Embedded AM-FM Signal Decomposition Algorithm for Continuous Human Activity Monitoring

AM-FM decomposition techniques have been successfully used for extracting significative features from a large variety of signals, helping realtime signal monitoring and pattern recognition, since they represent signals as a simultaneous composition of amplitude modulation and frequency modulation, where the carriers, amplitude envelopes, and the instantaneous frequencies are the features to be estimated. Human activities often involve repetitive movements, such as in running or cycling, where sinusoidal AM-FM decompositions of signals have already demonstrated to be useful to extract compact features to aid monitoring, classification, or detection. In this work we thus present the challenges and results of implementing the iterated coherent Hilbert decomposition (ICHD), a particularly effective algorithm to obtain an AM-FM decomposition, within a resource-constrained and low-power ARM Cortex-M4 microcontroller that is present in a wearable sensor we developed. We apply ICHD to the gyroscope data acquired from an inertial measurement unit (IMU) that is present in the sensor. Optimizing the implementation allowed us to achieve real-time performance using less then 16 % of the available CPU time, while consuming only about 5.4 mW of power, which results in a run-time of over 7 days using a small 250 mAh rechargeable cell

Influence of vitamin K antagonist treatment on activated partial thromboplastin time

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ECG-Based Arrhythmia Classification using Recurrent Neural Networks in Embedded Systems

Cardiac arrhythmia is one of the most important cardiovascular diseases (CVDs), causing million deaths every year. Moreover it is difficult to diagnose because it occurs intermittently and as such requires the analysis of large amount of data, collected during the daily life of patients. An important tool for CVD diagnosis is the analysis of electrocardiogram (ECG), because of its non-invasive nature and simplicity of acquisition. In this work we propose a classification algorithm for arrhythmia based on recurrent neural networks (RNNs) that operate directly on ECG data, exploring the effectiveness and efficiency of several variations of the general RNN, in particular using different types of layers implementing the network memory. We use the MIT-BIH arrhythmia database and the evaluation protocol recommended by the Association for the Advancement of Medical Instrumentation (AAMI). After designing and testing the effectiveness of the different networks, we then test its porting to an embedded platform, namely the STM32 microcontroller architecture from ST, using a specific framework to port a pre-built RNN to the embedded hardware, convert it to optimized code for the platform and evaluate its performance in terms of resource usage. Both in binary and multiclass classification, the basic RNN model outperforms the other architectures in terms of memory storage (∼117 KB), number of parameters (∼5 k) and inference time (∼150 ms), while the RNN LSTM-based achieved the best accuracy (∼90%)

U-health expert system with statistical neural network

Ubiquitous Health(U-Health) system witch focuses on automated applications that can provide healthcare to human anywhere and anytime using wired and wireless mobile technologies is becoming increasingly important. This system consists of a network system to collect data and a sensor module which measures pulse, blood pressure, diabetes, blood sugar, body fat diet with management and measurement of stress etc, by both wired and wireless and further portable mobile connections. In this paper, we propose an expert system using back-propagation to support the diagnosis of citizens in U-Health system

Intrapancreatic accessory spleen false positive to 68Ga-Dotatoc: case report and literature review

Background: Intrapancreatic accessory spleen (IPAS) is an uncommon finding of pancreatic mass. Differential diagnosis with pancreatic tumor, especially with non-functional neuroendocrine tumor (NF-NET), may be very hard and sometimes it entails unnecessary surgery. A combination of CT scan, MRI, and nuclear medicine can confirm the diagnosis of IPAS. 68-Ga-Dotatoc PET/CT is the gold standard in NET diagnosis and it can allow to distinguish between IPAS and NET. Case presentation: A 69-year-old man was admitted to our hospital for an incidental nodule in the tail of the pancreas with focal uptake of 68-Ga-dotatate at PET/CT. NET was suspected and open distal splenopancreatectomy was performed. Pathologic examination revealed an IPAS. Conclusion: This is the second IPAS case in which a positive 68Ga-Dotatoc uptake led to a false diagnosis of pancreatic NET. Here is a proposal of a literature review

Impact of the 2015 wildfires on Malaysian air quality and exposure: a comparative study of observed and modeled data

In September and October 2015, Equatorial Asia experienced the most intense biomass burning episodes over the past two decades. These events, mostly enhanced by the extremely dry weather associated with the occurrence of strong El Niño conditions, resulted in the transnational transport of hazardous pollutants from the originating sources in Indonesian Borneo and Sumatra to the highly populated Malaysian Peninsula. Quantifying the population exposure form this event is a major challenge, and only two model-based studies have been performed to date, with limited evaluation against measurements. This manuscript presents a new data set of 49 monitoring stations across Peninsular Malaysia and Malaysian Borneo active during the 2015 haze event, and performs the first comparative study of PM10 (particulate matter with diameter < 10 µm) and carbon monoxide (CO) against the output of a state-of-the-art regional model (WRF-Chem). WRF-Chem presents high skills in describing the spatio-temporal patterns of both PM10 and CO and thus was applied to estimate the impact of the 2015 wildfires on population exposure. This study showed that more than 60% of the population living in the highly populated region of the Greater Klang Valley was systematically exposed to unhealthy/hazardous air quality conditions associated with the increased pollutant concentrations from wildfires and that almost 40% of the Malaysian population was on average exposed to PM10 concentrations higher than 100 µg m−3 during September and October 2015

Quantitative scattering of melanin solutions

The optical scattering coefficient of a dilute, well solubilised eumelanin solution has been accurately measured as a function of incident wavelength, and found to contribute less than 6% of the total optical attenuation between 210 and 325nm. At longer wavelengths (325nm to 800nm) the scattering was less than the minimum sensitivity of our instrument. This indicates that UV and visible optical density spectra can be interpreted as true absorption with a high degree of confidence. The scattering coefficient vs wavelength was found to be consistent with Rayleigh Theory for a particle radius of 38+-1nm.Comment: 23 pages, 5 figure

The role of the heat shock protein B8 (HSPB8) in motoneuron diseases

Amyotrophic lateral sclerosis (ALS) and spinal and bulbar muscular atrophy (SBMA) are two motoneuron diseases (MNDs) characterized by aberrant protein behavior in affected cells. In familial ALS (fALS) and in SBMA specific gene mutations lead to the production of neurotoxic proteins or peptides prone to misfold, which then accumulate in form of aggregates. Notably, some of these proteins accumulate into aggregates also in sporadic ALS (sALS) even if not mutated. To prevent proteotoxic stresses detrimental to cells, misfolded and/or aggregated proteins must be rapidly removed by the protein quality control (PQC) system. The small heat shock protein B8 (HSPB8) is a chaperone induced by harmful events, like proteasome inhibition. HSPB8 is expressed both in motoneuron and muscle cells, which are both targets of misfolded protein toxicity in MNDs. In ALS mice models, in presence of the mutant proteins, HSPB8 is upregulated both in spinal cord and muscle. HSPB8 interacts with the HSP70 co-chaperone BAG3 and enhances the degradation of misfolded proteins linked to sALS, or causative of fALS and of SBMA. HSPB8 acts by facilitating autophagy, thereby preventing misfolded protein accumulation in affected cells. BAG3 and BAG1 compete for HSP70-bound clients and target them for disposal to the autophagy or proteasome, respectively. Enhancing the selective targeting of misfolded proteins by HSPB8-BAG3-HSP70 to autophagy may also decrease their delivery to the proteasome by the BAG1-HSP70 complex, thereby limiting possible proteasome overwhelming. Thus, approaches aimed at potentiating HSPB8-BAG3 may contribute to the maintenance of proteostasis and may delay MNDs progression

Review: The Efficacy of Cannabidiol (CBD) as Potential Antipsychotic Medication

Psychotic disorders such as schizophrenia are widespread and severely disabling; however, current pharmacological treatments are unsatisfactory due to major side effects. The current review discusses the therapeutic potential of cannabidiol (CBD), a non-psychoactive component of cannabis, as an antipsychotic drug. Research lines including studies based on animal models of psychosis, human experimental studies, neuroimaging studies, epidemiological studies, and clinical studies are reviewed. The studies described provide empirical support for the antipsychotic effects of CBD and indicate reduced side effects, high tolerability, and superior cost-effectiveness compared to regular antipsychotic medication. It is concluded that CBD may prove a safe and attractive alternative treatment for psychotic conditions. However, current evidence largely stems from experimental, non-clinical studies. Large-scale randomized clinical trials are needed before this can be implemented in practice