ECG Monitoring for Detection of Arrhythmias & Compression Techniques
Minimization of Storage Requirements
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The automated ECG system provides an alert when critical changes are detected by the system. The complete ECG diagnostic system includes a low power Instrumentation amplifier, filters, ADC, Microcontroller and ZIGBEE modules. MATLAB / LABVIEW are used for signal analysis and classification. These environments are capable of not only collecting, recording, transmitting, and displaying ECG data on a real time basis but also for analyzing the acquired ECG data in order to detect the cardiac abnormalities. The MIT-BIH database signals were used for validation and evaluation of classification algor...
The automated ECG system provides an alert when critical changes are detected by the system. The complete ECG diagnostic system includes a low power Instrumentation amplifier, filters, ADC, Microcontroller and ZIGBEE modules. MATLAB / LABVIEW are used for signal analysis and classification. These environments are capable of not only collecting, recording, transmitting, and displaying ECG data on a real time basis but also for analyzing the acquired ECG data in order to detect the cardiac abnormalities. The MIT-BIH database signals were used for validation and evaluation of classification algorithms. In order to reduce the memory requirements for storing the acquired ECG signals, ECG data was compressed. Discrete Cosine Transform (DCT) technique was applied for ECG data compression. Here DCT showed good performance with a Compression Ratio (CR) of 82-90.43% and Percent Root Mean Difference (PRD) of 7.9-0.93. Linear Vector Quantization method (LVQ)is used for identifying the abnormalities associated with the ECG signal. After training the LVQ process with a reasonable number of samples, the algorithm is used for classifying ECG signals.
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