Design and Implementation of a Device for Capturing Biological Signals Applied to the Treatment of Ischemic Stroke.

Posted on October 7, 2025October 7, 2025 by rookie

Introduction

This work is part of the STRIKE project, a multidisciplinary initiative aimed at developing new therapeutic strategies for the treatment of ischemic stroke, one of the leading causes of death and disability in Spain and worldwide. STRIKE integrates three techniques: transcranial magnetic stimulation (TMS), implantation of mesenchymal stem cells encapsulated in silk fibroin, and electrical stimulation of the auricular branch of the vagus nerve (aVNS).

In this context, the Ikki device has been designed and implemented a portable system for acquiring biological signals, specifically electrocardiogram (ECG) in rodents and electroencephalogram (EEG) in humans. This work primarily focuses on the electrical stimulation of the auricular branch of the vagus nerve, although it is partially related to transcranial magnetic stimulation. The main goal is to enable real-time monitoring of physiological responses to the applied therapies, thereby facilitating personalized treatment and experimental validation of the proposed techniques. The device has been validated in a real-world setting through human trials.

Keywords

Electrocardiogram, electroencephalogram, biological signals, electrophysiology, biomedical
device, portable, low power consumption, vagus nerve, ischemic stroke, treatment, embedded
system.

The problem

Clinical Context

Ischemic stroke accounts for more than 80% of stroke cases. Conventional treatment is based on early reperfusion and physical rehabilitation, but neurological recovery remains limited. Therefore, new strategies are needed to complement current treatments and improve neuroprotection and brain regeneration. The STRIKE project was born from the development of these new strategies.

Technological Need

To experimentally validate the techniques proposed in the STRIKE project, it is essential to have a device capable of accurately acquiring physiological signals in a portable, non-invasive, and user-friendly manner for researchers. ECG and EEG signals allow for the evaluation of the impact of therapies on the nervous and cardiovascular systems and are fundamental for establishing a personalized treatment approach.

Acquisition system for ECG and stimulation used in the CTB.

Currently, experiments conducted at the Center for Biomedical Technology (CTB) use very large devices that hinder researchers due to their size. Therefore, the long-term goal is to develop a device with a “closed-loop approach.” To achieve this, the signal acquisition device is developed first, and later stimulation will be integrated into the same device.

Proposed Solution

The Ikki device has been developed as a comprehensive solution for acquiring and transmitting biological signals in the context of ischemic stroke treatment. Its design meets criteria of portability, low energy consumption, data capture precision, and ease of use in experimental and clinical environments. The system has been divided into hardware and software components.

Hardware Development (HW)

The complete hardware system has been built around a custom board integrating the following modules:

MSP430FG479 Microcontroller: Responsible for acquiring signals from the acquisition circuits. It includes built-in SD16-type ADCs. This is the main microcontroller of the entire system.
Signal Acquisition Circuits: Composed of several modules that allow analog processing of the signals to be captured, with filtering and amplification adapted to each signal type.
Power Supply System: Composed of a PMIC, a linear regulator, and an inverter that enable symmetrical power supply to the entire system.
Communications: Includes test points that allow data collection through the main microcontroller.
Connectors and Electrodes: Adapted for use in humans and rodents, ensuring secure and stable connections during acquisition.

The design has gone through several iterations, from initial prototypes to the final version Ikki V1.3, optimized for real-world testing. The quality of the acquired signals has been validated through comparisons with commercial systems.

Software Development(SW)

The software is divided into four layers:

Acquisition Layer:
- Developed in C using Code Composer Studio.
- Programs the main microcontroller MSP430FG479.
- Acquires data via the microcontroller’s built-in ADCs, processed through the acquisition circuits.
- These data are then sent in a predefined format via SPI or UART to the bridge layer.
Communication layer:
- Defines a state machine to differentiate data acquired from various channels by the main microcontroller.
Bridge layer:
- Programs an ESP32 using ArduinoIDE.
- Receives data from the acquisition layer via SPI/UART and forwards them via BLE.
- Enables wireless functionality of the device.
Visualization layer:
- Programmed in Python.
- Establishes a connection with the bridge layer and displays the data received via BLE on a device screen, such as a laptop.

TFM: Design strategies for detecting action potentials in actions based on movements

Posted on January 30, 2018December 14, 2018 by anacp

This work is located in the studies of the brain and their signals. The puspose is to know when someone wants to make a movement. Thus, it might help to people that actually are not able to move a member of their body or more. Mainly, it is focused in the design of strategies for detection of action potentials or spikes when a movement wants to be made. This study is not looking for action potentials form, it is looking for patterns and characteristics that allow to recognize the movement. Although there are action potentials covered by the signals taken from the electrodes, but they are unavailable.

To accomplish the objective, it is used the EEG signals of a public data base. It is selected the ones related to the movement of the hands, concretely, the movement of open and close the fist. Signal sources of noise that dirty the signal are analyzed, they are called artifacts, and then, filtering stage comes, giving the signals of below for movement and no movement.

Now, possible algorithms are checked. It is decided to use the Wavelet transform and the way in which it obtains the energy of the signal. Thanks to the calculation of Wavelet energy in 22 subjects, it is reached to the conclusion that Wavelet energy for movement is higher than for no movement. So, electrodes that comply with this condition at 100% are 4.

The final algorithm is implemented three features: correlation, a parameter that gives a relation between two signals, their energy range and their energy average. It could be said that algorithm has two parts: a training stage and a decision stage. Inside decision part, there are three algorithms: ProMove, ProMove + improve and Logic. The basic difference among ProMove and Logic is an or (||) and an and (&). The improve is based on empiric knowledge.

Final conclusions show that the signals between subjects are very changing. Therefore, same algorithm is not useful for everybody. To some subjects, the successful probability is very high (92,86% – 1 fail), while for others is more low than what is expected (50% – 7 fail). With these test, the importance in the length of the signals is reflected, because if signals for subjects with more than 3 fails are inversely processed, the fails are reduced. The most useful algorithm for a larger number of subjects is ProMove + improve.

Detección de indicadores de fatiga basado en la obtención de imágenes en tiempo real.

Posted on November 23, 2017November 23, 2017 by Gis

Dentro del proyecto Simbiosys buscamos la detección de fatiga mediante imágenes obtenidas por cámara, como apoyo al sistema de detección de indicadores de fatiga mediante EEG.

Este módulo del sistema multisensor consiste en una cámara de bajo coste que obtiene las imágenes del sujeto para analizar en tiempo real. Además, es necesario que pueda detectar luz infrarroja, para los casos en los que la luz sea escasa. El módulo se basa en la detección facial de la cara, para poder obtener posteriormente la detección de ambos ojos.

El objetivo es obtener el porcentaje de tiempo en el que el ojo se encuentra cerrado durante un minuto (AVECLOS). Por tanto, si el porcentaje es mayor que el porcentaje normal de tiempo en el que una persona presenta los ojos cerrados, se considera que el sujeto se encuentra cansado o fatigado.

El sistema final comparará la información obtenida tanto como por el electroencefalograma como por la cámara, para obtener con mayor seguridad el estado en el que se encuentra el sujeto.

SIMBIOSYS: Simulator Biometric System plug-in

Posted on May 27, 2016June 19, 2017 by Gis

One of the major problems facing the drivers of different vehicles is the difficulty of anticipate and react to the health-related problems that the operator may have.

For this reason, the goal of this project is the development of a simulator system that allows to prevent and/or report states in the machine operator that can compromise the safety of the people. It will detect physical states (tachycardia, bradycardia, hypoxia, hypotension, etc.) as well as psychics (stress, drowsiness, alertness,etc.).

To achieve this, the system will use biometrics sensors, such as breast bands or weareable bracelets to obtain the measures of heart rate or oxygen saturation. But the main sensor we are interested in is a EEG sensor that sends the raw electroencephalography.

The B105 Electronic Systems Lab. as a representative of Technical University of Madrid(UPM) participates with Valoriza in this innovative research project. To develop it we have the support of the Industrial Technological Center (CDTI) and the Ministry of Economy and Competitiveness.

B105 lab

Failure is not an option

EEG