Recognizing diseases with multivariate physiological signals by a DeepCNN-LSTM network

Liao, Jun; Liu, Dandan; Su, Guoxin; Liu, Li

doi:10.1007/s10489-021-02309-2

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…To the best of our knowledge, this is the first investigation using deep learning methods to specifically investigate sex differences in response to rTMS based on EEG in the context of MDD. The proposed method is robust and automated due to the representation power of time‐invariant features from raw EEG signals (Liao et al., 2021 ). In this methodology, intraslice features of brain images are processed and extracted and can be adopted for other physiological data and imaging modalities such as fMRI, CT, or PET (Cheng & Liu, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

Sex differences in rTMS treatment response: A deep learning‐based EEG investigation

et al. 2022

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Introduction The present study aimed to investigate sex differences in response to repetitive transcranial magnetic stimulation (rTMS) in Major Depressive Disorder (MDD) patients. Identifying the factors that mediate treatment response to rTMS in MDD patients can guide clinicians to administer more appropriate, reliable, and personalized interventions. Methods In this paper, we developed a novel pipeline based on convolutional LSTM‐based deep learning (DL) to classify 25 female and 25 male patients based on their rTMS treatment response. Results Five different classification models were generated, namely pre‐/post‐rTMS female (model 1), pre‐/post‐rTMS male (model 2), pre‐rTMS female responder versus pre‐rTMS female nonresponders (model 3), pre‐rTMS male responder vs. pre‐rTMS male nonresponder (model 4), and pre‐rTMS responder versus nonresponder of both sexes (model 5), achieving 93.3%, 98%, 95.2%, 99.2%, and 96.6% overall test accuracy, respectively. Conclusion These results indicate the potential of our approach to be used as a response predictor especially regarding sex‐specific antidepressant effects of rTMS in MDD patients.

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Section: Discussionmentioning

confidence: 99%

Sex differences in rTMS treatment response: A deep learning‐based EEG investigation

et al. 2022

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“…However, a higher monitoring rate (every five minutes at least) is required for patients under anesthesia according to the recommendations of the American Society of Anesthesiologists (ASA) [5] for checking the functionality of the patient's circulatory. Motivated by the current trend of increased health awareness, the need arises for ubiquitous sensing and monitoring of critical biomarkers continuously [6,7]. Simple and continuous non-clinical BP monitoring becomes highly desirable nowadays for limiting the risk of cardiovascular diseases and hypertension.…”

Section: Introductionmentioning

confidence: 99%

Video-based beat-by-beat blood pressure monitoring via transfer deep-learning

Omer,

Salah,

Hassan

et al. 2024

Appl Intell

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Currently, learning physiological vital signs such as blood pressure (BP), hemoglobin levels, and oxygen saturation, from Photoplethysmography (PPG) signal, is receiving more attention. Despite successive progress that has been made so far, continuously revealing new aspects characterizes that field as a rich research topic. It includes a diverse number of critical points represented in signal denoising, data cleaning, employed features, feature format, feature selection, feature domain, model structure, problem formulation (regression or classification), and model combinations. It is worth noting that extensive research efforts are devoted to utilizing different variants of machine learning and deep learning models while transfer learning is not fully explored yet. So, in this paper, we are introducing a per-beat rPPG-to-BP mapping scheme based on transfer learning. An interesting representation of a 1-D PPG signal as a 2-D image is proposed for enabling powerful off-the-shelf image-based models through transfer learning. It resolves limitations about training data size due to strict data cleaning. Also, it enhances model generalization by exploiting underlying excellent feature extraction. Moreover, non-uniform data distribution (data skewness) is partially resolved by introducing logarithmic transformation. Furthermore, double cleaning is applied for training contact PPG data and testing rPPG beats as well. The quality of the segmented beats is tested by checking some of the related quality metrics. Hence, the prediction reliability is enhanced by excluding deformed beats. Varying rPPG quality is relaxed by selecting beats during intervals of the highest signal strength. Based on the experimental results, the proposed system outperforms the state-of-the-art systems in the sense of mean absolute error (MAE) and standard deviation (STD). STD for the test data is decreased to 5.4782 and 3.8539 for SBP and DBP, respectively. Also, MAE decreased to 2.3453 and 1.6854 for SBP and DBP, respectively. Moreover, the results for BP estimation from real video reveal that the STD reaches 8.027882 and 6.013052 for SBP and DBP, respectively. Also, MAE for the estimated BP from real videos reaches 7.052803 and 5.616028 for SBP and DBP, respectively. Graphical abstract Proposed camera-based blood pressure monitoring system

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Time Series Reconstruction and Classification: A Comprehensive Comparative Study

Pedrycz

Gacek

2022

Appl Intell

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Recognizing diseases with multivariate physiological signals by a DeepCNN-LSTM network

Cited by 7 publications

References 37 publications

Sex differences in rTMS treatment response: A deep learning‐based EEG investigation

Sex differences in rTMS treatment response: A deep learning‐based EEG investigation

Video-based beat-by-beat blood pressure monitoring via transfer deep-learning

Time Series Reconstruction and Classification: A Comprehensive Comparative Study

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