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Matlab Implementation of Pan Tompkins ECG QRS
Detector.
Hooman Sedghamiz
March, 2014
Abstract
This open source code is a complete matlab implementation of Pan Tompkins ECG
QRS detector. This work implements all of the main steps proposed in the original
algorithm. A preliminary comparison of the MIT-BIH indicated similar sensitivity and
accuracy between this implementation and the original proposed work.
1 Background
Pan and Tompkins proposed an accurate real-time ECG beat detector in 1985 that has been
successfully used in many commercial devices [1]. The algorithm was originally implemented
on a microprocessor and later in C programming language [2]. This work implements the
same algorithm in Matlab environment with the hope that it can be more easily accessible
to researchers in the field of biomedical engineering.
2 Method
2.1 Preprocessing:
Steps:
1. Bandpass filter(5-15 Hz).
2. Derivating filter to high light the QRS complex.
3. Signal is squared.
4. Signal is averaged (MWI) to remove high frequency noise (0.150 seconds length).
5. Depending on the sampling frequency of your signal the filtering, options above are
changed to best match the characteristics of the ECG signal
1
2.2 Decision Rule
At this point in the algorithm, previous steps have generated a roughly pulse-shaped wave-
form at the output of the signal. The decision as to whether a pulse corresponds to a QRS
complex (as opposed to a high-sloped T-wave or a noise artefact) is performed with an
adaptive thresholding operation and other decision rules outlined below;
•FIDUCIAL MARK - The waveform is first processed to produce a set of weighted
unit samples at the location of the MWI maxima. This is done in order to localize the
QRS complex to a single instant of time.
•THRESHOLDING - When analyzing the amplitude of the MWI output, the algo-
rithm uses two threshold values (T HRS IG and T H RN OI SE , appropriately initialized
during a brief 2 second training phase) that continuously adapt to changing ECG sig-
nal quality. The first pass through y[n] uses these thresholds to classify the each non-
zero sample (CU RREN T P EAK ) as either signal or noise: If C URREN T P EAK >
T H RSIG, that location is identified as a QRS complex candidate and the signal level
(SI GLE V ) is updated: SI GLE V = 0.125 ×C URRE N T P EAK + 0.875 ×SIGLE V
If T H RNOI SE < C URREN T P E AK < T H RSIG , then that location is identified as
a noise peak and the noise level (NOI SELEV ) is updated: N OIS ELE V = 0.125 ×
CU RREN T P EAK + 0.875 ×N OISELEV . Based on new estimates of the signal
and noise levels (SIGLEV and N OISELEV , respectively) at that point in the ECG,
the thresholds are adjusted as follows: T HRSI G =NOISELEV + 0.25 ×(SIGLEV −
NOI SELEV ); T H RNO I SE = 0.5×T HRS IG ;These adjustments lower the threshold
gradually in signal segments that are deemed to be of poorer quality.
•SEARCHBACK FOR MISSED QRS COMPLEXES - In the thresholding step
above, if CU RRE N T P EAK < T HRSI G, the peak is deemed not to have resulted
from a QRS complex. If however, an unreasonably long period has expired without an
abovethreshold peak, the algorithm will assume a QRS has been missed and perform
a searchback. This limits the number of false negatives. The minimum time used to
trigger a searchback is 1.66 times the current R peak to R peak time period (called the
RR interval). This value has a physiological origin - the time value between adjacent
heartbeats cannot change more quickly than this. The missed QRS complex is assumed
to occur at the location of the highest peak in the interval that lies between T HRSI G
and T H RNOISE. In this algorithm, two average RR intervals are stored,the first RR
interval is calculated as an average of the last eight QRS locations in order to adapt to
changing heart rate and the second RR interval mean is the mean of the most regular
RR intervals . The threshold is lowered if the heart rate is not regular to improve
detection.
•ELIMINATION OF MULTIPLE DETECTIONS WITHIN REFRACTORY
PERIOD - It is impossible for a legitimate QRS complex to occur if it lies within
200ms after a previously detected one. This constraint is a physiological one due to
the refractory period during which ventricular depolarization cannot occur despite a
stimulus [1]. As QRS complex candidates are generated, the algorithm eliminates such
physically impossible events, thereby reducing false positives.
2
•T WAVE DISCRIMINATION - Finally, if a QRS candidate occurs after the 200ms
refractory period but within 360ms of the previous QRS, the algorithm determines
whether this is a genuine QRS complex of the next heartbeat or an abnormally promi-
nent T wave. This decision is based on the mean slope of the waveform at that position.
A slope of less than one half that of the previous QRS complex is consistent with the
slower changing behaviour of a T wave otherwise, it becomes a QRS detection. Extra
concepts: beside the points mentioned in the paper, this code also checks if the occured
peak which is less than 360 msec latency has also a latency less than 0,5∗meanRRif
yes this is counted as noise!
3 Evaluation of several MITDB database
Table 1: Detailed Performance of the beat detector on 10 sample records of MITDB*
Record Total FP FN Failed Failed
(No.) (No. Beats) (Beats) (Beats) (Beats) (%)
100 2274 0 0 0 0
101 1874 0 6 6 0.32
102 2187 0 0 0 0
104 2230 1 2 3 0.13
105 2572 48 32 80 3.11
108 1824 61 71 132 7.24
200 2601 1 3 4 0.15
202 2146 0 6 6 0.279
219 2312 0 0 0 0
222 2634 131 2 133 5.04
*Sample recordings were chosen randomly.
References
[1] J. Pan and W. J. Tompkins, “A Real-Time QRS Detection Algorithm,” IEEE Transac-
tions on Biomedical Engineering, vol. BME-32, no. 3, pp. 230–236, 1985.
[2] P. S. Hamilton and W. J. Tompkins, “Quantitative Investigation of QRS Detection Rules
Using the MIT/BIH Arrhythmia Database,” IEEE Transactions on Biomedical Engineer-
ing, vol. BME-33, no. 12, pp. 1157–1165, 1986.
3
