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Article ; Online: A robust automatic mechanism for electrocardiogram interpretation in telehealthcare.

Te-Wei Ho / Feipei Lai

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2017 Volume 2017, Page(s) 3505–3508

Abstract: Telehealthcare has become increasingly popular in clinical practice as a means of providing ubiquitous healthcare through long-term informative interactions and health monitoring. We have delivered a synchronized telehealthcare program since 2009. We ... ...

Abstract	Telehealthcare has become increasingly popular in clinical practice as a means of providing ubiquitous healthcare through long-term informative interactions and health monitoring. We have delivered a synchronized telehealthcare program since 2009. We have implemented a web-based clinical decision support system with a knowledge-based electrocardiogram (ECG) recognition mechanism as an augmentation service to assist medical practitioners doing decision making in clinical practice. To evaluate the capability and usage limits of this automatic ECG interpretation, the aim of this study was to validate the stability and robustness of proposed mechanism using stress testing through six simulation scenarios. According to experimental results, both of the processing items and processing time augmented steadily by the resource of hardware. Besides, under the cross-validation using 327,058 ECG signals from our telehealthcare program, the recognition classifiers yielded 86.8% accuracy in sinus detection and 88.4% accuracy in atrial fibrillation detection. In the future, this prominent mechanism of automatic ECG interpretation could widely offer high accessibility in the field of medical service. The findings of the present study also encourage and augment further support to implementation of screening and monitoring as part of telehealthcare.
MeSH term(s)	Atrial Fibrillation ; Computer Systems ; Delivery of Health Care ; Electrocardiography ; Humans
Language	English
Publishing date	2017-10-20
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC.2017.8037612
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A nomogram for estimating intracranial pressure using acute subdural hematoma thickness and midline shift

Chun-Chih Liao / Heng-Chun Liao / Feipei Lai / Furen Xiao

Scientific Reports, Vol 10, Iss 1, Pp 1-

2020 Volume 7

Abstract: Abstract Although criteria for surgical treatment of acute subdural hematoma (SDH) have been proposed, interaction exists between SDH, midline shift (MLS), and intracranial pressure (ICP). Based on our half sphere finite-element model (FEM) of the ... ...

Abstract	Abstract Although criteria for surgical treatment of acute subdural hematoma (SDH) have been proposed, interaction exists between SDH, midline shift (MLS), and intracranial pressure (ICP). Based on our half sphere finite-element model (FEM) of the supratentorial brain parenchyma, tools for ICP estimation using SDH thickness (SDHx) and MLS were developed. We performed 60 single load step, structural static analyses, simulating a left-sided SDH compressing the cerebral hemispheres. The Young's modulus was taken as 10,000 Pa. The ICP loads ranged from 10 to 80 mmHg with Poisson's ratios between 0.25 and 0.49. The SDHx and the MLS results were stored in a lookup table. An ICP estimation equation was derived from these data and then was converted into a nomogram. Numerical convergence was achieved in 49 model analyses. Their SDHx ranged from 0.79 to 28.3 mm, and the MLS ranged from 1.5 to 16.9 mm. The estimation formula was log(ICP) = 0.614–0.520 log(SDHx) + 1.584 log(MLS). Good correlations were observed between invasive ICP measurements and those estimated from preoperative SDHx and MLS data on images using our model. These tools can be used to estimate ICP noninvasively, providing additional information for selecting the treatment strategy in patients with SDH.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2020-12-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep learning approach based on superpixel segmentation assisted labeling for automatic pressure ulcer diagnosis.

Che Wei Chang / Mesakh Christian / Dun Hao Chang / Feipei Lai / Tom J Liu / Yo Shen Chen / Wei Jen Chen

PLoS ONE, Vol 17, Iss 2, p e

2022 Volume 0264139

Abstract: A pressure ulcer is an injury of the skin and underlying tissues adjacent to a bony eminence. Patients who suffer from this disease may have difficulty accessing medical care. Recently, the COVID-19 pandemic has exacerbated this situation. Automatic ... ...

Abstract	A pressure ulcer is an injury of the skin and underlying tissues adjacent to a bony eminence. Patients who suffer from this disease may have difficulty accessing medical care. Recently, the COVID-19 pandemic has exacerbated this situation. Automatic diagnosis based on machine learning (ML) brings promising solutions. Traditional ML requires complicated preprocessing steps for feature extraction. Its clinical applications are thus limited to particular datasets. Deep learning (DL), which extracts features from convolution layers, can embrace larger datasets that might be deliberately excluded in traditional algorithms. However, DL requires large sets of domain specific labeled data for training. Labeling various tissues of pressure ulcers is a challenge even for experienced plastic surgeons. We propose a superpixel-assisted, region-based method of labeling images for tissue classification. The boundary-based method is applied to create a dataset for wound and re-epithelialization (re-ep) segmentation. Five popular DL models (U-Net, DeeplabV3, PsPNet, FPN, and Mask R-CNN) with encoder (ResNet-101) were trained on the two datasets. A total of 2836 images of pressure ulcers were labeled for tissue classification, while 2893 images were labeled for wound and re-ep segmentation. All five models had satisfactory results. DeeplabV3 had the best performance on both tasks with a precision of 0.9915, recall of 0.9915 and accuracy of 0.9957 on the tissue classification; and a precision of 0.9888, recall of 0.9887 and accuracy of 0.9925 on the wound and re-ep segmentation task. Combining segmentation results with clinical data, our algorithm can detect the signs of wound healing, monitor the progress of healing, estimate the wound size, and suggest the need for surgical debridement.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep learning approach based on superpixel segmentation assisted labeling for automatic pressure ulcer diagnosis

Che Wei Chang / Mesakh Christian / Dun Hao Chang / Feipei Lai / Tom J. Liu / Yo Shen Chen / Wei Jen Chen

PLoS ONE, Vol 17, Iss

2022 Volume 2

Abstract	A pressure ulcer is an injury of the skin and underlying tissues adjacent to a bony eminence. Patients who suffer from this disease may have difficulty accessing medical care. Recently, the COVID-19 pandemic has exacerbated this situation. Automatic diagnosis based on machine learning (ML) brings promising solutions. Traditional ML requires complicated preprocessing steps for feature extraction. Its clinical applications are thus limited to particular datasets. Deep learning (DL), which extracts features from convolution layers, can embrace larger datasets that might be deliberately excluded in traditional algorithms. However, DL requires large sets of domain specific labeled data for training. Labeling various tissues of pressure ulcers is a challenge even for experienced plastic surgeons. We propose a superpixel-assisted, region-based method of labeling images for tissue classification. The boundary-based method is applied to create a dataset for wound and re-epithelialization (re-ep) segmentation. Five popular DL models (U-Net, DeeplabV3, PsPNet, FPN, and Mask R-CNN) with encoder (ResNet-101) were trained on the two datasets. A total of 2836 images of pressure ulcers were labeled for tissue classification, while 2893 images were labeled for wound and re-ep segmentation. All five models had satisfactory results. DeeplabV3 had the best performance on both tasks with a precision of 0.9915, recall of 0.9915 and accuracy of 0.9957 on the tissue classification; and a precision of 0.9888, recall of 0.9887 and accuracy of 0.9925 on the wound and re-ep segmentation task. Combining segmentation results with clinical data, our algorithm can detect the signs of wound healing, monitor the progress of healing, estimate the wound size, and suggest the need for surgical debridement.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Progressively Expanded Database for Automated Lung Sound Analysis

Fu-Shun Hsu / Shang-Ran Huang / Chien-Wen Huang / Yuan-Ren Cheng / Chun-Chieh Chen / Jack Hsiao / Chung-Wei Chen / Feipei Lai

Applied Sciences, Vol 12, Iss 7623, p

An Update

2022 Volume 7623

Abstract: We previously established an open-access lung sound database, HF_Lung_V1, and developed deep learning models for inhalation, exhalation, continuous adventitious sound (CAS), and discontinuous adventitious sound (DAS) detection. The amount of data used ... ...

Abstract	We previously established an open-access lung sound database, HF_Lung_V1, and developed deep learning models for inhalation, exhalation, continuous adventitious sound (CAS), and discontinuous adventitious sound (DAS) detection. The amount of data used for training contributes to model accuracy. In this study, we collected larger quantities of data to further improve model performance and explored issues of noisy labels and overlapping sounds. HF_Lung_V1 was expanded to HF_Lung_V2 with a 1.43× increase in the number of audio files. Convolutional neural network–bidirectional gated recurrent unit network models were trained separately using the HF_Lung_V1 (V1_Train) and HF_Lung_V2 (V2_Train) training sets. These were tested using the HF_Lung_V1 (V1_Test) and HF_Lung_V2 (V2_Test) test sets, respectively. Segment and event detection performance was evaluated. Label quality was assessed. Overlap ratios were computed between inhalation, exhalation, CAS, and DAS labels. The model trained using V2_Train exhibited improved performance in inhalation, exhalation, CAS, and DAS detection on both V1_Test and V2_Test. Poor CAS detection was attributed to the quality of CAS labels. DAS detection was strongly influenced by the overlapping of DAS with inhalation and exhalation. In conclusion, collecting greater quantities of lung sound data is vital for developing more accurate lung sound analysis models.
Keywords	auscultation ; convolutional neural network ; deep learning ; gated recurrent unit ; lung sound ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	780
Language	English
Publishing date	2022-07-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Unified Framework for Automatic Detection of Wound Infection with Artificial Intelligence

Jin-Ming Wu / Chia-Jui Tsai / Te-Wei Ho / Feipei Lai / Hao-Chih Tai / Ming-Tsan Lin

Applied Sciences, Vol 10, Iss 5353, p

2020 Volume 5353

Abstract: Background: The surgical wound is a unique problem requiring continuous postoperative care, and mobile health technology is implemented to bridge the care gap. Our study aim was to design an integrated framework to support the diagnosis of wound ... ...

Abstract	Background: The surgical wound is a unique problem requiring continuous postoperative care, and mobile health technology is implemented to bridge the care gap. Our study aim was to design an integrated framework to support the diagnosis of wound infection. Methods: We used a computer-vision approach based on supervised learning techniques and machine learning algorithms, to help detect the wound region of interest (ROI) and classify wound infection features. The intersection-union test (IUT) was used to evaluate the accuracy of the detection of color card and wound ROI. The area under the receiver operating characteristic curve (AUC) of our model was adopted in comparison with different machine learning approaches. Results: 480 wound photographs were taken from 100 patients for analysis. The average value of IUT on the validation set with fivefold stratification to detect wound ROI was 0.775. For prediction of wound infection, our model achieved a significantly higher AUC score (83.3%) than the other three methods (kernel support vector machines, 44.4%; random forest, 67.1%; gradient boosting classifier, 66.9%). Conclusions: Our evaluation of a prospectively collected wound database demonstrates the effectiveness and reliability of the proposed system, which has been developed for automatic detection of wound infections in patients undergoing surgical procedures.
Keywords	artificial intelligence ; wound infection ; telecare ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2020-08-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Author Correction

Yuan-Chia Chu / Wen-Tsung Kuo / Yuan-Ren Cheng / Chung-Yuan Lee / Cheng-Ying Shiau / Der-Cherng Tarng / Feipei Lai

Scientific Reports, Vol 9, Iss 1, Pp 1-

A Survival Metadata Analysis Responsive Tool (SMART) for web-based analysis of patient survival and risk

2019 Volume 1

Abstract: A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper. ...

Abstract	A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2019-06-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery

Siangruei Wu / Yihong Wu / Haoyun Chang / Florence T. Su / Hengchun Liao / Wanju Tseng / Chunchih Liao / Feipei Lai / Fengming Hsu / Furen Xiao

Applied Sciences, Vol 11, Iss 9180, p

2021 Volume 9180

Abstract: Semantic segmentation of medical images with deep learning models is rapidly being developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset. The dataset consists of ... ...

Abstract	Semantic segmentation of medical images with deep learning models is rapidly being developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset. The dataset consists of 1688 patients with various brain lesions (pituitary tumors, meningioma, schwannoma, brain metastases, arteriovenous malformation, and trigeminal neuralgia), and we divided the dataset into a training set (1557 patients) and test set (131 patients). This study demonstrates the strengths and weaknesses of deep-learning algorithms in a fairly practical scenario. We compared the model performances concerning their sampling method, model architecture, and the choice of loss functions, identifying suitable settings for their applications and shedding light on the possible improvements. Evidence from this study led us to conclude that deep learning could be promising in assisting the segmentation of brain lesions even if the training dataset was of high heterogeneity in lesion types and sizes.
Keywords	deep learning ; image segmentation ; brain tumors ; radiosurgery ; magnetic resonance imaging ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2021-10-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Epileptic EEG visualization and sonification based on linear discriminate analysis.

Wei Chen / Chia-Ping Shen / Ming-Jang Chiu / Qibin Zhao / Cichocki, Andrzej / Jeng-Wei Lin / Feipei Lai

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2016 Volume 2015, Page(s) 4466–4469

Abstract: In this paper, we first presents a high accuracy epileptic electroencephalogram (EEG) classification algorithm. EEG data of epilepsy patients are preprocessed, segmented, and decomposed to intrinsic mode functions, from which features are extracted. Two ... ...

Abstract	In this paper, we first presents a high accuracy epileptic electroencephalogram (EEG) classification algorithm. EEG data of epilepsy patients are preprocessed, segmented, and decomposed to intrinsic mode functions, from which features are extracted. Two classifiers are trained based on linear discriminant analysis (LDA) to classify EEG data into three types, i.e., normal, spike, and seizure. We further in-depth investigate the changes of the decision values in LDA on continuous EEG data. An epileptic EEG visualization and sonification algorithm is proposed to provide both temporal and spatial information of spike and seizure of epilepsy patients. In the experiment, EEG data of six subjects (two normal and four seizure patients) are included. The experiment result shows the proposed epileptic EEG classification algorithm achieves high accuracy. As well, the visualization and sonification algorithm exhibits a great help in nursing seizure patients and localizing the area of seizures.
MeSH term(s)	Algorithms ; Discriminant Analysis ; Electroencephalography ; Epilepsy ; Humans ; Seizures
Language	English
Publishing date	2016-01-12
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC.2015.7319386
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Survival Metadata Analysis Responsive Tool (SMART) for web-based analysis of patient survival and risk

Yuan-Chia Chu / Wen-Tsung Kuo / Yuan-Ren Cheng / Chung-Yuan Lee / Cheng-Ying Shiau / Der-Cherng Tarng / Feipei Lai

Scientific Reports, Vol 8, Iss 1, Pp 1-

2018 Volume 9

Abstract: Abstract Health information systems contain extensive amounts of patient data. Information relevant to public health and individuals’ medical histories are both available. In clinical research, the prediction of patient survival rates and identification ... ...

Abstract	Abstract Health information systems contain extensive amounts of patient data. Information relevant to public health and individuals’ medical histories are both available. In clinical research, the prediction of patient survival rates and identification of prognosis factors are major challenges. To alleviate the difficulties related to these factors, Metadata Utilities was developed to help researchers manage column definitions and information such as import/query/generator Metadata files. These utilities also include an automatic update mechanism to ensure consistency between the data and parameters of the batch produced in the conversion procedure. Survival Metadata Analysis Responsive Tool (SMART) provides a comprehensive set of statistical tests that are easy to understand, including support for analyzing nominal variables, ordinal variables, interval variables or ratio variables as means, standard deviations, maximum values, minimum values, and percentages. In this article, the development of a raw data source and transfer mechanism, Extract-Transform-Load (ETL), is described for data cleansing, extraction, transformation and loading. We also built a handy method for data presentation, which can be customized to the trial design. As demonstrated here, SMART is useful for risk-adjusted baseline cohort and randomized controlled trials.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2018-08-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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