Real-time noise reduction through independent channel averaging for real-time biomedical signal acquisition

In this work, a strategy to obtain a lower noise floor from commercial multichannel Sigma-Delta analog-to-digital converters (ADCs) is presented. Specifically, data from ADS131E08, an 8-channel simultaneous sampling 24 bit converter, is captured, processed, and transmitted in real time using a MAX 10 FPGA included in a measurement system with medical grade isolation, thus able to acquire biomedical signals. Noise measurements show that the system is able to reduce the equivalent input voltage noise of the ADC by a factor of 2.8, extending the measurement dynamic range by 9 dB. In this way, the system improves the otherwise minimum available noise floor with no additional analog stages and allows using higher data rates while maintaining signal quality. Experimental electrocardiogram and electromyogram recordings were taken using non-invasive dry electrodes, validating the operation of the system as a biopotential acquisition platform. Under these experimental conditions, a noise reduction factor of 2.1 times for the noise floor of the measured biopotential signals was verified.Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señale

High Performance 128-Channel Acquisition System for Electrophysiological Signals

The increased popularity of investigations and exploits in the fields of neurological rehabilitation, human emotion recognition, and other relevant brain-computer interfaces demand the need for flexible electrophysiology data acquisition systems. Such systems often require to be multi-modal and multi-channel capable of acquiring and processing several different types of physiological signals simultaneously in realtime. Developments of modular and scalable electrophysiological data acquisition systems for experimental research enhance understanding and progress in the field. To contribute to such an endeavor, we present an open-source hardware project called High-Channel Count Electrophysiology or HiCCE, targeting to produce an easily-adaptable, cost-effective, and affordable electrophysiological acquisition system as an alternative solution for mostly available commercial tools and the current state of the art in the field. In this paper, we describe the design and validation of the entire chain of the HiCCE-128 electrophysiological data acquisition system. The system comprises of 128 independent channels capable of acquiring signal at 31.25 kHz, with 16 effective bits per channel with a measured noise level of about 3 μV. The reliability and feasibility of the implemented system have been confirmed through a series of tests and real-world applications. The modular design methodology based on the FPGA Mezzanine Card (FMC) standard allows the connection of the HiCCE-128 board to programmable system-on-chip carrier devices through the high-speed FMC link. The implemented architecture enables end users to add various high-response electrophysiological signal processing techniques in the field programmable gate arrays (FPGA) part of the system on chip (SoC) device on each channel in parallel according to application specification

Neonatal seizure detection based on single-channel EEG: instrumentation and algorithms

Seizure activity in the perinatal period, which constitutes the most common neurological emergency in the neonate, can cause brain disorders later in life or even death depending on their severity. This issue remains unsolved to date, despite the several attempts in tackling it using numerous methods. Therefore, a method is still needed that can enable neonatal cerebral activity monitoring to identify those at risk. Currently, electroencephalography (EEG) and amplitude-integrated EEG (aEEG) have been exploited for the identification of seizures in neonates, however both lack automation. EEG and aEEG are mainly visually analysed, requiring a specific skill set and as a result the presence of an expert on a 24/7 basis, which is not feasible. Additionally, EEG devices employed in neonatal intensive care units (NICU) are mainly designed around adults, meaning that their design specifications are not neonate specific, including their size due to multi-channel requirement in adults - adults minimum requirement is ≥ 32 channels, while gold standard in neonatal is equal to 10; they are bulky and occupy significant space in NICU. This thesis addresses the challenge of reliably, efficiently and effectively detecting seizures in the neonatal brain in a fully automated manner. Two novel instruments and two novel neonatal seizure detection algorithms (SDAs) are presented. The first instrument, named PANACEA, is a high-performance, wireless, wearable and portable multi-instrument, able to record neonatal EEG, as well as a plethora of (bio)signals. This device despite its high-performance characteristics and ability to record EEG, is mostly suggested to be used for the concurrent monitoring of other vital biosignals, such as electrocardiogram (ECG) and respiration, which provide vital information about a neonate's medical condition. The two aforementioned biosignals constitute two of the most important artefacts in the EEG and their concurrent acquisition benefit the SDA by providing information to an artefact removal algorithm. The second instrument, called neoEEG Board, is an ultra-low noise, wireless, portable and high precision neonatal EEG recording instrument. It is able to detect and record minute signals (< 10 nVp) enabling cerebral activity monitoring even from lower layers in the cortex. The neoEEG Board accommodates 8 inputs each one equipped with a patent-pending tunable filter topology, which allows passband formation based on the application. Both the PANACEA and the neoEEG Board are able to host low- to middle-complexity SDAs and they can operate continuously for at least 8 hours on 3-AA batteries. Along with PANACEA and the neoEEG Board, two novel neonatal SDAs have been developed. The first one, termed G prime-smoothed (G ́_s), is an on-line, automated, patient-specific, single-feature and single-channel EEG based SDA. The G ́_s SDA, is enabled by the invention of a novel feature, termed G prime (G ́) and can be characterised as an energy operator. The trace that the G ́_s creates, can also be used as a visualisation tool because of its distinct change at a presence of a seizure. Finally, the second SDA is machine learning (ML)-based and uses numerous features and a support vector machine (SVM) classifier. It can be characterised as automated, on-line and patient-independent, and similarly to G ́_s it makes use of a single-channel EEG. The proposed neonatal SDA introduces the use of the Hilbert-Huang transforms (HHT) in the field of neonatal seizure detection. The HHT analyses the non-linear and non-stationary EEG signal providing information for the signal as it evolves. Through the use of HHT novel features, such as the per intrinsic mode function (IMF) (0-3 Hz) sub-band power, were also employed. Detection rates of this novel neonatal SDA is comparable to multi-channel SDAs.Open Acces

NASA Tech Briefs, November 2002

Topics include: a technology focus on engineering materials, electronic components and systems, software, mechanics, machinery/automation, manufacturing, bio-medical, physical sciences, information sciences book and reports, and a special section of Photonics Tech Briefs