Airaksinen M - Search Results

1

An Open Source Classifier for Bed Mattress Signal in Infant Sleep Monitoring.

Ranta J, Airaksinen M, Kirjavainen T, Vanhatalo S, Stevenson NJ. Ranta J, et al. Among authors: airaksinen m. Front Neurosci. 2021 Jan 14;14:602852. doi: 10.3389/fnins.2020.602852. eCollection 2020. Front Neurosci. 2021. PMID: 33519357 Free PMC article.

2

Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors.

Airaksinen M, Räsänen O, Ilén E, Häyrinen T, Kivi A, Marchi V, Gallen A, Blom S, Varhe A, Kaartinen N, Haataja L, Vanhatalo S. Airaksinen M, et al. Sci Rep. 2020 Jan 13;10(1):169. doi: 10.1038/s41598-019-56862-5. Sci Rep. 2020. PMID: 31932616 Free PMC article.

3

Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization.

Moghadam SM, Pinchefsky E, Tse I, Marchi V, Kohonen J, Kauppila M, Airaksinen M, Tapani K, Nevalainen P, Hahn C, Tam EWY, Stevenson NJ, Vanhatalo S. Moghadam SM, et al. Among authors: airaksinen m. Front Hum Neurosci. 2021 May 31;15:675154. doi: 10.3389/fnhum.2021.675154. eCollection 2021. Front Hum Neurosci. 2021. PMID: 34135744 Free PMC article.

4

Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants.

Airaksinen M, Gallen A, Kivi A, Vijayakrishnan P, Häyrinen T, Ilén E, Räsänen O, Haataja LM, Vanhatalo S. Airaksinen M, et al. Commun Med (Lond). 2022 Jun 15;2:69. doi: 10.1038/s43856-022-00131-6. eCollection 2022. Commun Med (Lond). 2022. PMID: 35721830 Free PMC article.

5

An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation.

Moghadam SM, Airaksinen M, Nevalainen P, Marchi V, Hellström-Westas L, Stevenson NJ, Vanhatalo S. Moghadam SM, et al. Among authors: airaksinen m. Lancet Digit Health. 2022 Dec;4(12):e884-e892. doi: 10.1016/S2589-7500(22)00196-0. Lancet Digit Health. 2022. PMID: 36427950 Free article.

6

Comparison of End-to-End Neural Network Architectures and Data Augmentation Methods for Automatic Infant Motility Assessment Using Wearable Sensors.

Airaksinen M, Vanhatalo S, Räsänen O. Airaksinen M, et al. Sensors (Basel). 2023 Apr 6;23(7):3773. doi: 10.3390/s23073773. Sensors (Basel). 2023. PMID: 37050833 Free PMC article.

7

Charting infants' motor development at home using a wearable system: validation and comparison to physical growth charts.

Airaksinen M, Taylor E, Gallen A, Ilén E, Saari A, Sankilampi U, Räsänen O, Haataja LM, Vanhatalo S. Airaksinen M, et al. EBioMedicine. 2023 Jun;92:104591. doi: 10.1016/j.ebiom.2023.104591. Epub 2023 May 1. EBioMedicine. 2023. PMID: 37137181 Free PMC article.

8

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

Vaaras E, Airaksinen M, Vanhatalo S, Rasanen O. Vaaras E, et al. Among authors: airaksinen m. Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul;2023:1-6. doi: 10.1109/EMBC40787.2023.10340118. Annu Int Conf IEEE Eng Med Biol Soc. 2023. PMID: 38083169

9

Automatic assessment of infant carrying and holding using at-home wearable recordings.

Airaksinen M, Vaaras E, Haataja L, Räsänen O, Vanhatalo S. Airaksinen M, et al. Sci Rep. 2024 Feb 28;14(1):4852. doi: 10.1038/s41598-024-54536-5. Sci Rep. 2024. PMID: 38418850 Free PMC article.

10

Corrigendum: Building an open source classifier for the neonatal EEG background: a systematic feature-based approach from expert scoring to clinical visualization.

Montazeri S, Pinchefsky E, Tse I, Marchi V, Kohonen J, Kauppila M, Airaksinen M, Tapani K, Nevalainen P, Hahn C, Tam EWY, Stevenson NJ, Vanhatalo S. Montazeri S, et al. Among authors: airaksinen m. Front Hum Neurosci. 2024 Apr 29;18:1417744. doi: 10.3389/fnhum.2024.1417744. eCollection 2024. Front Hum Neurosci. 2024. PMID: 38741595 Free PMC article.

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