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Technology - ECAT- Electrocardiogram Analysis Tool

Sumugam Balachandran

ECAT- Electrocardiogram Analysis Tool -
For Assessment of Risk for Multiple Life-Threatening Cardiac Ailments

The research article presents a pioneering Non-Invasive methodology attempting to detect multiple cardiac ailments. A flexible software analysis tool has been developed which given an input rhythm strip of conventionally available high-resolution surface electrocardiogram (ECG) performs the wavelet analysis to detect the presence of cardiac abnormalities. ECAT has the ability to convert the acquired ECG data, pre-process the ECG signal, wavelet transform the processed signal and presents the results graphically in 3D. The design of the tool addresses the important issue of providing a user interface for clinical experts to choose the options to view the Wavelet Transformed ECG. This article specifically deals with the results of the analysis done to detect presence of Ventricular Arrhythmias and Tachycardia - Wide spread cardiac ailments.

Ventricular Tachycardia (V-Tach) – The signature signal for inducible patients is believed to have potentials known as late potentials in the magnitude of 40mV (typically) and of frequency of 40 – 200 Hz in the ST segment of the cardiac waveform.

Electrocardiogram (ECG) data is acquired using a high-resolution instrument, capable of resolving signal amplitudes as low as 2-3 mV with a sampling rate of 1000 samples per second.  The acquired data is in BARD-Binary format. The data is converted from the Bard-Binary format to a format suitable for the preferred development package – MATLAB®. The converted data is run through several Signal Preprocessing stages preparing the data for wavelet analysis. The choice of wavelet transform analysis makes this research effort novel in the realm to bio medical signal processing. The wavelet transform analysis has the advantage of a joint time and frequency domain representation compared to the traditional time or frequency domain representation using Fourier analyses. This is best suited for transient signals like the ECG/EEG signals where one is interested in the time localization of the frequency components. In the case of V-Tach detection, the need is to localize a 40mV anomaly in the 40-200Hz range at in time range of 200-300ms in a cardiac cycle that lasts for 600ms. The results using ECAT to assess risk for the presence of V-Tach are as shown:

For patients with the risk for V-Tach the ECAT tool has localized the presence of high amplitude coefficients in the time range of 250-300 ms and in the frequency range 40-200 Hz (corresponding to 10-50 in wavelet scale coefficients). The software has been used successfully to detect the presence of V-Tach as shown in this article; the initial results have been hugely promising. However, there large amount of data is required for more accurate testing.

(Sumugam Balachandran received his Bachelors in Engineering specializing in Instrumentation and Control systems from Madras University, India in 1999 and graduated with a Masters in EE from Tufts University specializing in DSP and Communications Systems in 2001. Sumugam has worked for Aware Inc., as a DSP Systems Engineer and is currently working as a Senior Systems Quality Engineer at Cedar Point Communications, NH. )

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A Typical ECG showing the waves and intervals

ECAT- System Level Block Diagram

Raw Patient Data Converted from Bard Binary format in to MATLAB® useable format

Filtered ECG data SIGNAL PREPROCESSING STAGE 1. Cycle by Cycle Registration by peak detection. 2. Averaging to improve signal to noise ration. 3. Vector Magnitude Summation. 4. Bi-Direction Parkes-McCellan High Pass Filtering

Wavelet Transformed Data – Late Potential Detection For Normal Individuals

For Patients with risk for V-Tach

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