Application of machine learning with electroencephalography in seizure detection.

Volschenk, Andre Daniel (2017-12)

Thesis (MEng)--Stellenbosch University, 2017.

Thesis

INTRODUCTION: Seizures are periods of abnormal electrical activity in the brain, which induce brain injury to the sufferer. A patient that suffer seizures may need to be monitored for several hours, days, or even weeks. Seizure identification using electroencephalography (EEG) can be achieved through the use of seizure detection algorithms. Continuous EEG monitoring with early-detection algorithms to warn of the onset of seizures has many benefits as it allows for early intervention. In this study, the desired seizure monitoring software is designed for immediate application in the clinical environment to any patient. The aim of this research is to develop a robust, completely automatic software solution intended for real-time whole-brain seizure detection that uses EEG data, and no patient- or seizure-specific tuning. The training and testing is performed using a large, publicly available data corpus. The current state-of-the-art algorithm is improved upon. Detection should be possible as soon as a patient is rushed into the intensive care unit (ICU) and the EEG electrodes are connected properly. METHODS: The CHB-MIT data corpus is used. Included for analysis are 24 patients, 185 seizures, 979.9 hours of data, and 18 channels. Independent training and testing sets are used, with a train:test ratio of 80:20. Preprocessing: If a frame is corrupted by abnormal channel amplitude, mains noise, or phase reversal, then it is rejected without being passed to the next processes. Otherwise, the frame is bandpass filtered between 0.5 and 70 Hz, and a 5-level db2 wavelet filterbank is used for sub-band coding. Frequency bands (high), (low), ß, α,Φ, and δ are thereby approximated. The Relative Average Amplitude (RAA), Relative Scale Energy (RSE), and Coefficient of Variation of Amplitude (CVA) features of bands ß, α, and Φ are taken. Classification: A probabilistic Bayes classifier is trained and used for classification. Ictal/inter-ictal and high-/low-α classifiers are used. A novel automatic procedure for α training-data selection is implemented. Postprocessing: A sequential hypothesis test and persistence is used for false positive reduction. The objective function in the train-validate phase is the F1 score, which is the harmonic mean of Positive Predictive Value (PPV ) and True Positive Rate (TPR). Leave-one-out-cross-validation (LOOCV) is used in the train-validate phase. The TPR, PPV , and False Positive Rate (FPR) are reported for convenience. RESULTS: The offline train-validate phase yielded TPR = 58.73 %, PPV = 59.89 %, FPR = 0.2045 /h. The online test phase yielded TPR = 58.5 %, PPV = 40.61 %, FPR = 0.3536 /h. CONCLUSIONS: The algorithm presented here is an improvement to the current state-of-the-art. For clinical applicability, the issues of overall algorithm performance and inter-patient variability should be further improved.

INLEIDING: Stuipe is periodes van abnormale elektriese aktiwiteit in die brein, wat breinbesering aan die lyer veroorsaak. ‘n Pasiënt wat aan stuipe ly moet vir ‘n paar uur, dae, of selfs weke gemonitor word. Die identifikasie van stuipe word gedoen met behulp van elektroënsefalografie (EEG) deur die gebruik van stuipe-opsporingsalgoritmes. Die gebruik van deurlopende EEG monitering met vroeë opsporingsalgoritmes waarsku teen die aanvang van stuipe en het baie voordele aangesien dit voorsiening maak vir vroeë ingryping. In hierdie studie is die gewenste stuipe-monitering sagteware ontwerp vir onmiddellike toepassing op enige pasiënt in die kliniese omgewing. Die doel van hierdie navorsing is om ‘n robuuste, heeltemal outomatiese sagteware-oplossing te ontwikkel wat gebruik kan word vir intydse hele-brein stuipe opsporing wat EEG data gebruik, en geen pasiënt- of stuip-spesifieke verfyning benodig nie. Die opleiding en toetsing is uitgevoer deur gebruik te maak van ‘n groot, openlik-beskikbare data corpus. Daar word verbeteringe aangebring op die huidige beste-van-die-beste algoritme. Opsporing moet moontlik wees sodra ‘n pasiënt in die intensiewe sorgeenheid ingebring word en die EEG-elektrodes behoorlik aangeheg is. METODES: Die KHB-MIT data corpus word gebruik. Vir analise is 24 pasiënte, 185 stuipe, 979.9 ure se data, en 18 kanale ingesluit. Onafhanklike opleiding- en toetsstelle word gebruik, met ‘n oplei:toets verhouding van 80:20. Voorverwerking: Indien ‘n raam besmet is deur abnormale kanaalamplitude,kraglyn-geraas of fase-omkering, dan word dit afgekeur sonder om aan die volgende prosesse oorgedra te word. Andersins word die raam deur ‘n banddeurlaat filter tussen 0.5 en 70 Hz gefiltreer, en ‘n 5-vlak db2 golfie filterbank word gebruik vir subband kodering. Frekwensiebande τ (hoog), τ(laag), ß, α, en Φ word sodoende beraam. Die relatiewe gemiddelde amplitude (RGA), relatiewe skaalenergie (RSE) en koeffisiënt van variasie van amplitude (KVA) eienskappe van bande ß, α, en Φword geneem. Klassifikasie: ‘n Waarskynlikheids Bayes-klassifiseerder word opgelei en gebruik vir klassifikasie. Ictale/inter-ictale en hoë/lae α klassifiseerders word gebruik. ‘n Nuwe outomatiese prosedure vir α opleiding-data seleksie word geïmplementeer. Na-verwerking: ‘n Opeenvolgende hipotese toets en blywendheid word gebruik vir vals-positiewe vermindering. Die teiken funksie in die opleidingsvalidereringsfase is die F1 telling, wat die harmoniese gemiddeld van Positiewe Voorspellende Waarde (PVW) en Ware Positiewe Koers (WPK) is. Laat-een-uit-kruis-validering (LEUKV) word gebruik in die opleidingsvalidereringsfase. Die WPK, PVW en vals-positiewe koers (V PK) word gemeld vir gerief. RESULTATE: Die aflyn opleidingsvalidereringsfase het WPK = 58.73 %, PVW = 59.89 %, V PK = 0.2045 /h opgelewer. Die aanlyn toetsfase het WPK = 58.5 %, PVW = 40.61 %, V PK = 0.3536 /h opgelewer. GEVOLGTREKKINGS: Die algoritme wat hier aangebied word is ’n verbetering van die huidige beste-van-die-beste. Vir kliniese toepaslikheid moet die kwessies van algehele algoritme prestasie en interpasiënt veranderlikheid verder verbeter word.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/102985
This item appears in the following collections: