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In this work, we propose an ECG analysis system to ischemia detection. This system is based on an original markovian approach for online beat detection and segmentation, providing a precise localization of all beat waves and particularly of the PQ and ST segments. Our approach addresses a large panel of topics never studied before in others HMM related works: multichannel beat detection and segmentation, waveform models and unsupervised patient adaptation. Thanks to the use of some heuristic rules defined by cardiologists, our system performs a reliable ischemic episode detection, showing to be a helpful tool to ambulatory ECG analysis. The performance was evaluated on the two-channel European ST-T database, following its ST episode definitions. The experimentation was performed over 48 files extracted from 90. Our best average statistic results are 83% sensitivity and 85% positive predictivity. Performance compares favorably to others reported in the literature.