A Novel Phase Congruency Based Algorithm for Online Data Reduction in Ambulatory EEG Systems

• Published in: IEEE transactions on biomedical engineering Vol. 58; no. 10; pp. 2825 - 2834
• Main Authors: Logesparan, Lojini, Rodriguez-Villegas, Esther
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Biological and medical sciences; Computerized, statistical medical data processing and models in biomedicine; Data reduction; EEG; Electrodiagnosis. Electric activity recording; Electroencephalography; Electroencephalography - methods; epilepsy; Epilepsy - diagnosis; Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy; Humans; Investigative techniques, diagnostic techniques (general aspects); Medical management aid. Diagnosis aid; Medical sciences; Monitoring; Monitoring, Ambulatory - methods; Muscles; Nervous system; Nervous system (semeiology, syndromes); Neurology; Noise; Noise reduction; online data reduction; phase congruency; Reproducibility of Results; ROC Curve; Scalp; Sensitivity; Signal Processing, Computer-Assisted; spike detection; Statistical methods; Studies; Nervous system diseases; Epilepsy; EEG; Electrophysiology; phase congruency; Electroencephalography; spike detection; Algorithm; Data reduction; Cerebral disorder; Electrodiagnosis; Central nervous system disease; On line processing; Ambulatory; Diagnosis; online data reduction; Biomedical engineering
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2011.2160639

Real signals are often corrupted by noise with a power spectrum variable over time. In applications involving these signals, it is expected that dynamically estimating and correcting for this noise would increase the amount of useful information extracted from the signal. One such application is scalp EEG monitoring in epilepsy, where electrical activity generated by cranio-facial muscles obscure the measured brainwaves. This paper presents a data-selection algorithm based on phase congruency to identify interictal spikes from background EEG; together with a novel statistical method that allows a more comprehensive trade-off based quantitative comparison of two algorithms which have been tested at a fixed threshold in the same database. Here, traditional phase congruency has been modified to incorporate a dynamic estimate of muscle activity present in the input scalp EEG signal. The proposed algorithm achieves 50% data reduction whilst detecting more than 80% of interictal spikes. This represents a significant improvement over the state-of-the-art denoising method for phase congruency.