ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients
Expert Systems with Applications: An International Journal
A vision-based analysis system for gait recognition in patients with Parkinson's disease
Expert Systems with Applications: An International Journal
A noninvasive intelligent approach for predicting the risk in dengue patients
Expert Systems with Applications: An International Journal
Prediction of Parkinson's disease tremor onset using radial basis function neural networks
Expert Systems with Applications: An International Journal
A sensor-based framework for detecting human gait cycles using acceleration signals
SoftCOM'09 Proceedings of the 17th international conference on Software, Telecommunications and Computer Networks
Daily living activity recognition based on statistical feature quality group selection
Expert Systems with Applications: An International Journal
Neural network diagnostic system for dengue patients risk classification
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Tremor is an involuntary movement characterized by regular or irregular oscillations of one or several body segments. Physiological and pathological tremor in motor control can be defined as roughly sinusoidal movements with particular amplitude and frequency profiles. The electrophysiological analysis of human tremor has a long tradition. Tremor time series belongs to stochastic signals. This because the mechanism of generating them is so complex and exposed to so many uncontrollable influence that mathematical equations describing them contain random quantities. In this study, we concerned with tremor classification for the purpose of medical diagnosis. Accelerometer based tremor signals belong to Parkinsonian, essential, and healthy subjects were considered for this aim. Following features were extracted from tremor signals for classification by artificial neural network (ANN); linear prediction coefficients, wavelet transform detail coefficients, wavelet transform based entropy and variance, power ratio, and higher-order cumulants. Scaled-conjugate (SCG) and BFGS (Broyden-Fletcher-Goldfarb-Shanno) gradient learning algorithms were used. Despite BFGS algorithm had more sensitivity value (92.27%), SCG algorithm had more specificity value (89.01%). According to overall performance, BFGS algorithm (91.02%) was better than SCG algorithm (88.48%).