Discovering hidden information in biosignals from patients using artificial intelligence

Yoon, Dukyong; Jang, Jong-Hwan; Choi, Bunsoon; Kim, Tae Young; Han, Changho

doi:10.4097/kja.19475

Cited by 16 publications

(11 citation statements)

References 32 publications

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“…Recently, several studies have been successful in estimating clinical information that was not explicitly provided, such as in the prediction of valve disease or hyperkalemia using deep learning in electrocardiogram studies ( Galloway et al, 2019 ; Kwon et al, 2020 ). This supports the hypothesis that hidden but clinically meaningful information may exist in bio-signals, and these patterns could be analyzed with deep learning methods ( Yoon et al, 2020 ).…”

Section: Discussionsupporting

confidence: 81%

Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia

Park

Choi

Lee

et al. 2022

Front. Neuroinform.

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There is a proven correlation between the severity of dementia and reduced brain volumes. Several studies have attempted to use activity data to estimate brain volume as a means of detecting reduction early; however, raw activity data are not directly interpretable and are unstructured, making them challenging to utilize. Furthermore, in the previous research, brain volume estimates were limited to total brain volume and the investigators were unable to detect reductions in specific regions of the brain that are typically used to characterize disease progression. We aimed to evaluate volume prediction of 116 brain regions through activity data obtained combining time-frequency domain- and unsupervised deep learning-based feature extraction methods. We developed a feature extraction model based on unsupervised deep learning using activity data from the National Health and Nutrition Examination Survey (NHANES) dataset (n = 14,482). Then, we applied the model and the time-frequency domain feature extraction method to the activity data of the Biobank Innovations for chronic Cerebrovascular disease With ALZheimer’s disease Study (BICWALZS) datasets (n = 177) to extract activity features. Brain volumes were calculated from the brain magnetic resonance imaging of the BICWALZS dataset and anatomically subdivided into 116 regions. Finally, we fitted linear regression models to estimate each regional volume of the 116 brain areas based on the extracted activity features. Regression models were statistically significant for each region, with an average correlation coefficient of 0.990 ± 0.006. In all brain regions, the correlation was > 0.964. Particularly, regions of the temporal lobe that exhibit characteristic atrophy in the early stages of Alzheimer’s disease showed the highest correlation (0.995). Through a combined deep learning-time-frequency domain feature extraction method, we could extract activity features based solely on the activity dataset, without including clinical variables. The findings of this study indicate the possibility of using activity data for the detection of neurological disorders such as Alzheimer’s disease.

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Section: Discussionsupporting

confidence: 81%

Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia

Park

Choi

Lee

et al. 2022

Front. Neuroinform.

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“…Previous studies have suggested that clinically important information remained undiscovered in the biosignal data [83,84]. In the ECG waveform data, atrial fibrillation could be detected regardless of whether the ECG waveforms maintained normal sinus rhythm [48].…”

Section: Discussionmentioning

confidence: 99%

Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success

Park

Kim

Jung

et al. 2021

IJERPH

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We evaluated new features from biosignals comprising diverse physiological response information to predict the outcome of weaning from mechanical ventilation (MV). We enrolled 89 patients who were candidates for weaning from MV in the intensive care unit and collected continuous biosignal data: electrocardiogram (ECG), respiratory impedance, photoplethysmogram (PPG), arterial blood pressure, and ventilator parameters during a spontaneous breathing trial (SBT). We compared the collected biosignal data’s variability between patients who successfully discontinued MV (n = 67) and patients who did not (n = 22). To evaluate the usefulness of the identified factors for predicting weaning success, we developed a machine learning model and evaluated its performance by bootstrapping. The following markers were different between the weaning success and failure groups: the ratio of standard deviations between the short-term and long-term heart rate variability in a Poincaré plot, sample entropy of ECG and PPG, α values of ECG, and respiratory impedance in the detrended fluctuation analysis. The area under the receiver operating characteristic curve of the model was 0.81 (95% confidence interval: 0.70–0.92). This combination of the biosignal data-based markers obtained during SBTs provides a promising tool to assist clinicians in determining the optimal extubation time.

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“…Machine learning, which can learn patterns from data, is the most widely used AI algorithm in perioperative medicine [ 11 ]. Machine learning algorithms are typically classified into three categories: supervised, unsupervised, and reinforcement learning ( Fig.…”

Section: Overview Of Ai Techniquesmentioning

confidence: 99%

“…Accurate perioperative risk stratification is important for facilitating shared decision-making and the allocation of medical resources. Several preoperative risk scores have been developed and used in clinical practice, including the American Society of Anesthesiologists Physical Status (ASA-PS) classification [ 11 ], American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator [ 25 ], surgical Apgar score [ 26 ], and Risk Stratification Index [ 27 ].…”

Section: Ai Models For Perioperative Risk Stratificationmentioning

confidence: 99%

Artificial intelligence in perioperative medicine: a narrative review

Yoon

Yang

Jung

et al. 2022

Korean J Anesthesiol

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Recent advancements in artificial intelligence (AI) techniques have enabled the development of accurate prediction models using clinical big data. AI models for perioperative risk stratification, intraoperative event prediction, biosignal analyses, and intensive care medicine have been developed in the field of perioperative medicine. Some of these models have been validated using external datasets and randomized controlled trials. Once these models are implemented in electronic health record systems or software medical devices, they could help anesthesiologists improve clinical outcomes by accurately predicting complications and suggesting optimal treatment strategies in real-time. This review provides an overview of the AI techniques used in perioperative medicine and a summary of the studies that have been published using these techniques. Understanding these techniques will aid in their appropriate application in clinical practice.

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Discovering hidden information in biosignals from patients using artificial intelligence

Cited by 16 publications

References 32 publications

Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia

Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia

Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success

Artificial intelligence in perioperative medicine: a narrative review

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