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The Wavelet Transform in its discrete form has been applied to a wide range of biomedical signals by now. Typically, its calculation is performed off-line and calculation systems suffer from limited autonomy, bulkiness and obtrusiveness. A surge in industrial, research and academic interest into telemedicine and medical embedded systems, has been noticed recently, where miniature, low-cost, autonomous and ultra-low-power devices play a major role. Such devices are usually based on microcontrollers, which in addition to other tasks need to perform signal processing, very often in real-time. This paper presents a methodology to perform wavelet transform on general purpose microcontrollers. By using its optimized versions the electrocardiogram and photoplethysmographic signals are processed in real time for the purposes of QRS complex extraction and denoising. After the theoretical considerations on wavelets and their optimization in integer arithmetic, the embedded hardware and software computation architectures are described. The following is the presentation of obtained results during intensive tests on real signals. The same approach can be applied with other signals where the embedded implementation of wavelets can be benefitial.