• Head of Research Group: Dr. Márton Áron GODA
  • Members of the Group: Dr. Janka HATVANI, Dr. Miklós KOLLER, Kristóf MÜLLER, Bálint KRISTÓF, Szabolcs Mátyás Péter
  • Founder of the Ultrasound Lab (DUSI): Dr. Miklós GYÖNGY
  • Contact: goda.marton.aron@itk.ppke.hu
  • AIMS-Lab develops innovative technologies for processing medical signals, including sensors, measurement systems, software tools, and AI-based solutions. Our work spans a wide range of biomedical data, from phonocardiography (PCG), electrocardiography (ECG), and photoplethysmography (PPG) to ultrasound imaging, cardiotocography (CTG), and other sensor- or imaging-derived sources.
  • Our Research
    • Wearable devices have become a key part of modern healthcare, enabling home monitoring, tracking fitness, stress, and potential heart conditions, while providing valuable insights for clinical care and everyday health management. Our lab focuses on developing standardized, robust, and validated solutions for medical signal processing to support next-generation AI-powered healthcare technologies.
  • Fetal Monitoring
    • Traditional fetal monitoring methods, such as ultrasound and cardiotocography, have limitations, especially for long-term home monitoring, precise detection of fetal heart sounds, and specialized fetal activity assessment.
    • Fetal phonocardiography (fPCG) offers a simple, cost-effective, and efficient alternative. Advances in personal smart devices have improved data collection, making fetal monitoring more accessible and opening new ways to track fetal development and overall health.
    • Our primary focus is fPCG-based signal processing, complemented by ultrasound measurements for validation. We developed pyPCG, a validated Python toolbox for analyzing phonocardiographic (PCG) signals. It integrates widely used processing methods and is designed for easy expansion with new features and advanced segmentation models.
  • Heart Pulse Monitoring
    • Monitoring heart pulse is essential for assessing cardiovascular health and detecting arrhythmias. This can be done with ECG devices, pulse oximeters, smartwatches, or chest straps.
    • Beyond health tracking, these technologies also have potential for biometric identification. Combined with tools like facial recognition, pulse analysis can enhance modern security systems.
    • In collaboration with the Technion – Israel Institute of Technology and Cambridge University, we developed pyPPG, an advanced open-source toolbox for photoplethysmography (PPG) analysis. Our goal is to implement these tools broadly in clinical settings, including applications such as heart failure prediction and risk assessment.
  • Our Collaborating Partners

Time and frequency analysis of Fetal Breathing Movement episodes in fetal phonocardiography signal. The FBM and heart sounds exhibit distinct frequency characteristics, with the FBM signal appearing at lower frequencies and the peak values of heart sounds being located at higher frequencies. The separation between the starting frequency of the FBM and the heart sounds is well-defined. Specifically, the FBM can be readily detected within the 20-30 Hz frequency band, whereas the heart sounds manifest themselves at higher frequency ranges.