The role of real-time in biomedical science: A meta-analysis on computational complexity, delay and speedup

Faust, Oliver; Yu, Wenwei; Acharya, U. Rajendra

doi:10.1016/j.compbiomed.2014.12.024

Cited by 14 publications

(5 citation statements)

References 135 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For texture algorithms, computational complexity is just equipment investment and computation time. In general, computational complexity of feature extraction algorithms impact on latency and cost [83]. The cost rises in accordance with efforts to keep the latency down.…”

Section: Discussionmentioning

confidence: 99%

Comparative assessment of texture features for the identification of cancer in ultrasound images: a review

Faust

Acharya

Meiburger

et al. 2018

Biocybernetics and Biomedical Engineering

Self Cite

View full text Add to dashboard Cite

In this paper, we review the use of texture features for cancer detection in Ultrasound (US) images of breast, prostate, thyroid, ovaries and liver for Computer-Aided Diagnosis (CAD) systems. This paper shows that texture features are a valuable tool to extract diagnostically relevant information from US images. This information helps practitioners to discriminate normal from abnormal tissues. A drawback of some classes of texture features comes from their sensitivity to both changes in image resolution and grayscale levels. These limitations pose a considerable challenge to CAD systems, because the information content of a specific texture feature depends on the US imaging system and its setup. Our review shows that single classes of texture features are insufficient, if considered alone, to create robust CAD systems, which can help to solve practical problems, such as cancer screening. Therefore, we recommend that the CAD system design involves testing a wide range of texture features along with features obtained with other image processing methods. Having such a competitive testing phase helps the designer to select the best feature combination for a particular problem. This approach will lead to practical US based cancer detection systems which deliver real benefits to patients by improving the diagnosis accuracy while reducing health care cost.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparative assessment of texture features for the identification of cancer in ultrasound images: a review

Faust

Acharya

Meiburger

et al. 2018

Biocybernetics and Biomedical Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…The use of an automated CDSS is one way to establish a reliable diagnosis with easy access and convenience [20,21]. Additionally, confidence in the CDSS system should be established by traceability [22]. To date, almost all studies have shown remarkable accuracy using multichannel EEG for sleep stage scoring [14,23].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Application to Clinical Decision Support System in Sleep Stage Classification

Kim

Lee

Woo

et al. 2022

JPM

View full text Add to dashboard Cite

Recently, deep learning for automated sleep stage classification has been introduced with promising results. However, as many challenges impede their routine application, automatic sleep scoring algorithms are not widely used. Typically, polysomnography (PSG) uses multiple channels for higher accuracy; however, the disadvantages include a requirement for a patient to stay one or more nights in the lab wearing uncomfortable sensors and wires. To avoid the inconvenience caused by the multiple channels, we aimed to develop a deep learning model for use in clinical decision support systems (CDSSs) and combined convolutional neural networks and a transformer for the supervised learning of three classes of sleep stages only with single-channel EEG data (C4-M1). The data for training, validation, and test were derived from 1590, 341, and 343 polysomnography recordings, respectively. The developed model yielded an overall accuracy of 91.4%, comparable with that of human experts. Based on the severity of obstructive sleep apnea, the model’s accuracy was 94.3%, 91.9%, 91.9%, and 90.6% in normal, mild, moderate, and severe cases, respectively. Our deep learning model enables accurate and rapid delineation of three-class sleep staging and could be useful as a CDSS for application in real-world clinical practice.

show abstract

“…Encouraged by the excellent detection capability [4], we designed an atrial fibrillation detection service based on internet of medical things technology [6]. The idea is that the measured signals flow from the patient to a cloud server where our algorithm analyses the signal in real time [8]. Hybrid diagnosis support will ensure proper human validation during the diagnostic process [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Hybrid diagnosis support will ensure proper human validation during the diagnostic process [9][10][11]. Having wireless connectivity and real time analysis extends the observation duration indefinitely and thereby presents a viable solution to the atrial fibrillation detection problem [8]. However, before the service can be deployed, it is necessary to validate the deep learning algorithm in a clinical environment.…”

Section: Introductionmentioning

confidence: 99%

Atrial fibrillation detection service validation tool

2021

Self Cite

View full text Add to dashboard Cite

We developed a software tool to validate a deep learning algorithm for an atrial fibrillation detection service with heart rate data from a clinical study. The deep learning algorithm analyses the measurement data and establishes an estimated atrial fibrillation probability for each heartbeat. The software tool displays both data and deep learning analysis results. Furthermore, the graphical user interface can be used by medical experts to detect atrial fibrillation periods in the data and establish a reference result which will be treated as ground truth in subsequent result analysis steps. Once both deep learning and expert results are available, a confusion matrix is produced and the algorithm performance is validated by establishing accuracy, sensitivity, specificity, and f1-score. The software tool was created in Python and the software incorporated a graphical user interface as well as functional elements for data display and deep learning. To establish the required functionality, we used three different parallel processing methods for: (1) user interface processing, (2) data handling, and (3) deep learning. This highlights the need for parallel processing methods even for projects with a low or midrange complexity. We have learned that the functionality of individual components can be expressed elegantly in Python. However, the lack of parallel debugging support makes it rather difficult to integrate functional components to establish a working solution.

show abstract

The role of real-time in biomedical science: A meta-analysis on computational complexity, delay and speedup

Cited by 14 publications

References 135 publications

Comparative assessment of texture features for the identification of cancer in ultrasound images: a review

Comparative assessment of texture features for the identification of cancer in ultrasound images: a review

Deep Learning Application to Clinical Decision Support System in Sleep Stage Classification

Atrial fibrillation detection service validation tool

Contact Info

Product

Resources

About