Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network

Parallel computing for brain simulation

[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function

Artificial neural network (ANN) is an established artificial intelligence technique that is widely used for solving numerous problems such as classification and clustering in various fields. However, the major problem with ANN is a factor of time. ANN takes a longer time to execute a huge number of neurons. In order to overcome this, ANN is implemented into hardware namely field-programmable-gate-array (FPGA). However, implementing the ANN into a field-programmable gate array (FPGA) has led to a new problem related to the sigmoid function implementation. Often used as the activation function for ANN, a sigmoid function cannot be directly implemented in FPGA. Owing to its accuracy, the lookup table (LUT) has always been used to implement the sigmoid function in FPGA. In this case, obtaining the high accuracy of LUT is expensive particularly in terms of its memory requirements in FPGA. Second-order nonlinear function (SONF) is an appealing replacement for LUT due to its small memory requirement. Although there is a trade-off between accuracy and memory size. Taking the advantage of the aforementioned approaches, this thesis proposed a combination of SONF and a modified LUT namely differential lookup table (dLUT). The deviation values between SONF and sigmoid function are used to create the dLUT. SONF is used as the first step to approximate the sigmoid function. Then it is followed by adding or deducting with the value that has been stored in the dLUT as a second step as demonstrated via simulation. This combination has successfully reduced the deviation value. The reduction value is significant as compared to previous implementations such as SONF, and LUT itself. Further simulation has been carried out to evaluate the accuracy of the ANN in detecting the object in an indoor environment by using the proposed method as a sigmoid function. The result has proven that the proposed method has produced the output almost as accurately as software implementation in detecting the target in indoor positioning problems. Therefore, the proposed method can be applied in any field that demands higher processing and high accuracy in sigmoid function outpu

Estudi comparatiu de la publicació científica en l’àmbit de la informàtica a la UPC vs. altres universitats d’àmbit nacional i internacional (2007-2017)

L'estudi analitza la producció científica de la UPC vinculada amb la informàtica i es compara amb la d'altres 16 universitats de l’estat espanyol, europees, dels Estats Units i asiàtiques, amb una notable activitat investigadora en aquest camp.Postprint (published version

A low power and high performance hardware design for automatic epilepsy seizure detection

An application specific integrated design using Quadrature Linear Discriminant Analysis is proposed for automatic detection of normal and epilepsy seizure signals from EEG recordings in epilepsy patients. Five statistical parameters are extracted to form the feature vector for training of the classifier. The statistical parameters are Standardised Moment, Co-efficient of Variance, Range, Root Mean Square Value and Energy. The Intellectual Property Core performs the process of filtering, segmentation, extraction of statistical features and classification of epilepsy seizure and normal signals. The design is implemented in Zynq 7000 Zc706 SoC with average accuracy of 99%, Specificity of 100%, F1 score of 0.99, Sensitivity of  98%  and Precision of 100 % with error rate of 0.0013/hr., which is approximately zero false detectio

A low power and high performance hardware design for automatic epilepsy seizure detection

An application specific integrated design using Quadrature Linear Discriminant Analysis is proposed for automatic detection of normal and epilepsy seizure signals from EEG recordings in epilepsy patients. Five statistical parameters are extracted to form the feature vector for training of the classifier. The statistical parameters are Standardised Moment, Co-efficient of Variance, Range, Root Mean Square Value and Energy. The Intellectual Property Core performs the process of filtering, segmentation, extraction of statistical features and classification of epilepsy seizure and normal signals. The design is implemented in Zynq 7000 Zc706 SoC with average accuracy of 99%, Specificity of 100%, F1 score of 0.99, Sensitivity of  98%  and Precision of 100 % with error rate of 0.0013/hr., which is approximately zero false detectio

Correlation analysis between journal metrics and subscription price for selected journals

The given article gives an account on the correlation analysis between the journal metrics (SNIP, SJR, IF) and the subscription price for two groups of journals (114 Economics journals and 150 Mathematics and Computer Science journals) which were presented on Elsevier website in October 201