Regional beat-to-beat variability of repolarization increases during ischemia and predicts imminent arrhythmias in a pig model of myocardial infarction.

Ventricular arrhythmia (VT/VF) can complicate acute myocardial ischemia (AMI). Regional instability of repolarization during AMI contributes to the substrate for VT/VF. Beat-to-beat variability of repolarization (BVR), a measure of repolarization lability increases during AMI. We hypothesized that its surge precedes VT/VF. We studied the spatial and temporal changes in BVR in relation to VT/VF during AMI. In 24 pigs, BVR was quantified on 12-lead electrocardiogram recorded at a sampling rate of 1 kHz. AMI was induced in 16 pigs by percutaneous coronary artery occlusion (MI), whereas 8 underwent sham operation (sham). Changes in BVR were assessed at 5 min after occlusion, 5 and 1 min pre-VF in animals that developed VF, and matched time points in pigs without VF. Serum troponin and ST deviation were measured. After 1 mo, magnetic resonance imaging and VT induction by programmed electrical stimulation were performed. During AMI, BVR increased significantly in inferior-lateral leads correlating with ST deviation and troponin increase. BVR was maximal 1 min pre-VF (3.78 ± 1.36 vs. 5 min pre-VF, 1.67 ± 1.56, P < 0.0001). After 1 mo, BVR was higher in MI than in sham and correlated with the infarct size (1.43 ± 0.50 vs. 0.57 ± 0.30, P = 0.009). VT was inducible in all MI animals and the ease of induction correlated with BVR. BVR increased during AMI and temporal BVR changes predicted imminent VT/VF, supporting a possible role in monitoring and early warning systems. BVR correlated to arrhythmia vulnerability suggesting utility in risk stratification post-AMI.NEW & NOTEWORTHY The key finding of this study is that BVR increases during AMI and surges before ventricular arrhythmia onset. This suggests that monitoring BVR may be useful for monitoring the risk of VF during and after AMI in the coronary care unit settings. Beyond this, monitoring BVR may have value in cardiac implantable devices or wearables.

Post-acquisition water-signal removal in 3D water-unsuppressed 1 H-MR spectroscopic imaging of the prostate.

PURPOSE: To develop a robust processing procedure of raw signals from water-unsuppressed MRSI of the prostate for the mapping of absolute tissue concentrations of metabolites. METHODS: Water-unsuppressed 3D MRSI data were acquired from a phantom, from healthy volunteers, and a patient with prostate cancer. Signal processing included sequential computation of the modulus of the FID to remove water sidebands, a Hilbert transformation, and k-space Hamming filtering. For the removal of the water signal, we compared Löwner tensor-based blind source separation (BSS) and Hankel Lanczos singular value decomposition techniques. Absolute metabolite levels were quantified with LCModel and the results were statistically analyzed to compare the water removal methods and conventional water-suppressed MRSI. RESULTS: The post-processing algorithms successfully removed the water signal and its sidebands without affecting metabolite signals. The best water removal performance was achieved by Löwner tensor-based BSS. Absolute tissue concentrations of citrate in the peripheral zone derived from water-suppressed and unsuppressed 1 H MRSI were the same and as expected from the known physiology of the healthy prostate. Maps for citrate and choline from water-unsuppressed 3D 1 H-MRSI of the prostate showed expected spatial variations in metabolite levels. CONCLUSION: We developed a robust relatively simple post-processing method of water-unsuppressed MRSI of the prostate to remove the water signal. Absolute quantification using the water signal, originating from the same location as the metabolite signals, avoids the acquisition of additional reference data.

Relating QRS voltages to left ventricular mass and body composition in elite endurance athletes.

PURPOSE: Electrocardiogram (ECG) QRS voltages correlate poorly with left ventricular mass (LVM). Body composition explains some of the QRS voltage variability. The relation between QRS voltages, LVM and body composition in endurance athletes is unknown. METHODS: Elite endurance athletes from the Pro@Heart trial were evaluated with 12-lead ECG for Cornell and Sokolow-Lyon voltage and product. Cardiac magnetic resonance imaging assessed LVM. Dual energy x-ray absorptiometry assessed fat mass (FM) and lean mass of the trunk and whole body (LBM). The determinants of QRS voltages and LVM were identified by multivariable linear regression. Models combining ECG, demographics, DEXA and exercise capacity to predict LVM were developed. RESULTS: In 122 athletes (19 years, 71.3% male) LVM was a determinant of the Sokolow-Lyon voltage and product (ß = 0.334 and 0.477, p < 0.001) but not of the Cornell criteria. FM of the trunk (ß = - 0.186 and - 0.180, p < 0.05) negatively influenced the Cornell voltage and product but not the Sokolow-Lyon criteria. DEXA marginally improved the prediction of LVM by ECG (r = 0.773 vs 0.510, p < 0.001; RMSE = 18.9 ± 13.8 vs 25.5 ± 18.7 g, p > 0.05) with LBM as the strongest predictor (ß = 0.664, p < 0.001). DEXA did not improve the prediction of LVM by ECG and demographics combined and LVM was best predicted by including VO2max (r = 0.845, RMSE = 15.9 ± 11.6 g). CONCLUSION: LVM correlates poorly with QRS voltages with adipose tissue as a minor determinant in elite endurance athletes. LBM is the strongest single predictor of LVM but only marginally improves LVM prediction beyond ECG variables. In endurance athletes, LVM is best predicted by combining ECG, demographics and VO2max.

Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis.

Data fusion refers to the joint analysis of multiple datasets that provide different (e.g., complementary) views of the same task. In general, it can extract more information than separate analyses can. Jointly analyzing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measurements has been proved to be highly beneficial to the study of the brain function, mainly because these neuroimaging modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The EEG-fMRI fusion methods that have been reported so far ignore the underlying multiway nature of the data in at least one of the modalities and/or rely on very strong assumptions concerning the relation of the respective datasets. For example, in multisubject analysis, it is commonly assumed that the hemodynamic response function is a priori known for all subjects and/or the coupling across corresponding modes is assumed to be exact (hard). In this article, these two limitations are overcome by adopting tensor models for both modalities and by following soft and flexible coupling approaches to implement the multimodal fusion. The obtained results are compared against those of parallel independent component analysis and hard coupling alternatives, with both synthetic and real data (epilepsy and visual oddball paradigm). Our results demonstrate the clear advantage of using soft and flexible coupled tensor decompositions in scenarios that do not conform with the hard coupling assumption.

Assessing Neurovascular Coupling Using Wavelet Coherence in Neonates with Asphyxia.

Brain monitoring is important in neonates with asphyxia in order to assess the severity of hypoxic ischaemic encephalopathy (HIE) and identify neonates at risk of adverse neurodevelopmental outcome. Previous studies suggest that neurovascular coupling (NVC), quantified as the interaction between electroencephalography (EEG) and near-infrared spectroscopy (NIRS)-derived regional cerebral oxygen saturation (rSO2) is a promising biomarker for HIE severity and outcome. In this study, we explore how wavelet coherence can be used to assess NVC. Wavelet coherence was computed in 18 neonates undergoing therapeutic hypothermia in the first 3 days of life, with varying HIE severities (mild, moderate, severe). We compared two pre-processing methods of the EEG prior to wavelet computation: amplitude integrated EEG (aEEG) and EEG bandpower. Furthermore, we proposed average real coherence as a biomarker for NVC. Our results indicate that NVC as assessed by wavelet coherence between EEG bandpower and rSO2 can be a valuable biomarker for HIE severity in neonates with peripartal asphyxia. More specifically, average real coherence in a very low frequency range (0.21-0.83 mHz) tends to be high (positive) in neonates with mild HIE, low (positive) in neonates with moderate HIE, and negative in neonates with severe HIE. Further investigation in a larger patient cohort is needed to validate our findings.

Neurocardiovascular coupling in congenital diaphragmatic hernia patients undergoing different types of surgical treatment.

BACKGROUND: The effect of peri-operative management on the neonatal brain is largely unknown. Triggers for perioperative brain injury might be revealed by studying changes in neonatal physiology peri-operatively. OBJECTIVE: To study neonatal pathophysiology and cerebral blood flow regulation peri-operatively using the neuro-cardiovascular graph. DESIGN: Observational, prospective cohort study on perioperative neuromonitoring. Neonates were included between July 2018 and April 2020. SETTING: Multicentre study in two high-volume tertiary university hospitals. PATIENTS: Neonates with congenital diaphragmatic hernia were eligible if they received surgical treatment within the first 28 days of life. Exclusion criteria were major cardiac or chromosomal anomalies, or syndromes associated with altered cerebral perfusion or major neurodevelopmental impairment. The neonates were stratified into different groups by type of peri-operative management. INTERVENTION: Each patient was monitored using near-infrared spectroscopy and EEG in addition to the routine peri-operative monitoring. Neurocardiovascular graphs were computed off-line. MAIN OUTCOME MEASURES: The primary endpoint was the difference in neurocardiovascular graph connectivity in the groups over time. RESULTS: Thirty-six patients were included. The intraoperative graph connectivity decreased in all patients operated upon in the operation room (OR) with sevoflurane-based anaesthesia ( P  < 0.001) but remained stable in all patients operated upon in the neonatal intensive care unit (NICU) with midazolam-based anaesthesia. Thoracoscopic surgery in the OR was associated with the largest median connectivity reduction (0.33 to 0.12, P  < 0.001) and a loss of baroreflex and neurovascular coupling. During open surgery in the OR, all regulation mechanisms remained intact. Open surgery in the NICU was associated with the highest neurovascular coupling values. CONCLUSION: Neurocardiovascular graphs provided more insight into the effect of the peri-operative management on the pathophysiology of neonates undergoing surgery. The neonate's clinical condition as well as the surgical and the anaesthesiological approach affected the neonatal physiology and CBF regulation mechanisms at different levels. TRIAL REGISTRATION: NL6972, URL: https://www.trialre-gister.nl/trial/6972 .

Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI data.

EEG-correlated fMRI analysis is widely used to detect regional BOLD fluctuations that are synchronized to interictal epileptic discharges, which can provide evidence for localizing the ictal onset zone. However, the typical, asymmetrical and mass-univariate approach cannot capture the inherent, higher order structure in the EEG data, nor multivariate relations in the fMRI data, and it is nontrivial to accurately handle varying neurovascular coupling over patients and brain regions. We aim to overcome these drawbacks in a data-driven manner by means of a novel structured matrix-tensor factorization: the single-subject EEG data (represented as a third-order spectrogram tensor) and fMRI data (represented as a spatiotemporal BOLD signal matrix) are jointly decomposed into a superposition of several sources, characterized by space-time-frequency profiles. In the shared temporal mode, Toeplitz-structured factors account for a spatially specific, neurovascular 'bridge' between the EEG and fMRI temporal fluctuations, capturing the hemodynamic response's variability over brain regions. By analyzing interictal data from twelve patients, we show that the extracted source signatures provide a sensitive localization of the ictal onset zone (10/12). Moreover, complementary parts of the IOZ can be uncovered by inspecting those regions with the most deviant neurovascular coupling, as quantified by two entropy-like metrics of the hemodynamic response function waveforms (9/12). Hence, this multivariate, multimodal factorization provides two useful sets of EEG-fMRI biomarkers, which can assist the presurgical evaluation of epilepsy. We make all code required to perform the computations available at https://github.com/svaneynd/structured-cmtf.

The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels.

OBJECTIVE: Wearable seizure detection devices could provide more reliable seizure documentation outside the hospital compared to seizure self-reporting by patients, which is the current standard. Previously, during the SeizeIT1 project, we studied seizure detection based on behind-the-ear electroencephalography (EEG). However, the obtained sensitivities were too low for practical use, because not all seizures are associated with typical ictal EEG patterns. Therefore, in this paper, we aim to develop a multimodal automated seizure detection algorithm integrating behind-the-ear EEG and electrocardiography (ECG) for detecting focal seizures. In this framework, we quantified the added value of ECG to behind-the-ear EEG. METHODS: This study analyzed three multicenter databases consisting of 135 patients having focal epilepsy and a total of 896 seizures. A patient-specific multimodal automated seizure detection algorithm was developed using behind-the-ear/temporal EEG and single-lead ECG. The EEG and ECG data were processed separately using machine learning methods. A late integration approach was applied for fusing those predictions. RESULTS: The multimodal algorithm outperformed the EEG-based algorithm in two of three databases, with an increase of 11% and 8% in sensitivity for the same false alarm rate. SIGNIFICANCE: ECG can be of added value to an EEG-based seizure detection algorithm using only behind-the-ear/temporal lobe electrodes for patients with focal epilepsy.

Supervised SVM Transfer Learning for Modality-Specific Artefact Detection in ECG.

The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating materials. However, as the ECG is no longer recorded in a controlled environment, this inevitably implies the presence of more artefacts. Artefact detection algorithms are used to detect and remove these. Typically, the training of a new algorithm requires a lot of ground truth data, which is costly to obtain. As many labelled contact ECG datasets exist, we could avoid the use of labelling new ccECG signals by making use of previous knowledge. Transfer learning can be used for this purpose. Here, we applied transfer learning to optimise the performance of an artefact detection model, trained on contact ECG, towards ccECG. We used ECG recordings from three different datasets, recorded with three recording devices. We showed that the accuracy of a contact-ECG classifier improved between 5 and 8% by means of transfer learning when tested on a ccECG dataset. Furthermore, we showed that only 20 segments of the ccECG dataset are sufficient to significantly increase the accuracy.

Capacitively-Coupled ECG and Respiration for Sleep-Wake Prediction and Risk Detection in Sleep Apnea Patients.

Obstructive sleep apnea (OSA) patients would strongly benefit from comfortable home diagnosis, during which detection of wakefulness is essential. Therefore, capacitively-coupled electrocardiogram (ccECG) and bioimpedance (ccBioZ) sensors were used to record the sleep of suspected OSA patients, in parallel with polysomnography (PSG). The three objectives were quality assessment of the unobtrusive signals during sleep, prediction of sleep-wake using ccECG and ccBioZ, and detection of high-risk OSA patients. First, signal quality indicators (SQIs) determined the data coverage of ccECG and ccBioZ. Then, a multimodal convolutional neural network (CNN) for sleep-wake prediction was tested on these preprocessed ccECG and ccBioZ data. Finally, two indices derived from this prediction detected patients at risk. The data included 187 PSG recordings of suspected OSA patients, 36 (dataset "Test") of which were recorded simultaneously with PSG, ccECG, and ccBioZ. As a result, two improvements were made compared to prior studies. First, the ccBioZ signal coverage increased significantly due to adaptation of the acquisition system. Secondly, the utility of the sleep-wake classifier increased as it became a unimodal network only requiring respiratory input. This was achieved by using data augmentation during training. Sleep-wake prediction on "Test" using PSG respiration resulted in a Cohen's kappa (κ) of 0.39 and using ccBioZ in κ = 0.23. The OSA risk model identified severe OSA patients with a κ of 0.61 for PSG respiration and κ of 0.39 using ccBioZ (accuracy of 80.6% and 69.4%, respectively). This study is one of the first to perform sleep-wake staging on capacitively-coupled respiratory signals in suspected OSA patients and to detect high risk OSA patients based on ccBioZ. The technology and the proposed framework could be applied in multi-night follow-up of OSA patients.

Classification with a Deferral Option and Low-Trust Filtering for Automated Seizure Detection.

Wearable technology will become available and allow prolonged electroencephalography (EEG) monitoring in the home environment of patients with epilepsy. Neurologists analyse the EEG visually and annotate all seizures, which patients often under-report. Visual analysis of a 24-h EEG recording typically takes one to two hours. Reliable automated seizure detection algorithms will be crucial to reduce this analysis. We investigated such algorithms on a dataset of behind-the-ear EEG measurements. Our first aim was to develop a methodology where part of the data is deferred to a human expert, who performs perfectly, with the goal of obtaining an (almost) perfect detection sensitivity (DS). Prediction confidences are determined by temperature scaling of the classification model outputs and trust scores. A DS of approximately 90% (99%) can be achieved when deferring around 10% (40%) of the data. Perfect DS can be achieved when deferring 50% of the data. Our second contribution demonstrates that a common modelling strategy, where predictions from several short EEG segments are combined to obtain a final prediction, can be improved by filtering out untrustworthy segments with low trust scores. The false detection rate shows a relative decrease between 21% and 43%, and the DS shows a small increase or decrease.

Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach.

Impedance pneumography has been suggested as an ambulatory technique for the monitoring of respiratory diseases. However, its ambulatory nature makes the recordings more prone to noise sources. It is important that such noisy segments are identified and removed, since they could have a huge impact on the performance of data-driven decision support tools. In this study, we investigated the added value of machine learning algorithms to separate clean from noisy bio-impedance signals. We compared three approaches: a heuristic algorithm, a feature-based classification model (SVM) and a convolutional neural network (CNN). The dataset consists of 47 chronic obstructive pulmonary disease patients who performed an inspiratory threshold loading protocol. During this protocol, their respiration was recorded with a bio-impedance device and a spirometer, which served as a gold standard. Four annotators scored the signals for the presence of artefacts, based on the reference signal. We have shown that the accuracy of both machine learning approaches (SVM: 87.77 ± 2.64% and CNN: 87.20 ± 2.78%) is significantly higher, compared to the heuristic approach (84.69 ± 2.32%). Moreover, no significant differences could be observed between the two machine learning approaches. The feature-based and neural network model obtained a respective AUC of 92.77±2.95% and 92.51±1.74%. These findings show that a data-driven approach could be beneficial for the task of artefact detection in respiratory thoracic bio-impedance signals.

Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions.

Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.

Visual seizure annotation and automated seizure detection using behind-the-ear electroencephalographic channels.

OBJECTIVE: Seizure diaries kept by patients are unreliable. Automated electroencephalography (EEG)-based seizure detection systems are a useful support tool to objectively detect and register seizures during long-term video-EEG recording. However, this standard full scalp-EEG recording setup is of limited use outside the hospital, and a discreet, wearable device is needed for capturing seizures in the home setting. We are developing a wearable device that records EEG with behind-the-ear electrodes. In this study, we determined whether the recognition of ictal patterns using only behind-the-ear EEG channels is possible. Second, an automated seizure detection algorithm was developed using only those behind-the-ear EEG channels. METHODS: Fifty-four patients with a total of 182 seizures, mostly temporal lobe epilepsy (TLE), and 5284 hours of data, were recorded with a standard video-EEG at University Hospital Leuven. In addition, extra behind-the-ear EEG channels were recorded. First, a neurologist was asked to annotate behind-the-ear EEG segments containing selected seizure and nonseizure fragments. Second, a data-driven algorithm was developed using only behind-the-ear EEG. This algorithm was trained using data from other patients (patient-independent model) or from the same patient (patient-specific model). RESULTS: The visual recognition study resulted in 65.7% sensitivity and 94.4% specificity. By using those seizure annotations, the automated algorithm obtained 64.1% sensitivity and 2.8 false-positive detections (FPs)/24 hours with the patient-independent model. The patient-specific model achieved 69.1% sensitivity and 0.49 FPs/24 hours. SIGNIFICANCE: Visual recognition of ictal EEG patterns using only behind-the-ear EEG is possible in a significant number of patients with TLE. A patient-specific seizure detection algorithm using only behind-the-ear EEG was able to detect more seizures automatically than what patients typically report, with 0.49 FPs/24 hours. We conclude that a large number of refractory TLE patients can benefit from using this device.

Enuretic children have a higher variability in REM sleep when comparing their sleep parameters with nonenuretic control children using a wearable sleep tracker at home.

PURPOSE: That children with nocturnal enuresis ("bedwetting") are deep sleepers is a fact that their parents often state when asking for advice. However, until today no clear difference in sleep has been observed between children who do and do not wet the bed. This study investigates the difference in sleep parameters and heart rate variability (HRV) between enuretic and control children in their home setting by using a wearable sleep tracker during a long observation period. METHODS: Twenty-one enuretic and 18 control children, aged 6 to 12 years old, slept with a wearable sleep tracker device, a Fitbit Charge 2, for 14 consecutive days. In addition, nocturnal urine production (voided volumes and/or weight of the diaper) were measured. The HRV was calculated using the standard time and frequency domain parameters. The Kruskal-Wallis test was applied to evaluate the differences in the sleep and HRV parameters between both groups. RESULTS: Compared with healthy controls, enuretic children showed a higher standard deviation (P = .0209) of minutes spent in rapid eye movement (REM) sleep among the different nights. In addition, they showed the tendencies to fewer awakenings (P = .1161), although this was not significant. Analyzing the wet nights of the enuretic children, they showed higher autonomic activity, lower sleep efficiency and a higher restlessness compared with their dry nights and to the control group. CONCLUSION: This 2-weeks sleep-study, using a wrist-worn sleep tracker device Fitbit Charge 2, in the normal home environment has shown that enuretic children have a larger variation in their REM sleep and sleepless efficiently during a wet night when compared with non-bedwetting children.

Nonlinear Transfer Entropy to Assess the Neurovascular Coupling in Premature Neonates.

In the adult brain, it is well known that increases in local neural activity trigger changes in regional blood flow and, thus, changes in cerebral energy metabolism. This regulation mechanism is called neurovascular coupling (NVC). It is not yet clear to what extent this mechanism is present in the premature brain. In this study, we explore the use of transfer entropy (TE) in order to compute the nonlinear coupling between changes in brain function, assessed by means of EEG, and changes in brain oxygenation, assessed by means of near-infrared spectroscopy (NIRS). In a previous study, we measured the coupling between both variables using a linear model to compute TE. The results indicated that changes in brain oxygenation were likely to precede changes in EEG activity. However, using a nonlinear and nonparametric approach to compute TE, the results indicate an opposite directionality of this coupling. The source of the different results provided by the linear and nonlinear TE is unclear and needs further research. In this study, we present the results from a cohort of 21 premature neonates. Results indicate that TE values computed using the nonlinear approach are able to discriminate between neonates with brain abnormalities and healthy neonates, indicating a less functional NVC in neonates with brain abnormalities.

Using Biosensors and Digital Biomarkers to Assess Response to Cardiac Rehabilitation: Observational Study.

BACKGROUND: Cardiac rehabilitation (CR) is known for its beneficial effects on functional capacity and is a key component within current cardiovascular disease management strategies. In addition, a larger increase in functional capacity is accompanied by better clinical outcomes. However, not all patients respond in a similar way to CR. Therefore, a patient-tailored approach to CR could open up the possibility to achieve an optimal increase in functional capacity in every patient. Before treatment can be optimized, the differences in response of patients in terms of cardiac adaptation to exercise should first be understood. In addition, digital biomarkers to steer CR need to be identified. OBJECTIVE: The aim of the study was to investigate the difference in cardiac response between patients characterized by a clear improvement in functional capacity and patients showing only a minor improvement following CR therapy. METHODS: A total of 129 patients in CR performed a 6-minute walking test (6MWT) at baseline and during four consecutive short-term follow-up tests while being equipped with a wearable electrocardiogram (ECG) device. The 6MWTs were used to evaluate functional capacity. Patients were divided into high- and low-response groups, based on the improvement in functional capacity during the CR program. Commonly used heart rate parameters and cardiac digital biomarkers representative of the heart rate behavior during the 6MWT and their evolution over time were investigated. RESULTS: All participating patients improved in functional capacity throughout the CR program (P<.001). The heart rate parameters, which are commonly used in practice, evolved differently for both groups throughout CR. The peak heart rate (HRpeak) from patients in the high-response group increased significantly throughout CR, while no change was observed in the low-response group (F4,92=8.321, P<.001). Similar results were obtained for the recovery heart rate (HRrec) values, which increased significantly over time during every minute of recuperation, for the high-response group (HRrec1: P<.001, HRrec2: P<.001, HRrec3: P<.001, HRrec4: P<.001, and HRrec5: P=.02). The other digital biomarkers showed that the evolution of heart rate behavior during a standardized activity test differed throughout CR between both groups. These digital biomarkers, derived from the continuous measurements, contribute to more in-depth insight into the progression of patients' cardiac responses. CONCLUSIONS: This study showed that when using wearable sensor technology, the differences in response of patients to CR can be characterized by means of commonly used heart rate parameters and digital biomarkers that are representative of cardiac response to exercise. These digital biomarkers, derived by innovative analysis techniques, allow for more in-depth insights into the cardiac response of cardiac patients during standardized activity. These results open up the possibility to optimized and more patient-tailored treatment strategies and to potentially improve CR outcome.

Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation.

Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for clinical acceptance, easy interpretability of the AI models is crucial. The goal of the present study was to investigate whether a multi-parameter sensor could be used during a standardized activity test to interpret functional capacity in the longitudinal follow-up of CR patients. A total of 129 patients were followed for 3 months during CR using 6-min walking tests (6MWT) equipped with a wearable ECG and accelerometer device. Functional capacity was assessed based on 6MWT distance (6MWD). Linear and nonlinear interpretable models were explored to predict 6MWD. The t-distributed stochastic neighboring embedding (t-SNE) technique was exploited to embed and visualize high dimensional data. The performance of support vector machine (SVM) models, combining different features and using different kernel types, to predict functional capacity was evaluated. The SVM model, using chronotropic response and effort as input features, showed a mean absolute error of 42.8 m (±36.8 m). The 3D-maps derived using the t-SNE technique visualized the relationship between sensor-derived biomarkers and functional capacity, which enables tracking of the evolution of patients throughout the CR program. The current study showed that wearable monitoring combined with interpretable ML can objectively track clinical progression in a CR population. These results pave the road towards ambulatory CR.

Sensorimotor cortex neurometabolite levels as correlate of motor performance in normal aging: evidence from a 1H-MRS study.

Aging is associated with gradual alterations in the neurochemical characteristics of the brain, which can be assessed in-vivo with proton-magnetic resonance spectroscopy (1H-MRS). However, the impact of these age-related neurochemical changes on functional motor behavior is still poorly understood. Here, we address this knowledge gap and specifically focus on the neurochemical integrity of the left sensorimotor cortex (SM1) and the occipital lobe (OCC), as both regions are main nodes of the visuomotor network underlying bimanual control. 1H-MRS data and performance on a set of bimanual tasks were collected from a lifespan (20-75 years) sample of 86 healthy adults. Results indicated that aging was accompanied by decreased levels of N-acetylaspartate (NAA), glutamate-glutamine (Glx), creatine â€‹+ â€‹phosphocreatine (Cr) and myo-inositol (mI) in both regions, and decreased Choline (Cho) in the OCC region. Lower NAA and Glx levels in the SM1 and lower NAA levels in the OCC were related to poorer performance on a visuomotor bimanual coordination task, suggesting that NAA could serve as a potential biomarker for the integrity of the motor system supporting bimanual control. In addition, lower NAA, Glx, and mI levels in the SM1 were found to be correlates of poorer dexterous performance on a bimanual dexterity task. These findings highlight the role for 1H-MRS to study neurochemical correlates of motor performance across the adult lifespan.

Ictal autonomic changes as a tool for seizure detection: a systematic review.

PURPOSE: Adequate epileptic seizure detection may have the potential to minimize seizure-related complications and improve treatment evaluation. Autonomic changes often precede ictal electroencephalographic discharges and therefore provide a promising tool for timely seizure detection. We reviewed the literature for seizure detection algorithms using autonomic nervous system parameters. METHODS: The PubMed and Embase databases were systematically searched for original human studies that validate an algorithm for automatic seizure detection based on autonomic function alterations. Studies on neonates only and pilot studies without performance data were excluded. Algorithm performance was compared for studies with a similar design (retrospective vs. prospective) reporting both sensitivity and false alarm rate (FAR). Quality assessment was performed using QUADAS-2 and recently reported quality standards on reporting seizure detection algorithms. RESULTS: Twenty-one out of 638 studies were included in the analysis. Fifteen studies presented a single-modality algorithm based on heart rate variability (n = 10), heart rate (n = 4), or QRS morphology (n = 1), while six studies assessed multimodal algorithms using various combinations of HR, corrected QT interval, oxygen saturation, electrodermal activity, and accelerometry. Most studies had small sample sizes and a short follow-up period. Only two studies performed a prospective validation. A tendency for a lower FAR was found for retrospectively validated algorithms using multimodal autonomic parameters compared to those using single modalities (mean sensitivity per participant 71-100% vs. 64-96%, and mean FAR per participant 0.0-2.4/h vs. 0.7-5.4/h). CONCLUSIONS: The overall quality of studies on seizure detection using autonomic parameters is low. Unimodal autonomic algorithms cannot reach acceptable performance as false alarm rates are still too high. Larger prospective studies are needed to validate multimodal automatic seizure detection.