An Artificial Neural Network to Eliminate the Detrimental Spectral Shift on Mid-Infrared Gas Spectroscopy.

We demonstrate the successful implementation of an artificial neural network (ANN) to eliminate detrimental spectral shifts imposed in the measurement of laser absorption spectrometers (LASs). Since LASs rely on the analysis of the spectral characteristics of biological and chemical molecules, their accuracy and precision is especially prone to the presence of unwanted spectral shift in the measured molecular absorption spectrum over the reference spectrum. In this paper, an ANN was applied to a scanning grating-based mid-infrared trace gas sensing system, which suffers from temperature-induced spectral shifts. Using the HITRAN database, we generated synthetic gas absorbance spectra with random spectral shifts for training and validation. The ANN was trained with these synthetic spectra to identify the occurrence of spectral shifts. Our experimental verification unambiguously proves that such an ANN can be an excellent tool to accurately retrieve the gas concentration from imprecise or distorted spectra of gas absorption. Due to the global shift of the measured gas absorption spectrum, the accuracy of the retrieved gas concentration using a typical least-mean-squares fitting algorithm was considerably degraded by 40.3%. However, when the gas concentration of the same measurement dataset was predicted by the proposed multilayer perceptron network, the sensing accuracy significantly improved by reducing the error to less than ±1% while preserving the sensing sensitivity.

Blood pressure monitoring during anesthesia induction using PPG morphology features and machine learning.

Blood pressure (BP) is a crucial biomarker giving valuable information regarding cardiovascular diseases but requires accurate continuous monitoring to maximize its value. In the effort of developing non-invasive, non-occlusive and continuous BP monitoring devices, photoplethysmography (PPG) has recently gained interest. Researchers have attempted to estimate BP based on the analysis of PPG waveform morphology, with promising results, yet often validated on a small number of subjects with moderate BP variations. This work presents an accurate BP estimator based on PPG morphology features. The method first uses a clinically-validated algorithm (oBPM®) to perform signal preprocessing and extraction of physiological features. A subset of features that best reflects BP changes is automatically identified by Lasso regression, and a feature relevance analysis is conducted. Three machine learning (ML) methods are then investigated to translate this subset of features into systolic BP (SBP) and diastolic BP (DBP) estimates; namely Lasso regression, support vector regression and Gaussian process regression. The accuracy of absolute BP estimates and trending ability are evaluated. Such an approach considerably improves the performance for SBP estimation over previous oBPM® technology, with a reduction in the standard deviation of the error of over 20%. Furthermore, rapid BP changes assessed by the PPG-based approach demonstrates concordance rate over 99% with the invasive reference. Altogether, the results confirm that PPG morphology features can be combined with ML methods to accurately track BP variations generated during anesthesia induction. They also reinforce the importance of adding a calibration measure to obtain an absolute BP estimate.

Machine Learning Approaches For Improved Continuous, Non-occlusive Arterial Pressure Monitoring Using Photoplethysmography.

Arterial pressure (AP) is a crucial biomarker for cardiovascular disease prevention and management. Photoplethysmography (PPG) could provide a novel, paradigm-shifting approach for continuous, non-obtrusive AP monitoring, comfortably integrated in wearable and mobile devices; yet, it still faces challenges in accuracy and robustness. In this work, we sought to integrate machine learning (ML) techniques into a previously established, clinically-validated classical approach (oBPM®) to develop new accurate AP estimation tools based on PPG, and at the same time improve our understanding of the underlying physiological parameters. In this novel approach, oBPM® was used to pre-process PPG signals and robustly extract physiological features, and ML models were trained on these features to estimate systolic AP (SAP). A feature relevance analysis showed that reference (calibration) information, followed by various morphological parameters of the PPG pulse wave, comprised the most important features for SAP estimation. A performance analysis then revealed that LASSO-regularized linear regression, Gaussian process regression and support vector regression are effective for SAP estimation, particularly when operating on reduced feature sets previously obtained with e.g. LASSO. These approaches yielded substantial reductions in error standard deviation of 9-15% relative to conventional oBPM®. Altogether, these results indicate that ML approaches are well-suited, and promising tools to help overcoming the challenges of ubiquitous AP monitoring.

Lesion Index-Guided Ablation Facilitates Continuous, Transmural, and Durable Lesions in a Porcine Recovery Model.

BACKGROUND: The Lesion Index (LSI) is a proprietary algorithm from Abbott Medical combining contact force, radiofrequency application duration, and radiofrequency current. It can be displayed during ablation with the TactiCath contact force catheter. The LSI Index was designed to provide real-time lesion formation feedback and is hypothesized to estimate the lesion diameter. METHODS AND RESULTS: Before ablation, animals underwent cardiac computed tomography to assess atrial tissue thickness. Ablation lines (n=2-3 per animal) were created in the right atrium of 7 Göttingen mini pigs with point lesions (25 W). Within each line of ablation, the catheter tip was moved a prescribed distance (D/mm) according to 1 of 3 strategies: D=LSI+0 mm; D=LSI+2 mm; or D=LSI+4 mm. Two weeks after ablation, serial sections of targeted atrial tissue were examined histologically to identify gaps in transmural ablation. LSI-guided lines had a lower incidence of histological gaps (4 gaps in 69 catheter moves, 5.8%) than LSI+2 mm lines (7 gaps in 33 catheter moves, 21.2%) and LSI+4 mm lines (15 gaps in 23 catheter moves, 65.2%, P<0.05 versus D=LSI). ΔLSI was calculated retrospectively as the distance between 2 adjacent lesions above the mean LSI of the 2 lesions. ΔLSI values of ≤1.5 were associated with no gaps in transmural ablation. CONCLUSIONS: In this model of chronic atrial ablation, delivery of uninterrupted transmural linear lesions may be facilitated by using LSI to guide catheter movement. When ΔLSI between adjacent lesions is ≤1.5 mm, no gaps in atrial linear lesions should be expected.

A High Baseline Electrographic Organization Level Is Predictive of Successful Termination of Persistent Atrial Fibrillation by Catheter Ablation.

OBJECTIVES: This study sought to investigate whether the level of organization of electrocardiographic (ECG) signals based on novel indexes is predictive of persistent atrial fibrillation (pAF) termination by catheter ablation (CA). BACKGROUND: Whether the level of ECG organization in pAF is correlated with the restoration of sinus rhythm by CA remains unknown. METHODS: Thirty consecutive patients who underwent stepwise CA for pAF (sustained duration 19 ± 11 months) were included in the study (derivation cohort). ECG lead V6 was placed on the patients' back (V6b) to improve left atrial (LA) recording. Two novel ECG indexes were computed using an adaptive harmonic frequency tracking scheme: 1) the adaptive organization index (AOI), which quantifies the cyclicity of AF harmonic oscillations; and 2) the adaptive phase index (API), which quantifies the phase coupling between the harmonic components. Index cutoff values predictive of procedural AF termination were then tested on a validation cohort of 8 consecutive patients. RESULTS: In the derivation cohort, CA terminated AF in 21 patients within the LA (70%; left-terminated [LT] group), whereas CA did not terminate AF in 9 patients (30%; non-left-terminated [NLT] group). LT patients displayed a higher ECG organization level at baseline than the NLT patients, with the best separation achieved by AOI and API computed on lead V1 (area under the curve [AUC] = 0.94 and AUC = 0.88, respectively; p < 0.05) and API on lead V6b (AUC = 0.83; p < 0.05). Similar results were obtained for both AOI and API in the validation cohort. CONCLUSIONS: Patients in whom pAF terminated within the LA exhibited a higher level of atrial ECG organization, which was suggestive of a limited number of LA drivers than that of patients in whom the pAF could not be terminated by CA.

Systematic comparison of non-invasive measures for the assessment of atrial fibrillation complexity: a step forward towards standardization of atrial fibrillation electrogram analysis.

AIMS: To present a comparison of electrocardiogram-based non-invasive measures of atrial fibrillation (AF) substrate complexity computed on invasive animal recordings to discriminate between short-term and long-term AF. The final objective is the selection of an optimal sub-set of measures for AF complexity assessment. METHODS AND RESULTS: High-density epicardial direct contact mapping recordings (234 leads) were acquired from the right and the left atria of 17 goats in which AF was induced for 3 weeks (short-term AF group, N = 10) and 6 months (long-term AF group, N = 7). Several non-invasive measures of AF organization proposed in the literature in the last decade were investigated to assess their power in discriminating between the short-term and long-term group. The best performing measures were identified, which when combined attained a correct classification rate of 100%. Their ability to predict standard invasive AF complexity measures was also tested, showing an average R(2) of 0.73 ± 0.04. CONCLUSION: An optimal set of measures of the AF substrate complexity was identified out of the set of non-invasive measures analysed in this study. These measures may contribute to improve patient-tailored diagnosis and therapy of sustained AF.

Alterations in postural control during the world's most challenging mountain ultra-marathon.

We investigated postural control (PC) effects of a mountain ultra-marathon (MUM): a 330-km trail run with 24000 m of positive and negative change in elevation. PC was assessed prior to (PRE), during (MID) and after (POST) the MUM in experienced ultra-marathon runners (n = 18; finish time = 126 ± 16 h) and in a control group (n = 8) with a similar level of sleep deprivation. Subjects were instructed to stand upright on a posturographic platform over a period of 51.2 seconds using a double-leg stance under two test conditions: eyes open (EO) and eyes closed (EC). Traditional measures of postural stability (center of pressure trajectory analysis) and stabilogram-diffusion analysis (SDA) parameters were analysed. For the SDA, a significantly greater short-term effective diffusion was found at POST compared with PRE in the medio-lateral (ML; Dxs) and antero-posterior (AP) directions (Dys) in runners (p<0.05) The critical time interval (Ctx) in the ML direction was significantly higher at MID (p<0.001) and POST (p<0.05) than at PRE in runners. At MID (p<0.001) and POST (p<0.05), there was a significant difference between the two groups. The critical displacement (Cdx) in the ML was significantly higher at MID and at POST (p<0.001) compared with PRE for runners. A significant difference in Cdx was observed between groups in EO at MID (p<0.05) and POST (p<0.005) in the ML direction and in EC at POST in the ML and AP directions (p<0.05). Our findings revealed significant effects of fatigue on PC in runners, including, a significant increase in Ctx (critical time in ML plan) in EO and EC conditions. Thus, runners take longer to stabilise their body at POST than at MID. It is likely that the mountainous characteristics of MUM (unstable ground, primarily uphill/downhill running, and altitude) increase this fatigue, leading to difficulty in maintaining balance.

Adaptive filtering methods for identifying cross-frequency couplings in human EEG.

Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.

Measures of spatiotemporal organization differentiate persistent from long-standing atrial fibrillation.

AIMS: This study presents an automatic diagnostic method for the discrimination between persistent and long-standing atrial fibrillation (AF) based on the surface electrocardiogram (ECG). METHODS AND RESULTS: Standard 12-lead ECG recordings were acquired in 53 patients with either persistent (N= 20) or long-standing AF (N= 33), the latter including both long-standing persistent and permanent AF. A combined frequency analysis of multiple ECG leads followed by the computation of standard complexity measures provided a method for the quantification of spatiotemporal AF organization. All possible pairs of precordial ECG leads were analysed by this method and resulting organization measures were used for automatic classification of persistent and long-standing AF signals. Correct classification rates of 84.9% were obtained, with a predictive value for long-standing AF of 93.1%. Spatiotemporal organization as measured in lateral precordial leads V5 and V6 was shown to be significantly lower during long-standing AF than persistent AF, suggesting that time-related alterations in left atrial electrical activity can be detected in the ECG. CONCLUSION: Accurate discrimination between persistent and long-standing AF based on standard surface recordings was demonstrated. This information could contribute to optimize the management of sustained AF, permitting appropriate therapeutic decisions and thereby providing substantial clinical cost savings.

Adaptive tracking of EEG oscillations.

Neuronal oscillations are an important aspect of EEG recordings. These oscillations are supposed to be involved in several cognitive mechanisms. For instance, oscillatory activity is considered a key component for the top-down control of perception. However, measuring this activity and its influence requires precise extraction of frequency components. This processing is not straightforward. Particularly, difficulties with extracting oscillations arise due to their time-varying characteristics. Moreover, when phase information is needed, it is of the utmost importance to extract narrow-band signals. This paper presents a novel method using adaptive filters for tracking and extracting these time-varying oscillations. This scheme is designed to maximize the oscillatory behavior at the output of the adaptive filter. It is then capable of tracking an oscillation and describing its temporal evolution even during low amplitude time segments. Moreover, this method can be extended in order to track several oscillations simultaneously and to use multiple signals. These two extensions are particularly relevant in the framework of EEG data processing, where oscillations are active at the same time in different frequency bands and signals are recorded with multiple sensors. The presented tracking scheme is first tested with synthetic signals in order to highlight its capabilities. Then it is applied to data recorded during a visual shape discrimination experiment for assessing its usefulness during EEG processing and in detecting functionally relevant changes. This method is an interesting additional processing step for providing alternative information compared to classical time-frequency analyses and for improving the detection and analysis of cross-frequency couplings.