Search Results for author: Sampsa Vanhatalo

Found 7 papers, 3 papers with code

Modeling 3D Infant Kinetics Using Adaptive Graph Convolutional Networks

1 code implementation22 Feb 2024 Daniel Holmberg, Manu Airaksinen, Viviana Marchi, Andrea Guzzetta, Anna Kivi, Leena Haataja, Sampsa Vanhatalo, Teemu Roos

Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of medical issues that may need prompt interventions.

Pose Estimation

Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors

1 code implementation16 May 2023 Einari Vaaras, Manu Airaksinen, Sampsa Vanhatalo, Okko Räsänen

The recently-developed infant wearable MAIJU provides a means to automatically evaluate infants' motor performance in an objective and scalable manner in out-of-hospital settings.

Human Activity Recognition Self-Supervised Learning

Development of Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels

no code implementations25 Aug 2022 Saeed Montazeri Moghadam, Päivi Nevalainen, Nathan J. Stevenson, Sampsa Vanhatalo

In addition to training and validating a single EEG channel quiet sleep detector, we constructed Sleep State Trend (SST), a bedside-ready means for visualizing classifier outputs.


Ensemble learning using individual neonatal data for seizure detection

1 code implementation11 Apr 2022 Ana Borovac, Steinn Gudmundsson, Gardar Thorvardsson, Saeed M. Moghadam, Päivi Nevalainen, Nathan Stevenson, Sampsa Vanhatalo, Thomas P. Runarsson

The weighted mean aggregation scheme showed best performance, it was only marginally outperformed by the Dawid--Skene method when local detectors approach performance of a single detector trained on all available data.

EEG Ensemble Learning +1

Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy

no code implementations24 Feb 2022 Karoliina T. Tapani, Päivi Nevalainen, Sampsa Vanhatalo, Nathan J. Stevenson

Clinical efficacy was tested by comparing how the SDA and human experts quantified seizure burden and identified clinically significant periods of seizure activity in the EEG.

EEG Seizure Detection

Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors

no code implementations21 Sep 2019 Manu Airaksinen, Okko Räsänen, Elina Ilén, Taru Häyrinen, Anna Kivi, Viviana Marchi, Anastasia Gallen, Sonja Blom, Anni Varhe, Nico Kaartinen, Leena Haataja, Sampsa Vanhatalo

These data were manually annotated for infant posture and movement based on video recordings of the sessions, and using a novel annotation scheme specifically designed to assess the overall movement pattern of infants in the given age group.

Cannot find the paper you are looking for? You can Submit a new open access paper.