Integration of EEG and SPECT data acquired from simultaneous examinations

Kowalczyk, Leszek; Bajera, Adam; Goszczyńska, Hanna; Zalewska, Ewa; Królicki, Leszek

doi:10.1016/j.bbe.2013.09.002

Cited by 3 publications

(5 citation statements)

References 20 publications

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“…This procedure uses our nine-ellipses model, also used for interpolation of EEG maps (Fig.2. ) [3]. Below, the main assumptions of IBEA algorithm are presented.…”

Section: Subject and Methodsmentioning

confidence: 99%

“…The method of EEG-SPECT data alignment described in [3] assumes that five electrodes (Cz, Oz, Fpz, T3, T4), so-called base electrodes, are visible in SPECT images. The algorithm of invisible base electrode approximation (IBEA) has been developed to approximate the coordinates of invisible base electrodes and, therefore, to enable superposition of SPECT images and EEG maps in case of incomplete data, i.e., not all base electrodes visible in SPECT images.…”

Section: Subject and Methodsmentioning

confidence: 99%

“…Electrodes placed according to the 10-20 system are placed on the head surface according to well defined rules [5] and are located closely to the theoretical positions defined by our nine-ellipses model, as described previously [3]. 2.…”

Section: A Ibea Algorithm Assumptionsmentioning

confidence: 99%

“…In our previous study we have developed a method for superposition of SPECT images and EEG maps implemented in our APW software when these two examinations are performed simultaneously [3]. This work was performed as part of a larger research concerning feasibility of improving the localization of epileptic foci, as compared to the standard SPECT examination, by applying the technique of EEG mapping.…”

Section: Introductionmentioning

confidence: 99%

“…This work was performed as part of a larger research concerning feasibility of improving the localization of epileptic foci, as compared to the standard SPECT examination, by applying the technique of EEG mapping. Developed method makes it possible, after further enhancements, to calculate the foci coordinates in SPECT scan space, to improve SPECT image co-registrations in differential techniques [13], and to estimate particular brain region activity during simultaneous EEG/SPECT examination The method relies on making a few EEG electrodes (socalled base electrodes) visible in SPECT images using an appropriate dose of radioisotope [3,4], calculating the coordinates of these electrodes in SPECT image space, approximating the coordinates of the remaining electrodes used in EEG recording and then computing a sequence of 3D EEG maps spanning on all electrodes (Fig.1.). SPECT examinations were performed using VARICAM SPECT system scintigraphic camera and SIEMENS SYNGO MI Work Station simultaneously with EEG recordings using Aura 24 system (Grass, USA).…”

Section: Introductionmentioning

confidence: 99%

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Invisible Base Electrode Coordinates Approximation for Simultaneous SPECT and EEG Data Visualization

Kowalczyk

Goszczyńska

Zalewska

et al. 2014

Measurement Science Review

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This work was performed as part of a larger research concerning the feasibility of improving the localization of epileptic foci, as compared to the standard SPECT examination, by applying the technique of EEG mapping. The presented study extends our previous work on the development of a method for superposition of SPECT images and EEG 3D maps when these two examinations are performed simultaneously. Due to the lack of anatomical data in SPECT images it is a much more difficult task than in the case of MRI/EEG study where electrodes are visible in morphological images. Using the appropriate dose of radioisotope we mark five base electrodes to make them visible in the SPECT image and then approximate the coordinates of the remaining electrodes using properties of the 10-20 electrode placement system and the proposed nine-ellipses model. This allows computing a sequence of 3D EEG maps spanning on all electrodes. It happens, however, that not all five base electrodes can be reliably identified in SPECT data. The aim of the current study was to develop a method for determining the coordinates of base electrode(s) missing in the SPECT image. The algorithm for coordinates approximation has been developed and was tested on data collected for three subjects with all visible electrodes. To increase the accuracy of the approximation we used head surface models. Freely available model from Oostenveld research based on data from SPM package and our own model based on data from our EEG/SPECT studies were used. For data collected in four cases with one electrode not visible we compared the invisible base electrode coordinates approximation for Oostenveld and our models. The results vary depending on the missing electrode placement, but application of the realistic head model significantly increases the accuracy of the approximation.

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“…This procedure uses our nine-ellipses model, also used for interpolation of EEG maps (Fig.2. ) [3]. Below, the main assumptions of IBEA algorithm are presented.…”

Section: Subject and Methodsmentioning

confidence: 99%

Section: Subject and Methodsmentioning

confidence: 99%

Section: A Ibea Algorithm Assumptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Invisible Base Electrode Coordinates Approximation for Simultaneous SPECT and EEG Data Visualization

Kowalczyk

Goszczyńska

Zalewska

et al. 2014

Measurement Science Review

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show abstract

Accuracy of the electrodes location method for simultaneous SPECT and EEG examinations

Kowalczyk

Goszczyńska

Zalewska

2015

Biocybernetics and Biomedical Engineering

Self Cite

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Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research Directions

et al. 2023

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Degenerative nerve diseases such as Alzheimer’s and Parkinson’s diseases have always been a global issue of concern. Approximately 1/6th of the world’s population suffers from these disorders, yet there are no definitive solutions to cure these diseases after the symptoms set in. The best way to treat these disorders is to detect them at an earlier stage. Many of these diseases are genetic; this enables machine learning algorithms to give inferences based on the patient’s medical records and history. Machine learning algorithms such as deep neural networks are also critical for the early identification of degenerative nerve diseases. The significant applications of machine learning and deep learning in early diagnosis and establishing potential therapies for degenerative nerve diseases have motivated us to work on this review paper. Through this review, we covered various machine learning and deep learning algorithms and their application in the diagnosis of degenerative nerve diseases, such as Alzheimer’s disease and Parkinson’s disease. Furthermore, we also included the recent advancements in each of these models, which improved their capabilities for classifying degenerative nerve diseases. The limitations of each of these methods are also discussed. In the conclusion, we mention open research challenges and various alternative technologies, such as virtual reality and Big data analytics, which can be useful for the diagnosis of degenerative nerve diseases.

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Integration of EEG and SPECT data acquired from simultaneous examinations

Cited by 3 publications

References 20 publications

Invisible Base Electrode Coordinates Approximation for Simultaneous SPECT and EEG Data Visualization

Invisible Base Electrode Coordinates Approximation for Simultaneous SPECT and EEG Data Visualization

Accuracy of the electrodes location method for simultaneous SPECT and EEG examinations

Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research Directions

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