Automated EEG signal analysis for identification of epilepsy seizures and brain tumour

Abstract

Electroencephalography (EEG) is a clinical test which records neuro-electrical activities generated by brain structures. EEG test results used to monitor brain diseases such as epilepsy seizure, brain tumours, toxic encephalopathies infections and cerebrovascular disorders. Due to the extreme variation in the EEG morphologies, manual analysis of the EEG signal is laborious, time consuming and requires skilled interpreters, who by the nature of the task are prone to subjective judegment and error. Further, manual analysis of the EEG results often fails to detect and uncover subtle features. This paper proposes an automated EEG analysis method by combining digital signal processing and neural network techniques, which will remove error and subjectivity associated with manual analysis and identifies the existence of epilepsy seizure and brain tumour diseases. The system uses multi-wavelet transform for feature extraction in which an input EEG signal is decomposed in a sub-signal. Irregularities and unpredictable fluctuations present in the decomposed signal are measured using approximate entropy. A feed-forward neural network is used to classify the EEG signal as a normal, epilepsy or brain tumour signal. The proposed technique is implemented and tested on data of 500 EEG signals for each disease. Results are promising, with classification accuracy of 98% for normal, 93% for epilepsy and 87% for brain tumour. Along with classification, the paper also highlights the EEG abnormalities associated with brain tumour and epilepsy seizure.

Brain computer interfacing: Applications and challenges

“…Sharanreddy and Kulkarni have proposed a system in [34] that recognizes EEG abnormalities associated with brain tumors and epilepsy seizures. Early detection of epilepsy seizure, one of the most common neurological disorders, and controlling its effects are presented in [35,36].…”

Artificial neural networks in neurosurgery

“…In neurosurgery, ANNs have been successfully used for diagnosis in paediatric posterior fossa tumours,8 9 low back pain,10–12 cervical spine vertebra,13 scoliosis spinal deformity,14 15 brain tumours,16–21 primary generalised epilepsy using the analysis of EEGs22 and tumour and non-tumour cerebral disorders 23. ANNs have been used to interpret radiographic images,24 25 to enhance surgical decision-making for traumatic brain injury,26 to recognise and correctly diagnose patients with different facial pain syndromes,27 28 to discriminate between the essential tremor and the tremor in Parkinson’s disease (PD),29 for identification of epilepsy seizures and brain tumour,30 for discriminating between normal and PD participants31 and for real-time tumour tracking 32. Each of them is summarised in online supplementary table S1.…”

Biointerfaced sensors for biodiagnostics

“…Alzheimer's EEG (slower) [79] VOCs [80] t-tau, p-tau181, Aβ42, and Aβ40 [81] Epilepsy EEG (pathological high-frequency oscillations) [82] Oxidative stress related markers; [83] VOCs [84] Multiple (eg, increased concentration of hsa-miR-106b-5p) [85] Parkinson's EEG (synchronized oscillation in the beta band) [79] VOCs [86] Dopamine [87] Cardiology Myocardial infarction Electrocardiography (ST elevation or depression, T-wave invention) [88] Irregular heartbeats, abnormalities in blood pressure [89] Troponin, [90,91] B-type natriuretic peptide (BNP), [91] N-terminal pro BNP (NT-pro BNP), [92] high sensitivity CRP (HS-CRP) [91] Atrial fibrillation Electrocardiography (no P waves, irregular RR intervals) [93] Irregular heartbeats, [94] diastolic and systolic blood pressure [95] BNP, NT-proBNP, CRP [96] Cancer Melanoma --pH, pressure, temperature, O 2 (local) [97] Many (eg, Human Melanoma Black-45) [98] Brain tumor EEG [99] pH, pressure, temperature, O 2 (local) [97] Many (MIC-1 GDF15 in glioblastoma) [100] Diabetes Diabetes --VOC (eg, acetone) [101] Glucose/insulin Infectious disease TB --Exhaled breath VOCs [102,103] MTB genomic DNA [104] ESAT-6 protein [105] Flu --Exhaled breath VOCs [106] DNA aptamer [107] Viral RNA [108] Neuraminidase [108] Hemagglutinin [108] Malaria --Exhaled breath VOCs [109] Enzymes [1,…”