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General General

Multi-feature fusion and improved BO and IGWO metaheuristics based models for automatically diagnosing the sleep disorders from sleep sounds.

In Computers in biology and medicine

A night of regular and quality sleep is vital in human life. Sleep quality has a great impact on the daily life of people and those around them. Sounds such as snoring reduce not only the sleep quality of the person but also reduce the sleep quality of the partner. Sleep disorders can be eliminated by examining the sounds that people make at night. It is a very difficult process to follow and treat this process by experts. Therefore, this study, it is aimed to diagnose sleep disorders using computer-aided systems. In the study, the used data set contains seven hundred sound data which has seven different sound class such as cough, farting, laugh, scream, sneeze, sniffle, and snore. In the model proposed in the study, firstly, the feature maps of the sound signals in the data set were extracted. Three different methods were used in the feature extraction process. These methods are MFCC, Mel-spectrogram, and Chroma. The features extracted in these three methods are combined. Thanks to this method, the features of the same sound signal extracted in three different methods are used. This increases the performance of the proposed model. Later, the combined feature maps were analyzed using the proposed New Improved Gray Wolf Optimization (NI-GWO), which is the improved version of the Improved Gray Wolf Optimization (I-GWO) algorithm, and the proposed Improved Bonobo Optimizer (IBO) algorithm, which is the improved version of the Bonobo Optimizer (BO). In this way, it is aimed to run the models faster, reduce the number of features, and obtain the most optimum result. Finally, Support Vector Machine (SVM) and k-nearest neighbors (KNN) supervised shallow machine learning methods were used to calculate the metaheuristic algorithms' fitness values. Different types of metrics such as accuracy, sensitivity, F1 etc., were used for the performance comparison. Using the feature maps optimized by the proposed NI-GWO and IBO algorithms, the highest accuracy value was obtained from the SVM classifier with 99.28% for both metaheuristic algorithms.

Akyol Sinem, Yildirim Muhammed, Alatas Bilal

2023-Mar-08

Bonobo optimizer, Chroma, Gray wolf optimization, MFCC, Mel-spectrogram

General General

Machine learning-based clinical decision support systems for pregnancy care: A systematic review.

In International journal of medical informatics ; h5-index 49.0

BACKGROUND : Clinical decision support systems (CDSSs) can provide various functions and advantages to healthcare delivery. Quality healthcare during pregnancy and childbirth is of vital importance, and machine learning-based CDSSs have shown positive impact on pregnancy care.

OBJECTIVE : This paper aims to investigate what has been done in CDSSs in the context of pregnancy care using machine learning, and what aspects require attention from future researchers.

METHODS : We conducted a systematic review of existing literature following a structured process of literature search, paper selection and filtering, and data extraction and synthesis.

RESULTS : 17 research papers were identified on the topic of CDSS development for different aspects of pregnancy care using various machine learning algorithms. We discovered an overall lack of explainability in the proposed models. We also observed a lack of experimentation, external validation and discussion around culture, ethnicity and race from the source data, with most studies using data from a single centre or country, and an overall lack of awareness of applicability and generalisability of the CDSSs regarding different populations. Finally, we found a gap between machine learning practices and CDSS implementation, and an overall lack of user testing.

CONCLUSION : Machine learning-based CDSSs are still under-explored in the context of pregnancy care. Despite the open problems that remain, the few studies that tested a CDSS for pregnancy care reported positive effects, reinforcing the potential of such systems to improve clinical practice. We encourage future researchers to take into consideration the aspects we identified in order for their work to translate into clinical use.

Du Yuhan, McNestry Catherine, Wei Lan, Antoniadi Anna Markella, McAuliffe Fionnuala M, Mooney Catherine

2023-Mar-08

Clinical decision support system, Explainable artificial intelligence, Machine learning, Pregnancy care

General General

Mittag-Leffler stability of fractional-order quaternion-valued memristive neural networks with generalized piecewise constant argument.

In Neural networks : the official journal of the International Neural Network Society

This paper studies the global Mittag-Leffler (M-L) stability problem for fractional-order quaternion-valued memristive neural networks (FQVMNNs) with generalized piecewise constant argument (GPCA). First, a novel lemma is established, which is used to investigate the dynamic behaviors of quaternion-valued memristive neural networks (QVMNNs). Second, by using the theories of differential inclusion, set-valued mapping, and Banach fixed point, several sufficient criteria are derived to ensure the existence and uniqueness (EU) of the solution and equilibrium point for the associated systems. Then, by constructing Lyapunov functions and employing some inequality techniques, a set of criteria are proposed to ensure the global M-L stability of the considered systems. The obtained results in this paper not only extends previous works, but also provides new algebraic criteria with a larger feasible range. Finally, two numerical examples are introduced to illustrate the effectiveness of the obtained results.

Wang Jingjing, Zhu Song, Liu Xiaoyang, Wen Shiping

2023-Mar-02

Fractional-order, Generalized piecewise constant argument, Memristive neural networks, Mittag-Leffler stability, Quaternion-valued

General General

Digital Holographic Technique based Breast Cancer Detection using Transfer Learning Method.

In Journal of biophotonics

The Digital Holographic technique is an interferometric method that provides comprehensive information on morphological traits such as cell layer thickness and shape as well as access to biophysical attributes of cells like refractive index, dry mass, and volume. This method helps characterize sample structures in three dimensions both statically and dynamically, even for transparent objects like living biological cells. This research work captures the digital holograms of breast tissues and analyzes the malignancy of the tissue using a deep learning technique. It enables dynamic measurement of the sample under investigation. Different transfer learning models such as Inception, DenseNet, SqueezeNet, VGG, and ResNet are incorporated in this work. The parameters accuracy, precision, sensitivity, and F1 score of different models are compared and found that the ResNet model outperforms better compared to other models. This article is protected by copyright. All rights reserved.

Thomas Leena, M K Sheeja

2023-Mar-11

breast tissue, deep learning, digital holography, interferometry, transfer learning

General General

New proposal of viral genome representation applied in the classification of SARS-CoV-2 with deep learning.

In BMC bioinformatics

BACKGROUND : In December 2019, the first case of COVID-19 was described in Wuhan, China, and by July 2022, there were already 540 million confirmed cases. Due to the rapid spread of the virus, the scientific community has made efforts to develop techniques for the viral classification of SARS-CoV-2.

RESULTS : In this context, we developed a new proposal for gene sequence representation with Genomic Signal Processing techniques for the work presented in this paper. First, we applied the mapping approach to samples of six viral species of the Coronaviridae family, which belongs SARS-CoV-2 Virus. We then used the sequence downsized obtained by the method proposed in a deep learning architecture for viral classification, achieving an accuracy of 98.35%, 99.08%, and 99.69% for the 64, 128, and 256 sizes of the viral signatures, respectively, and obtaining 99.95% precision for the vectors with size 256.

CONCLUSIONS : The classification results obtained, in comparison to the results produced using other state-of-the-art representation techniques, demonstrate that the proposed mapping can provide a satisfactory performance result with low computational memory and processing time costs.

de Souza Luísa C, Azevedo Karolayne S, de Souza Jackson G, Barbosa Raquel de M, Fernandes Marcelo A C

2023-Mar-11

CGR DFT, COVID-19, Deep learning, GSP, SARS-CoV-2

General General

Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals

ArXiv Preprint

Pain is a serious worldwide health problem that affects a vast proportion of the population. For efficient pain management and treatment, accurate classification and evaluation of pain severity are necessary. However, this can be challenging as pain is a subjective sensation-driven experience. Traditional techniques for measuring pain intensity, e.g. self-report scales, are susceptible to bias and unreliable in some instances. Consequently, there is a need for more objective and automatic pain intensity assessment strategies. In this research, we develop PainAttnNet (PAN), a novel transfomer-encoder deep-learning framework for classifying pain intensities with physiological signals as input. The proposed approach is comprised of three feature extraction architectures: multiscale convolutional networks (MSCN), a squeeze-and-excitation residual network (SEResNet), and a transformer encoder block. On the basis of pain stimuli, MSCN extracts short- and long-window information as well as sequential features. SEResNet highlights relevant extracted features by mapping the interdependencies among features. The third architecture employs a transformer encoder consisting of three temporal convolutional networks (TCN) with three multi-head attention (MHA) layers to extract temporal dependencies from the features. Using the publicly available BioVid pain dataset, we test the proposed PainAttnNet model and demonstrate that our outcomes outperform state-of-the-art models. These results confirm that our approach can be utilized for automated classification of pain intensity using physiological signals to improve pain management and treatment.

Zhenyuan Lu, Burcu Ozek, Sagar Kamarthi

2023-03-13