Artificial neural network and spectrum analysis methods for detecting brain diseases from the CNV response in the electroencephalogram

Jervis, B.W.; Saatchi, Mohammad; Lacey, Arron; Roberts, T.; Allen, Elizabeth S.; Hudson, N.R.; Oke, S.; Grimsley, Michael

doi:10.1049/ip-smt:19941480

Cited by 19 publications

(12 citation statements)

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“…Furthermore, ten atrial flutter cases have also been presented to both HFAs. The standard medical diagnostic parameters are used to evaluate the results [7]. Definitions of these parameters and our results are summarized in Tables IV and V. The pair of HFAs has been able to distinguish correctly 35 out of 37 ECGs recorded from patients suffering from atrial fibrillation.…”

Section: Resultsmentioning

confidence: 97%

“…Automatic diagnosis using the decision-theoretic approach is based upon extracting features from signals that bear the characteristics of a fault. Diagnostic tools that follow the decision-theoretic approach frequently employ adaptive signal processing techniques, such as wavelets [22], or neural networks [4]- [7], [9], [10], claiming that classical signal processing [23], [24] techniques are insufficient to deal with the imperfect and imprecise information obtained from the signal.…”

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confidence: 99%

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A syntactic methodology for automatic diagnosis by analysis of continuous time measurements using hierarchical signal representations

Tümer

Belfore

Ropella

2003

IEEE Trans. Syst., Man, Cybern. B

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Section: Resultsmentioning

confidence: 97%

mentioning

confidence: 99%

A syntactic methodology for automatic diagnosis by analysis of continuous time measurements using hierarchical signal representations

Tümer

Belfore

Ropella

2003

IEEE Trans. Syst., Man, Cybern. B

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“…Artificial neural network (ANN) has been implemented to identify brain diseases like Parkinson's, Schizophrenia, and Huntington's disease from the CNV response in electroencephalograph (Jervis et al, 1994). Multi layer perceptrons (MLPs) and probabilistic neural network (PNN) have been studied for prediction of osteoporosis with bone densitometry (Mantzaris et al, 2008).…”

Section: Background Workmentioning

confidence: 99%

Disease management: clustering-based disease prediction

Kaushik

Kapoor²,

Varadharajan

et al. 2014

IJCENT

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Abstract:The latest trends in healthcare industry have made enormous data available which is useful for disease prediction. This may be utilised by provider/payer for disease management study (DMS) for judging the present health condition and analysing disease trends. The present approach depends upon objective analysis which is based on binary value of symptoms and health indicators and direct relation between them. We propose a framework based on analytics to diagnose and predict diseases using enhanced genetic clustering using patient history, symptoms, and existing medical condition. New patient's profile is analysed against existing patterns and mapped to a particular cluster based on the patient's medical parameters. After cluster formation, cohesion and adhesion between the clusters is determined, which helps to predict present medical condition and probability for being prone to a disease in future. This framework provides an efficient disease management based on the preliminary diagnosis of the patient. 70 K. Kaushik et al.Keywords: disease management; disease prediction; disease analysis; clustering; healthcare; big data analytics.Reference to this paper should be made as follows: Kaushik, K., Kapoor, D., Varadharajan, V. and Nallusamy, R. (2014) Biographical notes: Kulvaibhav Kaushik working in research stream at Infosys Limited, has a BTech in Computer Science from IIT Roorkee. He has worked on mobile money, merchant transactions, healthcare, big data analytics, cloud computing, and gamification. Much of his research has been carried out over network security, elliptical curve cryptography, and searchable encryption. He has filed patents for methods of secure data storage at cloud and multiuser encryption methodology. He has a few publications in IEEE and others and has delivered keynote address at various national and international conferences. He is also an editor for a mobile payments magazine.Divya Kapoor is a confident healthcare Quality Analyst with more than five years of experience in insurance and provider sector. She is an American health insurance primer Part A and part B certified professional. She has worked on various information technology systems in claim adjudication, revenue cycle, management, and hospital ecosystem.Vijayaraghavan Varadharajan is Senior Research Scientist at Infosys Limited where he is doing research in the field of security assessment, watermarking techniques, network security, and security in converged networks. He has published many papers in international journals and conference proceedings and authored few patents in the area of network security. He has also refereed for many journals and served as a TPC Member of several IEEE and ACM international conferences. He holds a Bachelor of Engineering degree in EC Engineering, MTech in Advanced computing and PhD degree in Information and communication engineering.Rajarathnam Nallusamy is currently the Founder and Managing

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“…Amongst the interesting medical applications of SOFMs are their use for classification of craniofacial growth patterns [34]' the extraction of information from electromyographic signals with regard to motor unit action potentials [35], and magnetic-resonance image segmentation [36]. With regard to the first example, an SOFM was used to extract the most relevant information from mandibular growth data.…”

Section: Self-organizing Feature Mapsmentioning

confidence: 99%