Scale based entropy measures and deep learning methods for analyzing the dynamical characteristics of cardiorespiratory control system in COVID-19 subjects during and after recovery.

COVID-19, known as Coronavirus Disease 2019 primarily targets the respiratory system and can impact the cardiovascular system, leading to a range of cardiorespiratory complications. The current forefront in analyzing the dynamical characteristics of physiological systems and aiding clinical decision-making involves the integration of entropy-based complexity techniques with artificial intelligence. Entropy-based measures offer promising prospects for identifying disturbances in cardiorespiratory control system (CRCS) among COVID-19 patients by assessing the oxygen saturation variability (OSV) signals. In this investigation, we employ scale-based entropy (SBE) methods, including multiscale entropy (MSE), multiscale permutation entropy (MPE), and multiscale fuzzy entropy (MFE), to characterize the dynamical characteristics of OSV signals. These measurements serve as features for the application of traditional machine learning (ML) and deep learning (DL) approaches in the context of classifying OSV signals from COVID-19 patients during their illness and subsequent recovery. We use the Beurer PO-80 pulse oximeter which non-invasively acquired OSV and pulse rate data from COVID-19 infected patients during the active infection phase and after a two-month recovery period. The dataset comprises of 88 recordings collected from 44 subjects(26 men and 18 women), both during their COVID-19 illness and two months post-recovery. Prior to analysis, data preprocessing is performed to remove artifacts and outliers. The application of SBE measures to OSV signals unveils a reduction in signal complexity during the course of COVID-19. Leveraging these SBE measures as feature sets, we employ two DL techniques, namely the radial basis function network (RBFN) and RBFN with dynamic delay algorithm (RBFNDDA), for the classification of OSV data collected during and after COVID-19 recovery. To evaluate the classification performance, we employ standard metrics such as sensitivity, specificity, false positive rate (FPR), and the area under the receiver operator characteristic curve (AUC). Among the three scale-based entropy measures, MFE outperformed MSE and MPE by achieving the highest classification performance using RBFN with 13 best features having sensitivity (0.84), FPR (0.30), specificity (0.70) and AUC (0.77). The outcomes of our study demonstrate that SBE measures combined with DL methods offer a valuable approach for categorizing OSV signals obtained during and after COVID-19, ultimately aiding in the detection of CRCS dysfunction.

Complexity reduction of oxygen saturation variability signals in COVID-19 patients: Implications for cardiorespiratory control.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a respiratory illness that leads to severe acute respiratory syndrome and various cardiorespiratory complications, contributing to morbidity and mortality. Entropy analysis has demonstrated its ability to monitor physiological states and system dynamics during health and disease. The main objective of the study is to extract information about cardiorespiratory control by conducting a complexity analysis of OSV signals using scale-based entropy measures following a two-month timeframe after recovery. METHODS: This prospective study collected data from subjects meeting specific criteria, using a Beurer PO-80 pulse oximeter to measure oxygen saturation (SpO2) and pulse rate. Excluding individuals with a history of pulmonary/cardiovascular issues, the study analyzed 88 recordings from 44 subjects (26 men, 18 women, mean age 45.34 ± 14.40) during COVID-19 and two months post-recovery. Data preprocessing and scale-based entropy analysis were applied to assess OSV signals. RESULTS: The study found a significant difference in mean OSV during illness (95.08 ± 0.15) compared to post-recovery (95.59 ± 1.03), indicating reduced cardiorespiratory dynamism during COVID-19. Multiscale entropy analyses (MSE, MPE, MFE) confirmed lower entropy values during illness across all time scales, particularly at higher scales. Notably, the maximum distinction between illness and recovery phases was seen at specific time scales and similarity criteria for each entropy measure, showing statistically significant differences. CONCLUSIONS: The study demonstrates that the loss of complexity in OSV signals, quantified using scale-based entropy measures, has the potential to detect malfunctioning of cardiorespiratory control in COVID-19 patients. This finding suggests that OSV signals could serve as a valuable indicator for assessing the cardiorespiratory status of COVID-19 patients and monitoring their recovery progress.

Mitotic Nuclei Segmentation and Classification Using Chaotic Butterfly Optimization Algorithm with Deep Learning on Histopathology Images.

Histopathological grading of the tumors provides insights about the patient's disease conditions, and it also helps in customizing the treatment plans. Mitotic nuclei classification involves the categorization and identification of nuclei in histopathological images based on whether they are undergoing the cell division (mitosis) process or not. This is an essential procedure in several research and medical contexts, especially in diagnosis and prognosis of cancer. Mitotic nuclei classification is a challenging task since the size of the nuclei is too small to observe, while the mitotic figures possess a different appearance as well. Automated calculation of mitotic nuclei is a stimulating one due to their great similarity to non-mitotic nuclei and their heteromorphic appearance. Both Computer Vision (CV) and Machine Learning (ML) approaches are used in the automated identification and the categorization of mitotic nuclei in histopathological images that endure the procedure of cell division (mitosis). With this background, the current research article introduces the mitotic nuclei segmentation and classification using the chaotic butterfly optimization algorithm with deep learning (MNSC-CBOADL) technique. The main objective of the MNSC-CBOADL technique is to perform automated segmentation and the classification of the mitotic nuclei. In the presented MNSC-CBOADL technique, the U-Net model is initially applied for the purpose of segmentation. Additionally, the MNSC-CBOADL technique applies the Xception model for feature vector generation. For the classification process, the MNSC-CBOADL technique employs the deep belief network (DBN) algorithm. In order to enhance the detection performance of the DBN approach, the CBOA is designed for the hyperparameter tuning model. The proposed MNSC-CBOADL system was validated through simulation using the benchmark database. The extensive results confirmed the superior performance of the proposed MNSC-CBOADL system in the classification of mitotic nuclei.

Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet.

An aim of the analysis of biomedical signals such as heart rate variability signals, brain signals, oxygen saturation variability (OSV) signals, etc., is for the design and development of tools to extract information about the underlying complexity of physiological systems, to detect physiological states, monitor health conditions over time, or predict pathological conditions. Entropy-based complexity measures are commonly used to quantify the complexity of biomedical signals; however novel complexity measures need to be explored in the context of biomedical signal classification. In this work, we present a novel technique that used Haar wavelets to analyze the complexity of OSV signals of subjects during COVID-19 infection and after recovery. The data used to evaluate the performance of the proposed algorithms comprised recordings of OSV signals from 44 COVID-19 patients during illness and after recovery. The performance of the proposed technique was compared with four, scale-based entropy measures: multiscale entropy (MSE); multiscale permutation entropy (MPE); multiscale fuzzy entropy (MFE); multiscale amplitude-aware permutation entropy (MAMPE). Preliminary results of the pilot study revealed that the proposed algorithm outperformed MSE, MPE, MFE, and MMAPE in terms of better accuracy and time efficiency for separating during and after recovery the OSV signals of COVID-19 subjects. Further studies are needed to evaluate the potential of the proposed algorithm for large datasets and in the context of other biomedical signal classifications.

Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection.

An early diagnosis of lung and colon cancer (LCC) is critical for improved patient outcomes and effective treatment. Histopathological image (HSI) analysis has emerged as a robust tool for cancer diagnosis. HSI analysis for a LCC diagnosis includes the analysis and examination of tissue samples attained from the LCC to recognize lesions or cancerous cells. It has a significant role in the staging and diagnosis of this tumor, which aids in the prognosis and treatment planning, but a manual analysis of the image is subject to human error and is also time-consuming. Therefore, a computer-aided approach is needed for the detection of LCC using HSI. Transfer learning (TL) leverages pretrained deep learning (DL) algorithms that have been trained on a larger dataset for extracting related features from the HIS, which are then used for training a classifier for a tumor diagnosis. This manuscript offers the design of the Al-Biruni Earth Radius Optimization with Transfer Learning-based Histopathological Image Analysis for Lung and Colon Cancer Detection (BERTL-HIALCCD) technique. The purpose of the study is to detect LCC effectually in histopathological images. To execute this, the BERTL-HIALCCD method follows the concepts of computer vision (CV) and transfer learning for accurate LCC detection. When using the BERTL-HIALCCD technique, an improved ShuffleNet model is applied for the feature extraction process, and its hyperparameters are chosen by the BER system. For the effectual recognition of LCC, a deep convolutional recurrent neural network (DCRNN) model is applied. Finally, the coati optimization algorithm (COA) is exploited for the parameter choice of the DCRNN approach. For examining the efficacy of the BERTL-HIALCCD technique, a comprehensive group of experiments was conducted on a large dataset of histopathological images. The experimental outcomes demonstrate that the combination of AER and COA algorithms attain an improved performance in cancer detection over the compared models.

Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning.

Medical cyber-physical systems (MCPS) represent a platform through which patient health data are acquired by emergent Internet of Things (IoT) sensors, preprocessed locally, and managed through improved machine intelligence algorithms. Wireless medical cyber-physical systems are extensively adopted in the daily practices of medicine, where vast amounts of data are sampled using wireless medical devices and sensors and passed to decision support systems (DSSs). With the development of physical systems incorporating cyber frameworks, cyber threats have far more acute effects, as they are reproduced in the physical environment. Patients' personal information must be shielded against intrusions to preserve their privacy and confidentiality. Therefore, every bit of information stored in the database needs to be kept safe from intrusion attempts. The IWMCPS proposed in this work takes into account all relevant security concerns. This paper summarizes three years of fieldwork by presenting an IWMCPS framework consisting of several components and subsystems. The IWMCPS architecture is developed, as evidenced by a scenario including applications in the medical sector. Cyber-physical systems are essential to the healthcare sector, and life-critical and context-aware health data are vulnerable to information theft and cyber-okayattacks. Reliability, confidence, security, and transparency are some of the issues that must be addressed in the growing field of MCPS research. To overcome the abovementioned problems, we present an improved wireless medical cyber-physical system (IWMCPS) based on machine learning techniques. The heterogeneity of devices included in these systems (such as mobile devices and body sensor nodes) makes them prone to many attacks. This necessitates effective security solutions for these environments based on deep neural networks for attack detection and classification. The three core elements in the proposed IWMCPS are the communication and monitoring core, the computational and safety core, and the real-time planning and administration of resources. In this study, we evaluated our design with actual patient data against various security attacks, including data modification, denial of service (DoS), and data injection. The IWMCPS method is based on a patient-centric architecture that preserves the end-user's smartphone device to control data exchange accessibility. The patient health data used in WMCPSs must be well protected and secure in order to overcome cyber-physical threats. Our experimental findings showed that our model attained a high detection accuracy of 92% and a lower computational time of 13 sec with fewer error analyses.

An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques.

Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods.