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With the increasing use of wearable healthcare devices for remote patient monitoring, reliable signal quality assessment (SQA) is required to ensure the high accuracy of interpretation and diagnosis on the recorded data from patients. Photoplethysmographic (PPG) signals non-invasively measured by wearable devices are extensively used to provide information about the cardiovascular system and its associated diseases. In this study, we propose an approach to optimize the quality assessment of the PPG signals.We used an ensemble-based feature selection scheme to enhance the prediction performance of the classification model to assess the quality of the PPG signals. Our approach for feature and subset size selection yielded the best-suited feature subset, which was optimized to differentiate between the clean and artifact corrupted PPG segments.A high discriminatory power was achieved between two classes on the test data by the proposed feature selection approach, which led to strong performance on all dependent and independent test datasets. We achieved accuracy, sensitivity, and specificity rates of higher than 0.93, 0.89, and 0.97, respectively, for dependent test datasets, independent of heartbeat type, i.e., atrial fibrillation (AF) or non-AF data including normal sinus rhythm (NSR), premature atrial contraction (PAC), and premature ventricular contraction (PVC). For independent test datasets, accuracy, sensitivity, and specificity rates were greater than 0.93, 0.89, and 0.97, respectively, on PPG data recorded from AF and non-AF subjects. These results were found to be more accurate than those of all of the contemporary methods cited in this work.As the results illustrate, the advantage of our proposed scheme is its robustness against dynamic variations in the PPG signal during long-term 14-day recordings accompanied with different types of physical activities and a diverse range of fluctuations and waveforms caused by different individual hemodynamic characteristics, and various types of recording devices. This robustness instills confidence in the application of the algorithm to various kinds of wearable devices as a reliable PPG signal quality assessment approach.

Wrist-based wearables have been FDA approved for AF detection. However, the health behavior impact of false AF alerts from wearables on older patients at high risk for AF are not known. In this work, we analyzed data from the Pulsewatch (NCT03761394) study, which randomized patients (≥ 50 years) with history of stroke or transient ischemic attack to wear a patch monitor and a smartwatch linked to a smartphone running the Pulsewatch application vs to only the cardiac patch monitor over 14 days. At baseline and 14 days, participants completed validated instruments to assess for anxiety, patient activation, perceived mental and physical health, chronic symptom management self-efficacy, and medicine adherence. We employed linear regression to examine associations between false AF alerts with change in patient-reported outcomes. Receipt of false AF alerts was related to a dose-dependent decline in self-perceived physical health and levels of disease self-management. We developed a novel convolutional denoising autoencoder (CDA) to remove motion and noise artifacts in photoplethysmography (PPG) segments to optimize AF detection, which substantially reduced the number of false alerts. A promising approach to avoid negative impact of false alerts is to employ artificial intelligence driven algorithms to improve accuracy.

BACKGROUND Atrial fibrillation (AF) prevalence increases with age and may lead to stroke. Therefore, older adults are a group that may benefit most from AF screening. Yet older adult populations tend to lag younger groups in terms of adoption of, and comfort with, the use of mobile health applications. Furthermore, although mobile apps that can detect AF are available to the public, most of them are designed for intermittent AF detection and for younger users. No app designed for long-term AF monitoring has released detailed system design specifications that can handle especially large data collection, especially in this age group. OBJECTIVE In collaboration with AF patients and their medical providers, this study aimed to design an innovative smartwatch-based AF monitoring mHealth solution for the older adult population. METHODS Based on functionality that patients and caregivers suggested, we designed the Pulsewatch system. The resulting system consisted of linked smartwatch and smartphone applications, a website for data verification, and user data organization on a cloud server. The smartwatch in the Pulsewatch system continuously monitored the pulse rate with embedded AF detection algorithms, and the smartphone in the Pulsewatch system serves as the data transferring hub to our cloud storage server. The user interface of the smartwatch and smartphone apps were specifically designed for older adults at risk for AF. We improved our Pulsewatch system based on the feedback from focus groups consisting of stroke patients and healthcare providers. The Pulsewatch system was used by the intervention group for up to 6 weeks in the two Phases of our randomized clinical trial (NCT03761394). RESULTS At the conclusion of Phase 1, 90 trial participants who had used Pulsewatch app and smartwatch for 14 days completed a System Usability Scale (SUS) to assess the usability of the Pulsewatch system; 64% of participants endorsed that the smartwatch app is “easy to use.” For Phases 1 and 2 of the study, we collected 9224.4 hours of watch recordings from participants. The longest recording streak in Phase 2 was 21 days of consecutive recording out of the 30 days of data collection. CONCLUSIONS This is one of the first studies to provide a detailed design for a smartphone-smartwatch dyad for ambulatory AF monitoring. In it, we report on the system’s usability and opportunities to increase the acceptability of mHealth solutions among older patients with cognitive impairment. CLINICALTRIAL ClinicalTrials.gov NCT03761394 INTERNATIONAL REGISTERED REPORT RR2-10.1016/j.cvdhj.2021.07.002

Background: Tinnitus known as a central nervous system disorder is correlated with specific oscillatory activities within auditory and non-auditory brain areas. Several studies in the past few years have revealed that in the most tinnitus cases, the response pattern of neurons in auditory system is changed due to auditory deafferentation, which leads to variation and disruption of the brain networks. Objective: In this paper, we introduce an approach to automatically distinguish tinnitus individuals from healthy controls based on whole-brain functional connectivity and network analysis. Material and Methods: The functional connectivity analysis was applied to the resting state electroencephalographic (EEG) data of both groups using Weighted Phase Lag Index (WPLI) for various frequency bands in 2-44 Hz frequency range. In this case- control study, the classification was performed on graph theoretical measures using support vector machine (SVM) as a robust classification method. Results: Experimental results showed promising classification performance with a high accuracy, sensitivity, and specificity in all frequency bands, specifically in the beta2 frequency band. Conclusion: The current study provides substantial evidence that tinnitus network can be successfully detected by consistent measures of the brain networks based on EEG functional connectivity.

We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincar&eacute, plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC.

Methamphetamine (meth) is potently addictive and is closely linked to high crime rates in the world. Since meth withdrawal is very painful and difficult, most abusers relapse to abuse in traditional treatments. Therefore, developing accurate data-driven methods based on brain functional connectivity could be helpful in classifying and characterizing the neural features of meth dependence to optimize the treatments. Accordingly, in this study, computation of functional connectivity using resting-state EEG was used to classify meth dependence. Firstly, brain functional connectivity networks (FCNs) of 36 meth dependent individuals and 24 normal controls were constructed by weighted phase lag index, in six frequency bands: delta (1–4 Hz), theta (4–8 Hz), alpha (8–15 Hz), beta (15–30 Hz), gamma (30–45 Hz) and wideband (1–45 Hz).Then, significant differences in graph metrics and connectivity values of the FCNs were used to distinguish the two groups. Support vector machine classifier had the best performance with 93% accuracy, 100% sensitivity, 83% specificity and 0.94 F-score for differentiating between MDIs and NCs. The best performance yielded when selected features were the combination of connectivity values and graph metrics in the beta frequency band.

Event-Related Potentials (ERPs) have been used in addiction studies to evaluate cognitive performance and craving in individuals with Substance Use Dependence (SUD). This paper reviews studies that used ERPs to investigate cue reactivity, inhibitory control and error processing in SUDs. Five abused substances are included in the investigation, i.e. alcohol, nicotine, cannabis, cocaine, and methamphetamine. For each substance, the main recent findings related to the ERPs are specifically discussed, according to the latency of ERPs. The results show that individuals with SUDs allocate more attention resources to the cognitive processing of substance-related cues, indexed by increased amplitude of middle and late latency ERPs. Laboratory observations also show amplitude enlargement for early latency ERPs. SUDs reveal a deficiency in the inhibitory control and conscious error processing, indexed by attenuated N2 and Pe amplitude. The cognitive and motor inhibitory component (P3) changes show a controversial result. This study expands the findings of previous related reviews implying that substance abusers allocate more attentional resources to drug cues indexed by enlarged P3 and LPP amplitude. Regarding P3 elicited in inhibitory control tasks, there is not still convergent results, while N2 and Pe become attenuated as reported in previous reviews. The outcomes also show that the chance of relapse to substance abuse could be predicted by recording ERPs reflecting inhibitory control and error processing.

This study aimed to examine the effects of chronic methamphetamine use on the topological organization of whole-brain functional connectivity network (FCN) by reconstruction of neural-activity time series at resting-state. The EEG of 36 individuals with methamphetamine use disorder (IWMUD) and 24 normal controls (NCs) were recorded, pre-processed and source-reconstructed using standardized low-resolution tomography (sLORETA). The brain FCNs of participants were constructed and between-group differences in network topological properties were investigated using graph theoretical analysis. IWMUD showed decreased characteristic path length, increased clustering coefficient and small-world index at delta and gamma frequency bands compared to NCs. Moreover, abnormal changes in inter-regional connectivity and network hubs were observed in all the frequency bands. The results suggest that the IWMUD and NCs have distinct FCNs at all the frequency bands, particularly at the delta and gamma bands, in which deviated small-world brain topology was found in IWMUD.

Tinnitus is defined as the auditory phantom perception in the absence of any objective external sound source. In this paper, we used the resting-state electroencephalography (EEG) data to reconstruct the neural sources based on the unit-noise-gain linearly constrained minimum-variance (LCMV) beamformer and applied the effective connectivity analysis on the reconstructed sources to examine the directional neuronal interactions between brain regions in tinnitus patients compared to the healthy controls. We found significantly disrupted patterns of effective connectivity in several brain areas including the frontal, temporal, and occipital cortices as well as the caudate nucleus. Particularly, significant aberrant causal couplings were observed in the orbitofrontal cortex, inferior frontal gyrus_triangular, and parahippocampal region that could potentially illustrate the auditory information retrieval, perception, and evaluation of the phantom sound in the brain of tinnitus patients. Furthermore, topological alterations of the brain network were investigated using graph theoretical analysis. Our findings demonstrated significantly decreased both global integration and segregation of the brain network in tinnitus patients accompanied by the topological shift of tinnitus network to a more random structure in the high-frequency bands. These findings were consistent with the hypothesis of the brain network deviation from small-worldness topology accompanied by reduced global integration in brain-related disorders.

Background: Brain source imaging based on electroencephalogram (EEG) data aims to recover the neuron populations’ activity producing the scalp potentials. This procedure is known as the EEG inverse problem. Recently, beamformers have gained a lot of consideration in the EEG inverse problem. Objective: Beamformers lack acceptable performance in the case of correlated brain sources. These sources happen when some regions of the brain have simultaneous or correlated activities such as auditory stimulation or moving left and right extremities of the body at the same time. In this paper, we have developed a multichannel beamformer robust to correlated sources. Material and Methods: In this simulation study, we have looked at the problem of brain source imaging and beamforming from a blind source separation point of view. We focused on the spatially constraint independent component analysis (scICA) algorithm, which generally benefits from the pre-known partial information of mixing matrix, and modified the steps of the algorithm in a way that makes it more robust to correlated sources. We called the modified scICA algorithm Multichannel ICA based EEG Beamformer (MIEB). Results: We evaluated the proposed algorithm on simulated EEG data and compared its performance quantitatively with three algorithms: scICA, linearly-constrained minimum-variance (LCMV) and Dual-Core beamformers; it is considered that the latter is specially designed to reconstruct correlated sources. Conclusion: The MIEB algorithm has much better performance in terms of normalized mean squared error in recovering the correlated/uncorrelated sources both in noise free and noisy synthetic EEG signals. Therefore, it could be used as a robust beamformer in recovering correlated brain sources.

Sparse component analysis (SCA) is a popular method for addressing underdetermined blind source separation in array signal processing applications. We are motivated by problems that arise in the applications where the sources are densely sparse (i.e. the number of active sources is high and very close to the number of sensors). The separation performance of current underdetermined source recovery (USR) solutions, including the relaxation and greedy families, reduces with decreasing the mixing system dimension and increasing the sparsity level (k). In this paper, we present a k-SCA-based algorithm that is suitable for USR in low-dimensional mixing systems. Assuming the sources is at most $$(m-1$$ ) sparse where m is the number of mixtures; the proposed method is capable of recovering the sources from the mixtures given the mixing matrix using a subspace detection framework. Simulation results show that the proposed algorithm achieves better separation performance in k-SCA conditions compared to state-of-the-art USR algorithms such as basis pursuit, minimizing norm-L1, smoothed L0, focal underdetermined system solver and orthogonal matching pursuit.

Deformation of neuron structure can induce abnormalities in action potential propagation in nervous system, which is a potential threat from viewpoint of medical science. The effect of geometrical changes and deformation of neuron structure on the propagation of action potential has been studied theoretically. The theoretical model is based on modified cable equation considering spatial changes of the neuron structure, incorporating the different ionic currents' components. The results of our analysis reveal that the morphology of the neuron has a significant effect on properties of action potential propagation in the neuron. We have shown that alteration of the velocity of propagation, and the broadening of action potential pulses directly correspond to the changes in the morphology of neuron fiber. The rates of changes in the above mentioned electrophysiological parameters accord with the rate and type of shape deformation.

It is known that the transformations estimated in most of the non-rigid image registration methods are not invertible. This means that the transformation is not one-to-one and also it defines the deformation only in one direction making the registration methods become inconsistent. The symmetric image registration method is a solution to constrain the estimated transformation to become more consistent. This technique promotes the quality of registration significantly. In this paper we present symmetric non-rigid image registration method using multi-scale approach, defining the registration progressively in four different image scales which used result of the previous level as the initial condition for the next level. Our proposed algorithm uses a symmetric cost function which includes forward and backward transformations. Regularization term is used for both forward and reverse deformation fields to control the spatial behavior of the transformation. The results show improving the image similarity and consistency compared to asymmetric methods.