The Advanced level of the Aleph Biomedical Signal Analysis course track is for professionals who already have the foundations covered and need to develop the applied skills the medical-device software industry expects: designing real digital filters, implementing adaptive filtering for ambulatory and wearable signals, applying wavelet decomposition to non-stationary signals, implementing and benchmarking the canonical Pan-Tompkins QRS-detection algorithm against the MIT-BIH Arrhythmia Database, extracting EEG band-power features for sleep-staging applications, developing and evaluating machine-learning classifiers of physiological signals, and integrating the IEC 62304 software lifecycle, the FDA Software as a Medical Device (SaMD) framework, and the FDA AI/ML Predetermined Change Control Plan (PCCP) Final Guidance (December 2024) into real algorithm work.

This 32-hour Professional Certificate Course builds directly on the 16-hour Introductory level (BME-540-I). It does not repeat the Introductory content. Where the Introductory level introduces the FFT and the idea of a digital filter, the Advanced level designs FIR (window method and Parks–McClellan) and IIR (Butterworth, Chebyshev) filters against real specifications, applies them to denoising real ECG data, and verifies the result. The course culminates in a professional course exercise — an algorithm verification report on a chosen physiological-signal-processing algorithm, benchmarked against a PhysioNet dataset, with the verification methodology, performance metrics (sensitivity, specificity, PPV, F1, ROC/AUC where applicable), and IEC 62304 / FDA SaMD framing documented in a portfolio-grade artifact.

Throughout the course, regulatory and standards content is anchored in real public sources — IEC 62304:2006+AMD1:2015, ISO 14971:2019, the FDA Software Functions premarket guidance (June 2023), and the FDA AI/ML PCCP Final Guidance (December 2024). Algorithm-equity considerations, including documented pulse-oximeter accuracy disparities across skin-pigmentation groups and analogous concerns in ECG and EEG algorithms, are addressed as a working engineering competency.

Graduates of the Advanced course are positioned for applied work at medical-device manufacturers (cardiac rhythm management, ambulatory monitoring, neuromonitoring, wearables), at SaMD-focused digital-health companies, at contract research organizations, and at regulatory-affairs consultancies supporting clients with software-driven submissions. The algorithm verification report produced during the course is a portfolio-grade artifact that demonstrates working command of the methods, the benchmark-validation discipline, and the IEC 62304 and FDA SaMD framing that the field expects.

Participants who meet the course completion requirements receive a Certificate of Completion issued by Aleph University.