Respiratory Rate Extraction from Neonatal Near-Infrared Spectroscopy Signals.

Background: Near-infrared spectroscopy (NIRS) relative concentration signals contain 'noise' from physiological processes such as respiration and heart rate. Simultaneous assessment of NIRS and respiratory rate (RR) using a single sensor would facilitate a perfectly time-synced assessment of (cerebral) physiology. Our aim was to extract respiratory rate from cerebral NIRS intensity signals in neonates admitted to a neonatal intensive care unit (NICU). Methods: A novel algorithm, NRR (NIRS RR), is developed for extracting RR from NIRS signals recorded from critically ill neonates. In total, 19 measurements were recorded from ten neonates admitted to the NICU with a gestational age and birth weight of 38 ± 5 weeks and 3092 ± 990 g, respectively. We synchronously recorded NIRS and reference RR signals sampled at 100 Hz and 0.5 Hz, respectively. The performance of the NRR algorithm is assessed in terms of the agreement and linear correlation between the reference and extracted RRs, and it is compared statistically with that of two existing methods. Results: The NRR algorithm showed a mean error of 1.1 breaths per minute (BPM), a root mean square error of 3.8 BPM, and Bland-Altman limits of agreement of 6.7 BPM averaged over all measurements. In addition, a linear correlation of 84.5% (p < 0.01) was achieved between the reference and extracted RRs. The statistical analyses confirmed the significant (p < 0.05) outperformance of the NRR algorithm with respect to the existing methods. Conclusions: We showed the possibility of extracting RR from neonatal NIRS in an intensive care environment, which showed high correspondence with the reference RR recorded. Adding the NRR algorithm to a NIRS system provides the opportunity to record synchronously different physiological sources of information about cerebral perfusion and respiration by a single monitoring system. This allows for a concurrent integrated analysis of the impact of breathing (including apnea) on cerebral hemodynamics.

Estimation of Respiratory Rate during Biking with a Single Sensor Functional Near-Infrared Spectroscopy (fNIRS) System.

OBJECTIVE: The employment of wearable systems for continuous monitoring of vital signs is increasing. However, due to substantial susceptibility of conventional bio-signals recorded by wearable systems to motion artifacts, estimation of the respiratory rate (RR) during physical activities is a challenging task. Alternatively, functional Near-Infrared Spectroscopy (fNIRS) can be used, which has been proven less vulnerable to the subject's movements. This paper proposes a fusion-based method for estimating RR during bicycling from fNIRS signals recorded by a wearable system. METHODS: Firstly, five respiratory modulations are extracted, based on amplitude, frequency, and intensity of the oxygenated hemoglobin concentration (O2Hb) signal. Secondly, the dominant frequency of each modulation is computed using the fast Fourier transform. Finally, dominant frequencies of all modulations are fused, based on averaging, to estimate RR. The performance of the proposed method was validated on 22 young healthy subjects, whose respiratory and fNIRS signals were simultaneously recorded during a bicycling task, and compared against a zero delay Fourier domain band-pass filter. RESULTS: The comparison between results obtained by the proposed method and band-pass filtering indicated the superiority of the former, with a lower mean absolute error (3.66 vs. 11.06 breaths per minute, p<0.05). The proposed fusion strategy also outperformed RR estimations based on the analysis of individual modulation. SIGNIFICANCE: This study orients towards the practical limitations of traditional bio-signals for RR estimation during physical activities.

Estimation of Respiratory Rate from Functional Near-Infrared Spectroscopy (fNIRS): A New Perspective on Respiratory Interference.

OBJECTIVE: Respiration is recognized as a systematic physiological interference in functional near-infrared spectroscopy (fNIRS). However, it remains unanswered as to whether it is possible to estimate the respiratory rate (RR) from such interference. Undoubtedly, RR estimation from fNIRS can provide complementary information that can be used alongside the cerebral activity analysis, e.g., sport studies. Thus, the objective of this paper is to propose a method for RR estimation from fNIRS. Our primary presumption is that changes in the baseline wander of oxygenated hemoglobin concentration (O2Hb) signal are related to RR. METHODS: fNIRS and respiratory signals were concurrently collected from subjects during controlled breathing tasks at a constant rate from 0.1 Hz to 0.4 Hz. Firstly, the signal quality index algorithm is employed to select the best O2Hb signal, and then a band-pass filter with cut-off frequencies from 0.05 to 2 Hz is used to remove very low- and high-frequency artifacts. Secondly, troughs of the filtered O2Hb signal are localized for synthesizing the baseline wander (S1) using cubic spline interpolation. Finally, the fast Fourier transform of the S1 signal is computed, and its dominant frequency is considered as RR. In this paper, two different datasets were employed, where the first one was used for the parameter adjustment of the proposed method, and the second one was solely used for testing. RESULTS: The low mean absolute error between the reference and estimated RRs for the first and second datasets (2.6 and 1.3 breaths per minute, respectively) indicates the feasibility of the proposed method for RR estimation from fNIRS. SIGNIFICANCE: This paper provides a novel view on the respiration interference as a source of complementary information in fNIRS.

When the human brain goes diving: using near-infrared spectroscopy to measure cerebral and systemic cardiovascular responses to deep, breath-hold diving in elite freedivers.

Continuous measurements of haemodynamic and oxygenation changes in free living animals remain elusive. However, developments in biomedical technologies may help to fill this knowledge gap. One such technology is continuous-wave near-infrared spectroscopy (CW-NIRS)-a wearable and non-invasive optical technology. Here, we develop a marinized CW-NIRS system and deploy it on elite competition freedivers to test its capacity to function during deep freediving to 107 m depth. We use the oxyhaemoglobin and deoxyhaemoglobin concentration changes measured with CW-NIRS to monitor cerebral haemodynamic changes and oxygenation, arterial saturation and heart rate. Furthermore, using concentration changes in oxyhaemoglobin engendered by cardiac pulsation, we demonstrate the ability to conduct additional feature exploration of cardiac-dependent haemodynamic changes. Freedivers showed cerebral haemodynamic changes characteristic of apnoeic diving, while some divers also showed considerable elevations in venous blood volumes close to the end of diving. Some freedivers also showed pronounced arterial deoxygenation, the most extreme of which resulted in an arterial saturation of 25%. Freedivers also displayed heart rate changes that were comparable to diving mammals both in magnitude and patterns of change. Finally, changes in cardiac waveform associated with heart rates less than 40 bpm were associated with changes indicative of a reduction in vascular compliance. The success here of CW-NIRS to non-invasively measure a suite of physiological phenomenon in a deep-diving mammal highlights its efficacy as a future physiological monitoring tool for human freedivers as well as free living animals. This article is part of the theme issue 'Measuring physiology in free-living animals (Part II)'.

Neuroprotection of the Perinatal Brain by Early Information of Cerebral Oxygenation and Perfusion Patterns.

Abnormal patterns of cerebral perfusion/oxygenation are associated with neuronal damage. In preterm neonates, hypoxemia, hypo-/hypercapnia and lack of cerebral autoregulation are related to peri-intraventricular hemorrhages and white matter injury. Reperfusion damage after perinatal hypoxic ischemia in term neonates seems related with cerebral hyperoxygenation. Since biological tissue is transparent for near infrared (NIR) light, NIR-spectroscopy (NIRS) is a noninvasive bedside tool to monitor brain oxygenation and perfusion. This review focuses on early assessment and guiding abnormal cerebral oxygenation/perfusion patterns to possibly reduce brain injury. In term infants, early patterns of brain oxygenation helps to decide whether or not therapy (hypothermia) and add-on therapies should be considered. Further NIRS-related technical advances such as the use of (functional) NIRS allowing simultaneous estimation and integrating of heart rate, respiration rate and monitoring cerebral autoregulation will be discussed.

Localizing confined epileptic foci in patients with an unclear focus or presumed multifocality using a component-based EEG-fMRI method.

Precise localization of epileptic foci is an unavoidable prerequisite in epilepsy surgery. Simultaneous EEG-fMRI recording has recently created new horizons to locate foci in patients with epilepsy and, in comparison with single-modality methods, has yielded more promising results although it is still subject to limitations such as lack of access to information between interictal events. This study assesses its potential added value in the presurgical evaluation of patients with complex source localization. Adult candidates considered ineligible for surgery on account of an unclear focus and/or presumed multifocality on the basis of EEG underwent EEG-fMRI. Adopting a component-based approach, this study attempts to identify the neural behavior of the epileptic generators and detect the components-of-interest which will later be used as input in the GLM model, substituting the classical linear regressor. Twenty-eight sets interictal epileptiform discharges (IED) from nine patients were analyzed. In eight patients, at least one BOLD response was significant, positive and topographically related to the IEDs. These patients were rejected for surgery because of an unclear focus in four, presumed multifocality in three, and a combination of the two conditions in two. Component-based EEG-fMRI improved localization in five out of six patients with unclear foci. In patients with presumed multifocality, component-based EEG-fMRI advocated one of the foci in five patients and confirmed multifocality in one of the patients. In seven patients, component-based EEG-fMRI opened new prospects for surgery and in two of these patients, intracranial EEG supported the EEG-fMRI results. In these complex cases, component-based EEG-fMRI either improved source localization or corroborated a negative decision regarding surgical candidacy. As supported by the statistical findings, the developed EEG-fMRI method leads to a more realistic estimation of localization compared to the conventional EEG-fMRI approach, making it a tool of high value in pre-surgical evaluation of patients with refractory epilepsy. To ensure proper implementation, we have included guidelines for the application of component-based EEG-fMRI in clinical practice.

Signal quality index: an algorithm for quantitative assessment of functional near infrared spectroscopy signal quality.

We propose the signal quality index (SQI) algorithm as a novel tool for quantitatively assessing the functional near infrared spectroscopy (fNIRS) signal quality in a numeric scale from 1 (very low quality) to 5 (very high quality). The algorithm comprises two preprocessing steps followed by three consecutive rating stages. The results on a dataset annotated by independent fNIRS experts showed SQI performed significantly better (p<0.05) than PHOEBE (placing headgear optodes efficiently before experimentation) and SCI (scalp coupling index), two existing algorithms, in both quantitatively rating and binary classifying the fNIRS signal quality. Employment of the proposed algorithm to estimate the signal quality before processing the fNIRS signals increases certainty in the interpretations.

Proposing a convolutional neural network for stress assessment by means of derived heart rate from functional near infrared spectroscopy.

BACKGROUND: Stress is known as one of the major factors threatening human health. A large number of studies have been performed in order to either assess or relieve stress by analyzing the brain and heart-related signals. METHOD: In this study, a method based on the Convolutional Neural Network (CNN) approach is proposed to assess stress induced by the Montreal Imaging Stress Task. The proposed model is trained on the heart rate signal derived from functional Near-Infrared Spectroscopy (fNIRS), which is referred to as HRF. In this regard, fNIRS signals of 20 healthy volunteers were recorded using a configuration of 23 channels located on the prefrontal cortex. The proposed deep learning system consists of two main parts where in the first part, the one-dimensional convolutional neural network is employed to build informative activation maps, and then in the second part, a stack of deep fully connected layers is used to predict the stress existence probability. Thereafter, the employed CNN method is compared with the Dense Neural Network, Support Vector Machine, and Random Forest regarding various classification metrics. RESULTS: Results clearly showed the superiority of CNN over all other methods. Additionally, the trained HRF model significantly outperforms the model trained on the filtered fNIRS signals, where the HRF model could achieve 98.69 ± 0.45% accuracy, which is 10.09% greater than the accuracy obtained by the fNIRS model. CONCLUSIONS: Employment of the proposed deep learning system trained on the HRF measurements leads to higher stress classification accuracy than the accuracy reported in the existing studies where the same experimental procedure has been done. Besides, the proposed method suggests better stability with lower variation in prediction. Furthermore, its low computational cost opens up the possibility to be applied in real-time monitoring of stress assessment.

Stress assessment by means of heart rate derived from functional near-infrared spectroscopy.

Many studies have been carried out in order to detect and quantify the level of mental stress by means of different physiological signals. From the physiological point of view, stress promptly affects brain and cardiac function; therefore, stress can be assessed by analyzing the brain- and heart-related signals more efficiently. Signals produced by functional near-infrared spectroscopy (fNIRS) of the brain together with the heart rate (HR) are employed to assess the stress induced by the Montreal Imaging Stress Task. Two different versions of the HR are used in this study. The first one is the commonly used HR derived from the electrocardiogram (ECG) and is considered as the reference HR (RHR). The other is the HR computed from the fNIRS signal (EHR) by means of an effective combinational algorithm. fNIRS and ECG signals were simultaneously recorded from 10 volunteers, and EHR and RHR are derived from them, respectively. Our results showed a high degree of agreement [r > 0.9, BAR (Bland Altman ratio) <5 % ] between the two HR. A principal component analysis/support vector machine-based algorithm for stress classification is developed and applied to the three measurements of fNIRS, EHR, and RHR and a classification accuracy of 78.8%, 94.6%, and 62.2% were obtained for the three measurements, respectively. From these observations, it can be concluded that the EHR carries more useful information with regards to the mental stress than the RHR and fNIRS signals. Therefore, EHR can be used alone or in combination with the fNIRS signal for a more accurate and real-time stress detection and classification.