Multi-expert evolving system for objective psychophysiological monitoring and fast discovery of effective personalized therapies

Senyukova, Olga V.; Gavrishchaka, Valeriy V.; Tulnova, Ksenia

doi:10.1109/eais.2017.7954824

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…It turns out that our ensemble-based HRV indicators could be effectively used in detecting general changes in psychophysiological states [82]. This capability is illustrated in Figure 26, where psychophysiological states before and during Chi meditation are quantified (data from http://www.physionet.…”

Section: Subtle Psycho-physiological States Differentiation Based On mentioning

confidence: 99%

“…This could make the distinction between two states much less reliable. Similarly, the ensemblebased measure could be much more robust in early detection of subtle changes in psychological conditions and in their monitoring for an objective choice of optimal therapy and its further fine-tuning [82].…”

Section: Subtle Psycho-physiological States Differentiation Based On mentioning

confidence: 99%

See 1 more Smart Citation

Synergy of physics-based reasoning and machine learning in biomedical applications: towards unlimited deep learning with limited data

Gavrishchaka

Senyukova

Koepke

2019

Advances in Physics: X

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Technological advancements enable collecting vast data, i. e., Big Data, in science and industry including biomedical field. Increased computational power allows expedient analysis of collected data using statistical and machine-learning approaches. Historical data incompleteness problem and curse of dimensionality diminish practical value of pure data-driven approaches, especially in biomedicine. Advancements in deep learning (DL) frameworks based on deep neural networks (DNN) improved accuracy in image recognition, natural language processing, and other applications yet severe data limitations and/or absence of transferlearning-relevant problems drastically reduce advantages of DNN-based DL. Our earlier works demonstrate that hierarchical data representation can be alternatively implemented without NN, using boosting-like algorithms for utilization of existing domain knowledge, tolerating significant data incompleteness, and boosting accuracy of low-complexity models within the classifier ensemble, as illustrated in physiological-data analysis. Beyond obvious use in initial-factor selection, existing simplified models are effectively employed for generation of realistic synthetic data for later DNN pre-training. We review existing machine learning approaches, focusing on limitations caused by trainingdata incompleteness. We outline our hybrid framework that leverages existing domain-expert models/knowledge, boosting-like model combination, DNN-based DL and other machine learning algorithms for drastic reduction of training-data requirements. Applying this framework is illustrated in context of analyzing physiological data.

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Section: Subtle Psycho-physiological States Differentiation Based On mentioning

confidence: 99%

Section: Subtle Psycho-physiological States Differentiation Based On mentioning

confidence: 99%

Synergy of physics-based reasoning and machine learning in biomedical applications: towards unlimited deep learning with limited data

Gavrishchaka

Senyukova

Koepke

2019

Advances in Physics: X

Self Cite

View full text Add to dashboard Cite

show abstract

“…One of the well-known applications of this methodology is heart rate variability (HRV) analysis approved as one of the modalities for cardiac diagnostics [37]. Compared to traditional ECG analysis of waveforms, HRV metrics computed from time series of beat-to-beat (R-R) intervals are much more tolerant to noise and capable of detecting cardiac and non-cardiac (e.g., psychological) abnormalities lacking well-defined ECG waveform patterns [17], [18], [38]. HRV analysis is often based on complexity measures inspired by theoretical results in dynamics (NLD) and by spectral metrics heavily used in science and engineering for time series analysis [17], [37], [39]- [43].…”

Section: Application Examplesmentioning

confidence: 99%

Advantages of Hybrid Deep Learning Frameworks in Applications with Limited Data

2018

IJMLC

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Recent advancements in deep learning (DL) frameworks based on deep neural networks (DNN) drastically improved accuracy in image recognition, natural language processing and other applications. The key advantage of DL is systematic approach for independent training of groups of DNN layers including unsupervised training of auto-encoders for hierarchical representation of raw input data (i.e., automatic feature selection and dimensionality reduction) and supervised re-training of several final layers in the transfer learning that compensate for data incompleteness. However, severe data limitations and/or absence of relevant problem for transfer learning can drastically reduce advantages of DNN-based DL. For example, pure data-driven auto-encoders dealing with high-dimensional input data require large amount of data for effective operation. However, hierarchical data representations can be also implemented without NN. Previously we have shown robustness of boosting-like algorithms for effective utilization of existing domain knowledge (e.g. analytical models) via discovery of compact ensembles of complementary low-complexity components. This approach can tolerate significant data incompleteness and boost accuracy of individual base models as was demonstrated in cardiac diagnostics applications. Here we argue that hybrid DL framework with auto-encoders replaced by components discovered by boosting followed by supervised NN could be more tolerant to data incompleteness compared to pure DNN-based DL. Illustrations based on cardio data from www.physionet.org are presented. The proposed framework could be utilized in many applications dealing with incomplete data including personalized medicine and rare or complex abnormalities.

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