A New Framework to Estimate Breathing Rate from Electrocardiogram, Photoplethysmogram, and Blood Pressure Signals

Breathing Rate (BR) is a key physiological parameter measured in a wide range of clinical settings. However, it is still widely measured manually. In this paper, a novel framework is proposed to estimate the BR from an electrocardiogram (ECG), a photoplethysmogram (PPG), or a blood pressure (BP) signal. The framework uses Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) methods to extract respiratory signals, taking advantage of both time and frequency domain information. An Extended Kalman Filter (EKF), incorporating a Signal Quality Index (SQI), enabled our method to achieve acceptable performance even for significantly distorted periods of the signals. Using state vector fusion, the output signals are combined and finally the BR is estimated. The framework was tested on two publicly available clinical databases: the MIT-BIH Polysomnographic and BIDMC databases. Performance was evaluated using the mean absolute percentage error (MAPE). The results indicated high accuracy: MAPEs on the two databases of 3.9% and 3.6% for ECG signals, 6.0% for PPG, and 5.0% for BP signals. The results also indicated high robustness to noise down to 0dB. Therefore, this framework may have utility for BR monitoring in high noise settings

Extracting Prior Knowledge from Data Distribution to Migrate from Blind to Semi-Supervised Clustering

Although many studies have been conducted to improve the clustering efficiency, most of the state-of-art schemes suffer from the lack of robustness and stability. This paper is aimed at proposing an efficient approach to elicit prior knowledge in terms of must-link and cannot-link from the estimated distribution of raw data in order to convert a blind clustering problem into a semi-supervised one. To estimate the density distribution of data, Wiebull Mixture Model (WMM) is utilized due to its high flexibility. Another contribution of this study is to propose a new hill and valley seeking algorithm to find the constraints for semi-supervise algorithm. It is assumed that each density peak stands on a cluster center; therefore, neighbor samples of each center are considered as must-link samples while the near centroid samples belonging to different clusters are considered as cannot-link ones. The proposed approach is applied to a standard image dataset (designed for clustering evaluation) along with some UCI datasets. The achieved results on both databases demonstrate the superiority of the proposed method compared to the conventional clustering methods

A combinatorial deep learning structure for precise depth of anesthesia estimation from EEG signals

Electroencephalography (EEG) is commonly used to measure the depth of anesthesia (DOA) because EEG reflects surgical pain and state of the brain. However, precise and real-time estimation of DOA index for painful surgical operations is challenging due to problems such as postoperative complications and accidental awareness. To tackle these problems, we propose a new combinatorial deep learning structure involving convolutional neural networks (inspired by the inception module), bidirectional long short-term memory, and an attention layer. The proposed model uses the EEG signal to continuously predicts the bispectral index (BIS). It is trained over a large dataset, mostly from those under general anesthesia with few cases receiving sedation/analgesia and spinal anesthesia. Despite the imbalance distribution of BIS values in different levels of anesthesia, our proposed structure achieves convincing root mean square error of 5.59 ± 1.04 and mean absolute error of 4.3 ± 0.87, as well as improvement in area under the curve of 15% on average, which surpasses state-of-the-art DOA estimation methods. The DOA values are also discretized into four levels of anesthesia and the results demonstrate strong inter-subject classification accuracy of 88.7% that outperforms the conventional methods

Quantification of sEMG signals for automated muscle fatigue detection using nonlinear SVM

Fatigue is a multidimensional and subjective concept and is a complex phenomenon including various causes, mechanisms and forms of manifestation. Thus, it is crucial to delineate the different levels and to quantify selfperceived fatigue. The aim of this study was to introduce a method for automatic quantification and detection of muscle fatigue using surface EMG signals. Thus, sEMG signals from right sternocleidomastoid muscle of 9 healthy female subjects were recorded during neck flexion endurance test in Quaem hospital. Then six features in time, frequency and time- scale domains were extracted from signals. After dimensionality estimation and reduction, the SVM classifier was applied to the resulted feature vector. Then, the performance of linear SVM and nonlinear SVM with RBF kernel and the effect of show that the best accuracy is achieved using RBF kernel SVM with features using LLE criterion, were RMS, ZC and AIF. These results suggest that the selected features contained some information that could be used by nonlinear SVM with RBF kernel to best discriminate between fatigue and nonfatigue stages.    </p

Novel mutation identification and copy number variant detection via exome sequencing in congenital muscular dystrophy.

BACKGROUND: Congenital muscular dystrophy type 1A (MDC1A), also termed merosin-deficient congenital muscular dystrophy (CMD), is a severe form of CMD caused by mutations in the laminin α2 gene (LAMA2). Of the more than 300 likely pathogenic variants found in the Leiden Open Variant Database, the majority are truncating mutations leading to complete LAMA2 loss of function, but multiple copy number variants (CNVs) have also been reported with variable frequency. METHODS: We collected a cohort of individuals diagnosed with likely MDC1A and sought to identify both single nucleotide variants and small and larger CNVs via exome sequencing by extending the analysis of sequencing data to detect splicing changes and CNVs. RESULTS: Standard exome analysis identified multiple novel LAMA2 variants in our cohort, but only four cases carried biallelic variants. Since likely truncating LAMA2 variants are often found in heterozygosity without a second allele, we performed additional splicing and CNV analysis on exome data and identified one splice change outside of the canonical sequences and three CNVs, in the remaining four cases. CONCLUSIONS: Our findings support the expectation that a portion of MDC1A cases may be caused by at least one CNV allele and show how these changes can be effectively identified by additional analysis of existing exome data

BVVL/ FL: features caused by SLC52A3 mutations; WDFY4 and TNFSF13B may be novel causative genes

Brown-Vialetto-Van Laere (BVVL) and Fazio-Londe are disorders with amyotrophic lateral sclerosis-like features, usually with recessive inheritance. We aimed to identify causative mutations in 10 probands. Neurological examinations, genetic analysis, audiometry, magnetic resonance imaging, biochemical and immunological testings, and/or muscle histopathology were performed. Mutations in known causative gene SLC52A3 were found in 7 probands. More importantly, only 1 mutated allele was observed in several patients, and variable expressivity and incomplete penetrance were clearly noted. Environmental insults may contribute to variable presentations. Putative causative mutations in other genes were identified in 3 probands. Two of the genes, WDFY4 and TNFSF13B, have immune-related functions. Inflammatory responses were implicated in the patient with the WDFY4 mutation. Malfunction of the immune system and mitochondrial anomalies were shown in the patient with the TNFSF13B mutation. Prevalence of heterozygous SLC52A3 BVVL causative mutations and notable variability in expressivity of homozygous and heterozygous genotypes are being reported for the first time. Identification of WDFY4 and TNFSF13B as candidate causative genes supports conjectures on involvement of the immune system in BVVL and amyotrophic lateral sclerosis

Change in brain activity through virtual reality-based brain-machine communication in a chronic tetraplegic subject with muscular dystrophy

<p>Abstract</p> <p>Background</p> <p>For severely paralyzed people, a brain-computer interface (BCI) provides a way of re-establishing communication. Although subjects with muscular dystrophy (MD) appear to be potential BCI users, the actual long-term effects of BCI use on brain activities in MD subjects have yet to be clarified. To investigate these effects, we followed BCI use by a chronic tetraplegic subject with MD over 5 months. The topographic changes in an electroencephalogram (EEG) after long-term use of the virtual reality (VR)-based BCI were also assessed. Our originally developed BCI system was used to classify an EEG recorded over the sensorimotor cortex in real time and estimate the user's motor intention (MI) in 3 different limb movements: feet, left hand, and right hand. An avatar in the internet-based VR was controlled in accordance with the results of the EEG classification by the BCI. The subject was trained to control his avatar via the BCI by strolling in the VR for 1 hour a day and then continued the same training twice a month at his home.</p> <p>Results</p> <p>After the training, the error rate of the EEG classification decreased from 40% to 28%. The subject successfully walked around in the VR using only his MI and chatted with other users through a voice-chat function embedded in the internet-based VR. With this improvement in BCI control, event-related desynchronization (ERD) following MI was significantly enhanced (<it>p </it>< 0.01) for feet MI (from -29% to -55%), left-hand MI (from -23% to -42%), and right-hand MI (from -22% to -51%).</p> <p>Conclusions</p> <p>These results show that our subject with severe MD was able to learn to control his EEG signal and communicate with other users through use of VR navigation and suggest that an internet-based VR has the potential to provide paralyzed people with the opportunity for easy communication.</p