This document presents the methodology for designing an electrocardiograph capable of acquiring IEC 60601-2-25-compliant signals. The objective of developing this methodology is to address a signal incompatibility problem that has existed in academia for years, specifically in physiological processing research. This problem is related to the signal’s sampling rate and/or noise levels, and it becomes evident when one signal processing method is intended to work with another, either as a subsequent or simultaneous process. Even though matching algorithms can be implemented to remedy this incompatibility problem, the ultimate solution is the standardization of signals, which depends exclusively on the standardization of hardware. The signal incompatibility problem is urgent to solve because it makes the integration and scalability of different academic works difficult, preventing academia from reaching the stage of development that commercial equipment displays in automatic interpretation procedures. The design methodology presented in this document addresses the stated problem by creating an open-source hardware device capable of acquiring compliant signals, with careful consideration given to Signal Integrity and EMC concepts—a methodology that can be extended to other physiological acquisition systems. The expedited availability of the device’s design documentation and fabrication files is also an advantage of this work.

Numerous papers report the efficiency of the automatic interpretation capabilities of commercial algorithms. Unfortunately, these algorithms are proprietary, and academia has no means of directly contributing to these results. In fact, nothing at the same stage of development exists in academia. Despite the extensive research in ECG signal processing, from signal conditioning to expert systems, a cohesive single application for clinical use is not ready yet. This is due to a serious lack of coordination in the academic efforts, which involve not only algorithms for signal processing, but also the signal acquisition equipment itself. For instance, the different sampling rates and the different noise levels frequently found in the available signal databases can cause severe incompatibility problems when the integration of different algorithms is desired. Therefore, this work aims to solve this incompatibility problem by providing the academic community with a diagnostic-grade electrocardiograph. The intention is to create a new standardized ECG signals database in order to address the automatic interpretation problem and create an electrocardiography system that can fully assist clinical practitioners, as the proprietary systems do. Achieving this objective is expected through an open and coordinated collaboration platform for which a webpage has already been created.

This study presents an open and replicable methodology for multi-lead ECG signal quality assessment (SQA), implemented on a 12-lead embedded acquisition platform. Signal quality is a critical software component for diagnostic reliability and compliance with international standards such as IEC 60601-2-27 (clinical ECG monitors), IEC 60601-2-47 (ambulatory ECG systems), and IEC 62304 (software life cycle for medical devices) which define the essential engineering requirements and functional performance for medical devices. Unlike proprietary SQA algorithms embedded in closed commercial systems such as Philips DXL™, the proposed method provides a transparent and auditable framework that enables independent validation and supports adaptation for research and clinical prototyping. Our approach combines convolutional neural networks (CNNs) with FFT-derived spectrograms to perform four-level signal quality classification (High, Medium, Low, and Unidentifiable), achieving up to 95.67% accuracy on the test set, confirming the robustness of the CNN-based spectrogram classification model. The algorithm has been validated on a custom controlled dataset generated using the Fluke PS420™ hardware simulator, enabling controlled replication of signal artifacts for software-level evaluation. Designed for execution on resource-constrained embedded platforms, the system integrates real-time preprocessing and wireless transmission, demonstrating its feasibility for deployment in mobile or decentralized ECG monitoring solutions. These results establish a software validation proof-of-concept that goes beyond algorithmic performance, addressing regulatory expectations such as those outlined in FDA’s Good Machine Learning Practice (GMLP). While clinical validation remains pending, this work contributes a standards-aligned methodology to democratize advanced SQA functionality and support future regulatory-compliant development of embedded ECG system.

Surface electromyography (sEMG) is widely used to evaluate neuromuscular activity; however, objective and reproducible methodologies for assessing signal quality in embedded acquisition systems remain limited. This study presents a multichannel embedded sEMG acquisition platform based on an ADS1298 analog front-end (Texas Instruments, Dallas, TX, USA) and an STM32H743ZIT6 microcontroller (STMicroelectronics, Geneva, Switzerland), together with a signal-quality evaluation methodology guided by signal-integrity principles derived from IEC 60601-2-40. sEMG signals were acquired at 2 kHz from 10 healthy participants during standardized submaximal isometric and controlled isotonic contractions using consistent electrode placement and acquisition procedures. Signal quality was quantified using complementary temporal and spectral metrics, including signal-to-noise ratio (SNR), power-line interference ratio (PLI), baseline drift, root mean square stability, median frequency, spectral entropy, skewness, and kurtosis. Signal conditioning reduced the baseline drift from 0.32 ± 0.23 to 0.004 ± 0.003 and reduced PLI from 0.097 ± 0.135 to 0.010 ± 0.003 while preserving contraction-dependent temporal and spectral characteristics. In addition, envelope-based Spearman correlation analysis revealed contraction-dependent intermuscular coordination patterns between the long and short heads of the biceps brachii under controlled acquisition conditions. These findings demonstrate that combining standardized acquisition protocols, objective signal quality metrics, and interpretable correlation-based analysis provides a reproducible engineering framework for evaluating embedded sEMG systems and establishes a foundation for future machine learning approaches based on larger and well-characterized physiological datasets.