Biomedical Signal Processing and Control

Background and ObjectiveAccurately parameterizing dynamic, time-varying interactions in physiological systems is a methodological challenge, as global causal discovery methods may obscure transient, local fluctuations. This study introduces tempord, an open-source Python library designed to estimate local temporal orders and evaluate the short-term stability, directionality, and strength of causal links in non-stationary biological signals. MethodsThe algorithm estimates temporal relationships by keeping one signal stationary while iteratively shifting another one within a sliding window. To parameterize optimal inter-signal shifts (causal vector, CV), the framework utilizes linear modeling or time series distance metrics. The methodology was validated through a simulation study on synthetic bivariate signals with mathematically imposed dynamic phase delays, under both deterministic and noisy conditions. Furthermore, in-vivo capabilities were demonstrated by evaluating cardiorespiratory coupling dynamics across spontaneous and music-induced relaxation breathing states. ResultsThe simulation study demonstrated that the extracted CV trajectories precisely aligned with ground-truth temporal delays, assessed using mean absolute error and root mean square error for both noise-free and noisy synthetic data. In-vivo application demonstrated dynamic temporal stability and the detection of minor step changes during autonomic nervous system state transitions. ConclusionsThe tempord Python package bridges the gap between global causal discovery and local beat-by-beat statistical parameterization. It provides a robust "bottom-up" analytical instrument for investigating the transient mechanisms governing complex biological networks.

Surface electromyography (sEMG) offers a silent and wearable input modality, but its practical use is limited by variability across users and recording sessions. This study presents a compact CNN- Transformer model for decoding isolated handwritten digits from eight-channel sEMG signals. The model combines trainable signal preprocessing, convolutional feature extraction, and Transformerbased temporal modeling. It was evaluated on ten recordings from five participants using recordingseen classification, leave-one-recording-out (LORO) generalization, and few-shot adaptation. The model achieved a mean macro F1 score of 0.924 {+/-} 0.059 in the recording-seen setting and 0.619 {+/-} 0.252 under zero-shot LORO evaluation. Adaptation using two labeled trials per digit increased macro F1 to 0.828 {+/-} 0.112, while ten trials per digit achieved 0.925 {+/-} 0.053. The proposed architecture also outperformed classical and neural baselines in the controlled LORO benchmark. These results indicate that compact CNN-Transformer models, combined with lightweight target-recording calibration, provide a promising basis for adaptive sEMG-based input systems.

The features of the PPG (photoplethysmography) morphology are known to reflect age-related cardiac and vascular changes. In most contemporary wearables, PPG signals are acquired from distal sites such as the wrist and finger. The superficial temporal artery (STA), accessible at the temple region, is reached via a shorter arterial path from the aortic root than the radial circulation, and may therefore carry hemodynamic and aging information with less distance-dependent attenuation. We hypothesized that the morphology of the PPG at temple region (STA) would show stronger and more numerous age correlates than the PPG at the wrist. To test this, we extracted a common set of 89 pulse-morphology features, spanning raw-waveform timing/amplitude/area measures, ratios among them, derivative-based ratios, and spectral harmonic-ratio features. We compared an in-house temple-worn device which has PPG as one of the sensors, with a publicly available Microsoft Aurora-BP wrist-worn PPG dataset, and tested each feature's association with age. We identified 14 robust age correlates at the temple region, compared to 3 at the wrist. The temple's correlates spanned multiple morphological categories and showed a larger age-association than at the wrist. These results support the hypothesis that the temple region may be a more robust PPG measurement site than the wrist to extract age-related cardiovascular information, which motivates further investigation of temple-based cardiovascular sensing.

Interictal epileptiform discharges (IEDs) are diagnostically important EEG abnormalities observed between seizures. This study addresses a conditional spatial-classification task where every analyzed four-second epoch had already been reviewed and confirmed by experts as containing an IED, and the model assigned that epoch to one of five predefined scalp-distribution categories (generalized, frontal, temporal, occipital, or centro-parietal). The analysis therefore does not evaluate IED-versus-non-IED detection. After preprocessing, 2,514 IED-labelled epochs were analyzed using identical stratified epoch-level partitions, SMOTE based training, 26 handcrafted features per included channel, and multiple machine-learning classifiers. A staged channel ablation compared 19-channel scalp EEG, 21-channel EEG with ECG, and the complete 29-channel input containing scalp EEG, referential, ECG, and EMG channels. The best EEG-only result was obtained with linear discriminant analysis (88.89% test accuracy). CatBoost achieved 93.25% on EEG with ECG channel and 94.44% with the whole channel set. All eight directly comparable classifiers showed numerically higher test accuracy after ECG channel was added; for CatBoost, the increase was 6.35 percentage points. In the EEG with ECG channel, CatBoost model on ECG channel on right and left arm received respectively 15.79% and 15.12% of normalized global SHAP attribution, and beta-band power was the leading of all features (18.76%). These SHAP values indicate model-specific predictive contributions and do not establish physiological biomarkers, causal autonomic mechanisms, or clinical localization. The findings support a limited methodological conclusion which is ECG-derived features were associated with improved internal epoch-level categorization of expert-confirmed IED epochs. They do not establish IED detection, artifact rejection, independent EMG effects, or generalization to unseen patients.

The -Bonacci weighted sequence space {ell}(,) is introduced for [&ge;] 2, 1 [&le;] < {infty}, and > 0). It is proved to be a Banach space which is isometrically isomorphic to {ell}, with an unconditional Schauder basis. The space is also reflexive and uniformly convex. Its strict inclusion chain, continuous dual, and asymptotic truncation estimate of order - / are also established. Based on this framework, Scaled &#119898-BonacciNet is proposed as a lightweight CNN that rescales Fibonacci-Net channels using the dominant &#119898-Bonacci root and employs depth wise separable convolutions. Tribonacci-Net achieved 96.09% accuracy and a 0.9581 macro F1-score on the 15-class Brain MRI dataset using 0.85 million parameters and 0.366 GFLOPs. Pentanacci-Net attained 95.87% accuracy on the 44-class dataset while reducing parameters and FLOPs by 47.10% and 44.48%, respectively. On the CKD ultrasound dataset, Hexanacci-Net achieved 83.75% accuracy and a 0.7941 macro F1-score, with corresponding reductions of 50.37% and 45.10%. These findings support dominant-root scaling as a structured approach to lightweight CNN design.

EEG microstates are a distinct number of quasi-stable spatial distributions of brain activity. Microstate trajectories are strongly suspected to reflect the underlying neural mechanisms during information processing and are therefore also called the "building blocks" of human thought. In this study, we examined, if EEG microstate sequences can serve as potential triggers for a Brain-Computer Interface (BCI). To this end, a semi-supervised deep learning model architecture consisting of an LSTM-based autoencoder and a dense neural network was utilized to classify between left- and right-hand motor imagery EEG data, with the resulting classification output serving as the BCI trigger. On the one hand, this was done in a 2-step approach, in which the autoencoder and classifer have been trained separately. On the other hand, an end-to-end approach was employed, where training was performed by combining reconstruction and classification losses. Results show that the proposed model architecture was able to extract relevant features from microstate sequences and exploit them for within subjects and sessions classification. Applying transfer learning to session-to-session or across-subject transfer resulted in peak classification accuracies around 89%. We also investigated to what extent transfer learning has to be applied to reach considerable classification accuracies serving as the calibration time representative. We found that on average around 400s are needed for BCI calibration when emplyoing our approach to reach 80% classification accuracy. The present study signifies that the investigation of EEG microstate trajectories can be a promising approach for extracting BCI triggers, as it reduces the dimensionality of multi-channel recorded EEG signals to a distinct number of brain states over time. Deep learning methods, especially transfer learning, applied to EEG microstate trajectories seem promising regarding user-convenient and calibration-free BCIs in real-world applications.

Background and Objective: Heart rate variability (HRV) analysis provides a non-invasive method for quantifying autonomic modulation from electrocardiographic recordings. However, practical HRV analysis often depends on fragmented workflows, limited signal-quality review, and software tools optimized for either human or preclinical recordings, but not both. This study developed and evaluated HRV-GUI, a MATLAB-based graphical interface for electrocardiogram (ECG)-derived HRV analysis in translational biomedical research. Methods: The HRV-GUI integrates electrocardiographic and RR interval loading, human and rat analysis modes, preprocessing, segment selection, automated R-peak detection, manual peak correction, RR interval generation, multi-domain HRV computation, diagnostic visualization, result export, and session saving/loading. The software was evaluated using deterministic synthetic RR interval datasets, baseline recordings from healthy human controls and healthy rats, and a clinical use-case comparison between healthy controls and patients with diabetic gastroparesis. Results: The HRV-GUI produced expected outputs in synthetic RR validation tests, including constant RR sequences, alternating RR sequences, outlier-containing RR sequences, and low-frequency- or high-frequency-dominant sinusoidal RR modulation. The software generated physiologically plausible HRV profiles in both human and rat recordings. In the clinical use-case analysis, patients with diabetic gastroparesis showed higher heart rate and sympathetic index, together with lower respiratory sinus arrhythmia, absolute low- and high-frequency spectral power, standard deviation of normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD), percentage of successive RR intervals differing by more than 50 ms (pNN50), Poincare short-term variability (SD1), and Poincare long-term variability (SD2) compared with healthy controls. Conclusions: HRV-GUI provides an integrated biomedical software workflow for ECG-derived HRV analysis. The validation results support its use for controlled RR testing, human and rodent ECG recordings, and clinical autonomic assessment in diabetic gastroparesis.

Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder characterized by persistent symptoms of inattention, hyperactivity, and impulsivity that significantly affect cognitive, academic, and social functioning. Although electroencephalography (EEG) has emerged as a promising non-invasive tool for identifying objective neurophysiological biomarkers of ADHD because of its high temporal resolution and cost-effectiveness, many existing machine learning studies rely on single-domain EEG features, which may not adequately capture the complex functional organization of the brain. Methods: This study proposes a subject-level EEG classification framework that integrates three complementary feature domains: spectral characteristics, weighted Phase Lag Index (wPLI)-based functional connectivity, and topological descriptors derived from persistent homology. EEG recordings from 121 subjects were preprocessed and segmented into overlapping windows for feature extraction. Window-level features were aggregated to generate subject-level representations. Recursive Feature Elimination (RFE) was employed to select the 110 most informative features from an initial set of 1,043 descriptors. Five machine learning classifiers, including Support Vector Machine (SVM), Random Forest, XGBoost, CatBoost, and Multi-Layer Perceptron (MLP), were evaluated using stratified five-fold cross-validation. An ablation study was further conducted to assess the contribution of each feature domain. Model interpretability was investigated using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), two widely adopted Explainable Artificial Intelligence techniques for interpreting machine learning models. Results: Among the evaluated classifiers, the SVM achieved the highest classification performance, yielding an accuracy of 80.17%, an F1-score of 80.95%, a balanced accuracy of 80.14%, a receiver operating characteristic area under the curve (ROC-AUC) of 86.72%, and a precision recall area under the curve (PR-AUC) of 87.34%. The ablation study demonstrated that combining spectral, functional connectivity, and topological features consistently outperformed individual feature domains, highlighting the complementary nature of these representations. Explainable AI analyses further identified functional connectivity features as the primary contributors to classification performance while confirming the additional value of spectral and topological descriptors. Conclusions: The proposed framework demonstrates that integrating spectral analysis, functional connectivity, and topological data analysis provides an effective and interpretable approach for subject-level ADHD classification from EEG recordings. The combination of recursive feature selection, multi-domain feature fusion, ablation analysis, and Explainable Artificial Intelligence establishes a transparent and reproducible pipeline that may facilitate the development of reliable EEG-based clinical decision-support systems for ADHD.

AO_SCPLOWBSTRACTC_SCPLOWElectroencephalography (EEG) is a promising tool for automated detection of mild cognitive impairment (MCI) and dementia, but comparisons across studies are limited by inconsistent datasets and evaluation protocols. This study benchmarks ten deep learning models across four resting-state EEG datasets and eight binary classification tasks using a unified preprocessing pipeline and five-fold subject-wise cross-validation. Each experiment was repeated ten times. SCCNet obtained the highest mean subject-level accuracy, sensitivity, and F1 score, while ShallowConvNet achieved the highest mean segment-level accuracy, specificity, and precision. Subject-level aggregation improved mean accuracy for all evaluated models, and performance varied substantially across datasets and diagnostic tasks. Higher computational cost did not consistently correspond to better classification performance, with several compact architectures remaining competitive with substantially larger models. The results provide a reproducible reference for comparing EEG-based dementia classification models under consistent subject-independent evaluation conditions.

Prostate cancer, a leading cause of cancer-related deaths among men globally, necessitates the development of precise diagnostic and treatment strategies. Accurate segmentation of prostate cancer in medical imaging, particularly in MRI scans, is crucial for early diagnosis and clinical decisions. Traditional manual segmentation techniques, while efficient, are labor-intensive and necessitate significant expertise, resulting in an increasing demand for automated alternatives. The RSAUNet is a novel deep learning architecture designed to improve prostate cancer segmentation. It incorporates essential components, including Residual Blocks, Swin Transformer Blocks, and Attention Mechanisms within a U-Net architecture. This markedly enhances the model's capacity to discern complex anatomical features and accurately segment malignant tissues. Using sophisticated deep Learning methods, RSAUNet addresses the complexities of prostate imaging, delivering reliable, consistent segmentation results. The model was evaluated against various cutting-edge techniques on extensive multi-parametric MRI datasets, attaining an impressive Dice coefficient (DC) of 0.998 and a Jaccard Index (IoU) of 0.965. These findings highlight the innovative characteristics of RSAUNet and its capacity to transform prostate cancer diagnosis and treatment strategies. The proposed model surpasses current methods and shows potential for practical clinical applications, providing an efficient, precise, and scalable solution for automated prostate cancer segmentation and fostering optimism for the future of medical imaging and diagnosis.

Cerebral ischemia is a significant concern during high-risk surgeries, such as carotid endarterectomy (CEA). Continuous electroencephalography, monitored by neurophysiological experts, is used to detect cerebral ischemia during surgery; however, real-time visual interpretation is resource-intensive and error-prone. We evaluated machine learning (ML) models, including random forest (RF), eXtreme Gradient Boosting with a random forest base classifier (XGB), elastic-net logistic regression (LR), support vector classifier (SVC) with a radial basis function kernel, and naive Bayes (NB) classifier, for automated detection of cerebral ischemia during CEA using quantitative electroencephalographic (qEEG) features. RF achieved the highest sensitivity (0.79-0.83) and an area under the precision-recall curve (AUPRC) of 0.44, while XGB demonstrated the highest specificity (0.93-0.96) with an AUPRC of 0.36. Both models showed high negative predictive values and high area under the receiver operating characteristic (AUROC) scores. Feature-importance analysis identified alpha-band activity and hemispheric asymmetry as the most discriminative qEEG predictors of ischemia. These results highlight the potential of ML-assisted monitoring to support neurophysiology experts and enhance patient safety during high-risk surgical procedures.

Photoplethysmography (PPG, optical measurement of cardiac blood volume changes) is the foundation of wearable cardiac monitoring, but systematically fails on dark skin due to melanin absorption. We present the Melanin Absorption Invariance (MAI) framework: a label-free method that substantially reduces cross-skin-tone bias in cardiac feature extraction by preserving topological rather than geometric signal structure. We prove two theorems: Theorem 1 bounds attractor bias to O(SNR_eff^-1) under Z-normalization; Theorem 2 reduces residual bias to O(SNR_eff^-2) via SNR-adaptive correction. Empirical validation confirms these theoretical predictions on real dark-skin PPG signals. Comprehensive empirical validation on the complete MMPD dataset (Fitzpatrick III-VI, n = 656 recordings, 33 subjects, spanning all 4 lighting conditions and 5 motion types, Samsung Galaxy mobile phone) demonstrates MAI generalization across real-world deployment conditions. Results show substantial attractor bias reduction across all skin tone groups, with largest effects for Fitzpatrick IV and VI populations most affected by current systems. This work demonstrates a theoretically grounded, label-free, skin-tone-invariant cardiac monitoring framework.

Background: Precise localisation of intracranial electrode contacts is essential for the interpretation of stereoelectroencephalography recordings and planning epilepsy surgery. In current clinical practice, this is typically a manual process, which is time-consuming and prone to variability. Existing automated solutions are often fragmented across multiple tools requiring technical expertise, limiting their adoption in routine clinical workflows. This study presents an open-source extension for 3D Slicer that provides an integrated, user-friendly standalone solution for the direct automatic detection of electrode contacts within a widely used medical imaging platform. Results: The proposed method combines anchor bolt-based initialisation, probabilistic segmentation of electrode structures, and non-linear modelling to precisely track true electrode trajectories. The approach was evaluated on a dataset comprising 78 cases from 73 patients, including 1,078 electrodes with 14,480 contacts. The method achieved high localisation accuracy, with a median (interquartile range) deviation of 0.10 (0.06, 0.15) mm. Only 7/1078 (0.65%) electrodes required manual correction; these specific cases were handled using tools provided within the proposed extension. Conclusions: The presented extension enables fast, accurate, and reproducible electrode contact localisation within a single integrated environment. By combining automation with intuitive user interaction, it significantly reduces processing time while maintaining clinical reliability. The tool's free availability as an extension in 3D Slicer lowers the barrier to adoption and supports the standardisation of workflows across clinical and research centres.

ObjectiveElectroencephalography (EEG) is a key tool for studying auditory processing in cochlear implant (CI) users. In particular, EEG recordings obtained during continuous speech are becoming increasingly important for assessing speech and language processing in CI users, and may be utilized for neurofeedback. However, CIs also induce strong stimulation artifacts that are time-locked to the stimulus and mask the neural responses that have smaller magnitudes. Existing artifact reduction methods are typically based on event-related potentials (ERPs) or require manual component selection, making them unsuitable for naturalistic listening conditions or large datasets. ApproachWe develop CORSICA (CORrelation-baSed ICA artifact rejection), a reproducible, parameter-efficient method for CI artifact reduction in EEG responses to continuous speech. CORSICA operates on independent components (ICs) obtained through Infomax ICA and requires no manual component labelling, with performance governed by a single tunable threshold. It exploits the observation that CI artifacts temporally follow the audio signal without delay, whereas neural responses have an inherent lag due to auditory pathway latencies. For each IC, CORSICA computes the cross-correlation with the speech stimulus. Artifacts are identified by a high signal-to-noise ratio (SNR) of the correlation peak near zero lag, and the component is rejected if this SNR exceeds a threshold. To benchmark CORSICA, we evaluate two alternatives: a TRF-based SNR method, in which temporal response functions are fitted to each IC and artifact-driven peaks near zero lag are used for rejection, and a variant replacing ICA with second-order blind identification (SOBI) as the source separation step. Main resultsCORSICA effectively suppressed CI artifacts while preserving neural activity, enabling recovery of physiologically plausible TRFs with only 2% of ICs rejected. Both benchmark methods confirmed the validity of the SNR-based rejection framework, but CORSICA outperformed the TRF-based alternative in artifact suppression quality. Replacing ICA with SOBI as the source separation step required more ICs to be rejected, further supporting ICA as the preferred backbone for CORSICA. SignificanceCORSICA provides a fully objective, label-free approach to identifying CI artifacts in speech-evoked EEG data, with no manual intervention required. By centering artifact rejection on a single interpretable threshold, it offers a reproducible preprocessing standard for future EEG studies on speech processing in CI users. ConclusionOur findings demonstrate that objective CI artifact suppression in speech-evoked EEG data is feasible on the basis of the ICs temporal response patterns.

Objective: To develop and validate a dual-probe Diffuse Correlation Spectroscopy (DCS) system for non-invasive and simultaneous, monitoring of cerebral blood flow (CBF) in the bilateral Middle Cerebral Artery (MCA) territories, and expanding the utility of conventional DCS limited to cortical-volume-based CBF measurements to vessel-specific cerebral perfusion monitoring. Methods: A dual-probe DCS system was designed for non-invasive monitoring of MCA-specific perfusion. Probe placement and protocol optimization study has been performed using anatomical landmarks, motor and speech activation tasks in healthy volunteers. System stability and repeatability were further evaluated in a pilot cohort of 30 healthy (age, 25{+/-}7 years) participants using optimized probe position and protocol. A bilateral MCA ischemic Lacunar Infract stroke case report also validated the feasibility of the system in clinical settings. Results: Measurements demonstrated superior sensitivity towards MCA-territory perfusion at targeted probe locations compared to off-MCA positions. In pilot cohort, significant increase of 30.34 {+/-} 21.56% and 36.48 {+/-} 21.22% in rCBF corresponding to hand squeeze and speech task respectively showed reproducible physiological responsiveness of the system (p<0.001). Measurement done on a patient with bilateral MCA ischemic Lacunar Infract stroke showed a significant change of 30% during speech for both the MCAs but no significant change is observed for hand squeeze tasks. Conclusion: The custom built dual-probe DCS system enables non-invasive, operator-independent, targeted and continuous monitoring of rCBF within bilateral MCA territories. Significance: This approach enables the potential use of DCS system for bilateral and vessel-specific monitoring of cerebral perfusion in the MCA territories.

This study provides a direct and objective comparison of ECG signal quality and morphological feature preservation across four leading commercial smartwatches: the Apple Watch Series 9, Samsung Galaxy Watch 6, Fitbit Sense 2, and Withings ScanWatch, using a controlled dataset. The Philips TC30 is used as the reference standard. ECG signal quality is assessed using Signal-to-Noise Ratio (SNR), Standard Deviation of Baseline Wander, and Power Spectral Density (PSD). Preservation of morphological features is assessed by comparing the performance of our automatic J-point and ST deviation detection algorithms when applied separately to recordings from each smartwatch. J-point detection is evaluated by comparing each device's performance against the reference device, while ST deviation is assessed using simulator-defined ground truth values. The Apple Watch and Withings ScanWatch achieved the best SNR, while the Samsung Galaxy Watch showed substantially lower values. The Withings ScanWatch demonstrated the most stable baseline, whereas the Fitbit Sense 2 showed the greatest baseline variability. All devices exhibited a consistent negative bias in J-point detection, suggesting that internal signal processing tends to smooth small changes in waveforms. For ST-segment classification, the Apple Watch achieved the most balanced performance in terms of sensitivity and specificity. These results provide researchers and clinicians with data-driven guidance for selecting the most appropriate device for their specific needs.

Stroke remains one of the leading causes of disability and mortality worldwide, where timely and accurate diagnosis is critical for guiding treatment and improving patient outcomes. However, a global shortage of trained clinicians and radiologists continues to limit rapid and reliable interpretation of neuroimaging, particularly in resource-constrained settings. Artificial intelligence (AI) has emerged as a promising solution to this challenge by enabling efficient analysis of medical images. Here we present an Integrated Stroke Diagnosis System for MRI (ISDS-MRI), a unified framework that leverages graph neural networks and sequence-specific feature modeling for comprehensive ischemic stroke analysis. This framework is designed to detect ischemic stroke, segment lesions, and estimate lesion volume from multi-sequence MRI data, while accommodating incomplete combinations of MRI sequences. To ensure generalizability, we evaluate our approach across multiple publicly available MRI datasets and introduce a newly curated dataset, BGD-MRIS, comprising 532 MRI scans from three hospitals in Bangladesh. This newly curated dataset provides a multi-center MRI cohort from a resource-constrained setting, offering an additional test bed for evaluating stroke AI across heterogeneous clinical imaging protocols. Experimental results demonstrate that ISDS-MRI achieves a Dice score of 0.725 for lesion segmentation, a AUC of 0.962, and a lesion volume estimation relative error of 8.4%, outperforming comparison methods by 3.2% in Dice score and 2.6% in detection performance, while reducing volume estimation relative error by 1.9%. These results highlight the robustness and clinical potential of ISDS-MRI for scalable and comprehensive stroke diagnosis from MRI.

Deep learning models for electroencephalography (EEG) pathology detection achieve competitive accuracy but offer limited interpretability. In this paper, we introduce concept bottleneck models (CBMs) for EEG pathology detection which use clinical EEG biomarkers computed analytically from the signal as a bottleneck to be used for prediction. Those concepts include band power, spectral ratios, alpha peak frequency and amplitude, hemispheric asymmetry and phase locking values. With the backbone based on shallow convolutional neural network (CNN), we compare a black box baseline with two variants of CBMs. The hybrid variant is a classifier receives a residual projection of the backbone features. The pure variant is a CBM where the classifier receives only the predicted concepts. Models are evaluated on abnormality detection and dementia classification followed by concept prediction quality and leakage analysis. Both CBMs approach the baseline on TUAB (82.7-82.9% vs 83.6%) and are 2-5 percentage points lower on CAUEEG (57.9-60.5% vs 62.7%). The pure CBM exhibits concept fidelity concern where the predicted concepts deviate from their ground truth values. Replacing them with their true values causes severe cost on accuracy by 26 and 22 points on TUAB and CAUEEG. The concept fidelity concern is mitigated by increasing the weight of the concept loss to match the ground truth concepts. the mitigation comes with small accuracy cost on TUAB but a relatively larger cost on CAUEEG. The code of the experiments and full logs are available at https://www.github.com/mhmdrdwn/cbm.

Background and purpose: Accurate identification of cardiac MRI volume orientations is essential for reliable image interpretation and for enabling downstream automated analysis pipelines. However, orientation labels and commonly assigned manually, making the process time-consuming and prone to variability. Recent advances in vision-language models offers new opportunities for automated label generation by jointly modeling visual and semantic information. Methods: In this study, we systematically compare a conventional end-to-end transformer-based captioning model with a CLIPCap architecture for automated cardiac MRI view labelling. Both models were evaluated on a dataset of 1184 de-identified cardiac MR images spanning across six clinically relevant orientations: short axis, two-, three-, four-chamber views, left and right ventricular outflow tract views. The caption transformer was trained from scratch, whereas CLIPCap leveraged pretrained CLIP image embeddings combined with lightweight mapping network and a frozen language model. Results: Experimental results demonstrate a substantial performance advantage from CLIPCap, which achieved an overall accuracy of 98% compared to 68% for the caption transformer. CLIPCap consistently delivered high precision and recall across all orientation classes, including those of high clinical importance, while the end to end transformer exhibited unstable performance and complete failure in certain views. Conclusion: These findings highlight the benefits of leveraging large scale multimodal pretrained model for medical image labeling tasks, particularly in data limited settings. The study suggests that CLIPCap provides a more robust and clinically reliable solution for automated cardiac MRI orientation labeling, supporting its integration into efficient and scalable clinical workflows.

Background: Depth of anesthesia is routinely monitored using electroencephalographic (EEG) indices such as the Bispectral Index (BIS), which reduce cortical activity to a single numerical real-time series. However, these scalar descriptors fail to capture inter-individual variability, drug-specific EEG signatures, and transitions between brain states. Methods: We analyzed transient EEG patterns during general anesthesia induced by propofol and sevoflurane in 89 patients. Iso-electric suppressions (IES), $\alpha$-suppressions, and spectral power in the $\alpha$ and $\delta$ bands were quantified across controlled concentration plateaus. We compared conventional BIS monitoring with a novel State-Chart representation mapping EEG activity into discrete depth states. Results: Increasing hypnotic concentration produced a consistent shift from $\alpha$-dominant to $\delta$-dominant activity and a reduction in maximal $\alpha$ frequency. IES occurred predominantly at high propofol concentrations and remained infrequent under sevoflurane. Compared with BIS, the State-Chart showed a marked reduction in temporal variability (0.5 $\pm$ 0.6 vs 4.0 $\pm$ 2.9; $p<10^{-90}$). Importantly, our State-Chart separates stable from transition regimes and discriminates identical concentration levels. Statistics computed over prior dosing (ascending vs descending), reveals refined clustering not captured by BIS. Conclusions: Anesthesia depth drugs concentrations vs EEG effects is more accurately described by structured EEG state transitions than by a single numerical index. The State-Chart provides a stable, physiologically interpretable representation of brain activity and reveals transition states preceding deep cortical suppression, supporting individualized and predictive monitoring of anesthesia.