Bahador, N., & Kortelainen, J. (2022). Deep learning-based classification of multichannel bio-signals using directedness transfer learning. Biomedical Signal Processing and Control, 72, 103300. https://doi.org/10.1016/j.bspc.2021.103300
Deep learning-based classification of multichannel bio-signals using directedness transfer learning
|Author:||Bahador, Nooshin1; Kortelainen, Jukka1|
1Physiological Signal Analysis Team, Center for Machine Vision and Signal Analysis, MRC Oulu, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 4.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022041929573
|Publish Date:|| 2022-06-17
The problem with processing of multivariate/multichannel signals lies in adapting of existing classifiers on data. Reformulating time-series data as visual clues and assigning visual patterns to different categories help the classification of time series in a wide range of applications. These series-to-image transformations have benefits including better noise robustness and more options regarding augmentation. They also provide the possibility of achieving discriminative features by employing transfer learning paradigm in cases dealing with highly small training datasets. In this respect, this work aimed to encode spectral-phase information into a bi-dimensional map. Transferring knowledge was done using bi-dimensional transformation capitalizing on the direction and propagation pattern of one channel influence on the others. EEG data from patients diagnosed with delirium (N = 15) recorded using a 10-channel BrainStatus device were used for this analysis. Considering leave-one-subject-out cross-validation, classification outcomes demonstrated that directedness transfer learning via Alexnet yields a promising performance showing 97.17% precision and outperforming other approaches. Comparison with nine different deep networks pretrained on ImageNet database was included. Directedness transfer learning resulted in precision of 95.29 ± 1.46 (µ ± σ)% among all networks. For further evaluation, directedness bi-dimensional transformation was also compared with six other 2D maps. Applying different networks resulted in average precision of (91.99 ± 2.23)% for polar-, (91.69 ± 1.57)% for correlation-, (90.46 ± 1.71)% for Spectrogram-, (87.82 ± 2.16)% for Wavelet-, (84.24 ± 1.72)% for Wigner-Ville- and (82.84 ± 2.46)% for Mel-frequency Cepstrum maps. To conclude, the proposed technique shows significant benefit in compressing spatio-spectral patterns of multichannel signals in just a unified visual representation.
Biomedical signal processing and control
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported by a grant (No. 308935) from the Academy of Finland and Infotech. Nooshin Bahador would also like to thank following foundations for their support: 1- Orion Research Foundation sr, 2- Walter Ahlström Foundations, 3- Tauno Tönning Foundation.
|Academy of Finland Grant Number:||
308935 (Academy of Finland Funding decision)
© 2022 The Authors. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).