Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors

Auscultation is one of the most used techniques for detecting cardiovascular diseases, which is one of the main causes of death in the world. Heart murmurs are the most common abnormal finding when a patient visits the physician for auscultation. These heart sounds can either be innocent, which are harmless, or abnormal, which may be a sign of a more serious heart condition. However, the accuracy rate of primary care physicians and expert cardiologists when auscultating is not good enough to avoid most of both type-I (healthy patients are sent for echocardiogram) and type-II (pathological patients are sent home without medication or treatment) errors made. In this paper, the authors present a novel convolutional neural network based tool for classifying between healthy people and pathological patients using a neuromorphic auditory sensor for FPGA that is able to decompose the audio into frequency bands in real time. For this purpose, different networks have been trained with the heart murmur information contained in heart sound recordings obtained from nine different heart sound databases sourced from multiple research groups. These samples are segmented and preprocessed using the neuromorphic auditory sensor to decompose their audio information into frequency bands and, after that, sonogram images with the same size are generated. These images have been used to train and test different convolutional neural network architectures. The best results have been obtained with a modified version of the AlexNet model, achieving 97% accuracy (specificity: 95.12%, sensitivity: 93.20%, PhysioNet/CinC Challenge 2016 score: 0.9416). This tool could aid cardiologists and primary care physicians in the auscultation process, improving the decision making task and reducing type-I and type-II errors.Ministerio de Economía y Competitividad TEC2016-77785-

NAVIS: Neuromorphic Auditory VISualizer Tool

This software presents diverse utilities to perform the first post-processing layer taking the neuromorphic auditory sensors (NAS) information. The used NAS implements in FPGA a cascade filters architecture, imitating the behavior of the basilar membrane and inner hair cells and working with the sound information decomposed into its frequency components as spike streams. The well-known neuromorphic hardware interface Address-Event-Representation (AER) is used to propagate auditory information out of the NAS, emulating the auditory vestibular nerve. Using the information packetized into aedat files, which are generated through the jAER software plus an AER to USB computer interface, NAVIS implements a set of graphs that allows to represent the auditory information as cochleograms, histograms, sonograms, etc. It can also split the auditory information into different sets depending on the activity level of the spike streams. The main contribution of this software tool is that it allows complex audio post-processing treatments and representations, which is a novelty for spike-based systems in the neuromorphic community and it will help neuromorphic engineers to build sets for training spiking neural networks (SNN).Ministerio de Economía y Competitividad TEC2012-37868-C04-0

Stereo Matching in Address-Event-Representation (AER) Bio-Inspired Binocular Systems in a Field-Programmable Gate Array (FPGA)

In stereo-vision processing, the image-matching step is essential for results, although it involves a very high computational cost. Moreover, the more information is processed, the more time is spent by the matching algorithm, and the more ine cient it is. Spike-based processing is a relatively new approach that implements processing methods by manipulating spikes one by one at the time they are transmitted, like a human brain. The mammal nervous system can solve much more complex problems, such as visual recognition by manipulating neuron spikes. The spike-based philosophy for visual information processing based on the neuro-inspired address-event-representation (AER) is currently achieving very high performance. The aim of this work was to study the viability of a matching mechanism in stereo-vision systems, using AER codification and its implementation in a field-programmable gate array (FPGA). Some studies have been done before in an AER system with monitored data using a computer; however, this kind of mechanism has not been implemented directly on hardware. To this end, an epipolar geometry basis applied to AER systems was studied and implemented, with other restrictions, in order to achieve good results in a real-time scenario. The results and conclusions are shown, and the viability of its implementation is proven.Ministerio de Economía y Competitividad TEC2016-77785-

Live Demonstration: On the distance estimation of moving targets with a Stereo-Vision AER system

Distance calculation is always one of the most important goals in a digital stereoscopic vision system. In an AER system this goal is very important too, but it cannot be calculated as accurately as we would like. This demonstration shows a first approximation in this field, using a disparity algorithm between both retinas. The system can make a distance approach about a moving object, more specifically, a qualitative estimation. Taking into account the stereo vision system features, the previous retina positioning and the very important Hold&Fire building block, we are able to make a correlation between the spike rate of the disparity and the distance.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0

System based on inertial sensors for behavioral monitoring of wildlife

Sensors Network is an integration of multiples sensors in a system to collect information about different environment variables. Monitoring systems allow us to determine the current state, to know its behavior and sometimes to predict what it is going to happen. This work presents a monitoring system for semi-wild animals that get their actions using an IMU (inertial measure unit) and a sensor fusion algorithm. Based on an ARM-CortexM4 microcontroller this system sends data using ZigBee technology of different sensor axis in two different operations modes: RAW (logging all information into a SD card) or RT (real-time operation). The sensor fusion algorithm improves both the precision and noise interferences.Junta de Andalucía P12-TIC-130

A Sensor Fusion Horse Gait Classification by a Spiking Neural Network on SpiNNaker

The study and monitoring of the behavior of wildlife has always been a subject of great interest. Although many systems can track animal positions using GPS systems, the behavior classification is not a common task. For this work, a multi-sensory wearable device has been designed and implemented to be used in the Doñana National Park in order to control and monitor wild and semiwild life animals. The data obtained with these sensors is processed using a Spiking Neural Network (SNN), with Address-Event-Representation (AER) coding, and it is classified between some fixed activity behaviors. This works presents the full infrastructure deployed in Doñana to collect the data, the wearable device, the SNN implementation in SpiNNaker and the classification results.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130

Bio-Inspired Stereo Vision Calibration for Dynamic Vision Sensors

Many advances have been made in the eld of computer vision. Several recent research trends have focused on mimicking human vision by using a stereo vision system. In multi-camera systems, a calibration process is usually implemented to improve the results accuracy. However, these systems generate a large amount of data to be processed; therefore, a powerful computer is required and, in many cases, this cannot be done in real time. Neuromorphic Engineering attempts to create bio-inspired systems that mimic the information processing that takes place in the human brain. This information is encoded using pulses (or spikes) and the generated systems are much simpler (in computational operations and resources), which allows them to perform similar tasks with much lower power consumption, thus these processes can be developed over specialized hardware with real-time processing. In this work, a bio-inspired stereovision system is presented, where a calibration mechanism for this system is implemented and evaluated using several tests. The result is a novel calibration technique for a neuromorphic stereo vision system, implemented over specialized hardware (FPGA - Field-Programmable Gate Array), which allows obtaining reduced latencies on hardware implementation for stand-alone systems, and working in real time.Ministerio de Economía y Competitividad TEC2016-77785-PMinisterio de Economía y Competitividad TIN2016-80644-

Multilayer Spiking Neural Network for Audio Samples Classification Using SpiNNaker

Audio classification has always been an interesting subject of research inside the neuromorphic engineering field. Tools like Nengo or Brian, and hardware platforms like the SpiNNaker board are rapidly increasing in popularity in the neuromorphic community due to the ease of modelling spiking neural networks with them. In this manuscript a multilayer spiking neural network for audio samples classification using SpiNNaker is presented. The network consists of different leaky integrate-and-fire neuron layers. The connections between them are trained using novel firing rate based algorithms and tested using sets of pure tones with frequencies that range from 130.813 to 1396.91 Hz. The hit rate percentage values are obtained after adding a random noise signal to the original pure tone signal. The results show very good classification results (above 85 % hit rate) for each class when the Signal-to-noise ratio is above 3 decibels, validating the robustness of the network configuration and the training step.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130

Nueva población de Cynara tournefortii Boiss. & Reut. (Compositae) en Andalucía (S. España)

New record for Cynara tournefortii Boiss. & Reut. (Compositae) in Andalusia (S. Spain)Palabras clave. Cynara tournefortii, Compositae, corología, conservación, S. España.Key words. Cynara tournefortii, Compositae, chorology, conservation, S. Spain

A Binaural Neuromorphic Auditory Sensor for FPGA: A Spike Signal Processing Approach

This paper presents a new architecture, design flow, and field-programmable gate array (FPGA) implementation analysis of a neuromorphic binaural auditory sensor, designed completely in the spike domain. Unlike digital cochleae that decompose audio signals using classical digital signal processing techniques, the model presented in this paper processes information directly encoded as spikes using pulse frequency modulation and provides a set of frequency-decomposed audio information using an address-event representation interface. In this case, a systematic approach to design led to a generic process for building, tuning, and implementing audio frequency decomposers with different features, facilitating synthesis with custom features. This allows researchers to implement their own parameterized neuromorphic auditory systems in a low-cost FPGA in order to study the audio processing and learning activity that takes place in the brain. In this paper, we present a 64-channel binaural neuromorphic auditory system implemented in a Virtex-5 FPGA using a commercial development board. The system was excited with a diverse set of audio signals in order to analyze its response and characterize its features. The neuromorphic auditory system response times and frequencies are reported. The experimental results of the proposed system implementation with 64-channel stereo are: a frequency range between 9.6 Hz and 14.6 kHz (adjustable), a maximum output event rate of 2.19 Mevents/s, a power consumption of 29.7 mW, the slices requirements of 11 141, and a system clock frequency of 27 MHz.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130