A multi-layer monitoring system for clinical management of Congestive Heart Failure

BACKGROUND: Congestive Heart Failure (CHF) is a serious cardiac condition that brings high risks of urgent hospitalization and death. Remote monitoring systems are well-suited to managing patients suffering from CHF, and can reduce deaths and re-hospitalizations, as shown by the literature, including multiple systematic reviews. METHODS: The monitoring system proposed in this paper aims at helping CHF stakeholders make appropriate decisions in managing the disease and preventing cardiac events, such as decompensation, which can lead to hospitalization or death. Monitoring activities are stratified into three layers: scheduled visits to a hospital following up on a cardiac event, home monitoring visits by nurses, and patient's self-monitoring performed at home using specialized equipment. Appropriate hardware, desktop and mobile software applications were developed to enable a patient's monitoring by all stakeholders. For the first two layers, we designed and implemented a Decision Support System (DSS) using machine learning (Random Forest algorithm) to predict the number of decompensations per year and to assess the heart failure severity based on a variety of clinical data. For the third layer, custom-designed sensors (the Blue Scale system) for electrocardiogram (EKG), pulse transit times, bio-impedance and weight allowed frequent collection of CHF-related data in the comfort of the patient's home. We also performed a short-term Heart Rate Variability (HRV) analysis on electrocardiograms self-acquired by 15 healthy volunteers and compared the obtained parameters with those of 15 CHF patients from PhysioNet's PhysioBank archives. RESULTS: We report numerical performances of the DSS, calculated as multiclass accuracy, sensitivity and specificity in a 10-fold cross-validation. The obtained average accuracies are: 71.9% in predicting the number of decompensations and 81.3% in severity assessment. The most serious class in severity assessment is detected with good sensitivity and specificity (0.87 / 0.95), while, in predicting decompensation, high specificity combined with good sensitivity prevents false alarms. The HRV parameters extracted from the self-measured EKG using the Blue Scale system of sensors are comparable with those reported in the literature about healthy people. CONCLUSIONS: The performance of DSSs trained with new patients confirmed the results of previous work, and emphasizes the strong correlation between some CHF markers, such as brain natriuretic peptide (BNP) and ejection fraction (EF), with the outputs of interest. Comparing HRV parameters from healthy volunteers with HRV parameters obtained from PhysioBank archives, we confirm the literature that considers the HRV a promising method for distinguishing healthy from CHF patients

Pulse transit time measured by photoplethysmography improves the accuracy of heart rate as a surrogate measure of cardiac output, stroke volume and oxygen uptake in response to graded exercise

Heart rate (HR) is a valuable and widespread measure for physical training programs, although its description of conditioning is limited to the cardiac response to exercise. More comprehensive measures of exercise adaptation include cardiac output ((Q) over dot), stroke volume (SV) and oxygen uptake ((V) over dotO(2)), but these physiological parameters can be measured only with cumbersome equipment installed in clinical settings. In this work, we explore the ability of pulse transit time (PTT) to represent a valuable pairing with HR for indirectly estimating (Q) over dot, SV and (V) over dotO(2) non-invasively. PTT was measured as the time interval between the peak of the electrocardiographic (ECG) R-wave and the onset of the photoplethysmography (PPG) waveform at the periphery (i.e. fingertip) with a portable sensor. Fifteen healthy young subjects underwent a graded incremental cycling protocol after which HR and PTT were correlated with (Q) over dot, SV and (V) over dotO(2) using linear mixed models. The addition of PTT significantly improved the modeling of (Q) over dot, SV and (V) over dotO(2) at the individual level (R-1(2) = 0.419 for SV, 0.548 for (Q) over dot, and 0.771 for (V) over dotO(2)) compared to predictive models based solely on HR (R-1(2) = 0.379 for SV, 0.503 for (Q) over dot, and 0.745 for (V) over dotO(2)). While challenges in sensitivity and artifact rejection exist, combining PTT with HR holds potential for development of novel wearable sensors that provide exercise assessment largely superior to HR monitors

Standardising an infant fNIRS analysis pipeline to investigate neurodevelopment in global health

Data analysis methods for infant fNIRS data in global health are not standardised yet. This work proposes an analysis pipeline that improves the quality of the recovered HRF for use by other researchers in this field

Self-administered transcranial direct current stimulation treatment of knee osteoarthritis alters pain-related fNIRS connectivity networks

Epub 2023 Mar 31Significance: Knee osteoarthritis (OA) is a disease that causes chronic pain in the elderly population. Currently, OA is mainly treated pharmacologically with analgesics, although research has shown that neuromodulation via transcranial direct current stimulation (tDCS) may be beneficial in reducing pain in clinical settings. However, no studies have reported the effects of home-based self-administered tDCS on functional brain networks in older adults with knee OA. Aim: We used functional near-infrared spectroscopy (fNIRS) to investigate the functional connectivity effects of tDCS on underlying pain processing mechanisms at the central nervous level in older adults with knee OA. Approach: Pain-related brain connectivity networks were extracted using fNIRS at baseline and for three consecutive weeks of treatment from 120 subjects randomly assigned to two groups undergoing active tDCS and sham tDCS. Results: Our results showed that the tDCS intervention significantly modulated pain-related connectivity correlation only in the group receiving active treatment. We also found that only the active treatment group showed a significantly reduced number and strength of functional connections evoked during nociception in the prefrontal cortex, primary motor (M1), and primary somatosensory (S1) cortices. To our knowledge, this is the first study in which the effect of tDCS on pain-related connectivity networks is investigated using fNIRS. Conclusions: fNIRS-based functional connectivity can be effectively used to investigate neural circuits of pain at the cortical level in association with nonpharmacological, self-administered tDCS treatment.S.M.H. and L.P. would like to acknowledge the support of the National Science Foundation (Grant Nos. CNS 1650536 and 2137255) and I/UCRC for Building Reliable Advances and Innovation in Neurotechnology. LP also acknowledges the U.S. Fulbright Scholar Program and the Fulbright Spain Commission for sponsoring his stay at the Basque Center on Cognition, Brain and Language. The research reported in this publication was supported by the National Institute of Nursing Research of the National Institutes of Health (Award No. R15NR018050)

Human central auditory plasticity: A review of functional nearâ infrared spectroscopy (fNIRS) to measure cochlear implant performance and tinnitus perception

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146915/1/lio2185_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146915/2/lio2185.pd

Speech-evoked activation in adult temporal cortex measured using functional near-infrared spectroscopy (fNIRS): Are the measurements reliable?

Functional near-infrared spectroscopy (fNIRS) is a silent, non-invasive neuroimaging technique that is potentially well suited to auditory research. However, the reliability of auditory-evoked activation measured using fNIRS is largely unknown. The present study investigated the test-retest reliability of speech-evoked fNIRS responses in normally-hearing adults. Seventeen participants underwent fNIRS imaging in two sessions separated by three months. In a block design, participants were presented with auditory speech, visual speech (silent speechreading), and audiovisual speech conditions. Optode arrays were placed bilaterally over the temporal lobes, targeting auditory brain regions. A range of established metrics was used to quantify the reproducibility of cortical activation patterns, as well as the amplitude and time course of the haemodynamic response within predefined regions of interest. The use of a signal processing algorithm designed to reduce the influence of systemic physiological signals was found to be crucial to achieving reliable detection of significant activation at the group level. For auditory speech (with or without visual cues), reliability was good to excellent at the group level, but highly variable among individuals. Temporal-lobe activation in response to visual speech was less reliable, especially in the right hemisphere. Consistent with previous reports, fNIRS reliability was improved by averaging across a small number of channels overlying a cortical region of interest. Overall, the present results confirm that fNIRS can measure speech-evoked auditory responses in adults that are highly reliable at the group level, and indicate that signal processing to reduce physiological noise may substantially improve the reliability of fNIRS measurements

Anhedonia and reward-circuit connectivity distinguish nonresponders from responders to dorsomedial prefrontal rTMS in major depression

Background Depression is a heterogeneous mental illness. Neurostimulation treatments, by targeting specific nodes within the brain’s emotion-regulation network, may be useful both as therapies and as probes for identifying clinically relevant depression subtypes. Methods Here, we applied 20 sessions of magnetic resonance imaging-guided repetitive transcranial magnetic stimulation (rTMS) to the dorsomedial prefrontal cortex in 47 unipolar or bipolar patients with a medication-resistant major depressive episode. Results Treatment response was strongly bimodal, with individual patients showing either minimal or marked improvement. Compared with responders, nonresponders showed markedly higher baseline anhedonia symptomatology (including pessimism, loss of pleasure, and loss of interest in previously enjoyed activities) on item-by-item examination of Beck Depression Inventory-II and Quick Inventory of Depressive Symptomatology ratings. Congruently, on baseline functional magnetic resonance imaging, nonresponders showed significantly lower connectivity through a classical reward pathway comprising ventral tegmental area, striatum, and a region in ventromedial prefrontal cortex. Responders and nonresponders also showed opposite patterns of hemispheric lateralization in the connectivity of dorsomedial and dorsolateral regions to this same ventromedial region. Conclusions The results suggest distinct depression subtypes, one with preserved hedonic function and responsive to dorsomedial rTMS and another with disrupted hedonic function, abnormally lateralized connectivity through ventromedial prefrontal cortex, and unresponsive to dorsomedial rTMS. Future research directly comparing the effects of rTMS at different targets, guided by neuroimaging and clinical presentation, may clarify whether hedonia/reward circuit integrity is a reliable marker for optimizing rTMS target selection

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients. Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI

Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions

Plasticity in bilateral superior temporal cortex: effects of deafness and cochlear implantation on auditory and visual speech processing

While many individuals can benefit substantially from cochlear implantation, the ability to perceive and understand auditory speech with a cochlear implant (CI) remains highly variable amongst adult recipients. Importantly, auditory performance with a CI cannot be reliably predicted based solely on routinely obtained information regarding clinical characteristics of the CI candidate. This review argues that central factors, notably cortical function and plasticity, should also be considered as important contributors to the observed individual variability in CI outcome. Superior temporal cortex (STC), including auditory association areas, plays a crucial role in the processing of auditory and visual speech information. The current review considers evidence of cortical plasticity within bilateral STC, and how these effects may explain variability in CI outcome. Furthermore, evidence of audio-visual interactions in temporal and occipital cortices is examined, and relation to CI outcome is discussed. To date, longitudinal examination of changes in cortical function and plasticity over the period of rehabilitation with a CI has been restricted by methodological challenges. The application of functional near-infrared spectroscopy (fNIRS) in studying cortical function in CI users is becoming increasingly recognised as a potential solution to these problems. Here we suggest that fNIRS offers a powerful neuroimaging tool to elucidate the relationship between audio-visual interactions, cortical plasticity during deafness and following cochlear implantation, and individual variability in auditory performance with a CI