Backpropagation Artificial Neural Network Detects Changes in Electro-Encephalogram Power Spectra of Syncopic Patients

Sinha, Rakesh Kumar; Aggarwal, Yogender; Das, Barda Nand

doi:10.1007/s10916-006-9043-y

Cited by 10 publications

(7 citation statements)

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“…Lung function analysis plays an important role in the diagnosis and prognosis of respiratory disorders and spirometric investigation remains central in such clinical practices [22,17] . In this study, abnormalities of respiratory system have been detected using artificial neural networks which are appropriate alternatives to standard statistical methods [12,23].…”

Section: Resultsmentioning

confidence: 99%

“…The developed tests were evaluated by computing four evaluation indices [17,18] such as accuracy, sensitivity, specificity, and adjusted accuracy which are commonly used for validating medical and clinical tests [17,18] . …”

Section: Discussionmentioning

confidence: 99%

“…Artificial neural networks are appropriate alternatives to standard statistical methods [12,17] . Therefore, they were used in this study in order to yield more reliable and accurate results.…”

Section: Resaultsmentioning

confidence: 99%

“…In other words, when a problem is investigated by means of a number of neural networks, the final result is more reliable and suffers less error since malfunctioning of a network may be compensated by other networks and also each network receives inputs different from that of others and thus may yield more accurate results [10,13,14,15,17,19] . Data were divided into two groups (train and test) randomly.…”

Section: Resaultsmentioning

confidence: 99%

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Detection of Respiratory Abnormalities Using Artificial Neural Networks

Baemani¹,

Monadjemi²,

Moallem³

2008

J. of Computer Science

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Problem Statement: Lung disease is a major threat to the human health regarding the industrial life, air pollution, smoking, and infections. Lung function tests are often performed using spirometry. Approach: The present study aims at detecting obstructive and restrictive pulmonary abnormalities. Lung function tests are often performed using spirometry. In this study, the data were obtained from 250 volunteers with standard recording protocol in order to detect and classify pulmonary diseases into normal, obstructive and restrictive. Firstly, spirometric data was statistically analyzed concerning its significance for neural networks. Then, such parameters were presented as input to MLP and recurrent networks. Results: These two networks detected normal and abnormal disorders as well as obstructive and restrictive patterns, respectively. Moreover, the output data was confirmed by measuring accuracy and sensitivity. Conclusion: The results show that the proposed method could be useful for detecting the function of respiratory system.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Resaultsmentioning

confidence: 99%

Section: Resaultsmentioning

confidence: 99%

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Detection of Respiratory Abnormalities Using Artificial Neural Networks

Baemani¹,

Monadjemi²,

Moallem³

2008

J. of Computer Science

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“…The successful and effective use of ANNs for identifying changes in EEG power spectra has been discussed widely. 41,43,46,47 The present study was planned to quantify and recognize the variations in sleep-EEG, if any, due to 1 kHz square wave-modulated microwave of 2450 MHz. Further, ANN was used to compare the changes in EEG power spectra with the objective index derived from the experimental pathophysiological parameters changed due to microwave exposure such as behavior and thyroid hormones.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Evaluation of Chronic Non-Thermal Effects of Modulated 2450 MHz Microwave Radiation on Electroencephalogram

et al. 2008

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The effects of chronic exposure (2 h daily for 21 days) of 1 kHz square wave-modulated 2450 MHz microwave radiation (non-thermal) on sleep-EEG, open field behavior, and thyroid hormones (T(3), T(4), and TSH) have been analyzed in an animal model. Results revealed significant changes in these pathophysiological parameters (p < 0.05 or better), except body temperature, grooming behavior, and TSH levels. The sleep-EEG power spectrum data for slow wave sleep (SWS), rapid eye movement (REM) sleep, and awake (AWA) states in two experimental groups of rats (microwave exposed and the control) were tested by an artificial neural network (ANN), containing 60 nodes in input layer, weighted from power spectrum data from 0 to 30 Hz, 18 nodes in hidden layer and an output node. The target output values for this network were determined with another five-layered neural network (with the structure of 6-14-1-14-6). The input and output of this network was assigned with the six confirmed pathophysiological changes. The most important feature for chronic exposure of 2450 MHz microwave exposure and for control subjects was extracted from the third layer single neuron and used as the target value for the three-layered ANN. The network was found effective in recognizing the EEG power spectra with an average of 71.93% for microwave exposure and 93.13% for control subjects, respectively. However, the lower percentage of pattern identification agreement in the microwave-exposed group in comparison to the control group suggest only mild effects of microwave exposure with this experimental setup.

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