Giuseppe Griffo

Entropy Index in Quantitative EEG Measurement for Diagnosis Accuracy

Electroencephalogram (EEG) remains the most immediate, simple, and rich source of information for understanding phenomena related to brain electrical activities. It is certainly a source of basic and interesting information to be extracted using specific and appropriate techniques. The most important aspect in processing EEG signals is to use less co-lateral assets and instrumentation in order to carried out a possible diagnosis; this is the approach of early diagnosis. Advanced estimate spectral analysis can reveal new information encompassed in EEG signals by means of specific parameters or indices. The research proposes a multidimensional approach with a combined use of decimated signal diagonalization (DSD) as basis from which it is possible to work by finding appropriate signal windows for revealing expected information and overcoming signal processing limitations encountered in quantitative EEG. Important information, about the state of the patient under observation, must be extracted from calculated DSD bispectrum. For this aim, it is useful to define an assessment index about the dynamic process associated with the analyzed signal. This information is measured by means of entropy, since the degree of order/disorder of the recorded EEG signal will be reflected in the obtained DSD bispectrum. The general advantage of multidimensional approach is to reveal eventual stealth frequencies “in space and in time” giving a topological vision to be correlated to physical areas which these frequencies emerge from. Long term and sleeping EEG recorded are analyzed, and the results obtained are of interest for an accurate diagnosis of the patients clinical condition.

Mutidimensional analysis of EEG features using advanced spectral estimates for diagnosis accuracy

Electroencephalogram (EEG) is a source of interesting information if one is able to extract them according to appropriate techniques. The conditions of individual under EEG test is a key issue. In general, EEG feature extraction can be associated to other information like Electrocardiogram (ECG), ergospirometry and electromyogram (EMG). However, in some cases, a multidimensional representation is used; bispectrum is an example of such a representation. HOS (high order statistics), for instance, include the bispectrum and the trispectrum (third and fourth order statistics, respectively). Advanced estimate spectral analysis can reveal new information encompassed in EEG signals. That is the reason the author propose an algorithm based on DSD (Decimated Signal Diagonalization) that is able of processing exponentially dumped signals like those that regard EEG features. The version proposed here is a multidimensional one.

Wireless network acquisition of joint EEG-ECG-ergospirometric signals for epilepsy detection

Wireless network architecture allows the implementation of fatigue measurement that was once performed in a limited and constrained way due to the use of wired connections. This paper presents measurement acquisitions by means of wireless network that allows joint acquisition of electroencephalograms, electrocardiograms and ergospirometry signals. This opportunity permits to a person walking to a dedicated path (about 30 meters) to develop fatigue that is recorded in function of EEG and ECG. Only wireless configuration allows the patient under test to walk. Some issues have been developed to detect interesting features on transmitted signals for epilepsy detection.

Implantable Neurorecording Sensing System: Wireless Transmission of Measurements

Seizure detection and monitoring are generally carried out by electroencephalogram (EEG) instrumentation with electrodes located on the scalp. For 24 h monitoring, it is possible to use an EEG recorder brought by the patient positioned on a bed or normally moving in the hospital and doing everything. This paper presents findings in the design of a neurocase for hosting a neurorecording system and its conformity to IEEE 24451 limited to wireless aspects related to data transmission. The recording system also provides for suppressing spikes and surges in EEG signals since these signals can be considered as a series of voltages with a relationship with space and time. Nanotechnology solutions relative to materials have been illustrated. Moreover, a stress simulation has been also performed in order to verify the sustainability of the design. The studied system considers the implementation of the neurorecording system including, design of circuital electronic components and thermoelectric power board supply, and 3-D package.

EEG signal processing and acquisition for detecting abnormalities via bio-implantable devices

The proposed research illustrates an innovating implantable micro-apparatus to be encompassed under the scalp for monitoring and retrieving electrical cerebral activities. The illustrated system considers its theoretical realization including, design of circuital electronic components and energy harvesting, 3D package, chemical aspects concerning the utilization of UHMWPE (Ultra High Molecular Weight Polyethylene) polymeric materials for packaging including mechanical simulations and comparison with titanium material, and electromagnetic aspects regarding the Wi-Fi radiation. A full description of necessary circuitry is included. Moreover, for chemical viewpoint, requirements of polymeric nanomaterials, embedding silver or copper nanoparticles to be used for its fabrication, are discussed illustrating antibacterial and electromagnetic wall barrier properties. The study of the proposed work concerns the whole design of the system.

Smartness for Railway Transducers: Reliability Experimental Verifications and Accuracy

The development of electrical trains has been requiring advances in the construction of components that are usually supervised by smart systems. Safety aspects are given to redundant capabilities of the new equipments to overcome failures. The new components must obey to internationally recognized standards. For that reason, the electrical supply and the mechanical contacts between train and rails are the key issues to be supervised. The current ISO/IEC/IEEE 1451 is basically helpful for the electrical supply by means of the dedicated transducers. In this paper, a new voltage/current transducer for the train supply is shown and analyzed paying the attention on its reliability, availability, maintainability, and safety approach performed in an innovative way with a specific reference to guidelines of the ISO/IEC/IEEE 1451. At this aim, an extensive theoretical and experimental analysis has been performed using dedicated algorithms developed for the purposes of this research considering reliability aspects according to commercial and defense industry needs.

Design of a wearable sweat sensor for diagnosing Cystic Fibrosis in children

Cystic Fibrosis is an inherited genetic and debilitating pathology involving respiratory and digestive systems. The excessive production of thick sticky mucus on the outside of the cells is the main consequence of such disease. Mucous obstructs airways and ducts of several organs. So this pathology is cause of various symptoms. Although current treatments allow patient to improve his/her life quality, nowadays life expectation is about of 30 years. Sweat test is the gold standard or screening technique performed in laboratory to diagnose Cystic Fibrosis. Since individuals with this pathology have characteristic high sodium and chloride concentrations in the perspiration, the test is based on measuring concentrations of such electrolytes. False positive diagnoses are recurrent because of errors during the data interpretation. So improving acquisition and processing of sensor data is a primary goal. The authors propose the design of a wearable sweat sensor able to measure the concentration of the two electrolytes assessing suspicion of Cystic Fibrosis. The main contribution of the project is due to a combination of a Tow-Thomas biquadratic cell and a specific control circuit. This latter is devoted to control the effect of perspiration on Na+ and Cl- concentrations.

Sensing system for cystic fibrosis: Modeling the detection and characterization of sweat

Sweat assumes an important duty for the diagnostic of diverse pathologies since it is mostly a synthesis of human being physiological functions and its content is a dedicated indicator. It is an indicator of vegetative innervation, and disturbances of nervous systems. Sodium chloride, for instance, is one of the indicators of cystic fibrosis. The paper illustrates the modeling of a sensing system and its related architectures to detect the sodium chloride for the diagnosis of cystic fibrosis. The system is partly in micro and nanotechnology. The paper presents first a dedicated hardware for conditioning and processing signal deriving from a sensor for sweat characterization. The experimental board has been realized using common components but it can be reduced to micro and nanotechnology configuration. This realization, even carried out with a traditional board, demonstrates two positive results: nonlinearity of sodium concentration and reaction time. However, noise contributes to lower the experimental uncertainty. To improve the features, especially in terms of lowering uncertainty and further reaction time (response time), a design of a dedicated sensor for detecting sweat is performed. It is an ISFET (ion-sensitive field effect transistor) using MEMS technology.

A Distributed Edge Computing Architecture to Support Sensing and Detecting Leaks in Waterworks Based on Advanced FDM

High costs of urban services, namely, waterworks, transportation, waste collection, wastewater collection and treatment, energy, and public lighting, require their optimization in management. This optimization can be mostly achieved using dedicated technology and strategy by “building” smart cities and smart grids. This paper illustrates findings related to the application of a designed distributed edge computing system for supervising a network of sensors, located on a special configuration of a pipeline, to detect leaks. The plant to be supervised is a zigzag waterworks with leaks to be simulated by opening and closing taps. The pressure variation is detected by magnetic sensors, which convert pressure variation into electric signal to be processed on-line thanks to an advanced and robust algorithm called a filter diagonalization method that performs a spectral analysis. In this paper, we have also developed a 2-D representation of the leak within the pipeline or waterworks, which is a robust way to see the dimensions or the expansion of the leak in a specific space.

Leak detection in waterworks for preserving environment: A comparative study

Fast and robust algorithms are necessary for helping technicians in detecting leaks in waterworks and pipelines using spectral analysis. FDM (Filter Diagonalization Method) and DPA (Decimated Padé Approximant) have demonstrated their robustness in spectral analysis of leakage in waterworks. The input time signal points from an experimental plant or auto-correlation functions are given via measurements or computations, and the task is to reconstruct the unknown components as the harmonic variables in terms of the fundamental complex frequencies and amplitudes.This work presents a comparison between the aforementioned algorithms pointing out some drawbacks.