Análisis de Señales Biomédicas: Avanzado

Análisis de Señales Biomédicas: Avanzado

Biomedical Engineering — Applied Signal Processing and Medical-Device Software Regulation

Análisis de Señales Biomédicas: Avanzado

Move from foundations to applied biomedical signal processing — adaptive filtering, wavelets, QRS detection benchmarked against MIT-BIH, machine learning, and the IEC 62304 and FDA SaMD frameworks applied to a real algorithm.

BME-540-AAvanzado32 horasCertificado de Finalización

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Applied biomedical signal processing
FormatoCurso de Certificación Profesional
NivelAvanzado
Duración32 horas
IdiomaInglés
CertificadoCertificado de Finalización — Aleph University
Full course trackIntroductorio (16 h) + Avanzado (32 h) = 48 horas
Requisito previo / Preparación recomendada

Recommended prerequisite: completion of the Introductorio level for this training, or relevant professional experience subject to Aleph University review.

Descripción general

The Avanzado level of the Aleph Análisis de Señales Biomédicas 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 Curso de Certificación Profesional builds directly on the 16-hour Introductorio level (BME-540-I). It does not repeat the Introductorio content. Where the Introductorio level introduces the FFT and the idea of a digital filter, the Avanzado 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.

Lo que aprenderás
Design FIR digital filters using window-method and Parks–McClellan optimal-equiripple approaches and IIR filters using Butterworth and Chebyshev prototypes against real physiological-signal specifications.
Implement adaptive filtering (LMS, NLMS, RLS) for powerline-noise cancellation and motion-artifact compensation in ambulatory and wearable signals.
Apply Short-Time Fourier Transform and Continuous/Discrete Wavelet Transform methods to non-stationary physiological signals (EEG bursts, EMG transients, QRS onset).
Implement the Pan-Tompkins QRS-detection algorithm and evaluate its performance against the MIT-BIH Arrhythmia Database under the AAMI EC57 reporting standard.
Extract EEG band-power features (delta, theta, alpha, beta, gamma) using Welch’s-method power-spectral-density estimation and apply them to a Sleep-EDF recording.
Train and evaluate a binary classifier of physiological signals using k-fold cross-validation, ROC analysis, and stratified-by-subgroup performance reporting.
Produce an IEC 62304 software-safety classification analysis (Class A / B / C) for a chosen algorithm, with the FDA documentation level identified per the June 2023 Software Functions guidance.
Apply the FDA AI/ML Predetermined Change Control Plan (PCCP) Final Guidance (December 2024) to an AI/ML-enabled algorithm, including Description of Modifications, Modification Protocol, and Impact Assessment.
Author an algorithm verification report — a portfolio-grade course exercise — integrating specification, implementation, benchmark-validation methodology and results, and IEC 62304 / FDA SaMD framing.
course topics & modules
Relevancia profesional

Graduates of the Avanzado 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.

Certificado de Finalización

Los participantes que cumplan los requisitos de finalización del curso reciben un Certificado de Finalización emitido por Aleph University.

La finalización de los niveles Introductorio y Avanzado puede ser evaluada por Aleph University para su posible reconocimiento dentro de una ruta de posgrado aplicable, sujeto a revisión institucional y a las políticas académicas vigentes. El reconocimiento no es automático ni está garantizado.
Preguntas frecuentes
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