Biology subjects

Jalaleddini, K.

Publications and source records attributed to Jalaleddini, K..

5 recordsLinked to original sources

bioRxiv2026

Composite Certainty: Addressing Metric Degeneracy in Parameter Inference for Model-Based Diagnostics

Quantifying the diagnostic dispersion of inferred parameter distributions is a challenge in uncertainty-aware modeling. Scalar summaries such as credible interval width are topology-blind; fundamentally different posterior morphologies can yield identical scores, obscuring whether a parameter is precisely estimated or constrained to a range. We propose a Composite Certainty Framework that addresses this metric degeneracy by aggregating five complementary uncertainty metrics including interquartile range, standard deviation, full width at half maximum, Shannon entropy, and mass width. These metrics are aggregated through non-parametric Borda rank voting into a single, unitless consensus certainty score. Applied to a simulation-based inference pipeline for a finite-element model of the human middle ear tuned to cadaveric acoustic measurements, the framework reveals parameter-specific identifiability profiles invisible to any individual metric. It produces two actionable clinical thresholds: (1) the maximum tolerable measurement noise for reliable parameter recovery, and (2) the minimum simulation budget for posterior convergence. We demonstrated that no single metric captures all aspects of posterior dispersion, as spread-based metrics and entropy diverge systematically for clinically critical parameters, whereas their aggregation produces a consensus reflecting genuine diagnostic certainty. The framework is generalizable to any model-based diagnostic pipeline where posterior distribution not merely its coverage, but determines clinical certainty.

bioengineering↗

bioRxiv2026

Composite Biofidelity: Addressing Metric Degeneracy in Biomechanical Model Validation and Machine Learning Loss Design

Spectral similarity is often judged with a single metric such as RMSE, yet this can be misleading: physically different errors can produce similar scores. This is a critical limitation for computational biomechanics, where spectral agreement underpins both model validation and machine-learning loss design. Here, we develop a multi-metric framework for objective spectral biofidelity and test whether it better captures meaningful disagreement across complex frequency-domain responses. We evaluated 12 complementary similarity metrics, including CORA and ISO/TS 18571, using controlled spectral perturbations that mimic common real-world deviations such as resonance shifts, localized spikes, and broadband tilts. We then applied the framework to an SBI-tuned finite-element middle-ear model to assess convergence with training dataset size and robustness to measurement noise across repeated stochastic runs. No single metric performed reliably across all distortion types. Shape-based metrics tracked resonance morphology but could miss vertical scaling, whereas MaxError remained important for narrowband anomalies that smoother metrics underweighted. CORA and ISO 18571 did not consistently outperform simpler metrics. Rank aggregation using Borda count provided a robust consensus across metrics, enabling objective identification of training-data saturation and noise thresholds beyond which similarity rankings became unstable. These results show that spectral biofidelity cannot be reduced to a single norm. A multi-metric consensus provides a clearer and more physically meaningful basis for comparing experimental and simulated spectra, and offers a more defensible foundation for data-fidelity terms in physics-informed and simulation-based machine learning.

bioengineering↗

bioRxiv2019

Method for Modeling Residual Variance in Biomedical Signals Applied to Transcranial Doppler Ultrasonography Waveforms

Transcranial Doppler (TCD) ultrasonography measures pulsatile cerebral blood flow velocity in the arteries and veins of the head and neck. The velocity pulse waveform morphology has been shown to have physiological and diagnostic significance. However, the measured pulses may exhibit a high degree of variability that deteriorates the estimates of clinical parameters. This study characterizes the TCD residual variance that result in pulse variability.\n\nWe retrospectively utilized the data from 82 subjects. A trained sonographer insonated the middle cerebral arteries using a 2MHz hand-held probe. We implemented a multi-stage algorithm to identify the TCD residuals in each scan: pulses were identified; outlier pulses were flagged and removed; the average pulse waveform was taken as the ensemble average of the accepted pulse waveforms; finally, the resampled average pulse waveforms subtracted from individual pulses were taken as the TCD residuals. For each scan, we reported the signal to noise ratio and parameterized models for residuals: their amplitude structure using probability density function models and their temporal structure using autoregressive models.\n\nThe signal to noise ratio 90% range was [1.7, 18.2] dB. The estimated probability density functions were best characterized by a generalized normal distribution whose beta parameter was smaller than 2 in 93% of scans. The identified frequency structure showed the dynamics were low-pass in nature.\n\nAnalysis of the TCD residuals is useful in the assessment of the signal quality. Moreover, our identified models can also be used to generate synthetic TCD signal that enables future realistic simulation studies.

bioengineering↗

bioRxiv2019

Objective Assessment of Beat Quality in Transcranial Doppler Measurement of Blood Flow Velocity in Cerebral Arteries

ObjectiveTranscranial Doppler (TCD) ultrasonography measures pulsatile cerebral blood flow velocity in the arteries and veins of the head and neck. Similar to other real-time measurement modalities, especially in healthcare, the identification of high quality signals is essential for clinical interpretation. Our goal is to identify poor quality beats and remove them prior to further analysis of the TCD signal. MethodsWe selected objective features for this purpose including Euclidean distance between individual and average beat waveform, cross-correlation between individual and average beat waveform, ratio of the high frequency power to the total beat power, beat length, and variance of the diastolic portion of the beat waveform. We developed an iterative outlier detection algorithm to identify and remove the beats that are different from others in a recording. Finally, we tested the algorithm on a dataset consisting of more than 16 hours of TCD data recorded from 48 stroke and 35 in-hospital control subjects. ResultsWe assessed the performance of the algorithm in estimating clinically important TCD parameters by comparison to those identified from beats hand-annotated by an expert. The results show that there is strong correlation between the two that delineates the algorithm has successfully recovered clinically important features. We obtained significant improvement in estimating the TCD parameters using the algorithm accepted beats compared to using all beats (r>0.78, p<0.01). SignificanceOur algorithm provides a valuable tool to the clinicians for automated detection of the reliable portion of the data. Moreover, it can be used as a pre-processing tool to improve the data quality for machine learning algorithms for automated diagnosis of pathologic beat waveform.

bioinformatics↗

bioRxiv2018

Algorithm for Reliable Detection of Beat Onsets in Cerebral Blood Flow Velocity Signals

Transcranial Doppler (TCD) ultrasound has been demonstrated to be a valuable tool for assessing cerebral hemodynamics via measurement of cerebral blood flow velocity (CBFV), with a number of established clinical indications. However, CBFV waveform analysis depends on reliable pulse onset detection, an inherently difficult task for CBFV signals acquired via TCD. We study the application of a new algorithm for CBFV pulse segmentation, which locates pulse onsets in a sequential manner using a moving difference filter and adaptive thresholding. The test data set used in this study consists of 92,012 annotated CBFV pulses, whose quality is representative of real world data. On this test set, the algorithm achieves a true positive rate of 99.998% (2 false negatives), positive predictive value of 99.998% (2 false positives), and mean temporal offset error of 6.10 {+/-} 4.75 ms. We do note that in this context, the way in which true positives, false positives, and false negatives are defined caries some nuance, so care should be taken when drawing comparisons to other algorithms. Additionally, we find that 97.8% and 99.5% of onsets are detected within 10 ms and 30 ms, respectively, of the true onsets. The algorithms performance in spite of the large degree of variation in signal quality and waveform morphology present in the test data suggests that it may serve as a valuable tool for the accurate and reliable identification of CBFV pulse onsets in neurocritical care settings.

bioinformatics↗