Strength Of Dominant Frequency Biology

The goal of features is to characterize data by measurements whose values are very similar for objects in the same class and very different for objects in a different class. As well as providing discriminatory information, one of the most important functions of feature extraction is dimensionality reduction of the data. This classification algorithm extracts several features of respiratory signals were extracted and utilized for apnea detection. The feature extraction plays a very important role since the classification is completely based on the values of the extracted features. Feature extraction can be done through a technique called signal processing. It consists of theory, algorithms, architecture, implementation, and applications related to processing information contained in different formats broadly designated as signals.

Signal processing is an area of systems engineering, electrical engineering and applied mathematics that deals with operations on or analysis of signals in either discrete or continuous time. Signals of interest can include sound, images, time-varying measurement values and sensor data, for example biological data such as electrocardiograms, control system signals, telecommunication transmission signals and many others. Signals are analog or digital electrical representation of time-varying or spatial-varying physical quantities.

Here, we used Second order autoregressive modeling for the estimation of parameters. Since the respiratory signal does not have constant amplitude and variations, we cannot predict the features directly. So the respiratory signal is modeled as a second order AR equation and then the coefficients are calculated. Then by using the coefficient the features of the respiratory signal is extracted.

RESPIRATORY SIGNAL(MIT-BIH DATABASE)3.2 BLOCK DIAGRAMFEATURES EXTRACTIONSTRENGTH OF DOMINANT FREQUENCY (STR)DOMINANT FREQUENCY (FAR)RESPIRATION RATE(FZX)ENERGY INDEX(EI)BURG METHODLEAST SQUARES METHODAUTO REGRESSIVE (AR) MODELLLINGCLASSIFIED FEATURES3.3 FEATURES

Feature extraction involves simplifying the amount of resources required to describe a large set of data accurately. The fundamental features of the respiratory signal provide the numerical values which are compared with the threshold values and the classification results will be produced.

The fundamental features of respiratory signals are

1. Energy Index (EI)

2. Respiration frequency (FZX)

3. Dominant frequency estimated by AR modeling (FAR)

4. Strength of the dominant frequency estimated by AR modeling (STR)

To compute these features, the mean value was first removed from a given section of respiration recordings.

3.3.1 Energy Index (EI)

Energy index is the maximum amount of energy present in the signal.

Given a continuous-time signal f (t), the energy contained over a finite time interval is defined as follows

E(x1,x2)= f(t)|2.dt, T2 > T1 (3.1)

Ef =f(t)|2.dt

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