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Digital signal processing chip implementation for detection and analysis of intracardiac electrograms
C M Chiang1, J M Jenkins, L A DiCarlo
1Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor.
Pacing and Clinical Electrophysiology : PACE
|August 1, 1994
Summary
Digital signal processing (DSP) microchips enable real-time analysis of electrocardiographic (ECG) signals. A new system demonstrates high accuracy in detecting cardiac events and classifying arrhythmias, showing potential for medical devices.
Area of Science:
- Biomedical Engineering
- Digital Signal Processing
- Cardiology
Background:
- Digital signal processing (DSP) microchips offer enhanced computational speed for real-time analysis of electrocardiographic (ECG) signals.
- Customized architectures are needed to meet real-time requirements in cardiac signal processing.
Purpose of the Study:
- To design and evaluate a DSP-based system for real-time, cycle-by-cycle detection and waveform analysis of ECG signals.
- To assess the system's accuracy in classifying normal (sinus rhythm) and abnormal cardiac depolarizations.
Main Methods:
- A Motorola 56001 DSP chip was used to process intracardiac electrograms sampled at 1000 Hz.
- An adaptive trigger and time-domain template matching (correlation waveform analysis - CWA) were employed for event detection and waveform classification.
- The system was tested on 10 paired sets of electrograms containing sinus rhythm and arrhythmia segments.
Main Results:
- The adaptive trigger achieved 99.8% detection sensitivity and 99.6% specificity.
- Correlation waveform analysis (CWA) correctly identified 98.2% of sinus rhythm depolarizations and 99.4% of abnormal depolarizations.
- The system demonstrated high accuracy in classifying various cardiac rhythms, including ventricular tachycardia and paced rhythms.
Conclusions:
- The developed DSP-based system shows significant potential for real-time monitoring during electrophysiology studies.
- The algorithm's accuracy in detecting and classifying arrhythmias suggests its utility in implantable antitachycardia devices.