Retinal vessel segmentation underpins computer-assisted screening and monitoring of ocular and systemic disease. While encoder–decoder networks such as U-Net are widely used, their behavior is strongly shaped by the training objective. This work presents a controlled empirical study of loss functions for vessel segmentation using a U-Net architecture that employs strided convolutions in the encoder, together with a consistent pre-processing pipeline based on morphological enhancement and principal component analysis. We compare cross-entropy, weighted cross-entropy, and Dice losses on the DRIVE and STARE datasets under identical settings, reporting pixel-wise and overlap-based measures to reflect both detection and spatial agreement. The configuration with weighted cross-entropy provides a balanced outcome, achieving sensitivity and accuracy of 0.873 and 0.969 on DRIVE, and 0.821 and 0.961 on STARE. Rather than proposing architectural novelty, the contribution of this study is a reproducible data-driven comparison that clarifies the tradeoffs each loss imposes on recall, specificity, and boundary fidelity, offering practical guidance for selecting objectives in retinal vessel segmentation.

• The proposed SWF tokenization method, dynamically capturing emotional subtleties, noise robustness, and contextual information to robustly preserve speaker identity. • A transformative deep-self-supervised spectrogram transformer back-end, outperforming conventional approaches by effectively addressing their inherent limitations in preserving local features without the need for extensive labeled training data. • Complete elimination of dependency on additional speech enhancement, enabling seamless, efficient, and robust end-to-end learning tailored for real-world deployments. Identifying speakers in noisy and emotional conditions remains a significant challenge due to the distortion of spectral cues. This study proposes the Speech Without Filter (SWF) framework, a novel self-supervised learning paradigm that operates directly on raw spectrograms. Theoretically, this research introduces a progressive tokenization mechanism that acts as a structural inductive bias, mimicking the contracting path of a U-Net to preserve local spectro-temporal continuity. Unlike standard fixed-patch Transformers that often smooth over speaker-specific micro-textures, the SWF architecture integrates denoising and feature extraction into a single stage, challenging the traditional decoupled paradigm of speech enhancement and recognition. Using a sample of 1.58 million pre-training instances, the model was evaluated across English (RAVDESS), Arabic (ESD), and stressful (SUSAS) datasets. Results demonstrate significant improvements, with the SWF model achieving 91.01% accuracy in clean conditions and maintaining 88.5% in high-noise cocktail party environments, outperforming state-of-the-art models like WavLM and HuBERT. These findings suggest that architectural innovation in tokenization is as critical as pre-training scale for robust speech processing.

Long-term, continuous physiological recordings are currently being intensely investigated for tracking emotions. Emotional valence has been of more interest due to its relevance to cardiac and neurophysiological disease. In this research, multiple configurable convolutional neural networks (CNNs) were developed for different image-encoding techniques used as their input. Ensemble classification was then used to achieve a combined performance of the multiple CNNs by training a simple support vector machine (SVM) classifier using the last output layers of the CNNs as its input. Valence-labelled signals from the heart rate (HR) recorded using a wearable sensor from a wristband in a daily setting for one week from 80 participants were used for the image transforms. Accuracies of more than 91% were achieved with the classification ensembling, showing an improvement of the binary classification of emotional valence by more than 19% compared to using CNNs on their own.

OBJECTIVES: The purpose of this study was to set an optimal fit of the estimated LVEF at hourly intervals from 24-hour ECG recordings and compare it with the fit based on two gold-standard guidelines. METHODS: Support vector regression (SVR) models were applied to estimate LVEF from ECG derived heart rate variability (HRV) data in one-hour intervals from 24-hour ECG recordings of patients with either preserved, mid-range, or reduced LVEF, obtained from the Intercity Digital ECG Alliance (IDEAL) study. A step-wise feature selection approach was used to ensure the best possible estimations of LVEF levels. RESULTS: The experimental results have shown that the lowest Root Mean Square Error (RMSE) between the original and estimated LVEF levels was during 3-4 am, 5-6 am and 6-7 pm. CONCLUSION: The observations suggest these hours as possible times for intervention and optimal treatment outcomes. In addition, LVEF classifications following the ACCF/AHA guidelines leads to a more accurate assessment of mid-range LVEF. SIGNIFICANCE: This study paves the way to explore the use of HRV features in the prediction of LVEF percentages as an indicator of disease progression, which may lead to an automated classification process for CAD patients.

This paper investigates the problem of minimizing data transfer between different data centers of the cloud during the neurological diagnostics of cardiac autonomic neuropathy (CAN). This problem has never been considered in the literature before. All classifiers considered for the diagnostics of CAN previously assume complete access to all data, which would lead to enormous burden of data transfer during training if such classifiers were deployed in the cloud. We introduce a new model of clustering-based multi-layer distributed ensembles (CBMLDE). It is designed to eliminate the need to transfer data between different data centers for training of the classifiers. We conducted experiments utilizing a dataset derived from an extensive DiScRi database. Our comprehensive tests have determined the best combinations of options for setting up CBMLDE classifiers. The results demonstrate that CBMLDE classifiers not only completely eliminate the need in patient data transfer, but also have significantly outperformed all base classifiers and simpler counterpart models in all cloud frameworks.

Diabetic retinopathy (DR) is the leading cause of blindness in adults, but can be managed if detected early. Automated DR screening helps by indicating which patients should be referred to the doctor. However, current techniques of automated screening still depend too much on the detection of individual lesions. In this study, we bypass lesion detection, and directly train a classifier for DR referral. Additional novelties are the use of state-of-the-art mid-level features for the retinal images: BossaNova and Fisher Vector. Those features extend the classical Bags of Visual Words and greatly improve the accuracy of complex classification tasks. The proposed technique for direct referral is promising, achieving an area under the curve of 96.4%, thus, reducing the classification error by almost 40% over the current state of the art, held by lesion-based techniques.

Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.

In this study, a linear parametric modeling technique was applied to model ventricular repolarization (VR) dynamics. Three features were selected from the surface ECG recordings to investigate the changes in VR dynamics in healthy and cardiac autonomic neuropathy (CAN) participants with diabetes including heart rate variability (calculated from RR intervals), repolarization variability (calculated from QT intervals), and respiration [calculated by ECG-derived respiration (EDR)]. Surface ECGs were recorded in a supine resting position from 80 age-matched participants (40 with no cardiac autonomic neuropathy (NCAN) and 40 with CAN). In the CAN group, 25 participants had early/subclinical CAN (ECAN) and 15 participants were identified with definite/clinical CAN (DCAN). Detecting subclinical CAN is crucial for designing an effective treatment plan to prevent further cardiovascular complications. For CAN diagnosis, VR dynamics was analyzed using linear parametric autoregressive bivariate (ARXAR) and trivariate (ARXXAR) models, which were estimated using 250 beats of derived QT, RR, and EDR time series extracted from the first 5 min of the recorded ECG signal. Results showed that the EDR-based models gave a significantly higher fitting value (p < 0.0001) than models without EDR, which indicates that QT-RR dynamics is better explained by respiratory-information-based models. Moreover, the QT-RR-EDR model fitting values gradually decreased from the NCAN group to ECAN and DCAN groups, which indicate a decoupling of QT from RR and the respiration signal with the increase in severity of CAN. In this study, only the EDR-based model significantly distinguished ECAN and DCAN groups from the NCAN group (p < 0.05) with large effect sizes (Cohen's d > 0.75) showing the effectiveness of this modeling technique in detecting subclinical CAN. In conclusion, the EDR-based trivariate QT-RR-EDR model was found to be better in detecting the presence and severity of CAN than the bivariate QT-RR model. This finding also establishes the importance of adding respiratory information for analyzing the gradual deterioration of normal VR dynamics in pathological conditions, such as diabetic CAN.

Angiotensin converting enzyme (ACE) polymorphism has been shown to be important in hypertension progression and also in diabetes complications, especially associated with heart disease. Heart rate variability (HRV) is an established measure for classification of autonomic function regulating heart rate, based on the interbeat interval time series derived from a raw ECG recording. Results of this paper show that the length (number of interbeat intervals) and preprocessing of the tachogram affect the HRV analysis outcome. The comparison was based on tachogram lengths of 250, 300, 350, and 400 RR-intervals and five preprocessing approaches. An automated adaptive preprocessing method for the heart rate biosignal and tachogram length of 400 interbeat intervals provided the best classification. HRV results differed for the Type 2 Diabetes Mellitus (T2DM) group between the I/I genotype and the I/D and D/D genotypes, whereas for controls there was no significant difference in HRV between genotypes. Selecting an appropriate length of recording and automated preprocessing has confirmed that there is an effect of ACE polymorphism including the I/I genotype and that I/I should not be combined with I/D genotype in determining the extent of autonomic modulation of the heart rate.

This paper presents a person identification mechanism using electrocardiogram (ECG) signals with abnormal cardiac conditions in network environments. A total of 164 subjects were used in this paper using three different databases containing various irregular heart states from MIT-BIH arrhythmia database (MITDB), MIT-BIH supraventricular arrhythmia database (SVDB), and Charles Sturt diabetes complication screening initiative (DiSciRi) database. We proposed a simple yet effective biometric sample extraction technique for ECG samples with abnormal cardiac conditions to improve the person identification process. These sample points were then applied to four classifiers to verify the robustness of identification. Varying numbers of enrollment and recognition QRS complexes were used to validate the stability of the proposed method. Our experimentation results show that the biometric technique outperforms existing methods lacking the ability to efficiently extract features for biometric matching. This is evident by obtaining high accuracy results of 96.7% for MITDB, 96.4% for SVDB, and 99.3% for DiSciRi. Moreover, high sensitivity, specificity, positive predictive value, and Youden Index's values further verifies the reliability of the proposed method. This technique also suggests the possibility of improving the classification performance using ECG recordings with low sampling frequency and increased number of ECG samples.

Screening of Diabetic Retinopathy (DR) with timely treatment prevents blindness. Several researchers have focused their work on the development of computer-aided lesion-specific detectors. Combining detectors is a complex task as frequently the detectors have different properties and constraints and are not designed under a unified framework. We extend our previous work for detecting DR lesions based on points of interest and visual words to include additional detectors for the most common DR lesions and investigate fusion techniques to combine different classifiers for classification of normal or signs of diabetic retinopathy. The combination methods show promising results and shed light on the possible advantages of combining complementary lesion detectors for the DR diagnosis problem.

This article investigates a novel machine learning approach applying consensus clustering in conjunction with classification for the data mining of very large and highly dimensional ECG data sets. To obtain robust and stable clusterings, consensus functions can be applied for clustering ensembles combining a multitude of independent initial clusterings. Direct applications of consensus functions to highly dimensional ECG data sets remain computationally expensive and impracticable. We introduce a multistage scheme including various procedures for dimensionality reduction, consensus clustering of randomized samples, followed by the use of a fast supervised classification algorithm. Applying the Hybrid Bipartite Graph Formulation combined with rank ordering and SMO we obtained an area under the receiver operating curve of 0.987. The performance of the classification algorithm at the final stage is crucial for the effectiveness of this technique. It can be regarded as an indication of the reliability, quality and stability of the combined consensus clustering.

Cardiovascular autonomic neuropathy (CAN) has been frequently postulated to increase susceptibility to ventricular arrhythmias and sudden cardiac death in diabetic patients. The relation between the progression of CAN in diabetes and ventricular repolarization remains to be fully described. Therefore, this study examined QT interval variability and heart rate interbeat variability to identify any alterations of cardiac repolarization in diabetic patients in relation to severity of CAN. Seventy control participants without (CAN-) and 74 patients with CAN (CAN+) were enrolled in this study. Among 74 CAN + patients, 62 are early CAN + (eCAN +) , and 12 are definite CAN + (dCAN +) according to autonomic nervous system function tests as described by Ewing. The results showed that the QT variability index (QTVI) was significantly higher and positive in the dCAN + (0.51 ±1.32) group than in the eCAN + (-0.39 ±0.91) and CAN - (-0.54 ±0.72) groups. The QT variability to heart-rate variability ratio provides a measure of the balance between QT and heart interbeat variability. QTVI was more sensitive in identifying disease progression at all stages. Our study supports the hypothesis that QTVI could be used as a clinical test to identify early CAN and as a marker of CAN progression in diabetic patients and may help physicians in determining the best therapeutic strategy for these patients.

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al., 2004) and STARE (Hoover et al., 2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods.