ECE 5030

ECG recording from the hands.


In this assignment you will build a device to measure electrocardiogram between two electrodes attached only to the hands. The circuit will then be combined with the Finger Plethysmograph from the last assignment to estimate blood pressure.


Use the circuit given in Design of a Gel-Less Two Electrode ECG Monitor, but don't build the optional reference drive attached to the gain-setting resistor or the power supply. For the instrumentation amplifier, use a INA116. Electrodes will need to be large surface area for reasonable noise. YOu may need to provide a ground plane under the circuit to lower 60 Hz pickup.

If you nneed more filtering, a matlab program can be used to test filter techniques. The program simulates a QRS wave, then low pass, high pass, and LMS notch filter. The LMS filter is narrow band and only works for one frequency near 60 Hz. Adding four taps allows surpression of third harmonic distortion also. A more general n-tap LMS filter is shown in this program will surpress broadband interference.

You will need to implement Pan-Tompkins QRS detection. This matlab program uses physiobank data as input, adds noise, then filters to produce example output. Note that you cannot use the filter function for realtime filtering. You must implement the filter yourself.

You estimate blood pressure by the pulse pressure wave velocity.

More information:

Novel dry electrodes for ECG monitoring, Anna Gruetzmann, Stefan Hansen and Jorg Muller ,
Physiol. Meas. 28 (2007) 1375–1390 (pdf)

Design of a Gel-less Two-Electrode ECG Monitor, Emile Richard, Adrian D. C. Chan,
Medical Measurements and Applications Proceedings , April 30 2010-May 1 2010 (CU library link)

A Real-Time QRS Detection Algorithm, JIAPU PAN AND WILLIS J. TOMPKINS, (CU library link)

The Principles of Software QRS Detection, Reviw and compare algorithms,
Bert-Uwe Köhler, Carsten Hennig, Reinhold Orglmeister (CU library link)

Chapter 4 -- Event Detection, Dr. Bülent Yilmaz

Analysis of First-Derivative Based QRS Detection Algorithms

A Real-Time Microprocessor QRS Detector System with a 1-ms Timing Accuracy for the Measurement of Ambulatory HRV

Relationship between arterial pressure and pulse wave velocity using photoplethysmography during the post-exercise recovery period

Blood pressure estimation from pulse wave velocity measured on the chest


Optical pulse wave signal analysis for determination of early arterial ageing in diabetic patients

Case study of ECG signal used as a reference signal in optical pulse transit time measurement of blood flow –
The effect of different electrode placements on pulse transit time

Pulse transit time as an indicator of blood pressure.

Blood Pressure Estimation based on Pulse Transit Time and
Compensation of Vertical Position


  1. Build a circuit to safely record ECG from your hands.
  2. You may need to do digital filtering to remove noise.
  3. Using input from the IR pulse/ox channel and the ECG to estimate the blood pressure and compare to actual measured pressure.
  4. Use the USB-6008 to connect the circuit to your computer.
    Write a matlab program to:
    1. Record the resulting waveform using a scrolling display.
    2. Digital filter with GUI settable high pass and low pass filtering and optional notch filter for 60 Hz. The high/lowpass filters should be compatable with Pan_Tompkins detection.
    3. Plot a red dot above each detected QRS complex (on the ECG trace) to evaulate detection accuracy.
    4. Determine heart rate and plot it in real time as reciprocal of the last beat interval. Rate should be determined by the Pan_Tompkins algorithm.
    5. Plot the estimated blood pressure

Your written lab report should include the sections mentioned in the policy page, and :

  1. Estimates of signal to noise ratio, filter artifacts, and other electronic considerations.
  2. Comparision of you waveform with published ECG.
  3. Error estimates on the estimated blood pressure
  4. Observations concerning biological effects, such as electrode position or other effects.

February 27, 2014 Copyright Cornell university