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Nair SS, Guo A, Boen J, Aggarwal A, Chahal O, Tandon A, Patel M, Sankararaman S, Durr NJ, Azad TD, Pirracchio R, Stevens RD. A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit. Comput Biol Med 2024; 177:108677. [PMID: 38833800 DOI: 10.1016/j.compbiomed.2024.108677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/27/2024] [Accepted: 05/28/2024] [Indexed: 06/06/2024]
Abstract
Intracranial pressure (ICP) is commonly monitored to guide treatment in patients with serious brain disorders such as traumatic brain injury and stroke. Established methods to assess ICP are resource intensive and highly invasive. We hypothesized that ICP waveforms can be computed noninvasively from three extracranial physiological waveforms routinely acquired in the Intensive Care Unit (ICU): arterial blood pressure (ABP), photoplethysmography (PPG), and electrocardiography (ECG). We evaluated over 600 h of high-frequency (125 Hz) simultaneously acquired ICP, ABP, ECG, and PPG waveform data in 10 patients admitted to the ICU with critical brain disorders. The data were segmented in non-overlapping 10-s windows, and ABP, ECG, and PPG waveforms were used to train deep learning (DL) models to re-create concurrent ICP. The predictive performance of six different DL models was evaluated in single- and multi-patient iterations. The mean average error (MAE) ± SD of the best-performing models was 1.34 ± 0.59 mmHg in the single-patient and 5.10 ± 0.11 mmHg in the multi-patient analysis. Ablation analysis was conducted to compare contributions from single physiologic sources and demonstrated statistically indistinguishable performances across the top DL models for each waveform (MAE±SD 6.33 ± 0.73, 6.65 ± 0.96, and 7.30 ± 1.28 mmHg, respectively, for ECG, PPG, and ABP; p = 0.42). Results support the preliminary feasibility and accuracy of DL-enabled continuous noninvasive ICP waveform computation using extracranial physiological waveforms. With refinement and further validation, this method could represent a safer and more accessible alternative to invasive ICP, enabling assessment and treatment in low-resource settings.
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Affiliation(s)
- Shiker S Nair
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
| | - Alina Guo
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Joseph Boen
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Ataes Aggarwal
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Ojas Chahal
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Arushi Tandon
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Meer Patel
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Sreenidhi Sankararaman
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Nicholas J Durr
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Tej D Azad
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Romain Pirracchio
- Department of Anesthesia and Perioperative Care, UCSF, San Francisco, USA
| | - Robert D Stevens
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA; Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
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Peng Y, Zhao S, Zeng Z, Hu X, Yin Z. LGBMDF: A cascade forest framework with LightGBM for predicting drug-target interactions. Front Microbiol 2023; 13:1092467. [PMID: 36687573 PMCID: PMC9849804 DOI: 10.3389/fmicb.2022.1092467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 12/07/2022] [Indexed: 01/07/2023] Open
Abstract
Prediction of drug-target interactions (DTIs) plays an important role in drug development. However, traditional laboratory methods to determine DTIs require a lot of time and capital costs. In recent years, many studies have shown that using machine learning methods to predict DTIs can speed up the drug development process and reduce capital costs. An excellent DTI prediction method should have both high prediction accuracy and low computational cost. In this study, we noticed that the previous research based on deep forests used XGBoost as the estimator in the cascade, we applied LightGBM instead of XGBoost to the cascade forest as the estimator, then the estimator group was determined experimentally as three LightGBMs and three ExtraTrees, this new model is called LGBMDF. We conducted 5-fold cross-validation on LGBMDF and other state-of-the-art methods using the same dataset, and compared their Sn, Sp, MCC, AUC and AUPR. Finally, we found that our method has better performance and faster calculation speed.
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Meng Z, Yang X, Liu X, Wang D, Han X. Non-invasive blood pressure estimation combining deep neural networks with pre-training and partial fine-tuning. Physiol Meas 2022; 43. [PMID: 36301705 DOI: 10.1088/1361-6579/ac9d7f] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 10/25/2022] [Indexed: 02/07/2023]
Abstract
Objective. Daily blood pressure (BP) monitoring is essential since BP levels can reflect the functions of heart pumping and vasoconstriction. Although various neural network-based BP estimate approaches have been proposed, they have certain practical shortcomings, such as low estimation accuracy and poor model generalization. Based on the strategy of pre-training and partial fine-tuning, this work proposes a non-invasive method for BP estimation using the photoplethysmography (PPG) signal.Approach. To learn the PPG-BP relationship, the deep convolutional bidirectional recurrent neural network (DC-Bi-RNN) was pre-trained with data from the public medical information mark for intensive care (MIMIC III) database. A tiny quantity of data from the target subject was used to fine-tune the specific layers of the pre-trained model to learn more individual-specific information to achieve highly accurate BP estimation.Main results.The mean absolute error and the Pearson correlation coefficient (r) of the proposed algorithm are 3.21 mmHg and 0.919 for systolic BP, and 1.80 mmHg and 0.898 for diastolic BP (DBP). The experimental results show that our method outperforms other methods and meets the requirements of the Association for the Advancement of Medical Instrumentation standard, and received an A grade according to the British Hypertension Society standard.Significance.The proposed method applies the strategy of pre-training and partial fine-tuning to BP estimation and verifies its effectiveness in improving the accuracy of non-invasive BP estimation.
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Affiliation(s)
- Ziyan Meng
- School of Computer and Information, Hefei University of Technology, Hefei 230009, People's Republic of China.,Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei 230009, People's Republic of China
| | - Xuezhi Yang
- School of Computer and Information, Hefei University of Technology, Hefei 230009, People's Republic of China.,Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei 230009, People's Republic of China
| | - Xuenan Liu
- School of Computer and Information, Hefei University of Technology, Hefei 230009, People's Republic of China.,Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei 230009, People's Republic of China
| | - Dingliang Wang
- School of Computer and Information, Hefei University of Technology, Hefei 230009, People's Republic of China.,Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei 230009, People's Republic of China
| | - Xuesong Han
- School of Computer and Information, Hefei University of Technology, Hefei 230009, People's Republic of China.,Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei 230009, People's Republic of China
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Gupta K, Bajaj V, Ansari IA, Rajendra Acharya U. Hyp-Net: Automated detection of hypertension using deep convolutional neural network and Gabor transform techniques with ballistocardiogram signals. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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