated. Values for the intervals were calculated by finding the time difference between each neighbouring pair of points.
PEAK DETECTION
Peak detection is one of the most commonly encountered problems in the digital processing of biological signals. The main difficulty lies in distinguishing between artefactual peaks (caused for example, by patient movement) and authentic peaks. To overcome this problem it is often suggested that arbitrary thresholds be set to eliminate spurious peaks. Here, I have applied a threshold to the amplitude of the EDR signal to detect a valid respiration peak. This threshold has been decided based on the various testing carried out on the different data sets.
If the no of these peaks are above a certain limit, then the distance between consecutive peaks is checked. Peaks which are closer to each other than that distance (which is calculated heuristically) are taken as false and rejected. If the peaks count is less than the limit, it means the waveform is free of spurious peaks and the old threshold for peak detection can be used.
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Your autonomic nervous system is made up of nerves that control those “automatic”things you need to do to survive. A few of those necessary things include blood pressure, heart rate, sweating, and digestion of your food. Autonomic dysfunction or dysautonomia refers to problems with this autonomic nervous system.
The abnormalities in respiratory
modulation are an indication of autonomic dysfunction [34].
The remaining classes are named according to the diagnosed cardiac abnormality, premature ventricular contraction (PVC), Complete Heart Block (CHB), Sick Sinus Syndrome (SSS), Congestive heart failure (CHF), Ishemic/Dilated cardiomyoapathy (ISCDIL), Atrial Fibrillation (AF), and ventricular fibrillation (VF)
