Cardiovascular diseases are among the leading causes of death worldwide, highlighting the need to develop accessible and accurate diagnostic tools. This paper is meant to validate the proposed methodology for wave detection, developing an algorithm for automated detection of characteristic waves in 12-lead electrocardiogram (ECG) signals. An acquisition system based on the ADS1298 front-end was employed, along with simulator signals validated according to international standards. These signals are synthetic; they enable controlled testing but also constitute a limitation for direct clinical extrapolation. Signal preprocessing included IIR notch filtering and polynomial detrend baseline correction, significantly improving signal quality for wave detection. The BioSPPy library was integrated for R-wave detection, local minima detection for Q and S waves, adaptive temporal windows for T-wave identification, and phasor transform for identifying conducted and non-conducted P waves, the latter being particularly relevant for diagnosing atrioventricular blocks (AVB). The algorithm was evaluated in four scenarios: normal sinus rhythm, first-degree AVB, and second-degree AVB Mobitz types I and II. Unlike commercial algorithms with closed architectures, this proposal has the potential for implementation in embedded systems or digital clinical platforms, offering an effective solution for automated diagnosis.