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Real-time detection of REM phases with EEG records

100%

Bequart V. , Fermaut L. , Fertin M. , Robillart J. , Kotas R.

International Journal of Microelectronics and Computer Science

2018

tom Vol. 9, nr 4

155--160

The article presents the development of a program capable of real-time detection of REM phases during the human sleep. For this purpose, 39 electroencephalogram (EEG) recordings from PhysioNet were used. To achieve the goal of the project authors selected following set of parameters: the average amplitude of the signal, alpha and delta power band in frequency domain and the ratio Alpha-Delta, for 30 second interval. An Artificial Intelligence (AI) has been developed with Keras and trained with those parameters for 34 patients. Finally, the AI has been tested on the last 5 patients, by simulating a true night. It reaches 62% in sensibility for REM Phase detection, and 85% in specificity. Obtained results are promising in terms of real-time REM phase detection, but the approach needs further development.

Firmware development for the fibre-optic seismometer based on FOG

86%

Kamiński M. , Tylman W. , Jabłoński G. , Kotas R. , Amrozik P. , Sakowicz B. , Jaroszewicz L. R.

Opto-Electronics Review

2024

tom Vol. 32, No. 2

art. no. e150179

The main goal of the article is to present the concept of using a simulation environment when designing an advanced fibre-optic seismometer (FOS) using a field-programmable gate array (FPGA) computing system. The first part of the article presents the advanced requirements regarding the FOS principle of operation, as well as the measurement method using a closed-loop operation. The closed-loop control algorithm is developed using the high-level language C++ and then it is synthesised into an FPGA. The following part of the article describes the simulation environment developed to test the operation of the control algorithm. The environment includes a model of components of the measurement system, delays, and distortions in the signal processing path, and some of the measurement system surroundings. The article ends with a comparison of simulation data with measurements. The obtained results are consistent and prove correctness of the methodology adopted by the authors.

Automatic detection of heart diseases based on ECG signal analysis

86%

Delarue L. , Drouard P.-A. , Hourdé P. , Joulia J. , Renault V. , Kotas R.

International Journal of Microelectronics and Computer Science

2018

tom Vol. 9, nr 4

133--141

The authors provide overview of techniques used in ECG signal analysis and present their implementation in order to detect heart diseases (arrhythmias). This paper presents different means to study the ECG signals to develop automatic detection of heart diseases based on artificial intelligence.