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Abstract Chronic kidney disease (CKD) is a major worldwide health problem, affecting a large proportion of the world’s population and leading to higher morbidity and death rates. The early stages of CKD sometimes present without visible symptoms, causing patients to be unaware. Early detection and treatments are critical in reducing complications and improving the overall quality of life for people afflicted. In this work, we investigate the use of an explainable artificial intelligence (XAI)-based strategy, leveraging clinical characteristics, to predict CKD. This study collected clinical data from 491 patients, comprising 56 with CKD and 435 without CKD, encompassing clinical, laboratory, and demographic variables. To develop the predictive model, five machine learning (ML) methods, namely logistic regression (LR), random forest (RF), decision tree (DT), Naïve Bayes (NB), and extreme gradient boosting (XGBoost), were employed. The optimal model was selected based on accuracy and area under the curve (AUC). Additionally, the SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) algorithms were utilized to demonstrate the influence of the features on the optimal model. Among the five models developed, the XGBoost model achieved the best performance with an AUC of 0.9689 and an accuracy of 93.29%. The analysis of feature importance revealed that creatinine, glycosylated hemoglobin type A1C (HgbA1C), and age were the three most influential features in the XGBoost model. The SHAP force analysis further illustrated the model’s visualization of individualized CKD predictions. For further insights into individual predictions, we also utilized the LIME algorithm. This study presents an interpretable ML-based approach for the early prediction of CKD. The SHAP and LIME methods enhance the interpretability of ML models and help clinicians better understand the rationale behind the predicted outcomes more effectively.

Abstract Chronic kidney disease (CKD) remains one of the most prominent global causes of mortality worldwide, necessitating accurate prediction models for early detection and prevention. In recent years, machine learning (ML) techniques have exhibited promising outcomes across various medical applications. This study introduces a novel ML-driven nomogram approach for early identification of individuals at risk for developing CKD stages 3–5. This retrospective study employed a comprehensive dataset comprised of clinical and laboratory variables from a large cohort of diagnosed CKD patients. Advanced ML algorithms, including feature selection and regression models, were applied to build a predictive model. Among 467 participants, 11.56% developed CKD stages 3–5 over a 9-year follow-up. Several factors, such as age, gender, medical history, and laboratory results, independently exhibited significant associations with CKD (p < 0.05) and were utilized to create a risk function. The Linear regression (LR)-based model achieved an impressive R-score (coefficient of determination) of 0.954079, while the support vector machine (SVM) achieved a slightly lower value. An LR-based nomogram was developed to facilitate the process of risk identification and management. The ML-driven nomogram demonstrated superior performance when compared to traditional prediction models, showcasing its potential as a valuable clinical tool for the early detection and prevention of CKD. Further studies should focus on refining the model and validating its performance in diverse populations.

In cognitive radio technology, spectrum sensing is essential for detecting spectrum holes which may be allotted to secondary users. In this paper, an optimal voting rule is used for cooperative spectrum sensing while minimizing the total error rate (TER). The proposed spectrum sensing method is more energy-efficient and may be implemented in practice. It is relied upon in an improved energy detector whose utilization depends on the presence or absence of the primary user. Expressions for false alarm and missed detection probabilities are derived in the paper as well. Overall performance is analyzed both for AWGN and Rayleigh fading channels, in the presence of additive white Gaussian noise (AWGN). The optimum voting rule is applied to the cooperative spectrum sensing process in order to identify the optimum number of sensing nodes and the detection threshold. Finally, an energy-efficient spectrum sensing algorithm is proposed, requiring a lower number of cognitive users for a given error bound

Myocardial infarction (MI) occurs due to the decrease in the blood flow into one part of the heart, and it further causes damage to the heart muscle. The 12-channel electrocardiogram (ECG) has been widely used to detect and localize MI pathology in clinical studies. The vectorcardiogram (VCG) is a 3-channel recording system used to measure the heart’s electrical activity in sagittal, transverse, and frontal planes. The VCG signals have advantages over the 12-channel ECG to localize posterior MI pathology. Detection and localization of MI using VCG signals are vital in clinical practice. This paper proposes a multi-channel multi-scale two-stage deep-learning-based approach to detect and localize MI using VCG signals. In the first stage, the multivariate variational mode decomposition (MVMD) decomposes the three-channel-based VCG signal beat into five components along each channel. The multi-channel multi-scale VCG tensor is formulated using the modes of each channel of VCG data, and it is used as the input to the deep convolutional neural network (CNN) to classify MI and normal sinus rhythm (NSR) classes. In the second stage, the multi-class deep CNN is used for the categorization of anterior MI (AMI), anterior-lateral MI (ALMI), anterior-septal MI (ASMI), inferior MI (IMI), inferior-lateral MI (ILMI), inferior-posterior-lateral (IPLMI) classes using MI detected multi-channel multi-scale VCG instances from the first stage. The proposed approach is developed using the VCG data obtained from a public database. The results reveal that the approach has obtained the accuracy, sensitivity, and specificity values of 99.58%, 99.18%, and 99.87%, respectively, for MI detection. Moreover, for MI localization, we have obtained the overall accuracy value of 99.86% in the second stage for our proposed network. The proposed approach has demonstrated superior classification performance compared to the existing VCG signal-based MI detection and localization techniques.

The categorization of sleep stages helps to diagnose different sleep-related ailments. In this paper, an entropy-based information–theoretic approach is introduced for the automated categorization of sleep stages using multi-channel electroencephalogram (EEG) signals. This approach comprises of three stages. First, the decomposition of multi-channel EEG signals into sub-band signals or modes is performed using a novel multivariate projection-based fixed boundary empirical wavelet transform (MPFBEWT) filter bank. Second, entropy features such as bubble and dispersion entropies are computed from the modes of multi-channel EEG signals. Third, a hybrid learning classifier based on class-specific residuals using sparse representation and distances from nearest neighbors is used to categorize sleep stages automatically using entropy-based features computed from MPFBEWT domain modes of multi-channel EEG signals. The proposed approach is evaluated using the multi-channel EEG signals obtained from the cyclic alternating pattern (CAP) sleep database. Our results reveal that the proposed sleep staging approach has obtained accuracies of 91.77%, 88.14%, 80.13%, and 73.88% for the automated categorization of wake vs. sleep, wake vs. rapid eye movement (REM) vs. Non-REM, wake vs. light sleep vs. deep sleep vs. REM sleep, and wake vs. S1-sleep vs. S2-sleep vs. S3-sleep vs. REM sleep schemes, respectively. The developed method has obtained the highest overall accuracy compared to the state-of-art approaches and is ready to be tested with more subjects before clinical application.

This correction notice addresses errors in an article titled "A machine learning driven nomogram for predicting chronic kidney disease stages 3–5." The original version of the article misspelled the word "Nomogram" as "Monogram" in two instances. The correction has been made to accurately reflect the use of "Nomogram" throughout the article. The authors of the article are Samit Kumar Ghosh and Ahsan H. Khandoker. [Extracted from the article]

Background: Patients with chronic pain suffer from psychological effects such as anxiety due to the pain itself. Pain can not only impair activities of daily living (ADL) and quality of life (QOL), but also impair cognitive function. Therefore, in this study, we aimed to estimate the cognitive function of chronic pain patients using a deep neural network (DNN) model that has already been implemented in society. We investigated the characteristics of patients presumed to have mild cognitive impairment (MCI) and, at the same time, verified the relationship with the questionnaire commonly used in chronic pain research, which is administered by 43 university affiliated hospitals and medical institutions participating in the chronic pain research group of the Ministry of Health, Labor and Welfare in Japan (assessment batteries). Method: The study included 114 outpatients from a multidisciplinary pain clinic, and we estimated their Mini-Mental State Examination (MMSE) scores based on age and basic blood test data (23 items). Furthermore, we classified the estimated MMSE scores of chronic pain patients into two groups based on a cutoff score of 27, which indicates MCI, and compared the blood data and assessment batteries. Additionally, we used a control group of 252 healthy adults aged 45 years or older who visited a dementia prevention outpatient clinic for comparison with the MMSE scores of chronic pain patients. Result: The MMSE scores in chronic pain patients were below the cutoff for MCI. When classified into two groups based on the estimated MMSE score of 27 points, WBC, RBC, Hb, Hct, PLT, UA, BUN, creatinine, Triglyceride, and γ-GT were significantly higher in the blood data. In the MCI group, PDAS values were significantly lower. Furthermore, only in the non-MCI group, a significant correlation was found between the estimated MMSE value and BPI, PDAS, and Locomo. The estimated MMSE scores were significantly lower in chronic pain patients than in healthy adults (p = 0.04). Conclusion: Patients with chronic pain may exhibit cognitive impairment due to systemic metabolic disturbances. This suggests that chronic pain affects activities of daily living, resulting in systemic metabolic disorders. [ABSTRACT FROM AUTHOR]

One among the major causes of death in the country is Heart disease (HD). 17.3 million deaths per year are caused by cardiac diseases, which is the primary reason for death in the globe as per the World Health Organization (WHO). Hence, the early detection of heart disease increases the survival rate. For an efficient detection of HD, a wide variety of techniques are used. But, the techniques rely on limited data; this degrades the accuracy and the performance of the model. So, to deal with these shortcomings, this paper has proposed Gaussian Kaiming Variance-based Deep Learning Neural Network (GKVDLNN) classifier for HD. In this study, the problem of heart disease detection using ECG and PCG signals is investigated, which provides valuable information about heart function. Thereby, the possible abnormalities of the heart are identified. Moreover, large data are used in this work to ensure the model's accuracy. Firstly, from the publically accessible dataset, the input ECG along with PCG signals is gathered. Next, pre-processing is undergone by both signals. Utilizing Improved Empirical Mode Decomposition (IEMD), the signals are decomposed into bands after pre-processing. Then, for extracting the signal features, the decomposed bands are utilized. Then, for further process, the most suitable features are chosen as of the extracted features of both the ECG and PCG signals. After that, both the features are concatenated, and then classification is performed. In the classification process, the HD's type is identified and classified by GKVDLNN. Experimental results stated the proposed model's superiority and the proposed system withstands with 96.103% of accuracy with reduced costs. Overall, the method highlights the importance of early heart disease detection [32-33] and provides a novel approach for achieving this goal through the integration of ECG and PCG signals with advanced signal processing and machine learning techniques [31]. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is a major worldwide health problem, affecting a large proportion of the world's population and leading to higher morbidity and death rates. The early stages of CKD sometimes present without visible symptoms, causing patients to be unaware. Early detection and treatments are critical in reducing complications and improving the overall quality of life for people afflicted. In this work, we investigate the use of an explainable artificial intelligence (XAI)-based strategy, leveraging clinical characteristics, to predict CKD. This study collected clinical data from 491 patients, comprising 56 with CKD and 435 without CKD, encompassing clinical, laboratory, and demographic variables. To develop the predictive model, five machine learning (ML) methods, namely logistic regression (LR), random forest (RF), decision tree (DT), Naïve Bayes (NB), and extreme gradient boosting (XGBoost), were employed. The optimal model was selected based on accuracy and area under the curve (AUC). Additionally, the SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) algorithms were utilized to demonstrate the influence of the features on the optimal model. Among the five models developed, the XGBoost model achieved the best performance with an AUC of 0.9689 and an accuracy of 93.29%. The analysis of feature importance revealed that creatinine, glycosylated hemoglobin type A1C (HgbA1C), and age were the three most influential features in the XGBoost model. The SHAP force analysis further illustrated the model's visualization of individualized CKD predictions. For further insights into individual predictions, we also utilized the LIME algorithm. This study presents an interpretable ML-based approach for the early prediction of CKD. The SHAP and LIME methods enhance the interpretability of ML models and help clinicians better understand the rationale behind the predicted outcomes more effectively. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) remains one of the most prominent global causes of mortality worldwide, necessitating accurate prediction models for early detection and prevention. In recent years, machine learning (ML) techniques have exhibited promising outcomes across various medical applications. This study introduces a novel ML-driven monogram approach for early identification of individuals at risk for developing CKD stages 3–5. This retrospective study employed a comprehensive dataset comprised of clinical and laboratory variables from a large cohort of diagnosed CKD patients. Advanced ML algorithms, including feature selection and regression models, were applied to build a predictive model. Among 467 participants, 11.56% developed CKD stages 3–5 over a 9-year follow-up. Several factors, such as age, gender, medical history, and laboratory results, independently exhibited significant associations with CKD (p < 0.05) and were utilized to create a risk function. The Linear regression (LR)-based model achieved an impressive R-score (coefficient of determination) of 0.954079, while the support vector machine (SVM) achieved a slightly lower value. An LR-based monogram was developed to facilitate the process of risk identification and management. The ML-driven nomogram demonstrated superior performance when compared to traditional prediction models, showcasing its potential as a valuable clinical tool for the early detection and prevention of CKD. Further studies should focus on refining the model and validating its performance in diverse populations. [ABSTRACT FROM AUTHOR]

Introduction: Amplitude spectrum area (AMSA) is a well-established measure than can predict defibrillation outcome and guiding individualized resuscitation of ventricular fibrillation (VF) patients. However, accurate AMSA can only be calculated during cardiopulmonary resuscitation (CPR) pause due to artifacts produced by chest compression (CC). In this study, we developed a real-time AMSA estimation algorithm using a convolutional neural network (CNN). Methods: Data were collected from 698 patients, and the AMSA calculated from the uncorrupted signals served as the true value for both uncorrupted and the adjacent corrupted signals. An architecture consisting of a 6-layer 1D CNN and 3 fully connected layers was developed for AMSA estimation. A 5-fold crossvalidation procedure was used to train, validate and optimize the algorithm. An independent testing set comprised of simulated data, real-life CC corrupted data, and preshock data was used to evaluate the performance. Results: The mean absolute error, root mean square error, percentage root mean square difference and correlation coefficient were 2.182/1.951 mVHz, 2.957/ 2.574 mVHz, 22.887/28.649% and 0.804/0.888 for simulated and real-life testing data, respectively. The area under the receiver operating characteristic curve regarding predicting defibrillation success was 0.835, which was comparable to that of 0.849 using the true value of the AMSA. Conclusions: AMSA can be accurately estimated during uninterrupted CPR using the proposed method. [ABSTRACT FROM AUTHOR]

Remote Healthcare Monitoring Systems (RHMs) that employ fetal phonocardiography (fPCG) signals are highly efficient technologies for monitoring continuous and long-term fetal heart rate. Wearable devices used in RHMs still face a challenge that decreases their efficacy in terms of energy consumption because these devices have limited storage and are powered by batteries. This paper proposes an effective fPCG compression algorithm to reduce RHM energy consumption. In the proposed algorithm, the Discrete Orthogonal Charlier Moment (DOCMs) is used to extract features of the signal. The householder orthonormalization method (HOM) is used with the Charlier Moment to overcome the propagation of numerical errors that occur when computing high-order Charlier polynomials. The proposed algorithm's performance is evaluated in terms of CR, PRD, SNR, PSNR, and QS and provides the average values 18.33, 0.21, 48.85, 68.86, and 90.88, respectively. The results of the comparison demonstrate the proposed compression algorithm's superiority over other algorithms. It also tested in terms of compression speed and computational efficiency. The results indicate that the proposed algorithm has a high Compression speed (218.672 bps) and high computational efficiency (21.33). Additionally, the results reveal that the proposed algorithm decreases the energy consumption of a wearable device due to the transmission time decreasing for data by 3.68 s. [ABSTRACT FROM AUTHOR]

Regardless of whether the articles they examined were ultimately published, the editors would like to express their appreciation and thank the following reviewers for the time and dedication that they have shown I Sensors i :ely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). Thanks to the efforts of our reviewers in 2022, the median time to first decision was 16 days and the median time to publication was 38 days. I Sensors i was able to uphold its high standards for published papers due to the outstanding efforts of our reviewers. [Extracted from the article]

Phonocardiogram (PCG) signals are contaminated with various noise signals, which hinders the accurate diagnostic interpretation of the signal. Discrete wavelet transform (DWT) is a well-known technique used to remove noise from PCG signals and improve signal quality. The performance of DWT-based denoising depends upon several parameters involved in the process, such as mother wavelets used for decomposition, the number of decomposition levels (DLs), thresholding technique used and the threshold estimation rule followed. In this work, an investigative study is carried out to select the optimal parameter values which give the best denoising performance. The metrics such as mean-square error (MSE), normalized-mean-square error (NMSE), root-mean-square error (RMSE), percentage root-mean-square difference (PRD) and signal-to-noise ratio (SNR) are used to evaluate the performance of the denoising in this study. The results obtained show that the fifth-order Coiflet wavelet is best suited for denoising PCG signals when applied with the soft thresholding (ST) function and rigrsure threshold selection rule. Also, the optimum number of DLs resulting in better performance is level 6. SNR value obtained from the studies shows the efficacy of the parameters selected for denoising. The denoised PCG signals provide accurate information to determine various kinds of heart valve-related disorders (HVDs). [ABSTRACT FROM AUTHOR]

Myocardial infarction (MI) occurs due to the decrease in the blood flow into one part of the heart, and it further causes damage to the heart muscle. The 12-channel electrocardiogram (ECG) has been widely used to detect and localize MI pathology in clinical studies. The vectorcardiogram (VCG) is a 3-channel recording system used to measure the heart's electrical activity in sagittal, transverse, and frontal planes. The VCG signals have advantages over the 12-channel ECG to localize posterior MI pathology. Detection and localization of MI using VCG signals are vital in clinical practice. This paper proposes a multi-channel multi-scale two-stage deep-learning-based approach to detect and localize MI using VCG signals. In the first stage, the multivariate variational mode decomposition (MVMD) decomposes the three-channel-based VCG signal beat into five components along each channel. The multi-channel multi-scale VCG tensor is formulated using the modes of each channel of VCG data, and it is used as the input to the deep convolutional neural network (CNN) to classify MI and normal sinus rhythm (NSR) classes. In the second stage, the multi-class deep CNN is used for the categorization of anterior MI (AMI), anterior-lateral MI (ALMI), anterior-septal MI (ASMI), inferior MI (IMI), inferior-lateral MI (ILMI), inferior-posterior-lateral (IPLMI) classes using MI detected multi-channel multi-scale VCG instances from the first stage. The proposed approach is developed using the VCG data obtained from a public database. The results reveal that the approach has obtained the accuracy, sensitivity, and specificity values of 99.58%, 99.18%, and 99.87%, respectively, for MI detection. Moreover, for MI localization, we have obtained the overall accuracy value of 99.86% in the second stage for our proposed network. The proposed approach has demonstrated superior classification performance compared to the existing VCG signal-based MI detection and localization techniques. [ABSTRACT FROM AUTHOR]

The heart valve ailments (HVAs) are due to the defects in the valves of the heart and if untreated may cause heart failure, clots, and even sudden cardiac death. Automated early detection of HVAs is necessary in the hospitals for proper diagnosis of pathological cases, to provide timely treatment, and to reduce the mortality rate. The heart valve abnormalities will alter the heart sound and murmurs which can be faithfully captured by phonocardiogram (PCG) recordings. In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel-based Chirplet transform (SCT) is used to evaluate the time-frequency representation of PCG recording, and the features like L1-norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time-frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine- (ELM-) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology cases such as mitral valve prolapse (MVP), mitral regurgitation (MR), aortic stenosis (AS), and mitral stenosis (MS). The proposed approach has been evaluated using PCG recordings from both public and private databases, and the results demonstrated that an average sensitivity of 100%, 97.51%, 99.00%, 98.72%, and 99.13% are obtained for normal, MVP, MR, AS, and MS cases using the hold-out cross-validation (CV) method. The proposed approach is applicable for the Internet of Things- (IoT-) driven smart healthcare system for the accurate detection of HVAs. [ABSTRACT FROM AUTHOR]

The purpose of this study was to determine the incidence of wormian bones (WBs) in different head shapes of Nepalese skulls along with their distribution at various sites. This study was conducted on 70 Nepalese skulls obtained from the Department of Anatomy, Nepal Medical College, and the Institute of Medicine from September 2017 to January 2018. The skulls were examined for the presence and topographic distribution of WBs. The occurrence of WBs at various sites was correlated among different head shapes. The incidence of skulls showing WBs was 88.57%. The WBs were observed at the lambdoid (61.43%), parietomastoid (41.43%), occipitomastoid (27.14%), pterion (25.71%), asterion (24.29%), lambda (11.43%), sagittal (7.14%) and coronal sutures (4.28%). The dominant head type was dolichocephalic (44.29%) and the least dominant was brachycephalic (10%). The maximum number of WBs was shown on brachycephalic (mean 8.86 ± 7.13) then hyperdolichocephalic (mean 8.33 ± 9.15), mesaticephalic (mean 5.10 ± 4.45) and dolichocephalic heads (mean 4.16 ± 5.30). Brachycephalic heads frequently exhibited WBs at the pterion (57.14%) and at different sutures: lambdoid (71.42%), parietomastoid (57.14%), sagittal (28.57%) and squamous (14.28%). Hyperdolichocephalic heads displayed more lambda (33.33%) and coronal (8.33%) WBs. Similarly, dolichocephalic and mesaticephalic heads showed WBs at the occipitomastoid (35.48%) and asterion (30%), respectively. Inca bones were only identified in three dolichocephalic skulls. Neurosurgeons, radiologists and orthopedists should be careful when doing clinical and surgical procedures on different head shapes of the Nepalese population. [ABSTRACT FROM AUTHOR]

Objective: The present study was undertaken to know the anatomical basis of medial sural artery (MSA) and its perforators in Nepalese.Methods: The popliteal arteries of 16 preserved cadaveric lower limbs were injected with a mixture of red ink and glycerine. The number, location, diameter of perforators; length and intramuscular course of pedicle; the branching pattern of MSA were observed and measured.Results: The mean of 2.2 ± 1.2 perforators (range 0-4) was observed. The perforators were clustered between 8.6 and 25.7 cm from the popliteal crease and 0.3-7.5 cm from posterior midline of leg. The dominant perforators were observed in middle 1/3rd of the leg. The average pedicle length was 12.04 ± 3.27 cm. The intramuscular courses of pedicles were observed in deep and superficial strata in 65.7 and 34.3%, respectively. The MSA originated from popliteal artery in 62.5% and common sural artery in 37.5%. An accessory MSA was found in 12.5%. Type I and Type III branching patterns of MSA were observed in 31.2% each whereas Type II was found in 37.5%. The mean external diameter of perforators and MSA were 0.85 ± 0.27 mm and 2.2 ± 0.43 mm, respectively.Conclusions: The metrical presentation of this study provides an easy access to know about the distribution of perforators and branching pattern of MSA which will help the surgeons to make a convenient plan to harvest the MSA perforator flap in Nepalese population. [ABSTRACT FROM AUTHOR]