1. Peak Detection in ECG and ABP Signals using Empirical
Mode Decomposition
DEPARTMENT OF ELECTRONICS & COMMUNICATION
SHRI RAM MURTI SMARAK COLLEGE OF ENGINEERING
AND TECHNOLOGY,BAREILLY
SUBMITTED TO: SUBMITTED BY:
Mr.VivekYadavShreyas Singh
PiyushChaurasiya
Atal Singh Yadv
Gaurav Singh

2. INTRODUCTION
Automatic beat detection algorithms are extremely important for various biomedical signal
processing applications. These types of algorithms are mostly used for R-peak detection in ECG. The
ECG signal is a recording of electrical activity of heart. A single ECG cycle consists of P, Q, R, S, and T
waves. The QRS complex and especially R-peak detection is the most prominent feature in the ECG
signal and its accurate detection forms the basis of extraction of other features and parameters from
ECG signal. Since the QRS complex varies with different cardiac health conditions, therefore efficient
and automatic detection of QRS complex and R-Peak is essential for reliable health condition
monitoring.
Although many algorithms have been developed during the last five decades for accurate and
reliable detection of R-peaks in the ECG signal indicating high percentages of correct detection, there
are only a few publications that describe algorithms to detect features in pressure signals [10]–[12].
The earlier QRS complex detection algorithm involve a preprocessor stage, where the ECG signal is
transformed to accentuate the QRS complex, and a decision stage, where a QRS complex is detected
using thresholding, yielded 99.3% detection accuracy [1]. This was further improved to a detection
accuracy of 99.67% [2]. A QRS detection algorithm using hardware filter banks was proposed which
reported sensitivity of 99.59 % and positive predictivity of 99.56 % against the MIT-BIH Arrhythmia
Database [5]. A wavelet transforms based QRS detection algorithm was proposed which reported
0.15 % false detections [7]. A new wavelet based QRS detection algorithm was developed which
yielded very high detection accuracy of 99.99% [6].
There are numerous current and potential applications for Pressure beat detection algorithms. Many
pulse oximeters perform beat detection as part of the signal processing necessary to estimate
oxygen saturation. Identification of the pressure components is necessary for some methods that
assess the interaction between respiration and beat-by-beat ventricular parameters and the
modulation effects of respiration on left ventricular size and stroke volume [13]. In the present work
a beat detection algorithm for ECG and ABP signals based on empirical mode decomposition has
been proposed. The proposed beat detection algorithm was tested on different data records of
Fantasia database, Self- recorded signals and MIMIC database [9]. The algorithm was implemented
in MATLAB.

3. METHODOLOGY
Empirical Mode Decomposition (EMD) has been recently introduced by Huang for adaptively
decomposing signals in a sum of ―well-behaved‖ AM-FM components [15]. The EMD is defined by a
process called sifting. It decomposes a given signal x(t) into a set of AM–FM components, called
Intrinsic Mode Functions (IMF). Using this technique K modes dk(t) and a residual term r(t) are
obtained and expressed by: x(t) = k=1,2,…,K. (1) The EMD algorithm is summarized as below:
1. Start with the signal d1(t) = x(t), k = 1. Sifting process hj(t) =dk(t) , j = 0
2. Identify all local extrema of hj(t). 3. Compute the upper (EnvMax) and the lower envelopes
(EnvMin) by cubic spline lines interpolation of the maxima and the minima. 4. Calculate the mean of
the lower and upper envelopes,
3. Compute the upper (EnvMax) and the lower envelopes (EnvMin) by cubic spline lines interpolation
of the maxima and the minima. 4. Calculate the mean of the lower and upper envelopes,
4. Calculate the mean of the lower and upper envelopes,
m(t) = (EnvMin(t)+ EnvMax(t)
5. Extract the detail h j+1(t) =h j(t) −m(t).
6. If h j+1(t) is an IMF, go to step 7, else, iterate steps 2 to 5 up on the signal h j+1(t), j = j +1.
7. Extract the mode dk(t) =h j+1(t).
8. Calculate the residual rk(t) = x(t) −dk(t).
9. If rk(t) has less than 2 minima or 2 extrema, the extraction is finished r(t) =rk(t). Else iterate the
algorithm from Step1 upon the residual rk(t), k =k +1.

4. FLOWCHART
The flowchart of the algorithm is shown in figure 1. The ECG /ABP signal is decomposed
into IMF’s using EMD technique as shown in figure 2.
Figure1. Flowchart of the implemented algorithm

5. Fine to coarse approximation are determined by adding the IMF’s according to the following
equation
The fine to coarse approximations are shown in figure 3.The signal f2c7 (t) = y(t) signal has
been used in further processing of the signal.