Robust peak recognition in intracranial pressure signals

Artifact Management for Cerebral Near-Infrared Spectroscopy Signals: A Systematic Scoping Review

Artifacts induced during patient monitoring are a main limitation for near-infrared spectroscopy (NIRS) as a non-invasive method of cerebral hemodynamic monitoring. There currently does not exist a robust "gold-standard" method for artifact management for these signals. The objective of this review is to comprehensively examine the literature on existing artifact management methods for cerebral NIRS signals recorded in animals and humans. A search of five databases was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The search yielded 806 unique results. There were 19 articles from these results that were included in this review based on the inclusion/exclusion criteria. There were an additional 36 articles identified in the references of select articles that were also included. The methods outlined in these articles were grouped under two major categories: (1) motion and other disconnection artifact removal methods; (2) data quality improvement and physiological/other noise artifact filtering methods. These were sub-categorized by method type. It proved difficult to quantitatively compare the methods due to the heterogeneity of the effectiveness metrics and definitions of artifacts. The limitations evident in the existing literature justify the need for more comprehensive comparisons of artifact management. This review provides insights into the available methods for artifact management in cerebral NIRS and justification for a homogenous method to quantify the effectiveness of artifact management methods. This builds upon the work of two existing reviews that have been conducted on this topic; however, the scope is extended to all artifact types and all NIRS recording types. Future work by our lab in cerebral NIRS artifact management will lie in a layered artifact management method that will employ different techniques covered in this review (including dynamic thresholding, autoregressive-based methods, and wavelet-based methods) amongst others to remove varying artifact types.

Analysis of intracranial pressure pulse waveform in studies on cerebrospinal compliance: a narrative review

Continuous monitoring of mean intracranial pressure (ICP) has been an essential part of neurocritical care for more than half a century. Cerebrospinal pressure-volume compensation, i.e. the ability of the cerebrospinal system to buffer changes in volume without substantial increases in ICP, is considered an important factor in preventing adverse effects on the patient's condition that are associated with ICP elevation. However, existing assessment methods are poorly suited to the management of brain injured patients as they require external manipulation of intracranial volume. In the 1980s, studies suggested that spontaneous short-term variations in the ICP signal over a single cardiac cycle, called the ICP pulse waveform, may provide information on cerebrospinal compensatory reserve. In this review we discuss the approaches that have been proposed so far to derive this information, from pulse amplitude estimation and spectral techniques to most recent advances in morphological analysis based on artificial intelligence solutions. Each method is presented with focus on its clinical significance and the potential for application in standard clinical practice. Finally, we highlight the missing links that need to be addressed in future studies in order for ICP pulse waveform analysis to achieve widespread use in the neurocritical care setting.

Intracranial pressure management: moving beyond guidelines

PURPOSE OF REVIEW

The aim of this study was to provide an overview on advances in intracranial pressure (ICP) protocols for care, moving from traditional to more recent concepts.

RECENT FINDINGS

Deep understanding of mechanics and dynamics of fluids and solids have been introduced for intracranial physiology. The amplitude or the harmonics of the cerebral-spinal fluid and the cerebral blood waves shows more information about ICP than just a numeric threshold. When the ICP overcome the compensatory mechanisms that maintain the compliance within the skull, an intracranial compartment syndrome (ICCS) is defined. Autoregulation monitoring emerge as critical tool to recognize CPP management. Measurement of brain tissue oxygen will be a critical intervention for diagnosing an ICCS. Surgical procedures focused on increasing the physiological compliance and increasing the volume of the compartments of the skull.

SUMMARY

ICP management is a complex task, moving far than numeric thresholds for activation of interventions. The interactions of intracranial elements requires new interpretations moving beyond classical theories. Most of the traditional clinical studies supporting ICP management are not generating high class evidence. Recommendations for ICP management requires better designed clinical studies using new concepts to generate interventions according to the new era of personalized medicine.

Intracranial pressure analysis software: A mapping study and proposal

Introduction Intracranial pressure (ICP) monitoring and analysis are techniques that are, each year, applied to millions of patients with pathologies with million of patients annually. The detection of the so called A and B-waves, and the analysis of subtle changes in C-waves, which are present in ICP waveform, may indicate decreased intracranial compliance, and may improve the clinical outcome. Despite the advances in the field of computerized data analysis, the visual screening of ICP continues to be the means principally employed to detect these waves. To the best of our knowledge, no review study has addressed automated ICP analysis in sufficient detail and a need to research the state of the art of ICP analysis has, therefore, been identified. Methodology This paper presents a systematic mapping study to provide answers to 7 research questions: publication time, venue and source trends, medical tasks undertaken, research methods used, computational systems developed, validation methodology, tools and systems employed for evaluation and research problems identified. An ICP software prototype is presented and evaluated as a consequence of the results. Results A total of 23 papers, published between 1990 and 2020, were selected from 6 online databases. After analyzing these papers, the following information was obtained: diagnosis and monitoring medical tasks were addressed to the same extent, and the main research method used was evaluation research. Several computational systems were identified in the papers, the main one being image classification, while the main analysis objective was single pulse analysis. Correlation with expert analysis was the most frequent validation method, and few of the papers stated the use of a published dataset. Few authors referred to the tools used to build or evaluate the proposed solutions. The most frequent research problem was the need for new analysis methods. These results have inspired us to propose a software prototype with which provide an automated solution that integrates ICP analysis and monitoring techniques. Conclusions The papers in this study were selected and classified with regard to ICP automated analysis methods. Several research gaps were identified, which the authors of this study have employed as a based on which to recommend future work. Furthermore, this study has identified the need for an empirical comparison between methods, which will require the use and development of certain standard metrics. An in-depth analysis conducted by means of systematic literature review is also required. The software prototype evaluation provided positive results, showing that the prototype may be a reliable system for A-wave detection.

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis.

Patient-adaptable intracranial pressure morphology analysis using a probabilistic model-based approach

OBJECTIVE

We present a framework for analyzing the morphology of intracranial pressure (ICP). The analysis of ICP signals is challenging due to the non-linear and non-Gaussian characteristics of the signal dynamics, inevitable corruption by noise and artifacts, and variations in ICP pulse morphology among individuals with different neurological conditions. Existing frameworks make unrealistic assumptions regarding ICP dynamics and are not tuned for individual patients.

APPROACH

We propose a dynamic Bayesian network for automated detection of three major ICP pulsatile components. The proposed model captures the non-linear and non-Gaussian dynamics of ICP morphology and further adapts to a patient as the individual's ICP measurements are received. To make the approach more robust, we leverage evidence reversal and present an inference algorithm to obtain the posterior distribution over the locations of pulsatile components.

MAIN RESULTS

We evaluate our approach on a dataset with over 700 h of recordings from 66 neurological patients, where the pulsatile components were annotated by prior studies. The algorithm obtains accuracies of 96.56%, 92.39%, and 94.04% for the detection of each pulsatile component in the test set, showing significant improvement over existing approaches.

SIGNIFICANCE

Continuous ICP monitoring is essential in guiding the treatment of neurological conditions such as traumatic brain injuries. An automated approach for ICP morphology analysis is a step towards enhancing patient care with minimal supervision. Compared to previous methods, our framework offers several advantages. It learns the parameters that model each patient's ICP in an unsupervised manner, resulting in an accurate morphology analysis. The Bayesian model-based framework provides uncertainty estimates and reveals interesting facts about the ICP dynamics. The framework can readily be applied to replace existing morphological analysis methods and support the use of ICP pulse morphological features to aid the monitoring of pathophysiological changes of relevance to the care of patients with acute brain injuries.

Intracranial Pressure Monitoring Signals After Traumatic Brain Injury: A Narrative Overview and Conceptual Data Science Framework

Continuous intracranial pressure (ICP) monitoring is a cornerstone of neurocritical care after severe brain injuries such as traumatic brain injury and acts as a biomarker of secondary brain injury. With the rapid development of artificial intelligent (AI) approaches to data analysis, the acquisition, storage, real-time analysis, and interpretation of physiological signal data can bring insights to the field of neurocritical care bioinformatics. We review the existing literature on the quantification and analysis of the ICP waveform and present an integrated framework to incorporate signal processing tools, advanced statistical methods, and machine learning techniques in order to comprehensively understand the ICP signal and its clinical importance. Our goals were to identify the strengths and pitfalls of existing methods for data cleaning, information extraction, and application. In particular, we describe the use of ICP signal analytics to detect intracranial hypertension and to predict both short-term intracranial hypertension and long-term clinical outcome. We provide a well-organized roadmap for future researchers based on existing literature and a computational approach to clinically-relevant biomedical signal data.

Multi-Scale Peak and Trough Detection Optimised for Periodic and Quasi-Periodic Neuroscience Data

OBJECTIVES

The reliable detection of peaks and troughs in physiological signals is essential to many investigative techniques in medicine and computational biology. Analysis of the intracranial pressure (ICP) waveform is a particular challenge due to multi-scale features, a changing morphology over time and signal-to-noise limitations. Here we present an efficient peak and trough detection algorithm that extends the scalogram approach of Scholkmann et al., and results in greatly improved algorithm runtime performance.

MATERIALS AND METHODS

Our improved algorithm (modified Scholkmann) was developed and analysed in MATLAB R2015b. Synthesised waveforms (periodic, quasi-periodic and chirp sinusoids) were degraded with white Gaussian noise to achieve signal-to-noise ratios down to 5 dB and were used to compare the performance of the original Scholkmann and modified Scholkmann algorithms.

RESULTS

The modified Scholkmann algorithm has false-positive (0%) and false-negative (0%) detection rates identical to the original Scholkmann when applied to our test suite. Actual compute time for a 200-run Monte Carlo simulation over a multicomponent noisy test signal was 40.96 ± 0.020 s (mean ± 95%CI) for the original Scholkmann and 1.81 ± 0.003 s (mean ± 95%CI) for the modified Scholkmann, demonstrating the expected improvement in runtime complexity from [Formula: see text] to [Formula: see text].

CONCLUSIONS

The accurate interpretation of waveform data to identify peaks and troughs is crucial in signal parameterisation, feature extraction and waveform identification tasks. Modification of a standard scalogram technique has produced a robust algorithm with linear computational complexity that is particularly suited to the challenges presented by large, noisy physiological datasets. The algorithm is optimised through a single parameter and can identify sub-waveform features with minimal additional overhead, and is easily adapted to run in real time on commodity hardware.

Noise reduction in intracranial pressure signal using causal shape manifolds

We present the Iterative/Causal Subspace Tracking framework (I/CST) for reducing noise in continuously monitored quasi-periodic biosignals. Signal reconstruction of the basic segments of the noisy signal (e.g. beats) is achieved by projection to a reduced space on which probabilistic tracking is performed. The attractiveness of the presented method lies in the fact that the subspace, or manifold, is learned by incorporating temporal, morphological, and signal elevation constraints, so that segment samples with similar shapes, and that are close in time and elevation, are also close in the subspace representation. Evaluation of the algorithm's effectiveness on the intracranial pressure (ICP) signal serves as a practical illustration of how it can operate in clinical conditions on routinely acquired biosignals. The reconstruction accuracy of the system is evaluated on an idealized 20-min ICP recording established from the average ICP of patients monitored for various ICP related conditions. The reconstruction accuracy of the ground truth signal is tested in presence of varying levels of additive white Gaussian noise (AWGN) and Poisson noise processes, and measures significant increases of 758% and 396% in the average signal-to-noise ratio (SNR).

Intracranial pressure wave morphological classification: automated analysis and clinical validation

BACKGROUND

Recently, different software has been developed to automatically analyze multiple intracranial pressure (ICP) parameters, but the suggested methods are frequently complex and have no clinical correlation. The objective of this study was to assess the clinical value of a new morphological classification of the cerebrospinal fluid pulse pressure waveform (CSFPPW), comparing it to the elastance index (EI) and CSF-outflow resistance (Rout), and to test the efficacy of an automatic ICP analysis.

METHODS

An artificial neural network (ANN) was trained to classify 60 CSFPPWs in four different classes, according to their morphology, and its efficacy was compared to an expert examiner's classification. The morphology of CSFPPW, recorded in 60 patients at baseline, was compared to EI and Rout calculated at the end of an intraventricular infusion test to validate the utility of the proposed classification in patients' clinical evaluation.

RESULTS

The overall concordance in CSFPPW classification between the expert examiner and the ANN was 88.3 %. An elevation of EI was statistically related to morphological class' progression. All patients showing pathological baseline CSFPPW (class IV) revealed an alteration of CSF hydrodynamics at the end of their infusion test.

CONCLUSIONS

The proposed morphological classification estimates the global ICP wave and its ability to reflect or predict an alteration in CSF hydrodynamics. An ANN can be trained to efficiently recognize four different CSF wave morphologies. This classification seems helpful and accurate for diagnostic use.

An automatic method for arterial pulse waveform recognition using KNN and SVM classifiers

The measurement and analysis of the arterial pulse waveform (APW) are the means for cardiovascular risk assessment. Optical sensors represent an attractive instrumental solution to APW assessment due to their truly non-contact nature that makes the measurement of the skin surface displacement possible, especially at the carotid artery site. In this work, an automatic method to extract and classify the acquired data of APW signals and noise segments was proposed. Two classifiers were implemented: k-nearest neighbours and support vector machine (SVM), and a comparative study was made, considering widely used performance metrics. This work represents a wide study in feature creation for APW. A pool of 37 features was extracted and split in different subsets: amplitude features, time domain statistics, wavelet features, cross-correlation features and frequency domain statistics. The support vector machine recursive feature elimination was implemented for feature selection in order to identify the most relevant feature. The best result (0.952 accuracy) in discrimination between signals and noise was obtained for the SVM classifier with an optimal feature subset .

Monitoring of intracranial pressure in patients with traumatic brain injury

Since Monro published his observations on the nature of the contents of the intracranial space in 1783, there has been investigation of the unique relationship between the contents of the skull and the intracranial pressure (ICP). This is particularly true following traumatic brain injury (TBI), where it is clear that elevated ICP due to the underlying pathological processes is associated with a poorer clinical outcome. Consequently, there is considerable interest in monitoring and manipulating ICP in patients with TBI. The two techniques most commonly used in clinical practice to monitor ICP are via an intraventricular or intraparenchymal catheter with a microtransducer system. Both of these techniques are invasive and are thus associated with complications such as hemorrhage and infection. For this reason, significant research effort has been directed toward development of a non-invasive method to measure ICP. The principle aims of ICP monitoring in TBI are to allow early detection of secondary hemorrhage and to guide therapies that limit intracranial hypertension (ICH) and optimize cerebral perfusion. However, information from the ICP value and the ICP waveform can also be used to assess the intracranial volume-pressure relationship, estimate cerebrovascular pressure reactivity, and attempt to forecast future episodes of ICH.

Machine learning techniques for arterial pressure waveform analysis

The Arterial Pressure Waveform (APW) can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1) a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2) the acquired position and amplitude of onset, Systolic Peak (SP), Point of Inflection (Pi) and Dicrotic Wave (DW) were used for the computation of some morphological attributes; (3) pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4) classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic), J48 (decision tree) and RIPPER (rule-based induction); and (5) we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx). Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95%) and high area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve (0.961). Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation.

Continuous detection of cerebral vasodilatation and vasoconstriction using intracranial pulse morphological template matching

Although accurate and continuous assessment of cerebral vasculature status is highly desirable for managing cerebral vascular diseases, no such method exists for current clinical practice. The present work introduces a novel method for real-time detection of cerebral vasodilatation and vasoconstriction using pulse morphological template matching. Templates consisting of morphological metrics of cerebral blood flow velocity (CBFV) pulse, measured at middle cerebral artery using Transcranial Doppler, are obtained by applying a morphological clustering and analysis of intracranial pulse algorithm to the data collected during induced vasodilatation and vasoconstriction in a controlled setting. These templates were then employed to define a vasodilatation index (VDI) and a vasoconstriction index (VCI) for any inquiry data segment as the percentage of the metrics demonstrating a trend consistent with those obtained from the training dataset. The validation of the proposed method on a dataset of CBFV signals of 27 healthy subjects, collected with a similar protocol as that of training dataset, during hyperventilation (and CO₂ rebreathing tests) shows a sensitivity of 92% (and 82%) for detection of vasodilatation (and vasoconstriction) and the specificity of 90% (and 92%), respectively. Moreover, the proposed method of detection of vasodilatation (vasoconstriction) is capable of rejecting all the cases associated with vasoconstriction (vasodilatation) and outperforms other two conventional techniques by at least 7% for vasodilatation and 19% for vasoconstriction.

Intracranial hypertension prediction using extremely randomized decision trees

Intracranial pressure (ICP) elevation (intracranial hypertension, IH) in neurocritical care is typically treated in a reactive fashion; it is only delivered after bedside clinicians notice prolonged ICP elevation. A proactive solution is desirable to improve the treatment of intracranial hypertension. Several studies have shown that the waveform morphology of the intracranial pressure pulse holds predictors about future intracranial hypertension and could therefore be used to alert the bedside clinician of a likely occurrence of the elevation in the immediate future. In this paper, a computational framework is proposed to predict prolonged intracranial hypertension based on morphological waveform features computed from the ICP. A key contribution of this work is to exploit an ensemble classifier method based on extremely randomized decision trees (Extra-Trees). Experiments on a representative set of 30 patients admitted for various intracranial pressure related conditions demonstrate the effectiveness of the predicting framework on ICP pulses acquired under clinical conditions and the superior results of the proposed approach in comparison to linear and AdaBoost classifiers.

Bayesian tracking of intracranial pressure signal morphology

BACKGROUND

The waveform morphology of intracranial pressure (ICP) pulses holds essential informations about intracranial and cerebrovascular pathophysiological variations. Most of current ICP pulse analysis frameworks process each pulse independently and therefore do not exploit the temporal dependency existing between successive pulses. We propose a probabilistic framework that exploits this temporal dependency to track ICP waveform morphology in terms of its three peaks.

MATERIAL

ICP and electrocardiogram (ECG) signals were recorded from a total of 128 patients treated for various intracranial pressure related conditions.

METHODS

The tracking is posed as inference in a graphical model that associates a random variable to the position of each peak. A key contribution is to exploit a nonparametric Bayesian inference algorithm that offers robustness and real time performance. A simple, yet effective learning procedure estimates the statistical, nonlinear, dependencies between the peaks in a nonparametric way using evidence collected from manually annotated pulses.

RESULTS

Experiments demonstrate the effectiveness of the tracking framework on real ICP pulses and its robustness to occlusion and missing peaks. On artificialy distorted ICP sequences, the average error in latency in comparision with MOCAIP detector was reduced as follows: 11.88-8.09 ms, 11.80-6.90 ms, and 11.76-7.46 ms for the first, second, and third peak, respectively.

CONCLUSION

The proposed tracking algorithm sucessfuly increases the temporal resolution of detecting ICP pulse morphological changes from the minute-level to the beat-level.

Lack of consistent intracranial pressure pulse morphological changes during episodes of microdialysis lactate/pyruvate ratio increase

Lactate/pyruvate ratio (LPR) from microdialysis is a well-established marker of cerebral metabolic crisis. For brain injury patients, abnormally high LPR could indicate cerebral ischemia or failure of O(2) uptake. However, there is a debate on the primary factor responsible for LPR increase. Exploiting the potential of using the morphology of a high temporal resolution signal such as intracranial pulse (ICP) to characterize cerebrovascular changes, a data analysis experiment is taken to test whether consistent changes in ICP pulse morphological metrics accompany the LPR increase. We studied 3517 h of LPR and continuous ICP data from 19 severe traumatic brain injury patients. Our morphological clustering and analysis of intracranial pressure (MOCAIP) algorithm was applied to ICP pulses, which were matched in time to the LPR measurements, and 128 pulse morphological metrics were extracted. We automatically identified the episodes of LPR increases using a moving time window of 10-20 h. We then studied the trending patterns of each of the 128 ICP MOCAIP metrics within these identified periods and determined them to be one of the following three types: increasing, decreasing or no trend. A binomial test was employed to investigate whether any MOCAIP metrics show a consistent trend among all episodes of LPR increase per patient. Regardless of the selected values for different parameters of the proposed method, for the majority of the subjects in the study (78%), none of the ICP metrics show any consistent trend during the episodes of LPR increase. Even for the few subjects who have at least one ICP metric with a consistent trend during the LPR increase episodes, the number of such metrics is small and varies from subject to subject. Given the fact that ICP pulse morphology is influenced by the cerebral vasculature, our results suggest that a dominant cerebral vascular cause may be behind the changes in LPR when LPR trends correlate with ICP pulse morphological changes. However, the incidence of such correlation seems to be low.

Consistent changes in intracranial pressure waveform morphology induced by acute hypercapnic cerebral vasodilatation

BACKGROUND

Intracranial pressure (ICP) remains a pivotal physiological signal for managing brain injury and subarachnoid hemorrhage (SAH) patients in neurocritical care units. Given the vascular origin of the ICP, changes in ICP waveform morphology could be used to infer cerebrovascular changes. Clinical validation of this association in the setting of brain trauma, and SAH is challenging due to the multi-factorial influences on, and uncertainty of, the state of the cerebral vasculature.

METHODS

To gain a more controlled setting, in this articel, we study ICP signals recorded in four uninjured patients undergoing a CO2 inhalation challenge in which hypercapnia induced acute cerebral vasodilatation. We apply our morphological clustering and analysis of intracranial pressure (MOCAIP) algorithm to identify six landmarks on individual ICP pulses (based on the three established ICP sub-peaks; P1, P2, and P3) and extract 128 ICP morphological metrics. Then by comparing baseline, test, and post-test data, we assess the consistency and rate of change for each individual metric.

RESULTS

Acute vasodilatation causes consistent changes in a total of 72 ICP pulse morphological metrics and the P2 sub-region responds to cerebral vascular changes in the most consistent way with the greatest change as compared to P1 and P3 sub-regions.

CONCLUSIONS

Since the dilation/constriction of the cerebral vasculature resulted in detectable consistent changes in ICP MOCIAP metrics, by an extended monitoring practice of ICP that includes characterizing ICP pulse morphology, one can potentially detect cerebrovascular changes, continuously, for patients under neurocritical care.

Noninvasive intracranial pressure assessment based on a data-mining approach using a nonlinear mapping function

The current gold standard to determine intracranial pressure (ICP) involves an invasive procedure for direct access to the intracranial compartment. The risks associated with this invasive procedure include intracerebral hemorrhage, infection, and discomfort. We previously proposed an innovative data-mining framework of noninvasive ICP (NICP) assessment. The performance of the proposed framework relies on designing a good mapping function. We attempt to achieve performance gain by adopting various linear and nonlinear mapping functions. Our results demonstrate that a nonlinear mapping function based on the kernel spectral regression technique significantly improves the performance of the proposed data-mining framework for NICP assessment in comparison to other linear mapping functions.